Complete pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# Pandas is an open-source library that is made mainly for working with relational or labeled data both easily and intuitively.\n",
    "# Pandas generally provide two data structure for manipulating data\n",
    "# Series and DataFrame\n",
    "# Series is a one-dimensional labeled array capable of holding data of any type.\n",
    "# DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row1</th>\n",
       "      <td>janibasha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row2</th>\n",
       "      <td>shaik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Names\n",
       "Row1  janibasha\n",
       "Row2      shaik"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating DataFrames : pd.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)\n",
    "# data = any iterable(list,tuple,dictionary)\n",
    "\n",
    "# Creating DataFrame using lists\n",
    "\n",
    "Marks = ['janibasha','shaik']\n",
    "df=pd.DataFrame(Marks,index=['Row1','Row2'],columns=['Names'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shaik</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>94</td>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>97</td>\n",
       "      <td>98</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>75</td>\n",
       "      <td>73</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         jani  Basha  Shaik\n",
       "Maths      94     95     96\n",
       "Science    97     98     99\n",
       "English    75     73     70"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating DataFrame using Tuples\n",
    "\n",
    "Marks=[(94,95,96),(97,98,99),(75,73,70)]\n",
    "df=pd.DataFrame(Marks,index=['Maths','Science','English'],columns=['jani','Basha','Shaik'])\n",
    "df\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Creating DataFrame using Dictonary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak\n",
       "Maths      90     94     75\n",
       "Science    91     92     72\n",
       "English    93     93     90"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Marks3= {'Jani':(90,91,93),'Basha':(94,92,93),'Shiak':(75,72,90)}\n",
    "df=pd.DataFrame(Marks3,index=['Maths','Science','English'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_UT</th>\n",
       "      <th>year</th>\n",
       "      <th>Primary_Boys</th>\n",
       "      <th>Primary_Girls</th>\n",
       "      <th>Primary_Total</th>\n",
       "      <th>Upper Primary_Boys</th>\n",
       "      <th>Upper Primary_Girls</th>\n",
       "      <th>Upper Primary_Total</th>\n",
       "      <th>Secondary _Boys</th>\n",
       "      <th>Secondary _Girls</th>\n",
       "      <th>Secondary _Total</th>\n",
       "      <th>HrSecondary_Boys</th>\n",
       "      <th>HrSecondary_Girls</th>\n",
       "      <th>HrSecondary_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.68</td>\n",
       "      <td>Uppe_r_Primary</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.23</td>\n",
       "      <td>5.57</td>\n",
       "      <td>5.55</td>\n",
       "      <td>5.56</td>\n",
       "      <td>17.66</td>\n",
       "      <td>10.15</td>\n",
       "      <td>14.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.21</td>\n",
       "      <td>NR</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.51</td>\n",
       "      <td>8.36</td>\n",
       "      <td>5.98</td>\n",
       "      <td>7.2</td>\n",
       "      <td>18.94</td>\n",
       "      <td>12.2</td>\n",
       "      <td>15.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.69</td>\n",
       "      <td>11.47</td>\n",
       "      <td>8.16</td>\n",
       "      <td>9.87</td>\n",
       "      <td>21.05</td>\n",
       "      <td>12.21</td>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.36</td>\n",
       "      <td>12.21</td>\n",
       "      <td>13.25</td>\n",
       "      <td>12.72</td>\n",
       "      <td>2.66</td>\n",
       "      <td>NR</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.35</td>\n",
       "      <td>3.46</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3.78</td>\n",
       "      <td>11.95</td>\n",
       "      <td>13.37</td>\n",
       "      <td>12.65</td>\n",
       "      <td>12.65</td>\n",
       "      <td>10.85</td>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>3.44</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.63</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.31</td>\n",
       "      <td>16.73</td>\n",
       "      <td>19.77</td>\n",
       "      <td>18.34</td>\n",
       "      <td>8.03</td>\n",
       "      <td>7.76</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.47</td>\n",
       "      <td>5.84</td>\n",
       "      <td>2.88</td>\n",
       "      <td>4.3</td>\n",
       "      <td>16.33</td>\n",
       "      <td>19.06</td>\n",
       "      <td>17.8</td>\n",
       "      <td>8.18</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>All India</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>4.68</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.67</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.13</td>\n",
       "      <td>14.54</td>\n",
       "      <td>14.54</td>\n",
       "      <td>14.54</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>All India</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.14</td>\n",
       "      <td>4.34</td>\n",
       "      <td>3.09</td>\n",
       "      <td>4.49</td>\n",
       "      <td>3.77</td>\n",
       "      <td>17.93</td>\n",
       "      <td>17.79</td>\n",
       "      <td>17.86</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>All India</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>4.36</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3.49</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.03</td>\n",
       "      <td>17.21</td>\n",
       "      <td>16.88</td>\n",
       "      <td>17.06</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           State_UT     year Primary_Boys Primary_Girls Primary_Total  \\\n",
       "0     A & N Islands  2012-13         0.83          0.51          0.68   \n",
       "1     A & N Islands  2013-14         1.35          1.06          1.21   \n",
       "2     A & N Islands  2014-15         0.47          0.55          0.51   \n",
       "3    Andhra Pradesh  2012-13          3.3          3.05          3.18   \n",
       "4    Andhra Pradesh  2013-14         4.31          4.39          4.35   \n",
       "..              ...      ...          ...           ...           ...   \n",
       "105     West Bengal  2013-14         3.44          2.37          2.91   \n",
       "106     West Bengal  2014-15         2.13          0.79          1.47   \n",
       "107       All India  2012-13         4.68          4.66          4.67   \n",
       "108       All India  2013-14         4.53          4.14          4.34   \n",
       "109       All India  2014-15         4.36          3.88          4.13   \n",
       "\n",
       "    Upper Primary_Boys Upper Primary_Girls Upper Primary_Total  \\\n",
       "0       Uppe_r_Primary                1.09                1.23   \n",
       "1                   NR                1.54                0.51   \n",
       "2                 1.44                1.95                1.69   \n",
       "3                 3.21                3.51                3.36   \n",
       "4                 3.46                4.12                3.78   \n",
       "..                 ...                 ...                 ...   \n",
       "105               5.63                 3.1                4.31   \n",
       "106               5.84                2.88                 4.3   \n",
       "107                2.3                4.01                3.13   \n",
       "108               3.09                4.49                3.77   \n",
       "109               3.49                 4.6                4.03   \n",
       "\n",
       "    Secondary _Boys Secondary _Girls Secondary _Total HrSecondary_Boys  \\\n",
       "0              5.57             5.55             5.56            17.66   \n",
       "1              8.36             5.98              7.2            18.94   \n",
       "2             11.47             8.16             9.87            21.05   \n",
       "3             12.21            13.25            12.72             2.66   \n",
       "4             11.95            13.37            12.65            12.65   \n",
       "..              ...              ...              ...              ...   \n",
       "105           16.73            19.77            18.34             8.03   \n",
       "106           16.33            19.06             17.8             8.18   \n",
       "107           14.54            14.54            14.54               NR   \n",
       "108           17.93            17.79            17.86             1.48   \n",
       "109           17.21            16.88            17.06             0.25   \n",
       "\n",
       "    HrSecondary_Girls HrSecondary_Total  \n",
       "0               10.15             14.14  \n",
       "1                12.2             15.87  \n",
       "2               12.21             16.93  \n",
       "3                  NR              0.35  \n",
       "4               10.85             11.79  \n",
       "..                ...               ...  \n",
       "105              7.76               7.9  \n",
       "106              8.04              8.11  \n",
       "107                NR                NR  \n",
       "108              1.61              1.54  \n",
       "109                NR                NR  \n",
       "\n",
       "[110 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating DataFrame by loading exsting stroage (csv,sql,excel,json etc)\n",
    "\n",
    "# Creating DataFrame by loading csv file \n",
    "\n",
    "# csv : comma separeted values(in default pd sep=',')\n",
    "\n",
    "# We cause any sep= : ; etc but file should contain that sepated only\n",
    "\n",
    "\n",
    "df=pd.read_csv('dropout.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110, 14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the dataframe shape\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion : DataFrame contains 110 rows and 14 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viewing Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_UT</th>\n",
       "      <th>year</th>\n",
       "      <th>Primary_Boys</th>\n",
       "      <th>Primary_Girls</th>\n",
       "      <th>Primary_Total</th>\n",
       "      <th>Upper Primary_Boys</th>\n",
       "      <th>Upper Primary_Girls</th>\n",
       "      <th>Upper Primary_Total</th>\n",
       "      <th>Secondary _Boys</th>\n",
       "      <th>Secondary _Girls</th>\n",
       "      <th>Secondary _Total</th>\n",
       "      <th>HrSecondary_Boys</th>\n",
       "      <th>HrSecondary_Girls</th>\n",
       "      <th>HrSecondary_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.68</td>\n",
       "      <td>Uppe_r_Primary</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.23</td>\n",
       "      <td>5.57</td>\n",
       "      <td>5.55</td>\n",
       "      <td>5.56</td>\n",
       "      <td>17.66</td>\n",
       "      <td>10.15</td>\n",
       "      <td>14.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.21</td>\n",
       "      <td>NR</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.51</td>\n",
       "      <td>8.36</td>\n",
       "      <td>5.98</td>\n",
       "      <td>7.2</td>\n",
       "      <td>18.94</td>\n",
       "      <td>12.2</td>\n",
       "      <td>15.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.69</td>\n",
       "      <td>11.47</td>\n",
       "      <td>8.16</td>\n",
       "      <td>9.87</td>\n",
       "      <td>21.05</td>\n",
       "      <td>12.21</td>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.36</td>\n",
       "      <td>12.21</td>\n",
       "      <td>13.25</td>\n",
       "      <td>12.72</td>\n",
       "      <td>2.66</td>\n",
       "      <td>NR</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.35</td>\n",
       "      <td>3.46</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3.78</td>\n",
       "      <td>11.95</td>\n",
       "      <td>13.37</td>\n",
       "      <td>12.65</td>\n",
       "      <td>12.65</td>\n",
       "      <td>10.85</td>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         State_UT     year Primary_Boys Primary_Girls Primary_Total  \\\n",
       "0   A & N Islands  2012-13         0.83          0.51          0.68   \n",
       "1   A & N Islands  2013-14         1.35          1.06          1.21   \n",
       "2   A & N Islands  2014-15         0.47          0.55          0.51   \n",
       "3  Andhra Pradesh  2012-13          3.3          3.05          3.18   \n",
       "4  Andhra Pradesh  2013-14         4.31          4.39          4.35   \n",
       "\n",
       "  Upper Primary_Boys Upper Primary_Girls Upper Primary_Total Secondary _Boys  \\\n",
       "0     Uppe_r_Primary                1.09                1.23            5.57   \n",
       "1                 NR                1.54                0.51            8.36   \n",
       "2               1.44                1.95                1.69           11.47   \n",
       "3               3.21                3.51                3.36           12.21   \n",
       "4               3.46                4.12                3.78           11.95   \n",
       "\n",
       "  Secondary _Girls Secondary _Total HrSecondary_Boys HrSecondary_Girls  \\\n",
       "0             5.55             5.56            17.66             10.15   \n",
       "1             5.98              7.2            18.94              12.2   \n",
       "2             8.16             9.87            21.05             12.21   \n",
       "3            13.25            12.72             2.66                NR   \n",
       "4            13.37            12.65            12.65             10.85   \n",
       "\n",
       "  HrSecondary_Total  \n",
       "0             14.14  \n",
       "1             15.87  \n",
       "2             16.93  \n",
       "3              0.35  \n",
       "4             11.79  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing top 5 rows of DataFrame\n",
    "# df.head(n) ====> n= default 5 we can chookse any interger value\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_UT</th>\n",
       "      <th>year</th>\n",
       "      <th>Primary_Boys</th>\n",
       "      <th>Primary_Girls</th>\n",
       "      <th>Primary_Total</th>\n",
       "      <th>Upper Primary_Boys</th>\n",
       "      <th>Upper Primary_Girls</th>\n",
       "      <th>Upper Primary_Total</th>\n",
       "      <th>Secondary _Boys</th>\n",
       "      <th>Secondary _Girls</th>\n",
       "      <th>Secondary _Total</th>\n",
       "      <th>HrSecondary_Boys</th>\n",
       "      <th>HrSecondary_Girls</th>\n",
       "      <th>HrSecondary_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.68</td>\n",
       "      <td>Uppe_r_Primary</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.23</td>\n",
       "      <td>5.57</td>\n",
       "      <td>5.55</td>\n",
       "      <td>5.56</td>\n",
       "      <td>17.66</td>\n",
       "      <td>10.15</td>\n",
       "      <td>14.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.21</td>\n",
       "      <td>NR</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.51</td>\n",
       "      <td>8.36</td>\n",
       "      <td>5.98</td>\n",
       "      <td>7.2</td>\n",
       "      <td>18.94</td>\n",
       "      <td>12.2</td>\n",
       "      <td>15.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.69</td>\n",
       "      <td>11.47</td>\n",
       "      <td>8.16</td>\n",
       "      <td>9.87</td>\n",
       "      <td>21.05</td>\n",
       "      <td>12.21</td>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.36</td>\n",
       "      <td>12.21</td>\n",
       "      <td>13.25</td>\n",
       "      <td>12.72</td>\n",
       "      <td>2.66</td>\n",
       "      <td>NR</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.35</td>\n",
       "      <td>3.46</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3.78</td>\n",
       "      <td>11.95</td>\n",
       "      <td>13.37</td>\n",
       "      <td>12.65</td>\n",
       "      <td>12.65</td>\n",
       "      <td>10.85</td>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>6.57</td>\n",
       "      <td>6.89</td>\n",
       "      <td>6.72</td>\n",
       "      <td>5.09</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.2</td>\n",
       "      <td>15.81</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.71</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arunachal  Pradesh</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>11.54</td>\n",
       "      <td>10.22</td>\n",
       "      <td>10.89</td>\n",
       "      <td>4.44</td>\n",
       "      <td>6.74</td>\n",
       "      <td>5.59</td>\n",
       "      <td>16.08</td>\n",
       "      <td>12.75</td>\n",
       "      <td>14.49</td>\n",
       "      <td>18.57</td>\n",
       "      <td>15.49</td>\n",
       "      <td>17.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>15.84</td>\n",
       "      <td>14.44</td>\n",
       "      <td>15.16</td>\n",
       "      <td>5.86</td>\n",
       "      <td>9.06</td>\n",
       "      <td>7.47</td>\n",
       "      <td>13.99</td>\n",
       "      <td>11.77</td>\n",
       "      <td>12.93</td>\n",
       "      <td>7.85</td>\n",
       "      <td>2.14</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>11.51</td>\n",
       "      <td>10.09</td>\n",
       "      <td>10.82</td>\n",
       "      <td>5.31</td>\n",
       "      <td>8.08</td>\n",
       "      <td>6.71</td>\n",
       "      <td>18.33</td>\n",
       "      <td>15.81</td>\n",
       "      <td>17.11</td>\n",
       "      <td>19.37</td>\n",
       "      <td>17.44</td>\n",
       "      <td>18.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Assam</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>7.02</td>\n",
       "      <td>5.46</td>\n",
       "      <td>6.24</td>\n",
       "      <td>7.89</td>\n",
       "      <td>6.55</td>\n",
       "      <td>7.2</td>\n",
       "      <td>25.65</td>\n",
       "      <td>27.79</td>\n",
       "      <td>26.77</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             State_UT     year Primary_Boys Primary_Girls Primary_Total  \\\n",
       "0       A & N Islands  2012-13         0.83          0.51          0.68   \n",
       "1       A & N Islands  2013-14         1.35          1.06          1.21   \n",
       "2       A & N Islands  2014-15         0.47          0.55          0.51   \n",
       "3      Andhra Pradesh  2012-13          3.3          3.05          3.18   \n",
       "4      Andhra Pradesh  2013-14         4.31          4.39          4.35   \n",
       "5      Andhra Pradesh  2014-15         6.57          6.89          6.72   \n",
       "6  Arunachal  Pradesh  2013-14        11.54         10.22         10.89   \n",
       "7   Arunachal Pradesh  2012-13        15.84         14.44         15.16   \n",
       "8   Arunachal Pradesh  2014-15        11.51         10.09         10.82   \n",
       "9               Assam  2012-13         7.02          5.46          6.24   \n",
       "\n",
       "  Upper Primary_Boys Upper Primary_Girls Upper Primary_Total Secondary _Boys  \\\n",
       "0     Uppe_r_Primary                1.09                1.23            5.57   \n",
       "1                 NR                1.54                0.51            8.36   \n",
       "2               1.44                1.95                1.69           11.47   \n",
       "3               3.21                3.51                3.36           12.21   \n",
       "4               3.46                4.12                3.78           11.95   \n",
       "5               5.09                5.32                 5.2           15.81   \n",
       "6               4.44                6.74                5.59           16.08   \n",
       "7               5.86                9.06                7.47           13.99   \n",
       "8               5.31                8.08                6.71           18.33   \n",
       "9               7.89                6.55                 7.2           25.65   \n",
       "\n",
       "  Secondary _Girls Secondary _Total HrSecondary_Boys HrSecondary_Girls  \\\n",
       "0             5.55             5.56            17.66             10.15   \n",
       "1             5.98              7.2            18.94              12.2   \n",
       "2             8.16             9.87            21.05             12.21   \n",
       "3            13.25            12.72             2.66                NR   \n",
       "4            13.37            12.65            12.65             10.85   \n",
       "5             15.6            15.71               NR                NR   \n",
       "6            12.75            14.49            18.57             15.49   \n",
       "7            11.77            12.93             7.85              2.14   \n",
       "8            15.81            17.11            19.37             17.44   \n",
       "9            27.79            26.77             4.87               4.5   \n",
       "\n",
       "  HrSecondary_Total  \n",
       "0             14.14  \n",
       "1             15.87  \n",
       "2             16.93  \n",
       "3              0.35  \n",
       "4             11.79  \n",
       "5                NR  \n",
       "6             17.07  \n",
       "7              5.11  \n",
       "8             18.42  \n",
       "9              4.69  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if we want to view to 10 rows\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_UT</th>\n",
       "      <th>year</th>\n",
       "      <th>Primary_Boys</th>\n",
       "      <th>Primary_Girls</th>\n",
       "      <th>Primary_Total</th>\n",
       "      <th>Upper Primary_Boys</th>\n",
       "      <th>Upper Primary_Girls</th>\n",
       "      <th>Upper Primary_Total</th>\n",
       "      <th>Secondary _Boys</th>\n",
       "      <th>Secondary _Girls</th>\n",
       "      <th>Secondary _Total</th>\n",
       "      <th>HrSecondary_Boys</th>\n",
       "      <th>HrSecondary_Girls</th>\n",
       "      <th>HrSecondary_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>3.44</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.63</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.31</td>\n",
       "      <td>16.73</td>\n",
       "      <td>19.77</td>\n",
       "      <td>18.34</td>\n",
       "      <td>8.03</td>\n",
       "      <td>7.76</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.47</td>\n",
       "      <td>5.84</td>\n",
       "      <td>2.88</td>\n",
       "      <td>4.3</td>\n",
       "      <td>16.33</td>\n",
       "      <td>19.06</td>\n",
       "      <td>17.8</td>\n",
       "      <td>8.18</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>All India</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>4.68</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.67</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.13</td>\n",
       "      <td>14.54</td>\n",
       "      <td>14.54</td>\n",
       "      <td>14.54</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>All India</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.14</td>\n",
       "      <td>4.34</td>\n",
       "      <td>3.09</td>\n",
       "      <td>4.49</td>\n",
       "      <td>3.77</td>\n",
       "      <td>17.93</td>\n",
       "      <td>17.79</td>\n",
       "      <td>17.86</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>All India</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>4.36</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3.49</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.03</td>\n",
       "      <td>17.21</td>\n",
       "      <td>16.88</td>\n",
       "      <td>17.06</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State_UT     year Primary_Boys Primary_Girls Primary_Total  \\\n",
       "105  West Bengal  2013-14         3.44          2.37          2.91   \n",
       "106  West Bengal  2014-15         2.13          0.79          1.47   \n",
       "107    All India  2012-13         4.68          4.66          4.67   \n",
       "108    All India  2013-14         4.53          4.14          4.34   \n",
       "109    All India  2014-15         4.36          3.88          4.13   \n",
       "\n",
       "    Upper Primary_Boys Upper Primary_Girls Upper Primary_Total  \\\n",
       "105               5.63                 3.1                4.31   \n",
       "106               5.84                2.88                 4.3   \n",
       "107                2.3                4.01                3.13   \n",
       "108               3.09                4.49                3.77   \n",
       "109               3.49                 4.6                4.03   \n",
       "\n",
       "    Secondary _Boys Secondary _Girls Secondary _Total HrSecondary_Boys  \\\n",
       "105           16.73            19.77            18.34             8.03   \n",
       "106           16.33            19.06             17.8             8.18   \n",
       "107           14.54            14.54            14.54               NR   \n",
       "108           17.93            17.79            17.86             1.48   \n",
       "109           17.21            16.88            17.06             0.25   \n",
       "\n",
       "    HrSecondary_Girls HrSecondary_Total  \n",
       "105              7.76               7.9  \n",
       "106              8.04              8.11  \n",
       "107                NR                NR  \n",
       "108              1.61              1.54  \n",
       "109                NR                NR  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing bottom rows\n",
    "# df.tail(n) ====> n= default 5\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State_UT', 'year', 'Primary_Boys', 'Primary_Girls', 'Primary_Total',\n",
       "       'Upper Primary_Boys', 'Upper Primary_Girls', 'Upper Primary_Total',\n",
       "       'Secondary _Boys', 'Secondary _Girls', 'Secondary _Total',\n",
       "       'HrSecondary_Boys', 'HrSecondary_Girls', 'HrSecondary_Total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you want know what are the coulmns dataframe have then use df.columns\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_UT</th>\n",
       "      <th>year</th>\n",
       "      <th>Primary_Boys</th>\n",
       "      <th>Primary_Girls</th>\n",
       "      <th>Primary_Total</th>\n",
       "      <th>Upper Primary_Boys</th>\n",
       "      <th>Upper Primary_Girls</th>\n",
       "      <th>Upper Primary_Total</th>\n",
       "      <th>Secondary _Boys</th>\n",
       "      <th>Secondary _Girls</th>\n",
       "      <th>Secondary _Total</th>\n",
       "      <th>HrSecondary_Boys</th>\n",
       "      <th>HrSecondary_Girls</th>\n",
       "      <th>HrSecondary_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>96</td>\n",
       "      <td>106</td>\n",
       "      <td>107</td>\n",
       "      <td>106</td>\n",
       "      <td>81</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State_UT     year Primary_Boys Primary_Girls Primary_Total  \\\n",
       "count       110      110          110           110           110   \n",
       "unique       40        3           93            87            87   \n",
       "top     Manipur  2014-15           NR            NR            NR   \n",
       "freq          3       37           15            15            17   \n",
       "\n",
       "       Upper Primary_Boys Upper Primary_Girls Upper Primary_Total  \\\n",
       "count                 110                 110                 110   \n",
       "unique                 91                  97                  96   \n",
       "top                    NR                  NR                  NR   \n",
       "freq                   16                   8                   9   \n",
       "\n",
       "       Secondary _Boys Secondary _Girls Secondary _Total HrSecondary_Boys  \\\n",
       "count              110              110              110              110   \n",
       "unique             106              107              106               81   \n",
       "top                 NR               NR               NR               NR   \n",
       "freq                 4                4                4               28   \n",
       "\n",
       "       HrSecondary_Girls HrSecondary_Total  \n",
       "count                110               110  \n",
       "unique                76                78  \n",
       "top                   NR                NR  \n",
       "freq                  34                31  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you want know basic stastical information of colums then use \n",
    "# df.describe(percentiles=None, include=None, exclude=None)\n",
    "# include = which data types to include (float,integer,object)\n",
    "\n",
    "df.describe(percentiles=[0.25,0.50,0.75],include=['object','int','float'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['A & N Islands', '2012-13', '0.83', ..., '17.66', '10.15',\n",
       "        '14.14'],\n",
       "       ['A & N Islands', '2013-14', '1.35', ..., '18.94', '12.2',\n",
       "        '15.87'],\n",
       "       ['A & N Islands', '2014-15', '0.47', ..., '21.05', '12.21',\n",
       "        '16.93'],\n",
       "       ...,\n",
       "       ['All India', '2012-13', '4.68', ..., 'NR', 'NR', 'NR'],\n",
       "       ['All India', '2013-14', '4.53', ..., '1.48', '1.61', '1.54'],\n",
       "       ['All India', '2014-15', '4.36', ..., '0.25', 'NR', 'NR']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting DataFrame into numpy array use  df.to_numpy(dtype,copy)\n",
    "# dtype= str/int/object \n",
    "x=df.to_numpy()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Primary_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Primary_Total\n",
       "0          0.68\n",
       "1          1.21\n",
       "2          0.51\n",
       "3          3.18\n",
       "4          4.35"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.DataFrame(df['Primary_Total'].head())\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0.68'],\n",
       "       ['1.21'],\n",
       "       ['0.51'],\n",
       "       ['3.18'],\n",
       "       ['4.35']], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z=y.to_numpy()\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       A & N Islands\n",
       "1       A & N Islands\n",
       "2       A & N Islands\n",
       "3      Andhra Pradesh\n",
       "4      Andhra Pradesh\n",
       "            ...      \n",
       "105       West Bengal\n",
       "106       West Bengal\n",
       "107         All India\n",
       "108         All India\n",
       "109         All India\n",
       "Name: State_UT, Length: 110, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame contains columns and rows we can do operation add/delete/rename/select on columns and rows\n",
    "\n",
    "# selecting a single column by @\n",
    "\n",
    "df['State_UT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_UT</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2012-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2013-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>2014-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2012-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2013-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2013-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2014-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>All India</td>\n",
       "      <td>2012-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>All India</td>\n",
       "      <td>2013-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>All India</td>\n",
       "      <td>2014-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           State_UT     year\n",
       "0     A & N Islands  2012-13\n",
       "1     A & N Islands  2013-14\n",
       "2     A & N Islands  2014-15\n",
       "3    Andhra Pradesh  2012-13\n",
       "4    Andhra Pradesh  2013-14\n",
       "..              ...      ...\n",
       "105     West Bengal  2013-14\n",
       "106     West Bengal  2014-15\n",
       "107       All India  2012-13\n",
       "108       All India  2013-14\n",
       "109       All India  2014-15\n",
       "\n",
       "[110 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selceting multiple coulums by list of lists\n",
    "\n",
    "df[['State_UT','year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak\n",
       "Maths      90     94     75\n",
       "Science    91     92     72\n",
       "English    93     93     90"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding column to the exsting data frame df['Adding column']=list created for new column\n",
    "\n",
    "Marks3= {'Jani':(90,91,93),'Basha':(94,92,93),'Shiak':(75,72,90)}\n",
    "df=pd.DataFrame(Marks3,index=['Maths','Science','English'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "      <th>Total Marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>72</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak  Total Marks\n",
       "Maths      90     94     75          100\n",
       "Science    91     92     72          100\n",
       "English    93     93     90          100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outof= [100,100,100]\n",
    "df['Total Marks']=outof\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak\n",
       "Maths      90     94     75\n",
       "Science    91     92     72\n",
       "English    93     93     90"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deleting column form datafram using df.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')\n",
    "\n",
    "df.drop(['Total Marks'],axis=1,inplace=True)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak\n",
       "Maths      90     94     75\n",
       "Science    91     92     72"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Row selection in pandas using labes\n",
    "# df.loc['rowname']\n",
    "\n",
    "df.loc['Maths':'Science']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha\n",
       "Maths      90     94\n",
       "Science    91     92"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Row selection in pandas using index position\n",
    "# df.iloc[starting row: ending row , starting column : ending column]\n",
    "\n",
    "df.iloc[:2,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you want particular value in table then use iloc\n",
    "# df.iloc[row number,column position of value]\n",
    "\n",
    "df.iloc[1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak\n",
       "History    91     91     89"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding row to the existing dataframe \n",
    "# pd.concat(objs: Union[Iterable[‘DataFrame’], Mapping[Label, ‘DataFrame’]], axis='0', join: str = \"'outer'\", ignore_index: bool = 'False', keys='None', levels='None', names='None', verify_integrity: bool = 'False', sort: bool = 'False', copy: bool = 'True')\n",
    "\n",
    "new={'Jani':91,'Basha':91,'Shiak':89}\n",
    "new1=pd.DataFrame(new,index=['History'])\n",
    "new1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maths</th>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak\n",
       "History    91     91     89\n",
       "Maths      90     94     75\n",
       "Science    91     92     72\n",
       "English    93     93     90"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.concat([new1,df])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak\n",
       "History    91     91     89\n",
       "Science    91     92     72\n",
       "English    93     93     90"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deleting row using drop()\n",
    "# df.drop(['index labels'],inplace=True)\n",
    "\n",
    "df1.drop(['Maths'],inplace=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "      <th>Shiak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha  Shiak\n",
       "History    91     91     89\n",
       "Science    91     92     72\n",
       "English    93     93     90"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting rows between two indexs using index labels\n",
    "row=df1.loc['History':'English']\n",
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha\n",
       "History    91     91\n",
       "Science    91     92"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you want particular columns and particular rows then we use loc()\n",
    "# df.loc[['row1,row2,'],['cloumn1 ,column2']]\n",
    "\n",
    "df1.loc[['History','Science'],['Jani','Basha']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jani</th>\n",
       "      <th>Basha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Jani  Basha\n",
       "History    91     91\n",
       "Science    91     92"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can use numerical index position also using iloc()\n",
    "\n",
    "df1.iloc[:2,:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is function which is combination of both .loc[] and iloc[] =====>.ix[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In .ix[] we can use both index label and index position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manipulating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>21</td>\n",
       "      <td>BTech</td>\n",
       "      <td>Hyderabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robert</td>\n",
       "      <td>22</td>\n",
       "      <td>BSc</td>\n",
       "      <td>Bengluru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>21</td>\n",
       "      <td>MBA</td>\n",
       "      <td>Noida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age Degree    Address\n",
       "0     Ram   21  BTech  Hyderabad\n",
       "1  Robert   22    BSc   Bengluru\n",
       "2   Rahim   21    MBA      Noida"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding new column to the existed dataframe\n",
    "\n",
    "details=[('Ram',21,'BTech','Hyderabad'),('Robert',22,'BSc','Bengluru'),('Rahim',21,'MBA','Noida')]\n",
    "df2=pd.DataFrame(details,columns=['Name','Age','Degree','Address'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>21</td>\n",
       "      <td>BTech</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robert</td>\n",
       "      <td>22</td>\n",
       "      <td>BSc</td>\n",
       "      <td>Bengluru</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>21</td>\n",
       "      <td>MBA</td>\n",
       "      <td>Noida</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age Degree    Address  Height\n",
       "0     Ram   21  BTech  Hyderabad     123\n",
       "1  Robert   22    BSc   Bengluru     234\n",
       "2   Rahim   21    MBA      Noida     236"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding new column to the existing df using creating list and add to the dataframe\n",
    "\n",
    "New=(123,234,236)\n",
    "df2['Height']=New\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Pass out</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>21</td>\n",
       "      <td>BTech</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>123</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robert</td>\n",
       "      <td>22</td>\n",
       "      <td>BSc</td>\n",
       "      <td>Bengluru</td>\n",
       "      <td>234</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>21</td>\n",
       "      <td>MBA</td>\n",
       "      <td>Noida</td>\n",
       "      <td>236</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age Degree    Address  Height  Pass out\n",
       "0     Ram   21  BTech  Hyderabad     123      2017\n",
       "1  Robert   22    BSc   Bengluru     234      2018\n",
       "2   Rahim   21    MBA      Noida     236      2017"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding new column to the existing df using .insert() option\n",
    "# df.insert(loc, column, value, allow_duplicates=False)\n",
    "\n",
    "df2.insert(5,'Pass out',[2017,2018,2017],allow_duplicates=False)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Pass out</th>\n",
       "      <th>Hobbies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>21</td>\n",
       "      <td>BTech</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>123</td>\n",
       "      <td>2017</td>\n",
       "      <td>Chess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robert</td>\n",
       "      <td>22</td>\n",
       "      <td>BSc</td>\n",
       "      <td>Bengluru</td>\n",
       "      <td>234</td>\n",
       "      <td>2018</td>\n",
       "      <td>Cricket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>21</td>\n",
       "      <td>MBA</td>\n",
       "      <td>Noida</td>\n",
       "      <td>236</td>\n",
       "      <td>2017</td>\n",
       "      <td>Vollyball</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age Degree    Address  Height  Pass out    Hobbies\n",
       "0     Ram   21  BTech  Hyderabad     123      2017      Chess\n",
       "1  Robert   22    BSc   Bengluru     234      2018    Cricket\n",
       "2   Rahim   21    MBA      Noida     236      2017  Vollyball"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding new column to the existing df using .assign() option\n",
    "# df.assign('column name'= [values])\n",
    "\n",
    "df3=df2.assign(Hobbies =['Chess','Cricket','Vollyball'])\n",
    "df3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.to_csv('Details.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Pass out</th>\n",
       "      <th>Hobbies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ram</td>\n",
       "      <td>21</td>\n",
       "      <td>BTech</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>123</td>\n",
       "      <td>2017</td>\n",
       "      <td>Chess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Robert</td>\n",
       "      <td>22</td>\n",
       "      <td>BSc</td>\n",
       "      <td>Bengluru</td>\n",
       "      <td>234</td>\n",
       "      <td>2018</td>\n",
       "      <td>Cricket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Rahim</td>\n",
       "      <td>21</td>\n",
       "      <td>MBA</td>\n",
       "      <td>Noida</td>\n",
       "      <td>236</td>\n",
       "      <td>2017</td>\n",
       "      <td>Vollyball</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Name  Age Degree    Address  Height  Pass out    Hobbies\n",
       "0           0     Ram   21  BTech  Hyderabad     123      2017      Chess\n",
       "1           1  Robert   22    BSc   Bengluru     234      2018    Cricket\n",
       "2           2   Rahim   21    MBA      Noida     236      2017  Vollyball"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deleting column/row of df using .drop()\n",
    "# df.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')\n",
    "\n",
    "df4=pd.read_csv('Details.csv')\n",
    "df4\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting column form existing df\n",
    "df4.drop(['Unnamed: 0','Address'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Height</th>\n",
       "      <th>Pass out</th>\n",
       "      <th>Hobbies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>21</td>\n",
       "      <td>BTech</td>\n",
       "      <td>123</td>\n",
       "      <td>2017</td>\n",
       "      <td>Chess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robert</td>\n",
       "      <td>22</td>\n",
       "      <td>BSc</td>\n",
       "      <td>234</td>\n",
       "      <td>2018</td>\n",
       "      <td>Cricket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>21</td>\n",
       "      <td>MBA</td>\n",
       "      <td>236</td>\n",
       "      <td>2017</td>\n",
       "      <td>Vollyball</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age Degree  Height  Pass out    Hobbies\n",
       "0     Ram   21  BTech     123      2017      Chess\n",
       "1  Robert   22    BSc     234      2018    Cricket\n",
       "2   Rahim   21    MBA     236      2017  Vollyball"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting row from existin df\n",
    "\n",
    "df4.drop([1],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Height</th>\n",
       "      <th>Pass out</th>\n",
       "      <th>Hobbies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>21</td>\n",
       "      <td>BTech</td>\n",
       "      <td>123</td>\n",
       "      <td>2017</td>\n",
       "      <td>Chess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>21</td>\n",
       "      <td>MBA</td>\n",
       "      <td>236</td>\n",
       "      <td>2017</td>\n",
       "      <td>Vollyball</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age Degree  Height  Pass out    Hobbies\n",
       "0    Ram   21  BTech     123      2017      Chess\n",
       "2  Rahim   21    MBA     236      2017  Vollyball"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Ruler</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>India</td>\n",
       "      <td>Rupees</td>\n",
       "      <td>PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>US</td>\n",
       "      <td>Dollar</td>\n",
       "      <td>President</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R3</th>\n",
       "      <td>China</td>\n",
       "      <td>yan</td>\n",
       "      <td>king</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Currency      Ruler\n",
       "R1   India   Rupees         PM\n",
       "R2      US   Dollar  President\n",
       "R3   China      yan       king"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Truncate : if you want the data frame certain range only then truncate useful\n",
    "# df.truncate(before=None, after=None, axis=None, copy=True)\n",
    "\n",
    "money=[('India','Rupees','PM'),('US','Dollar','President'),('China','yan','king')]\n",
    "Df=pd.DataFrame(money,index=['R1','R2','R3'],columns=['Country','Currency','Ruler'])\n",
    "Df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Ruler</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>India</td>\n",
       "      <td>Rupees</td>\n",
       "      <td>PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>US</td>\n",
       "      <td>Dollar</td>\n",
       "      <td>President</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Currency      Ruler\n",
       "R1   India   Rupees         PM\n",
       "R2      US   Dollar  President"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Truncate by rows\n",
    "Df.truncate(before='R1',after='R2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Currency</th>\n",
       "      <th>Ruler</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>Rupees</td>\n",
       "      <td>PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>Dollar</td>\n",
       "      <td>President</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R3</th>\n",
       "      <td>yan</td>\n",
       "      <td>king</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Currency      Ruler\n",
       "R1   Rupees         PM\n",
       "R2   Dollar  President\n",
       "R3      yan       king"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Truncate by columns\n",
    "Df.truncate(before='Currency',after='Ruler',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R1 Country      India\n",
      "Currency    Rupees\n",
      "Ruler           PM\n",
      "Name: R1, dtype: object\n",
      "R2 Country            US\n",
      "Currency       Dollar\n",
      "Ruler       President\n",
      "Name: R2, dtype: object\n",
      "R3 Country     China\n",
      "Currency      yan\n",
      "Ruler        king\n",
      "Name: R3, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Pandas dataframe contains rows and columns we can iterate both columns and rows of df by using function\n",
    "# iteritems(),iterrows(),itertuples() ====> row iteration\n",
    "# df[[i][index number of column]]  =====> column iteration\n",
    "\n",
    "for i,j in Df.iterrows():\n",
    "    print(i,j)\n",
    "\n",
    "# It returns the key : value relation of data frame by index label(row names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country R1    India\n",
      "R2       US\n",
      "R3    China\n",
      "Name: Country, dtype: object\n",
      "Currency R1    Rupees\n",
      "R2    Dollar\n",
      "R3       yan\n",
      "Name: Currency, dtype: object\n",
      "Ruler R1           PM\n",
      "R2    President\n",
      "R3         king\n",
      "Name: Ruler, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for i,j in Df.iteritems():\n",
    "    print(i,j)\n",
    "\n",
    "# It returns the key : value  relation of dataframe by item(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas(Index='R1', Country='India', Currency='Rupees', Ruler='PM')\n",
      "Pandas(Index='R2', Country='US', Currency='Dollar', Ruler='President')\n",
      "Pandas(Index='R3', Country='China', Currency='yan', Ruler='king')\n"
     ]
    }
   ],
   "source": [
    "for i in Df.itertuples():\n",
    "    print(i)\n",
    "    \n",
    "# it return the each row information in tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column iteration by creating the list of column\n",
    "\n",
    "col=list(Df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Country', 'Currency', 'Ruler']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "China\n",
      "yan\n",
      "king\n"
     ]
    }
   ],
   "source": [
    "for i in col:\n",
    "    print(Df[i][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
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       "      <td>6:53 AM</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>4/23/1993</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
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       "      <td>1/24/1998</td>\n",
       "      <td>4:47 PM</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Henry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11/23/2014</td>\n",
       "      <td>6:09 AM</td>\n",
       "      <td>132483</td>\n",
       "      <td>16.655</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Phillip</td>\n",
       "      <td>Male</td>\n",
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       "      <td>6:30 AM</td>\n",
       "      <td>42392</td>\n",
       "      <td>19.675</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Russell</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/20/2013</td>\n",
       "      <td>12:39 PM</td>\n",
       "      <td>96914</td>\n",
       "      <td>1.421</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>4/20/2013</td>\n",
       "      <td>4:45 PM</td>\n",
       "      <td>60500</td>\n",
       "      <td>11.985</td>\n",
       "      <td>False</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/15/2012</td>\n",
       "      <td>6:24 PM</td>\n",
       "      <td>129949</td>\n",
       "      <td>10.169</td>\n",
       "      <td>True</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
       "0      Douglas    Male    8/6/1993        12:42 PM   97308    6.945   \n",
       "1       Thomas    Male   3/31/1996         6:53 AM   61933    4.170   \n",
       "2        Maria  Female   4/23/1993        11:17 AM  130590   11.858   \n",
       "3        Jerry    Male    3/4/2005         1:00 PM  138705    9.340   \n",
       "4        Larry    Male   1/24/1998         4:47 PM  101004    1.389   \n",
       "..         ...     ...         ...             ...     ...      ...   \n",
       "995      Henry     NaN  11/23/2014         6:09 AM  132483   16.655   \n",
       "996    Phillip    Male   1/31/1984         6:30 AM   42392   19.675   \n",
       "997    Russell    Male   5/20/2013        12:39 PM   96914    1.421   \n",
       "998      Larry    Male   4/20/2013         4:45 PM   60500   11.985   \n",
       "999     Albert    Male   5/15/2012         6:24 PM  129949   10.169   \n",
       "\n",
       "    Senior Management                  Team  \n",
       "0                True             Marketing  \n",
       "1                True                   NaN  \n",
       "2               False               Finance  \n",
       "3                True               Finance  \n",
       "4                True       Client Services  \n",
       "..                ...                   ...  \n",
       "995             False          Distribution  \n",
       "996             False               Finance  \n",
       "997             False               Product  \n",
       "998             False  Business Development  \n",
       "999              True                 Sales  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Working with missing data \n",
    "# isnull(),notna(),fillna(),dropna(),replace(),interpolate()\n",
    "\n",
    "D=pd.read_csv('employ.csv')\n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Last Login Time</th>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     First Name  Gender  Start Date  Last Login Time  Salary  Bonus %  \\\n",
       "0         False   False       False            False   False    False   \n",
       "1         False   False       False            False   False    False   \n",
       "2         False   False       False            False   False    False   \n",
       "3         False   False       False            False   False    False   \n",
       "4         False   False       False            False   False    False   \n",
       "..          ...     ...         ...              ...     ...      ...   \n",
       "995       False    True       False            False   False    False   \n",
       "996       False   False       False            False   False    False   \n",
       "997       False   False       False            False   False    False   \n",
       "998       False   False       False            False   False    False   \n",
       "999       False   False       False            False   False    False   \n",
       "\n",
       "     Senior Management   Team  \n",
       "0                False  False  \n",
       "1                False   True  \n",
       "2                False  False  \n",
       "3                False  False  \n",
       "4                False  False  \n",
       "..                 ...    ...  \n",
       "995              False  False  \n",
       "996              False  False  \n",
       "997              False  False  \n",
       "998              False  False  \n",
       "999              False  False  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find is there any null value present in dataframe we use isnull() or notna()\n",
    "\n",
    "D.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     First Name  Gender  Start Date  Last Login Time  Salary  Bonus %  \\\n",
       "0          True    True        True             True    True     True   \n",
       "1          True    True        True             True    True     True   \n",
       "2          True    True        True             True    True     True   \n",
       "3          True    True        True             True    True     True   \n",
       "4          True    True        True             True    True     True   \n",
       "..          ...     ...         ...              ...     ...      ...   \n",
       "995        True   False        True             True    True     True   \n",
       "996        True    True        True             True    True     True   \n",
       "997        True    True        True             True    True     True   \n",
       "998        True    True        True             True    True     True   \n",
       "999        True    True        True             True    True     True   \n",
       "\n",
       "     Senior Management   Team  \n",
       "0                 True   True  \n",
       "1                 True  False  \n",
       "2                 True   True  \n",
       "3                 True   True  \n",
       "4                 True   True  \n",
       "..                 ...    ...  \n",
       "995               True   True  \n",
       "996               True   True  \n",
       "997               True   True  \n",
       "998               True   True  \n",
       "999               True   True  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# notna() is opposite to the isnull()\n",
    "D.notna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First Name            67\n",
       "Gender               145\n",
       "Start Date             0\n",
       "Last Login Time        0\n",
       "Salary                 0\n",
       "Bonus %                0\n",
       "Senior Management     67\n",
       "Team                  43\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you want know how many null values present in column\n",
    "# df.isnull().sum()\n",
    "\n",
    "D.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>8/6/1993</td>\n",
       "      <td>12:42 PM</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>3/31/1996</td>\n",
       "      <td>6:53 AM</td>\n",
       "      <td>61933</td>\n",
       "      <td>4.170</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>4/23/1993</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>3/4/2005</td>\n",
       "      <td>1:00 PM</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/24/1998</td>\n",
       "      <td>4:47 PM</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>11/23/2014</td>\n",
       "      <td>6:09 AM</td>\n",
       "      <td>132483</td>\n",
       "      <td>16.655</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Phillip</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/31/1984</td>\n",
       "      <td>6:30 AM</td>\n",
       "      <td>42392</td>\n",
       "      <td>19.675</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Russell</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/20/2013</td>\n",
       "      <td>12:39 PM</td>\n",
       "      <td>96914</td>\n",
       "      <td>1.421</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>4/20/2013</td>\n",
       "      <td>4:45 PM</td>\n",
       "      <td>60500</td>\n",
       "      <td>11.985</td>\n",
       "      <td>False</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/15/2012</td>\n",
       "      <td>6:24 PM</td>\n",
       "      <td>129949</td>\n",
       "      <td>10.169</td>\n",
       "      <td>True</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
       "0      Douglas    Male    8/6/1993        12:42 PM   97308    6.945   \n",
       "1       Thomas    Male   3/31/1996         6:53 AM   61933    4.170   \n",
       "2        Maria  Female   4/23/1993        11:17 AM  130590   11.858   \n",
       "3        Jerry    Male    3/4/2005         1:00 PM  138705    9.340   \n",
       "4        Larry    Male   1/24/1998         4:47 PM  101004    1.389   \n",
       "..         ...     ...         ...             ...     ...      ...   \n",
       "995      Henry       0  11/23/2014         6:09 AM  132483   16.655   \n",
       "996    Phillip    Male   1/31/1984         6:30 AM   42392   19.675   \n",
       "997    Russell    Male   5/20/2013        12:39 PM   96914    1.421   \n",
       "998      Larry    Male   4/20/2013         4:45 PM   60500   11.985   \n",
       "999     Albert    Male   5/15/2012         6:24 PM  129949   10.169   \n",
       "\n",
       "    Senior Management                  Team  \n",
       "0                True             Marketing  \n",
       "1                True                     0  \n",
       "2               False               Finance  \n",
       "3                True               Finance  \n",
       "4                True       Client Services  \n",
       "..                ...                   ...  \n",
       "995             False          Distribution  \n",
       "996             False               Finance  \n",
       "997             False               Product  \n",
       "998             False  Business Development  \n",
       "999              True                 Sales  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To fill the null values or not avaiable values we use \n",
    "# fillna(),replace(),interpolate()\n",
    "\n",
    "# df.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)\n",
    "\n",
    "D.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>8/6/1993</td>\n",
       "      <td>12:42 PM</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>3/31/1996</td>\n",
       "      <td>6:53 AM</td>\n",
       "      <td>61933</td>\n",
       "      <td>4.170</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>4/23/1993</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>3/4/2005</td>\n",
       "      <td>1:00 PM</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/24/1998</td>\n",
       "      <td>4:47 PM</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Henry</td>\n",
       "      <td>-1</td>\n",
       "      <td>11/23/2014</td>\n",
       "      <td>6:09 AM</td>\n",
       "      <td>132483</td>\n",
       "      <td>16.655</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Phillip</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/31/1984</td>\n",
       "      <td>6:30 AM</td>\n",
       "      <td>42392</td>\n",
       "      <td>19.675</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Russell</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/20/2013</td>\n",
       "      <td>12:39 PM</td>\n",
       "      <td>96914</td>\n",
       "      <td>1.421</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>4/20/2013</td>\n",
       "      <td>4:45 PM</td>\n",
       "      <td>60500</td>\n",
       "      <td>11.985</td>\n",
       "      <td>False</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/15/2012</td>\n",
       "      <td>6:24 PM</td>\n",
       "      <td>129949</td>\n",
       "      <td>10.169</td>\n",
       "      <td>True</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
       "0      Douglas    Male    8/6/1993        12:42 PM   97308    6.945   \n",
       "1       Thomas    Male   3/31/1996         6:53 AM   61933    4.170   \n",
       "2        Maria  Female   4/23/1993        11:17 AM  130590   11.858   \n",
       "3        Jerry    Male    3/4/2005         1:00 PM  138705    9.340   \n",
       "4        Larry    Male   1/24/1998         4:47 PM  101004    1.389   \n",
       "..         ...     ...         ...             ...     ...      ...   \n",
       "995      Henry      -1  11/23/2014         6:09 AM  132483   16.655   \n",
       "996    Phillip    Male   1/31/1984         6:30 AM   42392   19.675   \n",
       "997    Russell    Male   5/20/2013        12:39 PM   96914    1.421   \n",
       "998      Larry    Male   4/20/2013         4:45 PM   60500   11.985   \n",
       "999     Albert    Male   5/15/2012         6:24 PM  129949   10.169   \n",
       "\n",
       "    Senior Management                  Team  \n",
       "0                True             Marketing  \n",
       "1                True                    -1  \n",
       "2               False               Finance  \n",
       "3                True               Finance  \n",
       "4                True       Client Services  \n",
       "..                ...                   ...  \n",
       "995             False          Distribution  \n",
       "996             False               Finance  \n",
       "997             False               Product  \n",
       "998             False  Business Development  \n",
       "999              True                 Sales  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')\n",
    "import numpy as np\n",
    "D.replace(to_replace=np.nan,value=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Male\n",
       "1           Male\n",
       "2         Female\n",
       "3           Male\n",
       "4           Male\n",
       "         ...    \n",
       "995    No Gender\n",
       "996         Male\n",
       "997         Male\n",
       "998         Male\n",
       "999         Male\n",
       "Name: Gender, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D['Gender'].fillna('No Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To drop/deleter the rows which contain null values\n",
    "# df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)\n",
    "\n",
    "# Drop row if any 1 nan value present\n",
    "x=D.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>8/6/1993</td>\n",
       "      <td>12:42 PM</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>4/23/1993</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>3/4/2005</td>\n",
       "      <td>1:00 PM</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/24/1998</td>\n",
       "      <td>4:47 PM</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>4/18/1987</td>\n",
       "      <td>1:35 AM</td>\n",
       "      <td>115163</td>\n",
       "      <td>10.125</td>\n",
       "      <td>False</td>\n",
       "      <td>Legal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>George</td>\n",
       "      <td>Male</td>\n",
       "      <td>6/21/2013</td>\n",
       "      <td>5:47 PM</td>\n",
       "      <td>98874</td>\n",
       "      <td>4.479</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Phillip</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/31/1984</td>\n",
       "      <td>6:30 AM</td>\n",
       "      <td>42392</td>\n",
       "      <td>19.675</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Russell</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/20/2013</td>\n",
       "      <td>12:39 PM</td>\n",
       "      <td>96914</td>\n",
       "      <td>1.421</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>4/20/2013</td>\n",
       "      <td>4:45 PM</td>\n",
       "      <td>60500</td>\n",
       "      <td>11.985</td>\n",
       "      <td>False</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/15/2012</td>\n",
       "      <td>6:24 PM</td>\n",
       "      <td>129949</td>\n",
       "      <td>10.169</td>\n",
       "      <td>True</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>764 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date Last Login Time  Salary  Bonus %  \\\n",
       "0      Douglas    Male   8/6/1993        12:42 PM   97308    6.945   \n",
       "2        Maria  Female  4/23/1993        11:17 AM  130590   11.858   \n",
       "3        Jerry    Male   3/4/2005         1:00 PM  138705    9.340   \n",
       "4        Larry    Male  1/24/1998         4:47 PM  101004    1.389   \n",
       "5       Dennis    Male  4/18/1987         1:35 AM  115163   10.125   \n",
       "..         ...     ...        ...             ...     ...      ...   \n",
       "994     George    Male  6/21/2013         5:47 PM   98874    4.479   \n",
       "996    Phillip    Male  1/31/1984         6:30 AM   42392   19.675   \n",
       "997    Russell    Male  5/20/2013        12:39 PM   96914    1.421   \n",
       "998      Larry    Male  4/20/2013         4:45 PM   60500   11.985   \n",
       "999     Albert    Male  5/15/2012         6:24 PM  129949   10.169   \n",
       "\n",
       "    Senior Management                  Team  \n",
       "0                True             Marketing  \n",
       "2               False               Finance  \n",
       "3                True               Finance  \n",
       "4                True       Client Services  \n",
       "5               False                 Legal  \n",
       "..                ...                   ...  \n",
       "994              True             Marketing  \n",
       "996             False               Finance  \n",
       "997             False               Product  \n",
       "998             False  Business Development  \n",
       "999              True                 Sales  \n",
       "\n",
       "[764 rows x 8 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(764, 8)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.764"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape[0]/D.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop row if all the values nan \n",
    "y=D.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4/23/1993</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
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       "      <td>138705</td>\n",
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       "    <tr>\n",
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       "      <td>Larry</td>\n",
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       "      <td>4:47 PM</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>995</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>11/23/2014</td>\n",
       "      <td>6:09 AM</td>\n",
       "      <td>132483</td>\n",
       "      <td>16.655</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Phillip</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/31/1984</td>\n",
       "      <td>6:30 AM</td>\n",
       "      <td>42392</td>\n",
       "      <td>19.675</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Russell</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/20/2013</td>\n",
       "      <td>12:39 PM</td>\n",
       "      <td>96914</td>\n",
       "      <td>1.421</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>4/20/2013</td>\n",
       "      <td>4:45 PM</td>\n",
       "      <td>60500</td>\n",
       "      <td>11.985</td>\n",
       "      <td>False</td>\n",
       "      <td>Business Development</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/15/2012</td>\n",
       "      <td>6:24 PM</td>\n",
       "      <td>129949</td>\n",
       "      <td>10.169</td>\n",
       "      <td>True</td>\n",
       "      <td>Sales</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
       "0      Douglas    Male    8/6/1993        12:42 PM   97308    6.945   \n",
       "1       Thomas    Male   3/31/1996         6:53 AM   61933    4.170   \n",
       "2        Maria  Female   4/23/1993        11:17 AM  130590   11.858   \n",
       "3        Jerry    Male    3/4/2005         1:00 PM  138705    9.340   \n",
       "4        Larry    Male   1/24/1998         4:47 PM  101004    1.389   \n",
       "..         ...     ...         ...             ...     ...      ...   \n",
       "995      Henry     NaN  11/23/2014         6:09 AM  132483   16.655   \n",
       "996    Phillip    Male   1/31/1984         6:30 AM   42392   19.675   \n",
       "997    Russell    Male   5/20/2013        12:39 PM   96914    1.421   \n",
       "998      Larry    Male   4/20/2013         4:45 PM   60500   11.985   \n",
       "999     Albert    Male   5/15/2012         6:24 PM  129949   10.169   \n",
       "\n",
       "    Senior Management                  Team  \n",
       "0                True             Marketing  \n",
       "1                True                   NaN  \n",
       "2               False               Finance  \n",
       "3                True               Finance  \n",
       "4                True       Client Services  \n",
       "..                ...                   ...  \n",
       "995             False          Distribution  \n",
       "996             False               Finance  \n",
       "997             False               Product  \n",
       "998             False  Business Development  \n",
       "999              True                 Sales  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop column which has atleast one nan\n",
    "\n",
    "z=D.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
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       "      <th>3</th>\n",
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       "      <td>19.675</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
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       "      <td>1.421</td>\n",
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       "      <th>998</th>\n",
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       "      <td>11.985</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>5/15/2012</td>\n",
       "      <td>6:24 PM</td>\n",
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       "      <td>10.169</td>\n",
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       "<p>1000 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     Start Date Last Login Time  Salary  Bonus %\n",
       "0      8/6/1993        12:42 PM   97308    6.945\n",
       "1     3/31/1996         6:53 AM   61933    4.170\n",
       "2     4/23/1993        11:17 AM  130590   11.858\n",
       "3      3/4/2005         1:00 PM  138705    9.340\n",
       "4     1/24/1998         4:47 PM  101004    1.389\n",
       "..          ...             ...     ...      ...\n",
       "995  11/23/2014         6:09 AM  132483   16.655\n",
       "996   1/31/1984         6:30 AM   42392   19.675\n",
       "997   5/20/2013        12:39 PM   96914    1.421\n",
       "998   4/20/2013         4:45 PM   60500   11.985\n",
       "999   5/15/2012         6:24 PM  129949   10.169\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.shape[1]/D.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>2/17/2012</td>\n",
       "      <td>10:20 AM</td>\n",
       "      <td>61602</td>\n",
       "      <td>11.849</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/29/1994</td>\n",
       "      <td>6:48 PM</td>\n",
       "      <td>58755</td>\n",
       "      <td>5.097</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>7/22/1990</td>\n",
       "      <td>2:53 PM</td>\n",
       "      <td>52119</td>\n",
       "      <td>11.343</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/22/1986</td>\n",
       "      <td>7:39 PM</td>\n",
       "      <td>63126</td>\n",
       "      <td>18.424</td>\n",
       "      <td>False</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/21/2011</td>\n",
       "      <td>1:45 AM</td>\n",
       "      <td>95327</td>\n",
       "      <td>15.120</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>5/23/2001</td>\n",
       "      <td>7:52 PM</td>\n",
       "      <td>103877</td>\n",
       "      <td>6.322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>8/23/2000</td>\n",
       "      <td>4:19 PM</td>\n",
       "      <td>95866</td>\n",
       "      <td>19.388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>9/15/1985</td>\n",
       "      <td>1:50 AM</td>\n",
       "      <td>133472</td>\n",
       "      <td>16.941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>7/30/2012</td>\n",
       "      <td>3:07 PM</td>\n",
       "      <td>107351</td>\n",
       "      <td>5.329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>9/14/2010</td>\n",
       "      <td>5:19 AM</td>\n",
       "      <td>143638</td>\n",
       "      <td>9.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date Last Login Time  Salary  Bonus %  \\\n",
       "101      Aaron    Male  2/17/2012        10:20 AM   61602   11.849   \n",
       "327      Aaron    Male  1/29/1994         6:48 PM   58755    5.097   \n",
       "440      Aaron    Male  7/22/1990         2:53 PM   52119   11.343   \n",
       "937      Aaron     NaN  1/22/1986         7:39 PM   63126   18.424   \n",
       "137       Adam    Male  5/21/2011         1:45 AM   95327   15.120   \n",
       "..         ...     ...        ...             ...     ...      ...   \n",
       "902        NaN    Male  5/23/2001         7:52 PM  103877    6.322   \n",
       "925        NaN  Female  8/23/2000         4:19 PM   95866   19.388   \n",
       "946        NaN  Female  9/15/1985         1:50 AM  133472   16.941   \n",
       "947        NaN    Male  7/30/2012         3:07 PM  107351    5.329   \n",
       "951        NaN  Female  9/14/2010         5:19 AM  143638    9.662   \n",
       "\n",
       "    Senior Management             Team  \n",
       "101              True        Marketing  \n",
       "327              True        Marketing  \n",
       "440              True  Client Services  \n",
       "937             False  Client Services  \n",
       "137             False     Distribution  \n",
       "..                ...              ...  \n",
       "902               NaN     Distribution  \n",
       "925               NaN            Sales  \n",
       "946               NaN     Distribution  \n",
       "947               NaN        Marketing  \n",
       "951               NaN              NaN  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the dataframe by particular column\n",
    "# df.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)\n",
    "\n",
    "D.sort_values('First Name',ascending=True,na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>7/22/1990</td>\n",
       "      <td>2:53 PM</td>\n",
       "      <td>52119</td>\n",
       "      <td>11.343</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/29/1994</td>\n",
       "      <td>6:48 PM</td>\n",
       "      <td>58755</td>\n",
       "      <td>5.097</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>2/17/2012</td>\n",
       "      <td>10:20 AM</td>\n",
       "      <td>61602</td>\n",
       "      <td>11.849</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/22/1986</td>\n",
       "      <td>7:39 PM</td>\n",
       "      <td>63126</td>\n",
       "      <td>18.424</td>\n",
       "      <td>False</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>10/8/2010</td>\n",
       "      <td>9:53 PM</td>\n",
       "      <td>45181</td>\n",
       "      <td>3.491</td>\n",
       "      <td>False</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>9/14/2010</td>\n",
       "      <td>5:19 AM</td>\n",
       "      <td>143638</td>\n",
       "      <td>9.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>8/2/1992</td>\n",
       "      <td>8:35 PM</td>\n",
       "      <td>145316</td>\n",
       "      <td>18.517</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>11/24/2015</td>\n",
       "      <td>3:11 AM</td>\n",
       "      <td>145329</td>\n",
       "      <td>7.100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/2/1988</td>\n",
       "      <td>12:49 PM</td>\n",
       "      <td>147309</td>\n",
       "      <td>1.702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2/23/2005</td>\n",
       "      <td>9:50 PM</td>\n",
       "      <td>149654</td>\n",
       "      <td>1.825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
       "440      Aaron    Male   7/22/1990         2:53 PM   52119   11.343   \n",
       "327      Aaron    Male   1/29/1994         6:48 PM   58755    5.097   \n",
       "101      Aaron    Male   2/17/2012        10:20 AM   61602   11.849   \n",
       "937      Aaron     NaN   1/22/1986         7:39 PM   63126   18.424   \n",
       "538       Adam    Male   10/8/2010         9:53 PM   45181    3.491   \n",
       "..         ...     ...         ...             ...     ...      ...   \n",
       "951        NaN  Female   9/14/2010         5:19 AM  143638    9.662   \n",
       "269        NaN  Female    8/2/1992         8:35 PM  145316   18.517   \n",
       "890        NaN    Male  11/24/2015         3:11 AM  145329    7.100   \n",
       "628        NaN     NaN    9/2/1988        12:49 PM  147309    1.702   \n",
       "186        NaN  Female   2/23/2005         9:50 PM  149654    1.825   \n",
       "\n",
       "    Senior Management             Team  \n",
       "440              True  Client Services  \n",
       "327              True        Marketing  \n",
       "101              True        Marketing  \n",
       "937             False  Client Services  \n",
       "538             False  Human Resources  \n",
       "..                ...              ...  \n",
       "951               NaN              NaN  \n",
       "269               NaN  Human Resources  \n",
       "890               NaN          Finance  \n",
       "628               NaN     Distribution  \n",
       "186               NaN            Sales  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.sort_values(['First Name','Salary'],ascending=True,na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/22/1986</td>\n",
       "      <td>7:39 PM</td>\n",
       "      <td>63126</td>\n",
       "      <td>18.424</td>\n",
       "      <td>False</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>2/17/2012</td>\n",
       "      <td>10:20 AM</td>\n",
       "      <td>61602</td>\n",
       "      <td>11.849</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/29/1994</td>\n",
       "      <td>6:48 PM</td>\n",
       "      <td>58755</td>\n",
       "      <td>5.097</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>7/22/1990</td>\n",
       "      <td>2:53 PM</td>\n",
       "      <td>52119</td>\n",
       "      <td>11.343</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>12/24/1990</td>\n",
       "      <td>8:57 PM</td>\n",
       "      <td>110194</td>\n",
       "      <td>14.727</td>\n",
       "      <td>True</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/17/2011</td>\n",
       "      <td>8:29 AM</td>\n",
       "      <td>41126</td>\n",
       "      <td>14.009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>2/4/2005</td>\n",
       "      <td>1:01 PM</td>\n",
       "      <td>40451</td>\n",
       "      <td>16.044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/18/2007</td>\n",
       "      <td>6:59 PM</td>\n",
       "      <td>40297</td>\n",
       "      <td>6.185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>6/24/2014</td>\n",
       "      <td>10:05 AM</td>\n",
       "      <td>38275</td>\n",
       "      <td>10.494</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>10/8/2012</td>\n",
       "      <td>1:12 AM</td>\n",
       "      <td>37076</td>\n",
       "      <td>18.576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
       "937      Aaron     NaN   1/22/1986         7:39 PM   63126   18.424   \n",
       "101      Aaron    Male   2/17/2012        10:20 AM   61602   11.849   \n",
       "327      Aaron    Male   1/29/1994         6:48 PM   58755    5.097   \n",
       "440      Aaron    Male   7/22/1990         2:53 PM   52119   11.343   \n",
       "141       Adam    Male  12/24/1990         8:57 PM  110194   14.727   \n",
       "..         ...     ...         ...             ...     ...      ...   \n",
       "51         NaN     NaN  12/17/2011         8:29 AM   41126   14.009   \n",
       "267        NaN    Male    2/4/2005         1:01 PM   40451   16.044   \n",
       "174        NaN     NaN   9/18/2007         6:59 PM   40297    6.185   \n",
       "504        NaN  Female   6/24/2014        10:05 AM   38275   10.494   \n",
       "25         NaN    Male   10/8/2012         1:12 AM   37076   18.576   \n",
       "\n",
       "    Senior Management             Team  \n",
       "937             False  Client Services  \n",
       "101              True        Marketing  \n",
       "327              True        Marketing  \n",
       "440              True  Client Services  \n",
       "141              True          Product  \n",
       "..                ...              ...  \n",
       "51                NaN            Sales  \n",
       "267               NaN     Distribution  \n",
       "174               NaN  Client Services  \n",
       "504               NaN     Distribution  \n",
       "25                NaN  Client Services  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.sort_values(['First Name','Salary'],ascending=[True,False],na_position='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=<object object>, observed=False, dropna=True)\n",
    "\n",
    "# groupby() mainly refers to the following one or more process steps \n",
    "# Splitting : splitting the data into groups\n",
    "# Applying : applying function on each group\n",
    "# Combing : combine the individual groups into single data set\n",
    "\n",
    "# Grrouping with single key\n",
    "gr=D.groupby(['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Female': Int64Index([  2,   6,   7,   8,   9,  10,  11,  14,  15,  18,\n",
      "            ...\n",
      "            977, 980, 982, 984, 986, 987, 988, 990, 991, 993],\n",
      "           dtype='int64', length=431), 'Male': Int64Index([  0,   1,   3,   4,   5,  12,  13,  16,  17,  21,\n",
      "            ...\n",
      "            978, 979, 981, 983, 992, 994, 996, 997, 998, 999],\n",
      "           dtype='int64', length=424)}\n"
     ]
    }
   ],
   "source": [
    "print(gr.groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data set into two groups on the basis of gender(Male/Female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>Maria</td>\n",
       "      <td>4/23/1993</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>8/6/1993</td>\n",
       "      <td>12:42 PM</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       First Name Start Date Last Login Time  Salary  Bonus %  \\\n",
       "Gender                                                          \n",
       "Female      Maria  4/23/1993        11:17 AM  130590   11.858   \n",
       "Male      Douglas   8/6/1993        12:42 PM   97308    6.945   \n",
       "\n",
       "        Senior Management       Team  \n",
       "Gender                                \n",
       "Female              False    Finance  \n",
       "Male                 True  Marketing  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First entites in all the groups\n",
    "gr.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouping with multiple keys\n",
    "gr1=D.groupby(['Gender','Team'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Male', 'Marketing'): Int64Index([  0,  21,  26,  74,  77, 101, 104, 112, 119, 150, 187, 204, 216,\n",
      "            252, 274, 293, 309, 327, 353, 403, 413, 452, 454, 461, 464, 484,\n",
      "            496, 503, 544, 550, 556, 571, 581, 696, 722, 798, 860, 867, 899,\n",
      "            947, 994],\n",
      "           dtype='int64'), ('Male', nan): Int64Index([  1,  23,  32, 109, 199, 258, 290, 314, 367, 438, 445, 479, 513,\n",
      "            520, 580, 634, 635, 647, 706, 726, 753, 781, 850, 851, 864, 912],\n",
      "           dtype='int64'), ('Female', 'Finance'): Int64Index([  2,   7,  14,  67,  84,  96, 100, 103, 105, 142, 160, 172, 179,\n",
      "            190, 226, 268, 276, 399, 405, 418, 425, 436, 442, 485, 526, 540,\n",
      "            547, 549, 595, 644, 646, 649, 650, 691, 702, 731, 775, 807, 836,\n",
      "            884, 889, 905, 907, 987],\n",
      "           dtype='int64'), ('Male', 'Finance'): Int64Index([  3,  46,  56,  68,  83, 117, 200, 205, 219, 222, 241, 286, 304,\n",
      "            318, 328, 335, 341, 348, 420, 428, 458, 493, 582, 618, 620, 685,\n",
      "            710, 725, 732, 773, 790, 809, 831, 837, 854, 861, 890, 891, 954,\n",
      "            992, 996],\n",
      "           dtype='int64'), ('Male', 'Client Services'): Int64Index([  4,  24,  25,  28,  34,  39,  82,  85, 102, 128, 163, 184, 196,\n",
      "            217, 227, 244, 254, 265, 325, 330, 334, 387, 392, 408, 440, 448,\n",
      "            472, 497, 506, 529, 560, 564, 577, 621, 655, 729, 742, 782, 877,\n",
      "            915, 918, 974],\n",
      "           dtype='int64'), ('Male', 'Legal'): Int64Index([  5,  29,  81,  94, 106, 107, 116, 136, 147, 162, 188, 221, 223,\n",
      "            238, 295, 310, 336, 431, 450, 469, 488, 510, 579, 584, 630, 654,\n",
      "            664, 668, 688, 708, 791, 819, 832, 887, 981],\n",
      "           dtype='int64'), ('Female', 'Product'): Int64Index([  6,  15,  19,  44,  55,  79, 115, 123, 124, 131, 146, 154, 157,\n",
      "            169, 178, 198, 231, 236, 261, 316, 342, 369, 388, 394, 518, 532,\n",
      "            552, 555, 570, 578, 598, 653, 678, 682, 736, 770, 786, 789, 803,\n",
      "            828, 842, 857, 868, 970, 977],\n",
      "           dtype='int64'), ('Female', 'Engineering'): Int64Index([  8,  30,  50,  54,  58,  59, 113, 122, 138, 173, 191, 209, 213,\n",
      "            214, 235, 262, 467, 475, 541, 553, 558, 561, 608, 619, 632, 633,\n",
      "            636, 707, 711, 724, 761, 797, 808, 816, 875, 908, 935, 948, 956,\n",
      "            958, 969, 980, 984, 993],\n",
      "           dtype='int64'), ('Female', 'Business Development'): Int64Index([  9,  33,  36,  38,  61,  66,  92, 111, 114, 118, 134, 155, 164,\n",
      "            182, 203, 249, 275, 279, 317, 324, 368, 370, 401, 406, 419, 441,\n",
      "            481, 487, 498, 509, 537, 583, 590, 638, 735, 746, 748, 772, 779,\n",
      "            802, 822, 841, 852, 859, 872, 898, 916, 933, 936, 950],\n",
      "           dtype='int64'), ('Female', nan): Int64Index([10, 139, 382, 434, 512, 567, 573, 626, 684, 774, 794, 853, 951], dtype='int64'), ('Female', 'Legal'): Int64Index([ 11,  42,  88,  89, 110, 135, 165, 185, 193, 210, 237, 256, 289,\n",
      "            308, 313, 354, 355, 412, 433, 457, 460, 465, 500, 516, 623, 741,\n",
      "            754, 763, 865, 888, 910, 929, 955, 976],\n",
      "           dtype='int64'), ('Male', 'Human Resources'): Int64Index([ 12,  16,  52,  57,  63, 127, 132, 156, 208, 259, 270, 281, 297,\n",
      "            302, 339, 345, 346, 377, 409, 426, 439, 508, 519, 525, 538, 572,\n",
      "            637, 643, 672, 674, 709, 743, 760, 766, 776, 780, 801, 820, 829,\n",
      "            833, 846, 858, 863, 966, 978],\n",
      "           dtype='int64'), ('Male', 'Sales'): Int64Index([ 13,  45,  71, 167, 189, 202, 225, 257, 312, 337, 340, 371, 373,\n",
      "            410, 421, 427, 466, 495, 515, 539, 592, 607, 609, 624, 690, 692,\n",
      "            727, 739, 764, 787, 862, 873, 886, 892, 896, 921, 957, 964, 999],\n",
      "           dtype='int64'), ('Male', 'Product'): Int64Index([ 17,  72, 141, 176, 195, 243, 272, 273, 322, 326, 332, 338, 358,\n",
      "            364, 378, 383, 384, 422, 463, 533, 546, 575, 576, 610, 681, 737,\n",
      "            757, 771, 799, 800, 812, 848, 849, 885, 909, 917, 953, 962, 979,\n",
      "            997],\n",
      "           dtype='int64'), ('Female', 'Client Services'): Int64Index([ 18,  37,  47,  95, 133, 145, 152, 197, 206, 207, 212, 215, 234,\n",
      "            284, 287, 291, 292, 333, 359, 385, 423, 489, 502, 543, 569, 574,\n",
      "            587, 605, 606, 651, 658, 670, 677, 679, 694, 728, 751, 755, 767,\n",
      "            796, 845, 866, 879, 920, 924, 932, 965, 990],\n",
      "           dtype='int64'), (nan, 'Legal'): Int64Index([ 20,  27,  90,  93, 108, 192, 230, 534, 562, 698, 701, 723, 749,\n",
      "            758, 827, 844, 961, 985, 989],\n",
      "           dtype='int64'), (nan, 'Client Services'): Int64Index([22, 70, 174, 282, 303, 315, 366, 389, 432, 524, 554, 631, 639, 937,\n",
      "            938, 939],\n",
      "           dtype='int64'), (nan, 'Product'): Int64Index([31, 121, 247, 482, 514, 527, 528, 545, 750, 783], dtype='int64'), ('Female', 'Sales'): Int64Index([ 35,  73,  78,  87, 166, 170, 186, 233, 320, 321, 323, 347, 349,\n",
      "            351, 360, 361, 386, 391, 395, 437, 443, 480, 494, 523, 548, 593,\n",
      "            622, 641, 657, 693, 768, 777, 784, 806, 843, 906, 925, 963, 975],\n",
      "           dtype='int64'), ('Male', 'Distribution'): Int64Index([ 40,  65, 137, 177, 181, 229, 248, 267, 362, 397, 416, 435, 444,\n",
      "            470, 486, 501, 566, 614, 665, 687, 697, 700, 704, 714, 756, 762,\n",
      "            778, 804, 830, 838, 902, 919, 931, 940, 944],\n",
      "           dtype='int64'), (nan, 'Business Development'): Int64Index([41, 64, 86, 151, 251, 603, 666, 788, 834, 895, 923], dtype='int64'), ('Female', 'Marketing'): Int64Index([ 43,  62,  98, 140, 158, 201, 220, 305, 319, 331, 379, 446, 468,\n",
      "            490, 531, 535, 568, 585, 586, 617, 625, 627, 629, 656, 675, 676,\n",
      "            730, 785, 811, 813, 817, 824, 881, 883, 894, 913, 930, 942, 986,\n",
      "            991],\n",
      "           dtype='int64'), ('Male', 'Business Development'): Int64Index([ 48,  99, 120, 129, 180, 280, 306, 344, 411, 414, 473, 477, 478,\n",
      "            492, 511, 536, 594, 596, 648, 673, 716, 721, 733, 738, 745, 747,\n",
      "            752, 810, 869, 893, 911, 922, 927, 928, 949, 959, 960, 971, 973,\n",
      "            998],\n",
      "           dtype='int64'), (nan, 'Sales'): Int64Index([49, 51, 80, 144, 148, 250, 253, 277, 296, 350, 375, 380, 390, 404,\n",
      "            825, 972],\n",
      "           dtype='int64'), (nan, 'Finance'): Int64Index([ 53,  69, 153, 218, 311, 363, 415, 613, 642, 686, 689, 744, 765,\n",
      "            847, 856, 880, 897],\n",
      "           dtype='int64'), (nan, 'Distribution'): Int64Index([ 60, 161, 183, 240, 288, 407, 491, 499, 600, 612, 628, 660, 683,\n",
      "            699, 792, 882, 945, 995],\n",
      "           dtype='int64'), ('Female', 'Human Resources'): Int64Index([ 75, 125, 126, 130, 211, 232, 255, 264, 269, 285, 299, 396, 398,\n",
      "            429, 449, 453, 455, 471, 476, 483, 517, 559, 565, 588, 589, 599,\n",
      "            695, 720, 734, 839, 874, 900, 903, 904, 952, 982, 988],\n",
      "           dtype='int64'), ('Female', 'Distribution'): Int64Index([ 76, 149, 194, 224, 260, 266, 278, 294, 307, 356, 374, 430, 504,\n",
      "            522, 530, 542, 557, 591, 597, 616, 640, 662, 663, 671, 713, 759,\n",
      "            793, 814, 818, 821, 840, 878, 901, 914, 926, 946, 968],\n",
      "           dtype='int64'), (nan, nan): Int64Index([91, 669, 826, 855], dtype='int64'), (nan, 'Marketing'): Int64Index([ 97, 159, 168, 271, 298, 400, 451, 459, 601, 611, 645, 659, 667,\n",
      "            740, 769, 870, 876],\n",
      "           dtype='int64'), (nan, 'Engineering'): Int64Index([143, 263, 393, 402, 456, 521, 602, 712], dtype='int64'), ('Male', 'Engineering'): Int64Index([171, 175, 228, 242, 245, 283, 300, 301, 329, 343, 352, 357, 372,\n",
      "            376, 381, 447, 462, 474, 505, 507, 551, 563, 604, 615, 652, 661,\n",
      "            680, 705, 715, 718, 719, 805, 823, 835, 871, 934, 941, 943, 967,\n",
      "            983],\n",
      "           dtype='int64'), (nan, 'Human Resources'): Int64Index([239, 246, 365, 417, 424, 703, 717, 795, 815], dtype='int64')}\n"
     ]
    }
   ],
   "source": [
    "print(gr1.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Female</th>\n",
       "      <th>Business Development</th>\n",
       "      <td>Frances</td>\n",
       "      <td>8/8/2002</td>\n",
       "      <td>6:51 AM</td>\n",
       "      <td>139852</td>\n",
       "      <td>7.524</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Client Services</th>\n",
       "      <td>Diana</td>\n",
       "      <td>10/23/1981</td>\n",
       "      <td>10:27 AM</td>\n",
       "      <td>132940</td>\n",
       "      <td>19.082</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distribution</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>9/10/1988</td>\n",
       "      <td>12:42 PM</td>\n",
       "      <td>131604</td>\n",
       "      <td>7.353</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering</th>\n",
       "      <td>Angela</td>\n",
       "      <td>11/22/2005</td>\n",
       "      <td>6:29 AM</td>\n",
       "      <td>95570</td>\n",
       "      <td>18.523</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>Maria</td>\n",
       "      <td>4/23/1993</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>Bonnie</td>\n",
       "      <td>7/2/1991</td>\n",
       "      <td>1:27 AM</td>\n",
       "      <td>104897</td>\n",
       "      <td>5.118</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legal</th>\n",
       "      <td>Julie</td>\n",
       "      <td>10/26/1997</td>\n",
       "      <td>3:19 PM</td>\n",
       "      <td>102508</td>\n",
       "      <td>12.637</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>Marilyn</td>\n",
       "      <td>12/7/1980</td>\n",
       "      <td>3:16 AM</td>\n",
       "      <td>73524</td>\n",
       "      <td>5.207</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>8/17/1987</td>\n",
       "      <td>4:20 PM</td>\n",
       "      <td>65476</td>\n",
       "      <td>10.012</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>Theresa</td>\n",
       "      <td>10/10/2006</td>\n",
       "      <td>1:12 AM</td>\n",
       "      <td>85182</td>\n",
       "      <td>16.675</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Male</th>\n",
       "      <th>Business Development</th>\n",
       "      <td>Clarence</td>\n",
       "      <td>3/26/1996</td>\n",
       "      <td>5:57 AM</td>\n",
       "      <td>93581</td>\n",
       "      <td>6.083</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Client Services</th>\n",
       "      <td>Larry</td>\n",
       "      <td>1/24/1998</td>\n",
       "      <td>4:47 PM</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distribution</th>\n",
       "      <td>Michael</td>\n",
       "      <td>10/10/2008</td>\n",
       "      <td>11:25 AM</td>\n",
       "      <td>99283</td>\n",
       "      <td>2.665</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering</th>\n",
       "      <td>Patrick</td>\n",
       "      <td>8/17/2007</td>\n",
       "      <td>3:16 AM</td>\n",
       "      <td>143499</td>\n",
       "      <td>17.495</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>3/4/2005</td>\n",
       "      <td>1:00 PM</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>12/1/1980</td>\n",
       "      <td>1:08 AM</td>\n",
       "      <td>112807</td>\n",
       "      <td>17.492</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legal</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>4/18/1987</td>\n",
       "      <td>1:35 AM</td>\n",
       "      <td>115163</td>\n",
       "      <td>10.125</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>8/6/1993</td>\n",
       "      <td>12:42 PM</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>12/7/1986</td>\n",
       "      <td>7:45 PM</td>\n",
       "      <td>111737</td>\n",
       "      <td>6.414</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>Gary</td>\n",
       "      <td>1/27/2008</td>\n",
       "      <td>11:40 PM</td>\n",
       "      <td>109831</td>\n",
       "      <td>5.831</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            First Name  Start Date Last Login Time  Salary  \\\n",
       "Gender Team                                                                  \n",
       "Female Business Development    Frances    8/8/2002         6:51 AM  139852   \n",
       "       Client Services           Diana  10/23/1981        10:27 AM  132940   \n",
       "       Distribution           Margaret   9/10/1988        12:42 PM  131604   \n",
       "       Engineering              Angela  11/22/2005         6:29 AM   95570   \n",
       "       Finance                   Maria   4/23/1993        11:17 AM  130590   \n",
       "       Human Resources          Bonnie    7/2/1991         1:27 AM  104897   \n",
       "       Legal                     Julie  10/26/1997         3:19 PM  102508   \n",
       "       Marketing               Marilyn   12/7/1980         3:16 AM   73524   \n",
       "       Product                    Ruby   8/17/1987         4:20 PM   65476   \n",
       "       Sales                   Theresa  10/10/2006         1:12 AM   85182   \n",
       "Male   Business Development   Clarence   3/26/1996         5:57 AM   93581   \n",
       "       Client Services           Larry   1/24/1998         4:47 PM  101004   \n",
       "       Distribution            Michael  10/10/2008        11:25 AM   99283   \n",
       "       Engineering             Patrick   8/17/2007         3:16 AM  143499   \n",
       "       Finance                   Jerry    3/4/2005         1:00 PM  138705   \n",
       "       Human Resources         Brandon   12/1/1980         1:08 AM  112807   \n",
       "       Legal                    Dennis   4/18/1987         1:35 AM  115163   \n",
       "       Marketing               Douglas    8/6/1993        12:42 PM   97308   \n",
       "       Product                   Shawn   12/7/1986         7:45 PM  111737   \n",
       "       Sales                      Gary   1/27/2008        11:40 PM  109831   \n",
       "\n",
       "                             Bonus %  Senior Management  \n",
       "Gender Team                                              \n",
       "Female Business Development    7.524               True  \n",
       "       Client Services        19.082              False  \n",
       "       Distribution            7.353               True  \n",
       "       Engineering            18.523               True  \n",
       "       Finance                11.858              False  \n",
       "       Human Resources         5.118               True  \n",
       "       Legal                  12.637               True  \n",
       "       Marketing               5.207               True  \n",
       "       Product                10.012               True  \n",
       "       Sales                  16.675              False  \n",
       "Male   Business Development    6.083               True  \n",
       "       Client Services         1.389               True  \n",
       "       Distribution            2.665               True  \n",
       "       Engineering            17.495               True  \n",
       "       Finance                 9.340               True  \n",
       "       Human Resources        17.492               True  \n",
       "       Legal                  10.125              False  \n",
       "       Marketing               6.945               True  \n",
       "       Product                 6.414              False  \n",
       "       Sales                   5.831              False  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr1.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>First Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aaron</th>\n",
       "      <td>235602</td>\n",
       "      <td>46.713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam</th>\n",
       "      <td>321978</td>\n",
       "      <td>38.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alan</th>\n",
       "      <td>193580</td>\n",
       "      <td>31.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albert</th>\n",
       "      <td>570154</td>\n",
       "      <td>75.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alice</th>\n",
       "      <td>774731</td>\n",
       "      <td>70.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walter</th>\n",
       "      <td>523442</td>\n",
       "      <td>59.457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanda</th>\n",
       "      <td>144245</td>\n",
       "      <td>26.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wayne</th>\n",
       "      <td>378262</td>\n",
       "      <td>52.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William</th>\n",
       "      <td>225419</td>\n",
       "      <td>26.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Willie</th>\n",
       "      <td>463265</td>\n",
       "      <td>31.117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Salary  Bonus %\n",
       "First Name                 \n",
       "Aaron       235602   46.713\n",
       "Adam        321978   38.365\n",
       "Alan        193580   31.254\n",
       "Albert      570154   75.585\n",
       "Alice       774731   70.183\n",
       "...            ...      ...\n",
       "Walter      523442   59.457\n",
       "Wanda       144245   26.827\n",
       "Wayne       378262   52.275\n",
       "William     225419   26.665\n",
       "Willie      463265   31.117\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.groupby('First Name').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Female', 'Business Development')\n",
      "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "9      Frances  Female    8/8/2002         6:51 AM  139852    7.524   \n",
      "33        Jean  Female  12/18/1993         9:07 AM  119082   16.180   \n",
      "36      Rachel  Female   2/16/2009         8:47 PM  142032   12.599   \n",
      "38   Stephanie  Female   9/13/1986         1:52 AM   36844    5.574   \n",
      "61      Denise  Female   11/6/2001        12:03 PM  106862    3.699   \n",
      "66       Nancy  Female  12/15/2012        11:57 PM  125250    2.672   \n",
      "92       Linda  Female   5/25/2000         5:45 PM  119009   12.506   \n",
      "111     Bonnie  Female  12/17/1999         3:12 PM   42153    8.454   \n",
      "114     Ashley  Female    8/4/2002        11:00 AM   58698    6.811   \n",
      "118     Andrea  Female   1/12/2012         5:43 AM  120204    9.557   \n",
      "134    Carolyn  Female    4/9/2011         5:38 AM  109260    2.650   \n",
      "155      Marie  Female   3/16/2003        12:12 AM  104058   17.695   \n",
      "164       Mary  Female   8/13/1999         1:03 AM  134645   18.197   \n",
      "182    Lillian  Female   6/19/1999         4:44 PM  123940   12.184   \n",
      "203      Diana  Female    1/4/2013         7:53 AM  103521    2.784   \n",
      "249      Betty  Female   6/12/2002         3:59 AM  104896   19.550   \n",
      "275      Debra  Female   2/22/1994        10:25 PM  104250   18.456   \n",
      "279       Ruby  Female   11/8/2000         7:35 PM  105946    1.139   \n",
      "317      Kathy  Female  12/18/2013         3:09 PM  132381    8.340   \n",
      "324       Ruby  Female  12/10/1988         8:27 PM   76707    6.031   \n",
      "368    Marilyn  Female  12/17/1982        12:10 PM  147183    8.748   \n",
      "370      Linda  Female   7/15/2009        10:12 PM  144001    2.194   \n",
      "401      Norma  Female   4/16/1996         5:40 AM   38872    9.302   \n",
      "406       Mary  Female   8/22/2010         8:03 AM   42214   17.538   \n",
      "419    Dorothy  Female   11/5/2013        10:50 PM  140136    3.120   \n",
      "441     Louise  Female   5/13/2003         9:47 AM   46666    1.886   \n",
      "481        NaN  Female   4/27/2013         6:40 AM   93847    1.085   \n",
      "487     Cheryl  Female   12/3/2014         9:27 AM   71751   15.918   \n",
      "498      Diana  Female   6/13/1994         4:21 PM   41831    4.548   \n",
      "509        Ann  Female   11/4/1984        12:17 PM   90719    6.220   \n",
      "537    Cynthia  Female   9/14/1991         1:08 PM   51633   13.472   \n",
      "583      Diane  Female    2/2/1988         6:14 PM   49501   13.506   \n",
      "590     Andrea  Female  10/30/1994        11:23 AM   87575   13.346   \n",
      "638    Frances  Female  11/12/1996         6:08 PM  112467    1.433   \n",
      "735    Barbara  Female  11/26/2002         5:32 AM   82884    6.837   \n",
      "746     Gloria  Female   8/19/2004        10:31 AM   46602    1.027   \n",
      "748     Martha  Female   7/17/1997        11:11 PM   94963   19.626   \n",
      "772    Lillian  Female   8/14/2009         5:41 AM  113554   18.018   \n",
      "779      Doris  Female    9/8/1984         7:03 PM  114360   17.799   \n",
      "802       Jane  Female   1/12/1992         1:23 PM   51923   13.623   \n",
      "822    Deborah  Female   2/26/2009        10:17 AM  118043    7.266   \n",
      "841       Ruby  Female   8/13/2006         6:27 PM   48354   19.501   \n",
      "852      Diana  Female  11/19/2004         8:47 AM  105066   17.343   \n",
      "859      Robin  Female   10/1/2012         2:44 AM   41808   19.239   \n",
      "872     Brenda  Female   1/18/2015         4:39 PM   73749   19.332   \n",
      "898       Jane  Female   4/27/2012         2:12 PM   59680   15.211   \n",
      "916    Marilyn  Female   1/16/1996         7:18 AM  118369    7.696   \n",
      "933      Doris  Female    7/5/1993         5:54 PM  141439    3.799   \n",
      "936      Maria  Female   3/14/2003         5:23 PM   96250   10.056   \n",
      "950      Paula  Female   5/21/1983        11:42 AM   58423   10.833   \n",
      "\n",
      "    Senior Management                  Team  \n",
      "9                True  Business Development  \n",
      "33              False  Business Development  \n",
      "36              False  Business Development  \n",
      "38               True  Business Development  \n",
      "61              False  Business Development  \n",
      "66               True  Business Development  \n",
      "92               True  Business Development  \n",
      "111              True  Business Development  \n",
      "114              True  Business Development  \n",
      "118             False  Business Development  \n",
      "134              True  Business Development  \n",
      "155              True  Business Development  \n",
      "164             False  Business Development  \n",
      "182              True  Business Development  \n",
      "203              True  Business Development  \n",
      "249              True  Business Development  \n",
      "275              True  Business Development  \n",
      "279             False  Business Development  \n",
      "317             False  Business Development  \n",
      "324             False  Business Development  \n",
      "368             False  Business Development  \n",
      "370             False  Business Development  \n",
      "401              True  Business Development  \n",
      "406              True  Business Development  \n",
      "419              True  Business Development  \n",
      "441              True  Business Development  \n",
      "481               NaN  Business Development  \n",
      "487             False  Business Development  \n",
      "498             False  Business Development  \n",
      "509             False  Business Development  \n",
      "537              True  Business Development  \n",
      "583             False  Business Development  \n",
      "590              True  Business Development  \n",
      "638             False  Business Development  \n",
      "735              True  Business Development  \n",
      "746              True  Business Development  \n",
      "748              True  Business Development  \n",
      "772              True  Business Development  \n",
      "779              True  Business Development  \n",
      "802             False  Business Development  \n",
      "822              True  Business Development  \n",
      "841             False  Business Development  \n",
      "852              True  Business Development  \n",
      "859             False  Business Development  \n",
      "872             False  Business Development  \n",
      "898              True  Business Development  \n",
      "916              True  Business Development  \n",
      "933             False  Business Development  \n",
      "936             False  Business Development  \n",
      "950             False  Business Development  \n",
      "('Female', 'Client Services')\n",
      "     First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "18        Diana  Female  10/23/1981        10:27 AM  132940   19.082   \n",
      "37        Linda  Female  10/19/1981         8:49 PM   57427    9.557   \n",
      "47        Kathy  Female   6/22/2005         4:51 AM   66820    9.000   \n",
      "95      Heather  Female   7/11/1998         2:17 PM   43026   14.166   \n",
      "133        Lois  Female  12/25/1987         4:16 PM  106317    2.235   \n",
      "145    Jennifer  Female    4/4/2009        10:47 PM   71715   13.079   \n",
      "152        Ruth  Female   8/19/1999         4:03 AM  129297    8.067   \n",
      "197     Carolyn  Female   11/6/2012         3:51 AM   69268    3.031   \n",
      "206    Patricia  Female   12/6/2012         4:01 AM  114079    8.399   \n",
      "207   Katherine  Female  11/14/1999         8:15 AM   57531    1.767   \n",
      "212        Lisa  Female   11/2/2009         9:42 AM  115387    1.821   \n",
      "215        Mary  Female   5/30/2009        11:41 PM   92544    3.800   \n",
      "234       Irene  Female    2/4/1982         5:26 PM   40837   12.182   \n",
      "284      Brenda  Female   10/6/2005         9:33 PM  141521    4.337   \n",
      "287        Lois  Female   11/9/2011         7:06 AM  147183    9.999   \n",
      "291       Tammy  Female  11/11/1984        10:30 AM  132839   17.463   \n",
      "292        Anne  Female    3/7/2000         6:45 AM   44537   18.284   \n",
      "333     Shirley  Female    6/8/1986         2:25 AM  110061    7.494   \n",
      "359       Sarah  Female   5/22/1994         7:06 AM   64207    7.824   \n",
      "385       Debra  Female   1/19/2010         8:48 AM   70492    8.895   \n",
      "423     Deborah  Female  11/25/1980         4:33 AM   46953   17.436   \n",
      "489      Sharon  Female    7/1/2011         9:50 AM   46007   19.731   \n",
      "502       Sarah  Female   11/3/1980         2:47 PM   37259    1.763   \n",
      "543        Anna  Female   4/15/2008         2:34 PM  117293    2.366   \n",
      "569        Ruby  Female    7/7/1980         3:42 AM  101262    6.773   \n",
      "574         NaN  Female   1/13/2009         1:01 PM  118736    7.421   \n",
      "587      Cheryl  Female   7/23/1982         6:28 AM   98841    8.945   \n",
      "605        Rose  Female   5/20/1982         9:14 AM   97691    2.142   \n",
      "606     Mildred  Female   2/21/1981         3:30 AM   47266   10.256   \n",
      "651     Deborah  Female  11/21/1985         1:40 PM  113129   17.371   \n",
      "658     Jessica  Female  10/19/1986         9:58 PM   90285   13.591   \n",
      "670      Martha  Female   9/14/2001         3:22 AM  135758   14.782   \n",
      "677  Jacqueline  Female   9/20/1994         6:07 PM   62371   15.770   \n",
      "679        Lori  Female   9/11/2002         7:36 AM   66029    3.345   \n",
      "694     Barbara  Female  11/12/2011        12:35 AM   85718   13.326   \n",
      "728     Dorothy  Female   10/8/2008        12:22 AM   82744   19.111   \n",
      "751        Tina  Female    8/6/1999         3:18 AM  102841    3.369   \n",
      "755      Denise  Female   2/19/1984         6:22 AM   86150    3.997   \n",
      "767       Janet  Female   7/12/2008        12:10 PM   36927   18.769   \n",
      "796   Catherine  Female   8/31/1986         6:20 PM   59970   12.801   \n",
      "845      Brenda  Female    1/3/1981         6:01 PM  131131   11.682   \n",
      "866      Bonnie  Female   8/25/2004         2:17 AM  131943   14.249   \n",
      "879         Amy  Female   5/20/2009         6:26 AM   75415   19.132   \n",
      "920        Rose  Female    1/2/2002         9:14 PM   49538    9.828   \n",
      "924     Deborah  Female  11/11/2003         4:53 PM   60003    9.624   \n",
      "932      Bonnie  Female  12/23/2008         9:26 AM   90427    2.010   \n",
      "965   Catherine  Female   9/25/1989         1:31 AM   68164   18.393   \n",
      "990       Robin  Female   7/24/1987         1:35 PM  100765   10.982   \n",
      "\n",
      "    Senior Management             Team  \n",
      "18              False  Client Services  \n",
      "37               True  Client Services  \n",
      "47               True  Client Services  \n",
      "95              False  Client Services  \n",
      "133              True  Client Services  \n",
      "145              True  Client Services  \n",
      "152              True  Client Services  \n",
      "197             False  Client Services  \n",
      "206              True  Client Services  \n",
      "207             False  Client Services  \n",
      "212             False  Client Services  \n",
      "215             False  Client Services  \n",
      "234              True  Client Services  \n",
      "284             False  Client Services  \n",
      "287              True  Client Services  \n",
      "291              True  Client Services  \n",
      "292              True  Client Services  \n",
      "333             False  Client Services  \n",
      "359              True  Client Services  \n",
      "385             False  Client Services  \n",
      "423             False  Client Services  \n",
      "489              True  Client Services  \n",
      "502             False  Client Services  \n",
      "543             False  Client Services  \n",
      "569             False  Client Services  \n",
      "574               NaN  Client Services  \n",
      "587              True  Client Services  \n",
      "605             False  Client Services  \n",
      "606             False  Client Services  \n",
      "651             False  Client Services  \n",
      "658              True  Client Services  \n",
      "670              True  Client Services  \n",
      "677              True  Client Services  \n",
      "679              True  Client Services  \n",
      "694             False  Client Services  \n",
      "728              True  Client Services  \n",
      "751             False  Client Services  \n",
      "755             False  Client Services  \n",
      "767             False  Client Services  \n",
      "796             False  Client Services  \n",
      "845             False  Client Services  \n",
      "866             False  Client Services  \n",
      "879             False  Client Services  \n",
      "920             False  Client Services  \n",
      "924             False  Client Services  \n",
      "932              True  Client Services  \n",
      "965             False  Client Services  \n",
      "990              True  Client Services  \n",
      "('Female', 'Distribution')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "76     Margaret  Female   9/10/1988        12:42 PM  131604    7.353   \n",
      "149         NaN  Female   8/17/2014         2:00 PM   86230    8.578   \n",
      "194       Irene  Female   1/30/2004         8:25 PM  131038    8.996   \n",
      "224       Sarah  Female   9/14/1995         7:50 AM   87298    2.311   \n",
      "260      Gloria  Female   3/27/2007         1:44 AM   90730    2.491   \n",
      "266         NaN  Female    8/5/2009         8:14 AM  115145   14.063   \n",
      "278       Betty  Female   6/28/2005         6:03 PM   51613   12.984   \n",
      "294    Virginia  Female  10/20/1999         6:23 AM   46905   19.154   \n",
      "307     Marilyn  Female   9/26/1981         2:23 AM   86386    2.937   \n",
      "356        Judy  Female    2/1/1990         3:32 PM   38092    5.668   \n",
      "374         NaN  Female  12/31/2006        11:02 PM   81444    3.171   \n",
      "430      Andrea  Female   10/1/2010        11:54 AM   79123   19.422   \n",
      "504         NaN  Female   6/24/2014        10:05 AM   38275   10.494   \n",
      "522   Catherine  Female   8/31/2013         7:24 PM   58047   14.858   \n",
      "530    Kathleen  Female   6/13/2014         9:16 AM   35575   14.595   \n",
      "542      Amanda  Female    8/1/2004         1:32 PM   80803   14.077   \n",
      "557        Jane  Female    6/1/1994         8:39 AM   42424   18.115   \n",
      "591      Rachel  Female   4/22/1988        12:01 PM  110924    7.808   \n",
      "597      Teresa  Female   6/24/1987         7:37 PM   69740    8.294   \n",
      "616    Kimberly  Female   12/6/1986         2:23 PM   37916   12.929   \n",
      "640    Kathleen  Female   8/28/2004        10:49 AM   42553    3.756   \n",
      "662   Katherine  Female  12/19/2000         1:40 AM   41643    4.659   \n",
      "663      Andrea  Female   8/31/1989        11:10 PM  113760   12.866   \n",
      "671       Laura  Female   7/12/2000         6:07 PM   84672    3.960   \n",
      "713         Ann  Female   9/28/1994         5:44 AM   79796    9.851   \n",
      "759        Ruth  Female    9/2/1980         6:52 AM   59678   10.895   \n",
      "793      Andrea  Female   7/22/1999         9:25 AM  149105   13.707   \n",
      "814      Rachel  Female   6/23/2011        12:52 AM   54941    3.221   \n",
      "818         Ann  Female   10/3/1980         1:08 AM   96941   10.048   \n",
      "821         NaN  Female  12/27/1980         2:27 AM   80399   12.254   \n",
      "840     Lillian  Female   8/26/2002         8:53 AM  103854    4.924   \n",
      "878  Jacqueline  Female   5/25/2003         7:13 PM  125418    8.064   \n",
      "901    Patricia  Female  10/10/1995         4:52 PM  119266    6.911   \n",
      "914         Ann  Female   9/28/2001         6:37 AM   71958    5.272   \n",
      "926      Judith  Female    3/1/2004         7:32 AM  109324   19.488   \n",
      "946         NaN  Female   9/15/1985         1:50 AM  133472   16.941   \n",
      "968      Louise  Female   3/27/1995        10:27 PM   43050   11.671   \n",
      "\n",
      "    Senior Management          Team  \n",
      "76               True  Distribution  \n",
      "149               NaN  Distribution  \n",
      "194             False  Distribution  \n",
      "224             False  Distribution  \n",
      "260             False  Distribution  \n",
      "266               NaN  Distribution  \n",
      "278             False  Distribution  \n",
      "294             False  Distribution  \n",
      "307             False  Distribution  \n",
      "356             False  Distribution  \n",
      "374               NaN  Distribution  \n",
      "430             False  Distribution  \n",
      "504               NaN  Distribution  \n",
      "522              True  Distribution  \n",
      "530             False  Distribution  \n",
      "542              True  Distribution  \n",
      "557             False  Distribution  \n",
      "591             False  Distribution  \n",
      "597             False  Distribution  \n",
      "616              True  Distribution  \n",
      "640              True  Distribution  \n",
      "662              True  Distribution  \n",
      "663              True  Distribution  \n",
      "671             False  Distribution  \n",
      "713             False  Distribution  \n",
      "759             False  Distribution  \n",
      "793              True  Distribution  \n",
      "814              True  Distribution  \n",
      "818              True  Distribution  \n",
      "821               NaN  Distribution  \n",
      "840              True  Distribution  \n",
      "878             False  Distribution  \n",
      "901             False  Distribution  \n",
      "914              True  Distribution  \n",
      "926             False  Distribution  \n",
      "946               NaN  Distribution  \n",
      "968             False  Distribution  \n",
      "('Female', 'Engineering')\n",
      "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "8       Angela  Female  11/22/2005         6:29 AM   95570   18.523   \n",
      "30   Christina  Female    8/6/2002         1:19 PM  118780    9.096   \n",
      "50       Nancy  Female   9/23/2000         8:05 AM   94976   13.830   \n",
      "54        Sara  Female   8/15/2007         9:23 AM   83677    8.999   \n",
      "58     Theresa  Female   4/11/2010         7:18 AM   72670    1.481   \n",
      "59       Irene  Female    5/7/1997         9:32 AM   66851   11.279   \n",
      "113       Tina  Female   6/12/2009         7:16 AM  114767    3.711   \n",
      "122  Christina  Female   4/13/2012         2:04 PM  110169   13.892   \n",
      "138     Ashley  Female   5/25/2006        11:30 AM  112238    6.030   \n",
      "173    Lillian  Female  12/21/2008         6:51 PM   85446    3.959   \n",
      "191       Lois  Female  10/18/2013         4:51 PM   36946    6.652   \n",
      "209      Emily  Female   9/20/2007        11:25 AM   89434   11.295   \n",
      "213     Evelyn  Female   5/24/1980        11:10 PM   81673   15.364   \n",
      "214      Julie  Female   7/23/1989         1:52 PM  109588    3.550   \n",
      "235      Norma  Female   2/11/2015        11:44 PM   94393    3.643   \n",
      "262       Anne  Female   7/16/1986         2:08 PM   69134    3.723   \n",
      "467        Amy  Female   6/19/2002         3:06 AM  122897    8.222   \n",
      "475  Stephanie  Female  11/26/1992        12:54 AM  122121    7.937   \n",
      "541       Ruby  Female    5/1/1999         3:36 AM  147362    7.851   \n",
      "553     Amanda  Female    6/7/2002         7:46 AM  109290   13.853   \n",
      "558      Linda  Female   8/18/1985         4:34 AM   51431   13.295   \n",
      "561   Kathleen  Female   9/20/1996         3:45 PM   71430    8.572   \n",
      "608        NaN  Female  10/24/1993         5:17 PM  116236   17.274   \n",
      "619    Beverly  Female  10/22/1990         2:45 PM   59070   19.064   \n",
      "632    Rebecca  Female   2/23/1990         3:21 PM  134673    6.878   \n",
      "633     Andrea  Female  11/17/2011         2:37 PM  123591    6.500   \n",
      "636    Marilyn  Female    7/2/1984         4:06 PM   92430    2.924   \n",
      "707   Patricia  Female    3/7/1998         1:10 AM   75825    7.839   \n",
      "711      Karen  Female    1/9/2014        10:09 PM   46478   16.552   \n",
      "724     Andrea  Female  12/10/2001         6:40 AM   37888   13.470   \n",
      "761   Jennifer  Female   3/31/2015         7:43 PM  132084   10.006   \n",
      "797       Rose  Female  12/31/1982         6:50 PM  145001   19.850   \n",
      "808      Julie  Female    3/8/1980         5:13 AM  145357    3.459   \n",
      "816      Kelly  Female   3/17/2007         6:20 PM   39371    4.068   \n",
      "875    Beverly  Female   4/11/1998         2:27 PM   76485    9.212   \n",
      "908     Janice  Female  12/17/2009         6:42 AM  102697    3.283   \n",
      "935      Alice  Female   3/23/1996         3:09 AM  131952   12.090   \n",
      "948     Ashley  Female   3/31/2006         1:24 PM  142410   11.048   \n",
      "956    Beverly  Female  10/17/1986        12:51 AM   80838    8.115   \n",
      "958     Gloria  Female  10/24/1987         4:43 AM   39833    9.631   \n",
      "969      Linda  Female    2/4/2010         8:49 PM   44486   17.308   \n",
      "980   Kimberly  Female   1/26/2013        12:57 AM   46233    8.862   \n",
      "984      Maria  Female  10/15/2011         4:53 PM   43455   13.040   \n",
      "993       Tina  Female   5/15/1997         3:53 PM   56450   19.040   \n",
      "\n",
      "    Senior Management         Team  \n",
      "8                True  Engineering  \n",
      "30               True  Engineering  \n",
      "50               True  Engineering  \n",
      "54              False  Engineering  \n",
      "58               True  Engineering  \n",
      "59              False  Engineering  \n",
      "113              True  Engineering  \n",
      "122              True  Engineering  \n",
      "138              True  Engineering  \n",
      "173              True  Engineering  \n",
      "191             False  Engineering  \n",
      "209             False  Engineering  \n",
      "213              True  Engineering  \n",
      "214             False  Engineering  \n",
      "235              True  Engineering  \n",
      "262              True  Engineering  \n",
      "467              True  Engineering  \n",
      "475              True  Engineering  \n",
      "541              True  Engineering  \n",
      "553             False  Engineering  \n",
      "558             False  Engineering  \n",
      "561             False  Engineering  \n",
      "608               NaN  Engineering  \n",
      "619              True  Engineering  \n",
      "632             False  Engineering  \n",
      "633              True  Engineering  \n",
      "636             False  Engineering  \n",
      "707             False  Engineering  \n",
      "711             False  Engineering  \n",
      "724             False  Engineering  \n",
      "761              True  Engineering  \n",
      "797             False  Engineering  \n",
      "808             False  Engineering  \n",
      "816             False  Engineering  \n",
      "875              True  Engineering  \n",
      "908             False  Engineering  \n",
      "935             False  Engineering  \n",
      "948              True  Engineering  \n",
      "956             False  Engineering  \n",
      "958             False  Engineering  \n",
      "969              True  Engineering  \n",
      "980              True  Engineering  \n",
      "984             False  Engineering  \n",
      "993              True  Engineering  \n",
      "('Female', 'Finance')\n",
      "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "2        Maria  Female   4/23/1993        11:17 AM  130590   11.858   \n",
      "7          NaN  Female   7/20/2015        10:43 AM   45906   11.598   \n",
      "14    Kimberly  Female   1/14/1999         7:13 AM   41426   14.543   \n",
      "67      Rachel  Female   8/16/1999         6:53 AM   51178    9.735   \n",
      "84       Doris  Female   8/20/2004         5:51 AM   83072    7.511   \n",
      "96     Cynthia  Female   3/21/1994         8:34 AM  142321    1.737   \n",
      "100    Melissa  Female   6/21/2005         6:33 AM   48109   14.995   \n",
      "103    Phyllis  Female  10/11/1996         9:30 PM  136984    8.932   \n",
      "105      Kathy  Female    3/9/1996         4:33 AM   91712    8.567   \n",
      "142  Elizabeth  Female   10/9/2003         5:53 PM  146129    5.687   \n",
      "160      Kathy  Female   3/18/2000         7:26 PM  149563   16.991   \n",
      "172       Sara  Female   9/23/1991         6:17 PM   97058    9.402   \n",
      "179    Jessica  Female  10/23/1995         5:53 AM   68759   19.343   \n",
      "190      Carol  Female   3/19/1996         3:39 AM   57783    9.129   \n",
      "226      Kathy  Female   4/19/1995        12:46 AM   50905    9.404   \n",
      "268      Irene  Female    3/3/1982         8:26 AM   56526    5.810   \n",
      "276  Christine  Female    7/3/1989         4:01 AM   94345   11.996   \n",
      "399    Kathryn  Female    6/9/1988         9:29 AM   86439    7.799   \n",
      "405     Sharon  Female    3/9/2014         4:39 PM   91522    7.564   \n",
      "418      Julia  Female    3/2/1982        12:52 PM   36403    2.664   \n",
      "425      Alice  Female    5/2/1986         1:50 AM   51395    2.378   \n",
      "436    Kathryn  Female   8/29/2007         6:10 AM   53061   11.864   \n",
      "442      Julie  Female   4/23/1998         5:52 AM   73437    2.518   \n",
      "485     Ashley  Female   8/15/1997         9:48 PM  142415    1.985   \n",
      "526    Barbara  Female   3/22/2004         8:11 AM  144677    8.696   \n",
      "540        NaN  Female   12/2/2009         3:30 AM   84746    4.820   \n",
      "547     Evelyn  Female   9/22/1998         7:55 PM   51525   10.366   \n",
      "549       Sara  Female   2/25/1980        10:50 PM   75484    3.186   \n",
      "595      Nancy  Female    5/7/1985        10:20 PM  121006    3.512   \n",
      "644  Katherine  Female   8/13/1996        12:21 AM  149908   18.912   \n",
      "646  Elizabeth  Female   2/21/2010         7:02 AM   79145   19.780   \n",
      "649       Ruth  Female   2/17/2004         5:38 AM   59969   14.064   \n",
      "650    Cynthia  Female    7/5/1986         1:24 AM   35381   11.749   \n",
      "691       Joan  Female   10/2/2007         2:53 AM  120941    3.694   \n",
      "702      Debra  Female   3/17/2011         1:12 PM   84693    6.976   \n",
      "731        Ann  Female   7/16/2001         9:27 AM  118431   12.772   \n",
      "775       Rose  Female   11/3/1999         9:06 AM   75181    6.060   \n",
      "807       Mary  Female   11/6/2011         8:32 AM  115057    2.089   \n",
      "836    Carolyn  Female   3/17/2004         6:38 AM  118037   13.492   \n",
      "884       Lisa  Female  10/17/2003         1:42 PM   73706   18.530   \n",
      "889   Margaret  Female    1/4/2001         3:07 AM  126924    1.552   \n",
      "905      Alice  Female    2/9/2013        12:49 AM  121250    4.363   \n",
      "907  Elizabeth  Female   7/27/1998        11:12 AM  137144   10.081   \n",
      "987     Gloria  Female   12/8/2014         5:08 AM  136709   10.331   \n",
      "\n",
      "    Senior Management     Team  \n",
      "2               False  Finance  \n",
      "7                 NaN  Finance  \n",
      "14               True  Finance  \n",
      "67               True  Finance  \n",
      "84              False  Finance  \n",
      "96              False  Finance  \n",
      "100             False  Finance  \n",
      "103              True  Finance  \n",
      "105             False  Finance  \n",
      "142             False  Finance  \n",
      "160              True  Finance  \n",
      "172             False  Finance  \n",
      "179              True  Finance  \n",
      "190             False  Finance  \n",
      "226              True  Finance  \n",
      "268              True  Finance  \n",
      "276             False  Finance  \n",
      "399             False  Finance  \n",
      "405             False  Finance  \n",
      "418              True  Finance  \n",
      "425              True  Finance  \n",
      "436              True  Finance  \n",
      "442              True  Finance  \n",
      "485              True  Finance  \n",
      "526             False  Finance  \n",
      "540               NaN  Finance  \n",
      "547             False  Finance  \n",
      "549             False  Finance  \n",
      "595              True  Finance  \n",
      "644             False  Finance  \n",
      "646             False  Finance  \n",
      "649              True  Finance  \n",
      "650             False  Finance  \n",
      "691              True  Finance  \n",
      "702              True  Finance  \n",
      "731              True  Finance  \n",
      "775              True  Finance  \n",
      "807             False  Finance  \n",
      "836             False  Finance  \n",
      "884             False  Finance  \n",
      "889              True  Finance  \n",
      "905              True  Finance  \n",
      "907             False  Finance  \n",
      "987              True  Finance  \n",
      "('Female', 'Human Resources')\n",
      "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "75      Bonnie  Female    7/2/1991         1:27 AM  104897    5.118   \n",
      "125   Patricia  Female    9/1/2003         6:46 AM   49368   14.226   \n",
      "126   Michelle  Female   3/30/1984         6:43 PM   57325   17.179   \n",
      "130    Shirley  Female    5/1/1984         1:15 PM   41334    6.219   \n",
      "211      Linda  Female   6/24/2006        12:04 PM  110967   19.612   \n",
      "232    Marilyn  Female    4/4/1997         9:28 PM  147663   10.263   \n",
      "255     Denise  Female   3/20/2009         7:57 AM  115118    5.108   \n",
      "264  Stephanie  Female   3/24/1983        11:15 AM   50141   13.218   \n",
      "269        NaN  Female    8/2/1992         8:35 PM  145316   18.517   \n",
      "285       Judy  Female   9/16/1997        12:10 AM   65931    2.304   \n",
      "299      Emily  Female   1/13/1988         6:42 AM   36711   19.028   \n",
      "396       Rose  Female    7/6/2002         3:57 PM   63494   19.385   \n",
      "398      Doris  Female   7/29/1992         6:40 PM   85215    7.425   \n",
      "429       Rose  Female   5/28/2015         8:40 AM  149903    5.630   \n",
      "449    Beverly  Female  11/30/2005         2:57 AM  107163    3.665   \n",
      "453      Annie  Female  12/19/1994         6:19 AM   40119    4.338   \n",
      "455       Ruth  Female  10/24/2000         4:13 AM   69579   18.029   \n",
      "471     Sharon  Female   3/12/2002         6:32 AM  147635   14.693   \n",
      "476      Kathy  Female  10/25/1996        12:59 PM  143541    8.461   \n",
      "483        NaN  Female   4/26/1992         8:01 PM  115436    3.099   \n",
      "517       Ruth  Female  12/12/1996        10:07 AM   97915    3.997   \n",
      "559     Ashley  Female  11/17/1999         9:18 PM  120675    6.510   \n",
      "565    Marilyn  Female    8/9/2006        11:24 AM   87145   17.330   \n",
      "588    Cynthia  Female   5/23/1980         3:19 AM   78226    2.419   \n",
      "589     Sandra  Female   4/24/1995         7:14 PM  116931    9.657   \n",
      "599        NaN  Female  10/11/1990        10:57 PM   98385   10.925   \n",
      "695      Sarah  Female    4/6/1999        11:17 AM  109517    5.146   \n",
      "720      Marie  Female    4/8/1983         2:01 PM  145988   18.685   \n",
      "734        NaN  Female  12/18/1994         8:42 PM  138807    1.400   \n",
      "839      Joyce  Female   7/25/2001         6:04 AM   51065   16.807   \n",
      "874    Melissa  Female    9/8/1994         4:11 AM   98858    3.525   \n",
      "900  Christina  Female   6/23/2002         3:18 PM   35477   18.178   \n",
      "903    Heather  Female   7/29/1983        10:48 AM   47605   14.955   \n",
      "904  Stephanie  Female   6/26/1988         5:48 AM  136604    6.160   \n",
      "952     Teresa  Female   1/22/2013         3:17 PM  113425   11.907   \n",
      "982       Rose  Female    4/6/1982        10:43 AM   91411    8.639   \n",
      "988      Alice  Female   10/5/2004         9:34 AM   47638   11.209   \n",
      "\n",
      "    Senior Management             Team  \n",
      "75               True  Human Resources  \n",
      "125             False  Human Resources  \n",
      "126              True  Human Resources  \n",
      "130              True  Human Resources  \n",
      "211              True  Human Resources  \n",
      "232             False  Human Resources  \n",
      "255             False  Human Resources  \n",
      "264              True  Human Resources  \n",
      "269               NaN  Human Resources  \n",
      "285             False  Human Resources  \n",
      "299              True  Human Resources  \n",
      "396              True  Human Resources  \n",
      "398              True  Human Resources  \n",
      "429             False  Human Resources  \n",
      "449              True  Human Resources  \n",
      "453              True  Human Resources  \n",
      "455              True  Human Resources  \n",
      "471             False  Human Resources  \n",
      "476             False  Human Resources  \n",
      "483               NaN  Human Resources  \n",
      "517              True  Human Resources  \n",
      "559             False  Human Resources  \n",
      "565             False  Human Resources  \n",
      "588             False  Human Resources  \n",
      "589              True  Human Resources  \n",
      "599               NaN  Human Resources  \n",
      "695             False  Human Resources  \n",
      "720              True  Human Resources  \n",
      "734               NaN  Human Resources  \n",
      "839             False  Human Resources  \n",
      "874              True  Human Resources  \n",
      "900             False  Human Resources  \n",
      "903              True  Human Resources  \n",
      "904              True  Human Resources  \n",
      "952              True  Human Resources  \n",
      "982              True  Human Resources  \n",
      "988             False  Human Resources  \n",
      "('Female', 'Legal')\n",
      "     First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "11        Julie  Female  10/26/1997         3:19 PM  102508   12.637   \n",
      "42      Beverly  Female    9/9/1998         8:26 PM  121918   15.835   \n",
      "88        Donna  Female  11/27/1991         1:59 PM   64088    6.155   \n",
      "89       Janice  Female   3/12/2016        12:40 AM   51082   11.955   \n",
      "110     Shirley  Female   6/20/2001        10:39 PM  147113   16.135   \n",
      "135      Gloria  Female   6/29/2007         5:34 AM  134148    8.833   \n",
      "165         NaN  Female   3/23/2014         1:28 PM   59148    9.061   \n",
      "185      Sandra  Female   1/19/1983         7:25 AM   42090    8.842   \n",
      "193     Rebecca  Female   2/18/1995         6:50 AM  109259    4.443   \n",
      "210        Ruth  Female   8/12/1986         7:29 PM   44639    9.148   \n",
      "237      Cheryl  Female   9/23/2008         2:57 AM   52080    9.375   \n",
      "256       Debra  Female   3/13/1998        11:16 PM   48696    4.750   \n",
      "289     Jessica  Female   9/27/1985         1:35 PM   75145    6.388   \n",
      "308      Cheryl  Female    9/8/2009        10:16 AM   81308    2.196   \n",
      "313        Judy  Female    7/1/1991         2:33 AM  109510   13.457   \n",
      "354   Elizabeth  Female  11/10/2005         4:56 AM  106406    1.782   \n",
      "355      Andrea  Female   9/14/2003        10:30 PM  115913   12.121   \n",
      "412  Jacqueline  Female   2/11/2007         2:01 AM   66604   14.609   \n",
      "433       Wanda  Female   7/20/2008         1:44 PM   65362    7.132   \n",
      "457    Patricia  Female    1/9/2015         4:16 AM  121232   16.624   \n",
      "460        Tina  Female   1/17/2005         9:11 AM   88276   14.248   \n",
      "465       Helen  Female  10/31/1997         2:43 AM   52875    4.188   \n",
      "500   Christine  Female    2/1/2003         1:11 PM   72613   11.126   \n",
      "516      Gloria  Female   4/12/1992         8:26 PM   66224   15.979   \n",
      "623       Irene  Female  11/29/2006         2:14 PM  125018    9.601   \n",
      "741        Jane  Female   7/10/1999         7:02 PM  128540   15.941   \n",
      "754        Lisa  Female   4/11/2007         1:04 AM  128042    2.030   \n",
      "763  Jacqueline  Female   7/27/2008        12:08 PM  125298    3.019   \n",
      "865       Karen  Female   8/29/1997         4:56 PM   80633   16.306   \n",
      "888     Marilyn  Female   10/8/2007        12:32 AM  115149   11.934   \n",
      "910     Melissa  Female  10/22/2002         1:20 AM   45223    8.879   \n",
      "929     Theresa  Female   4/27/2001         9:17 PM   75661    1.079   \n",
      "955       Sarah  Female   8/17/2014        11:08 PM  127118   11.176   \n",
      "976      Denise  Female  10/19/1992         5:42 AM  137954    4.195   \n",
      "\n",
      "    Senior Management   Team  \n",
      "11               True  Legal  \n",
      "42              False  Legal  \n",
      "88               True  Legal  \n",
      "89              False  Legal  \n",
      "110             False  Legal  \n",
      "135              True  Legal  \n",
      "165               NaN  Legal  \n",
      "185              True  Legal  \n",
      "193              True  Legal  \n",
      "210              True  Legal  \n",
      "237             False  Legal  \n",
      "256             False  Legal  \n",
      "289              True  Legal  \n",
      "308              True  Legal  \n",
      "313              True  Legal  \n",
      "354              True  Legal  \n",
      "355             False  Legal  \n",
      "412             False  Legal  \n",
      "433              True  Legal  \n",
      "457             False  Legal  \n",
      "460             False  Legal  \n",
      "465             False  Legal  \n",
      "500             False  Legal  \n",
      "516              True  Legal  \n",
      "623              True  Legal  \n",
      "741             False  Legal  \n",
      "754              True  Legal  \n",
      "763              True  Legal  \n",
      "865             False  Legal  \n",
      "888              True  Legal  \n",
      "910              True  Legal  \n",
      "929              True  Legal  \n",
      "955             False  Legal  \n",
      "976              True  Legal  \n",
      "('Female', 'Marketing')\n",
      "     First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "43      Marilyn  Female   12/7/1980         3:16 AM   73524    5.207   \n",
      "62          NaN  Female   6/12/2007         5:25 PM   58112   19.414   \n",
      "98         Tina  Female   6/16/2016         7:47 PM  100705   16.961   \n",
      "140     Shirley  Female   2/28/1981         1:23 PM  113850    1.854   \n",
      "158       Norma  Female   2/28/1999         8:45 PM  114412    8.756   \n",
      "201    Kimberly  Female   7/15/1997         5:57 AM   36643    7.953   \n",
      "220         NaN  Female   6/17/1991        12:49 PM   71945    5.560   \n",
      "305    Margaret  Female    2/6/1993         1:05 PM  125220    3.733   \n",
      "319  Jacqueline  Female  11/25/1981         3:01 PM  145988   18.243   \n",
      "331      Evelyn  Female    9/3/1983         1:58 PM   36759   17.269   \n",
      "379         NaN  Female   9/18/2002        12:39 PM  118906    4.537   \n",
      "446      Cheryl  Female   8/16/1994         8:33 AM   67150   15.850   \n",
      "468      Janice  Female   6/28/1997         1:48 PM  136032   10.696   \n",
      "490      Judith  Female  11/23/2007         1:22 PM  117055    7.461   \n",
      "531    Virginia  Female    5/2/2010         9:10 PM  123649   10.154   \n",
      "535      Louise  Female   9/18/1981         5:47 AM   91462    8.205   \n",
      "568       Susan  Female   4/18/1986         9:31 AM   90829   19.142   \n",
      "585     Shirley  Female   4/16/1988        11:09 AM  132156    2.754   \n",
      "586        Rose  Female  10/30/2004         4:34 PM   56961    7.585   \n",
      "617       Kathy  Female   1/14/2001         5:11 AM   45682    1.451   \n",
      "625     Cynthia  Female   6/29/2015         6:38 AM   82408    8.701   \n",
      "627        Anne  Female  11/21/1984        12:30 PM  128305   16.636   \n",
      "629       Debra  Female   3/12/1983        12:19 PM   74911   19.513   \n",
      "656        Lisa  Female    2/9/1982         6:44 PM  113592   17.108   \n",
      "675       Diane  Female  10/30/2009         6:56 PM  130577   12.791   \n",
      "676       Annie  Female    6/6/1992        10:04 AM  138925    9.801   \n",
      "730      Nicole  Female   4/26/2009        12:40 AM   66047   18.674   \n",
      "785         NaN  Female   7/24/1982        12:00 AM   57811    8.941   \n",
      "811      Judith  Female    9/3/1989        11:16 AM  134048    6.818   \n",
      "813      Evelyn  Female   2/10/2002         4:44 AM  123621   19.767   \n",
      "817        Ruby  Female   5/31/2008         8:41 PM   83112    4.083   \n",
      "824       Julia  Female   8/18/2014         3:12 AM   97566    2.147   \n",
      "881        Ruby  Female   1/28/1980        11:08 PM  142868    6.318   \n",
      "883     Kathryn  Female  10/27/1995         3:39 PM   57300   18.015   \n",
      "894       Betty  Female  11/19/2002        10:40 AM   37005    7.645   \n",
      "913        Lois  Female  12/11/2015         4:10 PM   99747    6.168   \n",
      "930       Nancy  Female   9/10/2001        11:57 PM   85213    2.386   \n",
      "942        Lori  Female  11/20/2015         1:15 PM   75498    6.537   \n",
      "986       Donna  Female  11/26/1982         7:04 AM   82871   17.999   \n",
      "991        Rose  Female   8/25/2002         5:12 AM  134505   11.051   \n",
      "\n",
      "    Senior Management       Team  \n",
      "43               True  Marketing  \n",
      "62                NaN  Marketing  \n",
      "98               True  Marketing  \n",
      "140             False  Marketing  \n",
      "158              True  Marketing  \n",
      "201             False  Marketing  \n",
      "220               NaN  Marketing  \n",
      "305             False  Marketing  \n",
      "319             False  Marketing  \n",
      "331              True  Marketing  \n",
      "379               NaN  Marketing  \n",
      "446              True  Marketing  \n",
      "468              True  Marketing  \n",
      "490             False  Marketing  \n",
      "531              True  Marketing  \n",
      "535             False  Marketing  \n",
      "568             False  Marketing  \n",
      "585             False  Marketing  \n",
      "586             False  Marketing  \n",
      "617              True  Marketing  \n",
      "625              True  Marketing  \n",
      "627             False  Marketing  \n",
      "629             False  Marketing  \n",
      "656              True  Marketing  \n",
      "675             False  Marketing  \n",
      "676              True  Marketing  \n",
      "730              True  Marketing  \n",
      "785               NaN  Marketing  \n",
      "811              True  Marketing  \n",
      "813              True  Marketing  \n",
      "817             False  Marketing  \n",
      "824             False  Marketing  \n",
      "881             False  Marketing  \n",
      "883             False  Marketing  \n",
      "894              True  Marketing  \n",
      "913             False  Marketing  \n",
      "930              True  Marketing  \n",
      "942              True  Marketing  \n",
      "986             False  Marketing  \n",
      "991              True  Marketing  \n",
      "('Female', 'Product')\n",
      "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "6         Ruby  Female   8/17/1987         4:20 PM   65476   10.012   \n",
      "15     Lillian  Female    6/5/2016         6:09 AM   59414    1.256   \n",
      "19       Donna  Female   7/22/2010         3:48 AM   81014    1.894   \n",
      "44     Cynthia  Female  11/16/1988         6:54 PM  145146    7.482   \n",
      "55       Karen  Female  11/30/1999         7:46 AM  102488   17.653   \n",
      "79      Bonnie  Female  11/13/1988         3:30 PM  115814    4.990   \n",
      "115     Pamela  Female    7/1/1982         6:51 AM   54585    4.166   \n",
      "123      Helen  Female  11/29/2001         2:30 AM   73789   14.841   \n",
      "124    Marilyn  Female  11/16/1983         8:29 AM   76078    2.401   \n",
      "131    Rebecca  Female   7/10/1992        12:23 AM   94231   17.517   \n",
      "146   Patricia  Female  11/25/1992         2:24 AM   95322    4.150   \n",
      "154    Rebecca  Female  11/15/1980         4:13 AM   85730    5.359   \n",
      "157        NaN  Female   7/27/2005         8:32 AM   79536   14.443   \n",
      "169   Michelle  Female   11/4/1996         9:34 PM   53754    5.455   \n",
      "178       Jane  Female    9/3/1997         2:01 AM  144474   17.648   \n",
      "198      Maria  Female  12/27/1990         9:57 PM   36067    9.640   \n",
      "231      Diane  Female   4/18/1993         3:22 AM   64084   17.018   \n",
      "236      Laura  Female    4/2/2006         1:57 PM   42087    2.624   \n",
      "261      Marie  Female    8/6/1995         1:58 PM  100308   13.677   \n",
      "316      Marie  Female   10/3/2013         8:05 PM  123711   10.966   \n",
      "342      Marie  Female  10/20/2011        10:09 PM   62666   10.247   \n",
      "369       Mary  Female  10/18/2009         6:32 PM   87721   12.484   \n",
      "388     Gloria  Female   9/19/1989         7:40 PM  131045   11.312   \n",
      "394      Robin  Female    1/8/1998         2:12 AM  111163    5.025   \n",
      "518      Diana  Female   1/19/1995        12:37 AM   86883   18.003   \n",
      "532       Lisa  Female   2/26/2005         6:33 PM   38078   10.280   \n",
      "552    Barbara  Female    9/2/1991         3:41 PM  127297   11.905   \n",
      "555       Anne  Female  10/26/1996         8:09 PM   71930   18.451   \n",
      "570      Kelly  Female   8/16/1996         3:09 AM   41427    1.431   \n",
      "578     Amanda  Female   3/17/1982        11:46 PM  107111    1.438   \n",
      "598    Frances  Female   7/21/2001         2:34 AM   91996   11.506   \n",
      "653      Debra  Female   5/28/2006         6:51 AM   42296   16.922   \n",
      "678     Brenda  Female   4/17/1984         7:12 AM   87715    2.675   \n",
      "682    Deborah  Female   4/23/2000         6:55 PM  105573    5.268   \n",
      "736       Judy  Female    4/5/2011         1:44 AM   48668   11.716   \n",
      "770  Katherine  Female   9/22/2006         8:08 PM   72002   13.178   \n",
      "786     Sandra  Female    9/7/1989         4:17 PM  111468    2.580   \n",
      "789       Lisa  Female    7/8/2000         9:19 PM   40121    6.293   \n",
      "803       Lois  Female   7/28/2012        11:39 AM   53954   19.075   \n",
      "828    Cynthia  Female   7/12/2006         8:55 AM  149684    7.864   \n",
      "842     Sandra  Female    2/7/1998        12:39 PM  132327   19.264   \n",
      "857      Marie  Female   12/8/1993         6:28 PM   98406    7.943   \n",
      "868  Katherine  Female   8/18/2013         3:58 PM   97443   13.657   \n",
      "970      Alice  Female    9/3/1988         8:54 PM   63571   15.397   \n",
      "977      Sarah  Female   12/4/1995         9:16 AM  124566    5.949   \n",
      "\n",
      "    Senior Management     Team  \n",
      "6                True  Product  \n",
      "15              False  Product  \n",
      "19              False  Product  \n",
      "44               True  Product  \n",
      "55               True  Product  \n",
      "79              False  Product  \n",
      "115             False  Product  \n",
      "123              True  Product  \n",
      "124              True  Product  \n",
      "131             False  Product  \n",
      "146             False  Product  \n",
      "154              True  Product  \n",
      "157               NaN  Product  \n",
      "169              True  Product  \n",
      "178             False  Product  \n",
      "198              True  Product  \n",
      "231             False  Product  \n",
      "236             False  Product  \n",
      "261             False  Product  \n",
      "316             False  Product  \n",
      "342             False  Product  \n",
      "369             False  Product  \n",
      "388              True  Product  \n",
      "394              True  Product  \n",
      "518              True  Product  \n",
      "532              True  Product  \n",
      "552              True  Product  \n",
      "555              True  Product  \n",
      "570             False  Product  \n",
      "578              True  Product  \n",
      "598             False  Product  \n",
      "653             False  Product  \n",
      "678             False  Product  \n",
      "682              True  Product  \n",
      "736              True  Product  \n",
      "770              True  Product  \n",
      "786              True  Product  \n",
      "789             False  Product  \n",
      "803             False  Product  \n",
      "828             False  Product  \n",
      "842             False  Product  \n",
      "857              True  Product  \n",
      "868             False  Product  \n",
      "970              True  Product  \n",
      "977             False  Product  \n",
      "('Female', 'Sales')\n",
      "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "35     Theresa  Female  10/10/2006         1:12 AM   85182   16.675   \n",
      "73     Frances  Female    4/4/1999         4:19 PM   90582    4.709   \n",
      "78       Robin  Female    6/4/1983         3:15 PM  114797    5.965   \n",
      "87       Annie  Female   1/30/1993         2:05 AM  144887    8.276   \n",
      "166        NaN  Female    7/9/1991         6:52 PM   42341    7.014   \n",
      "170      Irene  Female   1/29/1992        11:36 AM  133772   19.020   \n",
      "186        NaN  Female   2/23/2005         9:50 PM  149654    1.825   \n",
      "233    Kathryn  Female    2/4/2004         1:47 PM   73935   14.752   \n",
      "320        NaN  Female    7/8/2008        11:40 PM   62960   14.356   \n",
      "321      Julie  Female   3/23/1986         9:26 AM   56926    7.507   \n",
      "323      Linda  Female  12/16/1990         2:20 AM  115658    3.041   \n",
      "347       Lori  Female   3/31/1988        10:59 PM   95389   14.223   \n",
      "349    Phyllis  Female  11/24/2005        11:57 PM  140347    8.723   \n",
      "351    Cynthia  Female   12/3/2012        11:03 PM   74287   10.751   \n",
      "360      Susan  Female   9/25/2004         1:30 AM   80688   18.892   \n",
      "361   Margaret  Female    5/5/2014         6:01 AM   55044    4.078   \n",
      "386    Phyllis  Female   1/15/1995         5:27 AM  125881   16.697   \n",
      "391    Marilyn  Female   8/15/1989         9:14 AM  140502    9.989   \n",
      "395      Kathy  Female  11/25/2001         9:55 PM   93753    7.094   \n",
      "437     Denise  Female   3/18/2001        12:02 AM   36697   11.196   \n",
      "443      Kathy  Female    5/8/1987         6:19 PM   86318   18.492   \n",
      "480        Amy  Female   3/30/2008        10:54 PM  106249   15.967   \n",
      "494    Barbara  Female   3/21/2002         8:02 AM   47322    7.250   \n",
      "523      Diane  Female    7/5/1982         4:56 PM  124889   15.026   \n",
      "548     Janice  Female    1/2/1984         9:06 PM   41190    3.311   \n",
      "593      Marie  Female  11/23/2000         5:06 AM  125574    4.644   \n",
      "622      Irene  Female   6/23/2005        11:03 AM   89780    8.999   \n",
      "641     Amanda  Female   1/25/1995         5:06 AM  102081    5.203   \n",
      "657    Rebecca  Female    3/5/2000        10:33 AM   46750   11.367   \n",
      "693      Alice  Female  10/16/1995         9:19 PM   92799    2.782   \n",
      "768      Robin  Female   3/26/2004         5:41 PM   70248    9.660   \n",
      "777   Kimberly  Female   4/20/1986         3:55 PM   52970    4.513   \n",
      "784  Stephanie  Female   9/14/1982         8:14 AM   96649    3.453   \n",
      "806    Kathryn  Female   1/29/2008        12:29 AM   86676    6.081   \n",
      "843     Louise  Female   2/26/1982         1:03 AM  106362    8.965   \n",
      "906    Frances  Female   5/16/2014         8:31 AM   35884   17.667   \n",
      "925        NaN  Female   8/23/2000         4:19 PM   95866   19.388   \n",
      "963        Ann  Female   9/23/1994        11:15 AM   89443   17.940   \n",
      "975      Susan  Female    4/7/1995        10:05 PM   92436   12.467   \n",
      "\n",
      "    Senior Management   Team  \n",
      "35              False  Sales  \n",
      "73               True  Sales  \n",
      "78               True  Sales  \n",
      "87               True  Sales  \n",
      "166               NaN  Sales  \n",
      "170              True  Sales  \n",
      "186               NaN  Sales  \n",
      "233             False  Sales  \n",
      "320               NaN  Sales  \n",
      "321             False  Sales  \n",
      "323              True  Sales  \n",
      "347             False  Sales  \n",
      "349             False  Sales  \n",
      "351             False  Sales  \n",
      "360              True  Sales  \n",
      "361             False  Sales  \n",
      "386             False  Sales  \n",
      "391              True  Sales  \n",
      "395              True  Sales  \n",
      "437              True  Sales  \n",
      "443              True  Sales  \n",
      "480             False  Sales  \n",
      "494              True  Sales  \n",
      "523              True  Sales  \n",
      "548              True  Sales  \n",
      "593             False  Sales  \n",
      "622              True  Sales  \n",
      "641             False  Sales  \n",
      "657              True  Sales  \n",
      "693             False  Sales  \n",
      "768              True  Sales  \n",
      "777             False  Sales  \n",
      "784             False  Sales  \n",
      "806             False  Sales  \n",
      "843             False  Sales  \n",
      "906             False  Sales  \n",
      "925               NaN  Sales  \n",
      "963              True  Sales  \n",
      "975             False  Sales  \n",
      "('Male', 'Business Development')\n",
      "    First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "48    Clarence   Male   3/26/1996         5:57 AM   93581    6.083   \n",
      "99      Harold   Male    1/2/1985         9:40 PM   77544   12.447   \n",
      "120      Peter   Male  11/17/1994         6:15 PM   84885   15.402   \n",
      "129    Antonio   Male  10/25/1988         1:50 PM   60866   13.101   \n",
      "180       Mark   Male   4/12/2008         5:40 PM   57286    5.025   \n",
      "280      Kevin   Male   7/12/1997         2:07 AM   46080    9.635   \n",
      "306       Mark   Male    4/1/1984         1:21 PM  121477   17.440   \n",
      "344      Scott   Male   5/12/2009         4:36 AM   58248    3.914   \n",
      "411      Kevin   Male   6/23/1981         8:26 PM  134598   11.699   \n",
      "414       Gary   Male   8/18/2011         8:12 AM   49101   11.900   \n",
      "473   Clarence   Male   8/20/1992         8:23 PM  124365    8.457   \n",
      "477     Albert   Male   8/14/2008         5:43 PM  137840    9.705   \n",
      "478    Michael   Male   5/22/2011        10:42 AM   73354   18.357   \n",
      "492      Jerry   Male   1/30/1995         7:51 PM  121357   18.845   \n",
      "511        Joe   Male    7/4/1983         7:32 AM   62161    8.130   \n",
      "536   Clarence   Male   8/26/1982         9:47 AM  146589    4.905   \n",
      "594      Louis   Male   4/15/2011         5:02 AM   95198    2.075   \n",
      "596       Mark   Male   10/3/1984         5:31 AM   95728    6.752   \n",
      "648    Gregory   Male  11/15/1999         5:22 PM  128031   15.857   \n",
      "673      Ralph   Male   3/24/1996        11:54 AM   50455   16.248   \n",
      "716       Eric   Male   6/15/1993         3:56 AM   51070   13.806   \n",
      "721     Harold   Male   4/16/2010         5:13 AM  147417   11.626   \n",
      "733        NaN   Male   6/17/2009         1:00 PM  136681    3.655   \n",
      "738     Joseph   Male  10/10/2010        11:12 PM  107050   12.737   \n",
      "745      James   Male   2/16/2008         2:49 AM   69111   14.625   \n",
      "747     Ernest   Male   6/30/2001         1:46 PM   61181   16.559   \n",
      "752      Henry   Male   2/23/2007         3:24 AM   49665   18.338   \n",
      "810      Ralph   Male   2/10/2000         4:48 PM   89854    7.227   \n",
      "869    Matthew   Male    8/3/1995         5:39 AM  135352    7.986   \n",
      "893      Frank   Male   7/27/2013         4:32 PM   91406    5.681   \n",
      "911       Carl   Male    7/7/2010         1:33 PM   54033   15.528   \n",
      "922     Arthur   Male   12/5/2013         8:44 AM   86615    3.412   \n",
      "927     Philip   Male   3/11/2005        10:56 AM  103557   16.014   \n",
      "928    Jeffrey   Male   9/20/1986         6:06 AM  111376    2.673   \n",
      "949      Scott   Male   8/20/2011         8:08 AM   37385    8.226   \n",
      "959     Albert   Male   9/19/1992         2:35 AM   45094    5.850   \n",
      "960    Stephen   Male  10/29/1989        11:34 PM   93997   18.093   \n",
      "971    Patrick   Male  12/30/2002         2:01 AM   75423    5.368   \n",
      "973    Russell   Male   5/10/2013        11:08 PM  137359   11.105   \n",
      "998      Larry   Male   4/20/2013         4:45 PM   60500   11.985   \n",
      "\n",
      "    Senior Management                  Team  \n",
      "48               True  Business Development  \n",
      "99              False  Business Development  \n",
      "120             False  Business Development  \n",
      "129              True  Business Development  \n",
      "180              True  Business Development  \n",
      "280             False  Business Development  \n",
      "306              True  Business Development  \n",
      "344             False  Business Development  \n",
      "411             False  Business Development  \n",
      "414              True  Business Development  \n",
      "473             False  Business Development  \n",
      "477             False  Business Development  \n",
      "478             False  Business Development  \n",
      "492             False  Business Development  \n",
      "511              True  Business Development  \n",
      "536              True  Business Development  \n",
      "594             False  Business Development  \n",
      "596              True  Business Development  \n",
      "648              True  Business Development  \n",
      "673             False  Business Development  \n",
      "716              True  Business Development  \n",
      "721              True  Business Development  \n",
      "733               NaN  Business Development  \n",
      "738             False  Business Development  \n",
      "745              True  Business Development  \n",
      "747             False  Business Development  \n",
      "752             False  Business Development  \n",
      "810             False  Business Development  \n",
      "869              True  Business Development  \n",
      "893              True  Business Development  \n",
      "911              True  Business Development  \n",
      "922              True  Business Development  \n",
      "927              True  Business Development  \n",
      "928              True  Business Development  \n",
      "949              True  Business Development  \n",
      "959              True  Business Development  \n",
      "960              True  Business Development  \n",
      "971              True  Business Development  \n",
      "973             False  Business Development  \n",
      "998             False  Business Development  \n",
      "('Male', 'Client Services')\n",
      "      First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "4          Larry   Male   1/24/1998         4:47 PM  101004    1.389   \n",
      "24          John   Male    7/1/1992        10:08 PM   97950   13.873   \n",
      "25           NaN   Male   10/8/2012         1:12 AM   37076   18.576   \n",
      "28         Terry   Male  11/27/1981         6:30 PM  124008   13.464   \n",
      "34         Jerry   Male   1/10/2004        12:56 PM   95734   19.096   \n",
      "39           NaN   Male   1/29/2016         2:33 AM  122173    7.797   \n",
      "82        Steven   Male   3/30/1980         9:20 PM   35095    8.379   \n",
      "85        Jeremy   Male    2/1/2008         8:50 AM  100238    3.887   \n",
      "102         Jack   Male   1/12/1997         5:51 AM  103902   12.159   \n",
      "128      Kenneth   Male   8/25/1987         9:16 PM  127654   16.439   \n",
      "163        Terry   Male    9/3/1990         9:15 PM   52226   19.135   \n",
      "184        Jerry   Male  12/18/2003         6:46 AM  140810    9.177   \n",
      "196       Steven   Male  11/10/1985        10:53 AM   62719   19.127   \n",
      "217      Douglas   Male    9/3/1999         4:00 PM   83341    1.015   \n",
      "227         Todd   Male   4/13/1999        10:31 AM   59728   11.226   \n",
      "244     Clarence   Male   10/8/1996        12:53 PM  142561    8.866   \n",
      "254       Edward   Male   5/27/1985        10:13 PM  110485    7.995   \n",
      "265          Roy   Male   9/23/2004         1:31 PM  101941    3.450   \n",
      "325      Russell   Male    1/2/2005         1:48 AM   60388   17.885   \n",
      "330        Randy   Male   3/16/2014         6:24 PM  135119    7.887   \n",
      "334         Todd   Male   5/23/2011         4:14 PM   85074    1.844   \n",
      "387       Robert   Male  10/29/1994         4:26 AM  123294   19.894   \n",
      "392        Ralph   Male  10/27/2010        10:28 AM  106310    4.030   \n",
      "408          Joe   Male   12/5/2010         4:20 PM  144082    1.620   \n",
      "440        Aaron   Male   7/22/1990         2:53 PM   52119   11.343   \n",
      "448        Wayne   Male   1/30/1992         3:01 PM   81183   17.066   \n",
      "472       Joshua   Male   7/10/1984         7:22 PM   68230    8.047   \n",
      "497     Benjamin   Male    5/5/2013         3:28 PM  114356    7.123   \n",
      "506       Donald   Male   7/14/1995         7:51 AM  106472    6.499   \n",
      "529  Christopher   Male   4/23/2014         7:52 AM   82401    7.252   \n",
      "560        Shawn   Male   12/3/2009        10:24 AM   96610    2.097   \n",
      "564       Andrew   Male   3/29/1985         6:57 PM   43414    7.563   \n",
      "577        Jerry   Male    5/4/2003         4:29 AM   98393   11.393   \n",
      "621       Justin   Male   8/10/1997        11:00 PM  128036   18.824   \n",
      "655       Walter   Male   9/24/2007        11:16 PM  125382   13.613   \n",
      "729       Steven   Male    9/7/1986        12:32 AM   43252   18.892   \n",
      "742       Martin   Male   2/25/2016         6:29 PM   61117    2.844   \n",
      "782          NaN   Male    5/6/1986         4:41 PM   74104   17.680   \n",
      "877          NaN   Male    9/5/1980         7:36 AM  114896   13.823   \n",
      "915         Todd   Male    1/4/1983        12:34 PM  115566    6.716   \n",
      "918         Ryan   Male   4/27/1999         3:41 AM   85858   19.475   \n",
      "974        Harry   Male   8/30/2011         6:31 PM   67656   16.455   \n",
      "\n",
      "    Senior Management             Team  \n",
      "4                True  Client Services  \n",
      "24              False  Client Services  \n",
      "25                NaN  Client Services  \n",
      "28               True  Client Services  \n",
      "34              False  Client Services  \n",
      "39                NaN  Client Services  \n",
      "82               True  Client Services  \n",
      "85               True  Client Services  \n",
      "102             False  Client Services  \n",
      "128              True  Client Services  \n",
      "163             False  Client Services  \n",
      "184              True  Client Services  \n",
      "196             False  Client Services  \n",
      "217              True  Client Services  \n",
      "227              True  Client Services  \n",
      "244             False  Client Services  \n",
      "254             False  Client Services  \n",
      "265             False  Client Services  \n",
      "325             False  Client Services  \n",
      "330             False  Client Services  \n",
      "334             False  Client Services  \n",
      "387             False  Client Services  \n",
      "392              True  Client Services  \n",
      "408              True  Client Services  \n",
      "440              True  Client Services  \n",
      "448             False  Client Services  \n",
      "472             False  Client Services  \n",
      "497             False  Client Services  \n",
      "506              True  Client Services  \n",
      "529             False  Client Services  \n",
      "560              True  Client Services  \n",
      "564              True  Client Services  \n",
      "577             False  Client Services  \n",
      "621             False  Client Services  \n",
      "655              True  Client Services  \n",
      "729             False  Client Services  \n",
      "742             False  Client Services  \n",
      "782               NaN  Client Services  \n",
      "877               NaN  Client Services  \n",
      "915              True  Client Services  \n",
      "918             False  Client Services  \n",
      "974              True  Client Services  \n",
      "('Male', 'Distribution')\n",
      "    First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "40     Michael   Male  10/10/2008        11:25 AM   99283    2.665   \n",
      "65       Steve   Male  11/11/2009        11:44 PM   61310   12.428   \n",
      "137       Adam   Male   5/21/2011         1:45 AM   95327   15.120   \n",
      "177      Wayne   Male    4/7/2012         8:00 AM  102652   14.085   \n",
      "181      Randy   Male  11/14/1999        12:12 PM   58129    1.952   \n",
      "229     Jeremy   Male    6/8/2000        12:15 AM   49542    1.679   \n",
      "248     Justin   Male   12/6/1992         5:58 PM   82782    4.366   \n",
      "267        NaN   Male    2/4/2005         1:01 PM   40451   16.044   \n",
      "362     Joshua   Male   11/9/2009         6:32 AM   72893    9.555   \n",
      "397   Clarence   Male   1/13/2005         9:00 AM  116693   13.835   \n",
      "416        NaN   Male   6/29/2008         5:23 AM  111043    5.966   \n",
      "435      Billy   Male   12/1/2006         3:32 PM  144709   10.069   \n",
      "444        NaN   Male   5/24/2016         9:17 PM   76409    7.008   \n",
      "470       Ryan   Male   7/20/1993        10:18 PM  139917   11.466   \n",
      "486     Howard   Male    4/9/2012         6:36 AM   37984    2.021   \n",
      "501       Sean   Male   2/11/2013         7:07 PM   42748    9.765   \n",
      "566     Johnny   Male    1/8/1995         1:35 PM   91124   12.986   \n",
      "614       Eric   Male  11/12/2004         9:16 PM   65168   11.513   \n",
      "665    Anthony   Male   2/13/2013         1:35 PM  146141    3.645   \n",
      "687        NaN   Male  12/27/1996         7:07 AM  139959    8.992   \n",
      "697        NaN   Male   5/23/1990         2:09 AM  136655    9.801   \n",
      "700      Frank   Male    3/2/2009         9:15 PM   78891    7.927   \n",
      "704     Thomas   Male    9/7/1991         9:51 AM   65251   11.211   \n",
      "714   Jonathan   Male   7/30/1984         6:02 PM   83809   12.922   \n",
      "756    Stephen   Male  10/21/1984         6:26 AM  121816   10.615   \n",
      "762      Terry   Male  11/10/2004         4:33 AM   35633    3.947   \n",
      "778    Antonio   Male  12/28/2003         4:17 AM  137979    5.266   \n",
      "804      Shawn   Male   3/17/2008         2:12 PM   39335   10.664   \n",
      "830    Michael   Male   8/31/2002         1:20 AM   81206   19.908   \n",
      "838      Billy   Male    4/6/2000         3:14 PM  115280    9.153   \n",
      "902        NaN   Male   5/23/2001         7:52 PM  103877    6.322   \n",
      "919       Sean   Male   3/21/2009        11:38 AM  131423    8.957   \n",
      "931     Harold   Male   6/23/2012        12:40 PM  140444    3.771   \n",
      "940     Andrew   Male   9/28/1990         9:38 AM  137386    8.611   \n",
      "944    Kenneth   Male   5/10/2006         8:24 AM  101914    1.905   \n",
      "\n",
      "    Senior Management          Team  \n",
      "40               True  Distribution  \n",
      "65               True  Distribution  \n",
      "137             False  Distribution  \n",
      "177              True  Distribution  \n",
      "181              True  Distribution  \n",
      "229              True  Distribution  \n",
      "248              True  Distribution  \n",
      "267               NaN  Distribution  \n",
      "362             False  Distribution  \n",
      "397              True  Distribution  \n",
      "416               NaN  Distribution  \n",
      "435              True  Distribution  \n",
      "444               NaN  Distribution  \n",
      "470             False  Distribution  \n",
      "486             False  Distribution  \n",
      "501             False  Distribution  \n",
      "566              True  Distribution  \n",
      "614             False  Distribution  \n",
      "665              True  Distribution  \n",
      "687               NaN  Distribution  \n",
      "697               NaN  Distribution  \n",
      "700              True  Distribution  \n",
      "704             False  Distribution  \n",
      "714             False  Distribution  \n",
      "756              True  Distribution  \n",
      "762              True  Distribution  \n",
      "778             False  Distribution  \n",
      "804             False  Distribution  \n",
      "830              True  Distribution  \n",
      "838             False  Distribution  \n",
      "902               NaN  Distribution  \n",
      "919             False  Distribution  \n",
      "931             False  Distribution  \n",
      "940              True  Distribution  \n",
      "944              True  Distribution  \n",
      "('Male', 'Engineering')\n",
      "    First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "171    Patrick   Male   8/17/2007         3:16 AM  143499   17.495   \n",
      "175     Willie   Male   2/17/1998         8:20 PM  146651    1.451   \n",
      "228      Bobby   Male   9/23/1994         1:29 AM   51685   17.445   \n",
      "242     Robert   Male  10/27/1990         7:15 PM   38041   18.428   \n",
      "245     Victor   Male   4/11/1991         7:44 AM   70817   17.138   \n",
      "283       Todd   Male   3/11/2009         3:43 AM  107281    1.612   \n",
      "300       Alan   Male   6/26/1988         3:54 AM  111786    3.592   \n",
      "301      James   Male   7/11/2015         8:52 PM   72257   13.023   \n",
      "329        NaN   Male   1/27/2010         2:57 AM   87760   14.987   \n",
      "343     Ronald   Male   2/24/2009         2:09 PM   96633    4.990   \n",
      "352        NaN   Male   10/9/2011         9:29 AM   69906    4.844   \n",
      "357    Russell   Male   8/13/2014         3:51 PM  121160    7.843   \n",
      "372     Albert   Male    2/1/1997         4:20 PM   67827   19.717   \n",
      "376    Brandon   Male  10/16/2015         2:44 AM  112548    4.664   \n",
      "381      Scott   Male    8/3/1996         1:50 AM   64172   10.894   \n",
      "447    Gregory   Male   5/15/2009         3:52 PM  142208   11.204   \n",
      "462      Craig   Male   4/14/1996         9:17 AM  125556    7.996   \n",
      "474   Jonathan   Male   8/15/2002        12:01 AM  104749   11.364   \n",
      "505        NaN   Male   4/20/1980         1:17 PM   71520   13.248   \n",
      "507   Clarence   Male   11/3/2015        12:11 AM  103684   18.654   \n",
      "551     Philip   Male   7/18/1985         6:36 AM  122319   19.122   \n",
      "563      Randy   Male    5/2/2002         7:39 PM   86723   14.842   \n",
      "604      Bruce   Male   3/15/2013        11:13 PM  141335   15.427   \n",
      "615     Edward   Male   8/30/2001         1:24 AM   73105    6.083   \n",
      "652     Willie   Male   12/5/2009         5:39 AM  141932    1.017   \n",
      "661      Craig   Male   8/21/1995         2:38 AM  123876    4.225   \n",
      "680     Albert   Male   6/17/1992         3:25 AM   86818   14.301   \n",
      "705      Harry   Male   9/23/2015         1:36 AM  129148   15.193   \n",
      "715      Peter   Male   3/22/1982         7:28 AM   77933   13.132   \n",
      "718      Terry   Male  10/21/2010        11:18 PM   58357    7.969   \n",
      "719       John   Male   1/29/1999         3:07 AM   67165   13.001   \n",
      "805     Thomas   Male    5/3/1990         9:29 AM  111371   15.081   \n",
      "823        NaN   Male  11/12/2009         2:30 PM  109411    9.494   \n",
      "835    Douglas   Male    8/4/2007         5:23 AM  132175    2.280   \n",
      "871     Gerald   Male   3/29/2003        10:37 AM   96329    2.469   \n",
      "934     Samuel   Male    8/7/1997        12:40 PM   43694    3.787   \n",
      "941    William   Male   6/26/1997         8:33 AM  104840   15.653   \n",
      "943      Wayne   Male    9/8/2006        11:09 AM   67471    2.728   \n",
      "967     Thomas   Male   3/12/2016         3:10 PM  105681   19.572   \n",
      "983       John   Male  12/23/1982        10:35 PM  146907   11.738   \n",
      "\n",
      "    Senior Management         Team  \n",
      "171              True  Engineering  \n",
      "175              True  Engineering  \n",
      "228              True  Engineering  \n",
      "242              True  Engineering  \n",
      "245             False  Engineering  \n",
      "283              True  Engineering  \n",
      "300              True  Engineering  \n",
      "301             False  Engineering  \n",
      "329               NaN  Engineering  \n",
      "343              True  Engineering  \n",
      "352               NaN  Engineering  \n",
      "357             False  Engineering  \n",
      "372              True  Engineering  \n",
      "376             False  Engineering  \n",
      "381              True  Engineering  \n",
      "447              True  Engineering  \n",
      "462             False  Engineering  \n",
      "474             False  Engineering  \n",
      "505               NaN  Engineering  \n",
      "507              True  Engineering  \n",
      "551             False  Engineering  \n",
      "563             False  Engineering  \n",
      "604              True  Engineering  \n",
      "615              True  Engineering  \n",
      "652              True  Engineering  \n",
      "661             False  Engineering  \n",
      "680              True  Engineering  \n",
      "705              True  Engineering  \n",
      "715              True  Engineering  \n",
      "718             False  Engineering  \n",
      "719             False  Engineering  \n",
      "805              True  Engineering  \n",
      "823               NaN  Engineering  \n",
      "835             False  Engineering  \n",
      "871              True  Engineering  \n",
      "934              True  Engineering  \n",
      "941              True  Engineering  \n",
      "943             False  Engineering  \n",
      "967             False  Engineering  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "983             False  Engineering  \n",
      "('Male', 'Finance')\n",
      "      First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "3          Jerry   Male    3/4/2005         1:00 PM  138705    9.340   \n",
      "46         Bruce   Male  11/28/2009        10:47 PM  114796    6.796   \n",
      "56          Carl   Male    5/3/2006         5:55 PM  130276   16.084   \n",
      "68          Jose   Male  10/30/2004         1:39 PM   84834   14.330   \n",
      "83         Shawn   Male   9/23/2005         2:55 AM  148115    6.539   \n",
      "117       Steven   Male    3/1/1995         3:03 PM  109095    9.494   \n",
      "200         Gary   Male   8/12/1987        12:04 AM   89661    8.525   \n",
      "205      Brandon   Male   3/27/2006         5:54 PM  115711    8.012   \n",
      "219        Billy   Male   3/13/1995        12:05 PM  120444    7.768   \n",
      "222        Jason   Male  10/17/1999        10:09 PM   78417    3.067   \n",
      "241       Walter   Male    7/6/1983        11:54 PM  127813    5.961   \n",
      "286         Todd   Male    2/2/1984        10:13 AM   69989   10.985   \n",
      "304       Jeremy   Male   8/12/1981        12:05 AM   46930   18.702   \n",
      "318          Roy   Male    8/6/2006         7:52 AM  148225    1.841   \n",
      "328       Samuel   Male  12/19/1991         2:54 AM   76076    5.319   \n",
      "335       Robert   Male  11/18/2014         5:00 AM   85799   19.930   \n",
      "341       Carlos   Male   9/19/2007         1:01 AM   77327   11.580   \n",
      "348       Philip   Male    8/2/1989        11:21 AM  129968   19.897   \n",
      "420       Justin   Male   5/22/1986         6:30 AM  121508   19.334   \n",
      "428  Christopher   Male   5/13/1999        10:48 AM   68028    7.869   \n",
      "458       Albert   Male   9/30/2007         5:34 PM  102626   15.843   \n",
      "493        Craig   Male   11/1/2005        11:31 PM   44857   13.266   \n",
      "582        Harry   Male  11/19/1982        11:25 AM   59277   10.200   \n",
      "618        Peter   Male  11/24/1992         5:43 PM   69297    1.268   \n",
      "620        Ralph   Male    3/7/2012         2:25 AM   81215    8.400   \n",
      "685          NaN   Male  10/20/1989         9:37 AM   88086   19.387   \n",
      "710       Edward   Male  11/27/1997         1:00 AM   58327   17.095   \n",
      "725       Jeremy   Male   5/19/1991         1:40 PM  131513    1.876   \n",
      "732        Henry   Male   5/12/1986         2:04 AM   59943    1.432   \n",
      "773          NaN   Male  10/24/1986         9:23 AM   47176   10.736   \n",
      "790         Fred   Male    3/3/1989         2:27 AM  129712   11.058   \n",
      "809       George   Male   9/27/1995         5:04 PM   36749   19.754   \n",
      "831      Kenneth   Male   1/15/2015         2:41 PM   69112    7.588   \n",
      "837       Joseph   Male   5/23/1985        10:16 PM  139570   15.804   \n",
      "854        Harry   Male   2/10/1985         7:46 PM   63046   10.411   \n",
      "861        Jerry   Male   3/26/1989         4:15 AM  140850   18.855   \n",
      "890          NaN   Male  11/24/2015         3:11 AM  145329    7.100   \n",
      "891      Timothy   Male   8/25/1991        10:37 AM   92587    8.475   \n",
      "954          Joe   Male   1/19/1980         4:06 PM  119667    1.148   \n",
      "992      Anthony   Male  10/16/2011         8:35 AM  112769   11.625   \n",
      "996      Phillip   Male   1/31/1984         6:30 AM   42392   19.675   \n",
      "\n",
      "    Senior Management     Team  \n",
      "3                True  Finance  \n",
      "46              False  Finance  \n",
      "56               True  Finance  \n",
      "68               True  Finance  \n",
      "83               True  Finance  \n",
      "117             False  Finance  \n",
      "200             False  Finance  \n",
      "205              True  Finance  \n",
      "219              True  Finance  \n",
      "222             False  Finance  \n",
      "241             False  Finance  \n",
      "286              True  Finance  \n",
      "304              True  Finance  \n",
      "318             False  Finance  \n",
      "328              True  Finance  \n",
      "335             False  Finance  \n",
      "341              True  Finance  \n",
      "348             False  Finance  \n",
      "420              True  Finance  \n",
      "428              True  Finance  \n",
      "458             False  Finance  \n",
      "493             False  Finance  \n",
      "582             False  Finance  \n",
      "618             False  Finance  \n",
      "620             False  Finance  \n",
      "685               NaN  Finance  \n",
      "710              True  Finance  \n",
      "725              True  Finance  \n",
      "732             False  Finance  \n",
      "773               NaN  Finance  \n",
      "790             False  Finance  \n",
      "809             False  Finance  \n",
      "831              True  Finance  \n",
      "837              True  Finance  \n",
      "854             False  Finance  \n",
      "861             False  Finance  \n",
      "890               NaN  Finance  \n",
      "891             False  Finance  \n",
      "954              True  Finance  \n",
      "992              True  Finance  \n",
      "996             False  Finance  \n",
      "('Male', 'Human Resources')\n",
      "    First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "12     Brandon   Male   12/1/1980         1:08 AM  112807   17.492   \n",
      "16      Jeremy   Male   9/21/2010         5:56 AM   90370    7.369   \n",
      "52        Todd   Male   2/18/1990         2:41 AM   49339    1.695   \n",
      "57       Henry   Male   6/26/1996         1:44 AM   64715   15.107   \n",
      "63     Matthew   Male    1/2/2013        10:33 PM   35203   18.040   \n",
      "127    William   Male   9/29/2002         4:09 PM   66521    5.830   \n",
      "132     Carlos   Male    1/4/1995         7:02 AM  146670   10.763   \n",
      "156     Howard   Male   4/27/1992         1:10 AM  105062    1.563   \n",
      "208   Jonathan   Male  10/12/1987         2:59 AM  141069    4.903   \n",
      "259      Henry   Male    2/6/1995         8:34 AM   89258   15.585   \n",
      "270     Thomas   Male    4/6/2011         1:04 PM  103235    9.554   \n",
      "281     Arthur   Male    1/1/2014         7:46 PM  134610    6.147   \n",
      "297     Daniel   Male   9/15/2007        10:16 PM  123811    7.664   \n",
      "302       Adam   Male    7/5/2007        11:59 AM   71276    5.027   \n",
      "339    Michael   Male  11/22/1991        12:57 PM   98753   16.443   \n",
      "345     Steven   Male  11/21/2006         8:30 AM   83706    6.960   \n",
      "346      James   Male  11/22/1998         3:48 AM   74086    5.778   \n",
      "377    Stephen   Male    4/9/1987        12:47 PM  111249   10.574   \n",
      "409     Jeremy   Male   5/29/1994         6:24 AM   47885   19.276   \n",
      "426       Todd   Male   3/16/2016         2:45 PM  134408    3.560   \n",
      "439   Nicholas   Male    3/1/2013         9:26 PM  101036    2.826   \n",
      "508      Scott   Male  11/23/1996         7:36 PM   96111   15.592   \n",
      "519    Raymond   Male   2/16/2009        10:38 PM   37812    3.178   \n",
      "525      Steve   Male   8/22/1995         6:58 AM   67780    9.540   \n",
      "538       Adam   Male   10/8/2010         9:53 PM   45181    3.491   \n",
      "572    Raymond   Male   1/13/1996         4:14 PM  114244   16.690   \n",
      "637      Wayne   Male    9/2/2009         1:37 AM  126956   18.396   \n",
      "643      Shawn   Male   9/29/1987        10:11 PM   71975    9.770   \n",
      "672     Robert   Male   11/5/1982        12:41 PM  111580   10.982   \n",
      "674        NaN   Male   8/28/2012         7:56 PM   88733    1.932   \n",
      "709        NaN   Male    4/7/1995         4:32 AM   60411   12.048   \n",
      "743     Steven   Male   5/30/1980         8:25 PM  100949   13.813   \n",
      "760      Shawn   Male  12/29/2006         3:05 AM   57871    4.020   \n",
      "766     Justin   Male   7/25/1991        12:45 AM  112975    9.699   \n",
      "776      Bobby   Male   9/30/2004        10:52 PM   79047   18.784   \n",
      "780     Steven   Male    7/5/2009         4:55 AM  110306   16.843   \n",
      "801       Ryan   Male   4/15/1988         5:18 AM   91109   17.499   \n",
      "820    William   Male  11/18/1993        12:27 PM   54058    5.182   \n",
      "829     Johnny   Male   1/14/2007         2:54 PM   71383    2.097   \n",
      "833     Gerald   Male   5/28/2010        12:26 PM   96511    9.331   \n",
      "846    Stephen   Male   9/10/1990        10:42 PM  129663   15.574   \n",
      "858       Sean   Male   4/25/2006         4:51 PM  108581    9.634   \n",
      "863    Phillip   Male   5/15/1984         6:15 AM  134120    6.842   \n",
      "966      Louis   Male   8/16/2011         5:19 PM   93022    9.146   \n",
      "978       Sean   Male   1/17/1983         2:23 PM   66146   11.178   \n",
      "\n",
      "    Senior Management             Team  \n",
      "12               True  Human Resources  \n",
      "16              False  Human Resources  \n",
      "52               True  Human Resources  \n",
      "57               True  Human Resources  \n",
      "63              False  Human Resources  \n",
      "127             False  Human Resources  \n",
      "132             False  Human Resources  \n",
      "156             False  Human Resources  \n",
      "208             False  Human Resources  \n",
      "259              True  Human Resources  \n",
      "270              True  Human Resources  \n",
      "281              True  Human Resources  \n",
      "297              True  Human Resources  \n",
      "302              True  Human Resources  \n",
      "339              True  Human Resources  \n",
      "345              True  Human Resources  \n",
      "346              True  Human Resources  \n",
      "377              True  Human Resources  \n",
      "409              True  Human Resources  \n",
      "426              True  Human Resources  \n",
      "439              True  Human Resources  \n",
      "508             False  Human Resources  \n",
      "519             False  Human Resources  \n",
      "525              True  Human Resources  \n",
      "538             False  Human Resources  \n",
      "572             False  Human Resources  \n",
      "637             False  Human Resources  \n",
      "643             False  Human Resources  \n",
      "672             False  Human Resources  \n",
      "674               NaN  Human Resources  \n",
      "709               NaN  Human Resources  \n",
      "743              True  Human Resources  \n",
      "760              True  Human Resources  \n",
      "766             False  Human Resources  \n",
      "776             False  Human Resources  \n",
      "780              True  Human Resources  \n",
      "801              True  Human Resources  \n",
      "820              True  Human Resources  \n",
      "829              True  Human Resources  \n",
      "833             False  Human Resources  \n",
      "846             False  Human Resources  \n",
      "858             False  Human Resources  \n",
      "863             False  Human Resources  \n",
      "966              True  Human Resources  \n",
      "978             False  Human Resources  \n",
      "('Male', 'Legal')\n",
      "      First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "5         Dennis   Male   4/18/1987         1:35 AM  115163   10.125   \n",
      "29      Benjamin   Male   1/26/2005        10:06 PM   79529    7.008   \n",
      "81   Christopher   Male   3/30/2008        10:52 AM   47369   14.822   \n",
      "94         Harry   Male   8/26/1981         3:16 PM  130620    7.030   \n",
      "106         Paul   Male    8/4/1993         7:25 PM   42146    3.046   \n",
      "107       Steven   Male   4/23/2013        10:12 PM   68680   16.565   \n",
      "116          NaN   Male   6/22/1991         8:58 PM   76189   18.988   \n",
      "136        Henry   Male   4/24/1995         4:18 AM   43542   19.687   \n",
      "147       Philip   Male   7/17/2008         1:30 PM   89227    3.996   \n",
      "162       Arthur   Male   4/13/1998        11:28 AM   89786   14.422   \n",
      "188      Charles   Male  10/14/2000         9:40 PM   71749   15.931   \n",
      "221      Gregory   Male   1/29/2000         6:01 AM  109564    3.845   \n",
      "223       Daniel   Male   4/30/2010         3:48 AM  106947   15.866   \n",
      "238        Kevin   Male   3/25/1982         7:31 AM   35061    5.128   \n",
      "295        Jesse   Male    3/2/1981         7:26 PM   79582    3.873   \n",
      "310       Harold   Male   2/20/1990        11:00 PM   66775    2.158   \n",
      "336         Mark   Male   1/22/2007         2:37 PM   75150   12.182   \n",
      "431      Charles   Male  12/16/1999         8:28 PM  104014    7.077   \n",
      "450       Willie   Male   8/22/2009         1:03 PM   55038   19.691   \n",
      "469      Stephen   Male    5/9/2015         7:07 PM  141958    9.078   \n",
      "488       Robert   Male   3/11/2007        11:20 AM  135882   19.944   \n",
      "510        Frank   Male   4/27/2003        12:04 PM  140303   17.070   \n",
      "579       Harold   Male  10/18/2010         8:45 PM   65673    1.187   \n",
      "584         Jack   Male   5/30/2012         3:17 AM   70367   18.266   \n",
      "630      Michael   Male   7/13/1988         1:48 PM   47079    2.617   \n",
      "654         Carl   Male  12/14/1984         8:49 PM   63395   11.411   \n",
      "664      Timothy   Male   10/6/2010        10:49 PM   49473   12.463   \n",
      "668        James   Male   12/5/2001         7:06 AM   67789   17.105   \n",
      "688        Brian   Male    4/7/2007        10:47 PM   93901   17.821   \n",
      "708        Steve   Male   1/11/2002         9:17 PM   51821    1.197   \n",
      "791       Joseph   Male   11/4/1982         9:46 PM  126010   19.601   \n",
      "819      Kenneth   Male  12/14/1985         6:55 AM   47232   17.862   \n",
      "832        Keith   Male   2/12/2003         3:02 PM  120672   19.467   \n",
      "887        David   Male   12/5/2009         8:48 AM   92242   15.407   \n",
      "981        James   Male   1/15/1993         5:19 PM  148985   19.280   \n",
      "\n",
      "    Senior Management   Team  \n",
      "5               False  Legal  \n",
      "29               True  Legal  \n",
      "81              False  Legal  \n",
      "94              False  Legal  \n",
      "106             False  Legal  \n",
      "107             False  Legal  \n",
      "116               NaN  Legal  \n",
      "136             False  Legal  \n",
      "147             False  Legal  \n",
      "162              True  Legal  \n",
      "188             False  Legal  \n",
      "221             False  Legal  \n",
      "223              True  Legal  \n",
      "238             False  Legal  \n",
      "295             False  Legal  \n",
      "310              True  Legal  \n",
      "336              True  Legal  \n",
      "431             False  Legal  \n",
      "450             False  Legal  \n",
      "469              True  Legal  \n",
      "488             False  Legal  \n",
      "510             False  Legal  \n",
      "579              True  Legal  \n",
      "584              True  Legal  \n",
      "630             False  Legal  \n",
      "654             False  Legal  \n",
      "664             False  Legal  \n",
      "668              True  Legal  \n",
      "688              True  Legal  \n",
      "708              True  Legal  \n",
      "791             False  Legal  \n",
      "819              True  Legal  \n",
      "832             False  Legal  \n",
      "887             False  Legal  \n",
      "981             False  Legal  \n",
      "('Male', 'Marketing')\n",
      "    First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "0      Douglas   Male    8/6/1993        12:42 PM   97308    6.945   \n",
      "21     Matthew   Male    9/5/1995         2:12 AM  100612   13.645   \n",
      "26       Craig   Male   2/27/2000         7:45 AM   37598    7.757   \n",
      "74      Thomas   Male    6/4/1995         2:24 PM   62096   17.029   \n",
      "77     Charles   Male   9/14/2004         8:13 PM  107391    1.260   \n",
      "101      Aaron   Male   2/17/2012        10:20 AM   61602   11.849   \n",
      "104       John   Male  12/23/1989         7:01 AM   80740   19.305   \n",
      "112     Willie   Male  11/27/2003         6:21 AM   64363    4.023   \n",
      "119       Paul   Male    6/3/2008         3:05 PM   41054   12.299   \n",
      "150       Sean   Male    5/4/1996         8:59 PM  135490   19.934   \n",
      "187      Roger   Male  11/19/2004         3:55 PM   51430    6.460   \n",
      "204     Willie   Male    6/6/2006         9:45 AM   55281    4.935   \n",
      "216    Matthew   Male   7/31/2013         8:04 AM  142373    2.462   \n",
      "252      Ralph   Male  11/11/2004         1:35 AM   71896    4.232   \n",
      "274     Howard   Male   10/5/1982         7:22 AM   97490    5.460   \n",
      "293      Jesse   Male  10/25/1999         3:35 PM  118733    9.653   \n",
      "309   Benjamin   Male   9/11/1984         6:37 AM   84810   15.794   \n",
      "327      Aaron   Male   1/29/1994         6:48 PM   58755    5.097   \n",
      "353        NaN   Male   4/22/1997         9:36 PM   65078    3.095   \n",
      "403      Craig   Male    2/8/1984         4:45 AM  113506   19.642   \n",
      "413     Johnny   Male   9/14/1992         8:21 AM  115194    5.861   \n",
      "452      Scott   Male  11/17/2012         2:47 PM  146812    1.965   \n",
      "454        NaN   Male   2/21/1995        12:26 AM  136602   10.429   \n",
      "461     Jeremy   Male    8/9/2001         3:56 AM   43354   13.946   \n",
      "464   Lawrence   Male   5/13/2005         1:25 AM   74640   18.641   \n",
      "484        Joe   Male   4/14/2000         3:14 PM   50645   11.119   \n",
      "496     Johnny   Male   2/26/2016         7:39 PM   76394    5.437   \n",
      "503      Roger   Male    8/3/2015         4:56 PM  125033    4.887   \n",
      "544      Roger   Male    7/3/2010         5:11 PM  105689   13.320   \n",
      "550    Gregory   Male   6/18/1984         3:46 AM   82726   16.304   \n",
      "556     Arthur   Male  12/16/2014         4:56 PM   66819    6.639   \n",
      "571   Jonathan   Male    4/6/1995        10:38 PM   56993   18.623   \n",
      "581     Ernest   Male   1/28/1990        12:08 AM   81919   15.118   \n",
      "696     Samuel   Male   5/25/2012         4:40 PM   85550   11.593   \n",
      "722     Joshua   Male   6/27/1997         3:23 PM   95003    5.197   \n",
      "798   Lawrence   Male   10/1/1984         2:06 PM  102589   17.952   \n",
      "860    Phillip   Male   10/7/1984        11:05 AM   36837   14.660   \n",
      "867       Earl   Male   6/15/2013         3:00 AM   48046    7.737   \n",
      "899     Walter   Male   5/21/1992        12:39 AM  144701   16.323   \n",
      "947        NaN   Male   7/30/2012         3:07 PM  107351    5.329   \n",
      "994     George   Male   6/21/2013         5:47 PM   98874    4.479   \n",
      "\n",
      "    Senior Management       Team  \n",
      "0                True  Marketing  \n",
      "21              False  Marketing  \n",
      "26               True  Marketing  \n",
      "74              False  Marketing  \n",
      "77               True  Marketing  \n",
      "101              True  Marketing  \n",
      "104             False  Marketing  \n",
      "112             False  Marketing  \n",
      "119             False  Marketing  \n",
      "150             False  Marketing  \n",
      "187             False  Marketing  \n",
      "204              True  Marketing  \n",
      "216             False  Marketing  \n",
      "252              True  Marketing  \n",
      "274              True  Marketing  \n",
      "293             False  Marketing  \n",
      "309             False  Marketing  \n",
      "327              True  Marketing  \n",
      "353               NaN  Marketing  \n",
      "403             False  Marketing  \n",
      "413              True  Marketing  \n",
      "452              True  Marketing  \n",
      "454               NaN  Marketing  \n",
      "461             False  Marketing  \n",
      "464             False  Marketing  \n",
      "484             False  Marketing  \n",
      "496              True  Marketing  \n",
      "503              True  Marketing  \n",
      "544              True  Marketing  \n",
      "550              True  Marketing  \n",
      "556              True  Marketing  \n",
      "571             False  Marketing  \n",
      "581             False  Marketing  \n",
      "696              True  Marketing  \n",
      "722              True  Marketing  \n",
      "798              True  Marketing  \n",
      "860             False  Marketing  \n",
      "867             False  Marketing  \n",
      "899              True  Marketing  \n",
      "947               NaN  Marketing  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "994              True  Marketing  \n",
      "('Male', 'Product')\n",
      "    First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "17       Shawn   Male   12/7/1986         7:45 PM  111737    6.414   \n",
      "72       Bobby   Male    5/7/2007        10:01 AM   54043    3.833   \n",
      "141       Adam   Male  12/24/1990         8:57 PM  110194   14.727   \n",
      "176     Victor   Male    1/8/2003         1:02 PM  124486   10.166   \n",
      "195     Ronald   Male   4/30/2014         2:19 PM  121068   12.757   \n",
      "243      Jimmy   Male   10/7/1986         5:53 PM  126310    5.413   \n",
      "272       Fred   Male   2/20/1980         2:25 AM   74129   18.225   \n",
      "273   Nicholas   Male   4/12/1994         8:21 PM   74669    1.113   \n",
      "322    Douglas   Male    1/8/2002         6:42 PM   41428   14.372   \n",
      "326    Jeffrey   Male   6/18/1997         8:01 PM   45150   12.075   \n",
      "332       Fred   Male   2/13/1987         2:15 PM  121723   18.645   \n",
      "338    Richard   Male   9/27/2005         1:41 AM   86326   10.717   \n",
      "358      Scott   Male  12/17/2011         3:45 AM   90429    4.450   \n",
      "364       Juan   Male   4/16/2002        11:09 PM   97364    3.595   \n",
      "378    Russell   Male  10/30/1994         5:46 AM  114334    9.669   \n",
      "383     Carlos   Male   7/28/2010         3:08 PM   50167    2.362   \n",
      "384       Jack   Male  12/24/1980        10:58 PM  106995   15.723   \n",
      "422     Victor   Male   9/24/2005        12:04 PM  123144   16.261   \n",
      "463       Jose   Male   7/11/2002         9:15 AM   59862    3.269   \n",
      "533       Earl   Male   2/11/2014         9:03 PM   52620   13.773   \n",
      "546     Joseph   Male   9/10/1992         6:50 PM  102555    3.672   \n",
      "575       John   Male   1/22/1995        12:17 AM   66077    5.809   \n",
      "576    Michael   Male   7/30/1993         5:35 PM   35013   14.879   \n",
      "610       Alan   Male   2/17/2012        12:26 AM   41453   10.084   \n",
      "681      Frank   Male  11/23/2002         6:10 PM   75147   17.398   \n",
      "737   Lawrence   Male   7/23/2011         3:41 AM  122971   14.618   \n",
      "757   Benjamin   Male   4/12/1988        11:32 PM  123409    7.783   \n",
      "771      Peter   Male   5/22/1991         1:39 AM  102577   12.026   \n",
      "799    Raymond   Male  12/12/1986        12:18 PM   47529    2.712   \n",
      "800   Clarence   Male    8/5/1989         6:11 PM  148941   11.517   \n",
      "812        NaN   Male  12/13/1994        10:34 AM  141311    5.478   \n",
      "848      Bobby   Male    6/5/1984         7:40 AM   93368    2.880   \n",
      "849     Ernest   Male    9/6/1997         2:02 PM   53335    9.192   \n",
      "885       Juan   Male   10/6/1993         8:52 AM   85871    9.160   \n",
      "909     Donald   Male    8/1/1991         2:25 AM   61999    6.466   \n",
      "917      Shawn   Male   3/13/2008         5:36 PM   51667    6.339   \n",
      "953      Randy   Male   2/25/2012         5:33 AM   57266   14.077   \n",
      "962   Jonathan   Male   8/21/2013        12:45 AM  121797   16.923   \n",
      "979     Ernest   Male   7/20/2013         6:41 AM  142935   13.198   \n",
      "997    Russell   Male   5/20/2013        12:39 PM   96914    1.421   \n",
      "\n",
      "    Senior Management     Team  \n",
      "17              False  Product  \n",
      "72              False  Product  \n",
      "141              True  Product  \n",
      "176             False  Product  \n",
      "195              True  Product  \n",
      "243              True  Product  \n",
      "272             False  Product  \n",
      "273              True  Product  \n",
      "322             False  Product  \n",
      "326              True  Product  \n",
      "332              True  Product  \n",
      "338             False  Product  \n",
      "358             False  Product  \n",
      "364             False  Product  \n",
      "378             False  Product  \n",
      "383             False  Product  \n",
      "384             False  Product  \n",
      "422              True  Product  \n",
      "463             False  Product  \n",
      "533             False  Product  \n",
      "546              True  Product  \n",
      "575              True  Product  \n",
      "576             False  Product  \n",
      "610             False  Product  \n",
      "681             False  Product  \n",
      "737             False  Product  \n",
      "757             False  Product  \n",
      "771              True  Product  \n",
      "799              True  Product  \n",
      "800             False  Product  \n",
      "812               NaN  Product  \n",
      "848              True  Product  \n",
      "849             False  Product  \n",
      "885             False  Product  \n",
      "909             False  Product  \n",
      "917             False  Product  \n",
      "953             False  Product  \n",
      "962             False  Product  \n",
      "979              True  Product  \n",
      "997             False  Product  \n",
      "('Male', 'Sales')\n",
      "      First Name Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "13          Gary   Male   1/27/2008        11:40 PM  109831    5.831   \n",
      "45         Roger   Male   4/17/1980        11:32 AM   88010   13.886   \n",
      "71        Johnny   Male   11/6/2009         4:23 PM  118172   16.194   \n",
      "167  Christopher   Male  12/24/2011        12:22 PM  142178   17.984   \n",
      "189     Clarence   Male    5/2/1998         3:16 AM   85700    1.215   \n",
      "202        Roger   Male   11/8/1982         2:32 AM  140558    5.084   \n",
      "225        Harry   Male   10/1/2015         7:47 PM   64579   15.266   \n",
      "257          NaN   Male  11/25/1999         6:16 AM   42676   15.517   \n",
      "312       Gerald   Male   4/16/2001        10:09 PM  121604    1.923   \n",
      "337         Earl   Male    1/5/2000         5:46 PM   91344    4.035   \n",
      "340       Steven   Male   5/12/2009         2:14 PM  113060    2.846   \n",
      "371        Larry   Male   8/27/2003         1:00 PM   91133    5.140   \n",
      "373      Kenneth   Male   4/13/1999        10:28 PM   81839   12.072   \n",
      "410       Ernest   Male   8/14/2012        11:25 AM  126232    6.191   \n",
      "421        Kevin   Male  11/20/1986         6:59 PM   79906   18.021   \n",
      "427        Frank   Male   1/10/1981         1:30 PM   71853    8.037   \n",
      "466       Walter   Male    8/4/2007         1:59 PM   58789    5.461   \n",
      "495       Eugene   Male   5/24/1984        10:54 AM   81077    2.117   \n",
      "515        Larry   Male    1/8/1999         2:05 AM   97370   10.458   \n",
      "539       Justin   Male    9/6/1999        10:30 AM   62454    3.459   \n",
      "592       Justin   Male   5/24/1981        11:15 PM   78351   15.221   \n",
      "607          NaN   Male  10/13/1983        11:59 PM  139754   12.740   \n",
      "609         Todd   Male   2/16/2010        11:29 AM  103405   15.910   \n",
      "624       Jeremy   Male  11/26/1997         8:22 AM  133033   12.200   \n",
      "690        Bruce   Male   1/28/2007        11:10 PM  134988   13.926   \n",
      "692       Harold   Male   5/15/2007         6:37 PM  118753    8.162   \n",
      "727          Roy   Male   5/10/2004         1:22 PM   46875   12.942   \n",
      "739       Carlos   Male   1/25/1981        10:00 AM  138598   14.737   \n",
      "764        Roger   Male    5/2/1988        10:04 PM  115582   15.343   \n",
      "787        Kevin   Male    7/1/2005         3:22 PM  141498    4.135   \n",
      "862       Ronald   Male   5/25/1983        10:05 AM   50426   18.536   \n",
      "873        Jason   Male    5/1/1998         6:05 PM   75607    4.299   \n",
      "886      Anthony   Male   1/30/2014         9:04 PM   96795   14.837   \n",
      "892      Brandon   Male  10/23/1995        12:04 PM   60263    2.709   \n",
      "896       Victor   Male   3/10/2010         5:01 PM   45267    3.942   \n",
      "921       George   Male    6/5/2015         5:40 AM   50369   18.900   \n",
      "957      Jeffrey   Male    2/3/1984         1:26 PM   70990   15.901   \n",
      "964        Bruce   Male    5/7/1980         8:00 PM   35802   12.391   \n",
      "999       Albert   Male   5/15/2012         6:24 PM  129949   10.169   \n",
      "\n",
      "    Senior Management   Team  \n",
      "13              False  Sales  \n",
      "45               True  Sales  \n",
      "71               True  Sales  \n",
      "167              True  Sales  \n",
      "189             False  Sales  \n",
      "202              True  Sales  \n",
      "225              True  Sales  \n",
      "257               NaN  Sales  \n",
      "312              True  Sales  \n",
      "337              True  Sales  \n",
      "340              True  Sales  \n",
      "371             False  Sales  \n",
      "373             False  Sales  \n",
      "410              True  Sales  \n",
      "421              True  Sales  \n",
      "427              True  Sales  \n",
      "466             False  Sales  \n",
      "495             False  Sales  \n",
      "515             False  Sales  \n",
      "539              True  Sales  \n",
      "592             False  Sales  \n",
      "607               NaN  Sales  \n",
      "609             False  Sales  \n",
      "624             False  Sales  \n",
      "690              True  Sales  \n",
      "692             False  Sales  \n",
      "727              True  Sales  \n",
      "739             False  Sales  \n",
      "764              True  Sales  \n",
      "787              True  Sales  \n",
      "862              True  Sales  \n",
      "873              True  Sales  \n",
      "886             False  Sales  \n",
      "892             False  Sales  \n",
      "896              True  Sales  \n",
      "921              True  Sales  \n",
      "957              True  Sales  \n",
      "964              True  Sales  \n",
      "999              True  Sales  \n"
     ]
    }
   ],
   "source": [
    "# Iterating over splitter groups\n",
    "\n",
    "for name,group in gr1:\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female\n",
      "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
      "2        Maria  Female   4/23/1993        11:17 AM  130590   11.858   \n",
      "6         Ruby  Female   8/17/1987         4:20 PM   65476   10.012   \n",
      "7          NaN  Female   7/20/2015        10:43 AM   45906   11.598   \n",
      "8       Angela  Female  11/22/2005         6:29 AM   95570   18.523   \n",
      "9      Frances  Female    8/8/2002         6:51 AM  139852    7.524   \n",
      "..         ...     ...         ...             ...     ...      ...   \n",
      "987     Gloria  Female   12/8/2014         5:08 AM  136709   10.331   \n",
      "988      Alice  Female   10/5/2004         9:34 AM   47638   11.209   \n",
      "990      Robin  Female   7/24/1987         1:35 PM  100765   10.982   \n",
      "991       Rose  Female   8/25/2002         5:12 AM  134505   11.051   \n",
      "993       Tina  Female   5/15/1997         3:53 PM   56450   19.040   \n",
      "\n",
      "    Senior Management                  Team  \n",
      "2               False               Finance  \n",
      "6                True               Product  \n",
      "7                 NaN               Finance  \n",
      "8                True           Engineering  \n",
      "9                True  Business Development  \n",
      "..                ...                   ...  \n",
      "987              True               Finance  \n",
      "988             False       Human Resources  \n",
      "990              True       Client Services  \n",
      "991              True             Marketing  \n",
      "993              True           Engineering  \n",
      "\n",
      "[431 rows x 8 columns]\n",
      "Male\n",
      "    First Name Gender Start Date Last Login Time  Salary  Bonus %  \\\n",
      "0      Douglas   Male   8/6/1993        12:42 PM   97308    6.945   \n",
      "1       Thomas   Male  3/31/1996         6:53 AM   61933    4.170   \n",
      "3        Jerry   Male   3/4/2005         1:00 PM  138705    9.340   \n",
      "4        Larry   Male  1/24/1998         4:47 PM  101004    1.389   \n",
      "5       Dennis   Male  4/18/1987         1:35 AM  115163   10.125   \n",
      "..         ...    ...        ...             ...     ...      ...   \n",
      "994     George   Male  6/21/2013         5:47 PM   98874    4.479   \n",
      "996    Phillip   Male  1/31/1984         6:30 AM   42392   19.675   \n",
      "997    Russell   Male  5/20/2013        12:39 PM   96914    1.421   \n",
      "998      Larry   Male  4/20/2013         4:45 PM   60500   11.985   \n",
      "999     Albert   Male  5/15/2012         6:24 PM  129949   10.169   \n",
      "\n",
      "    Senior Management                  Team  \n",
      "0                True             Marketing  \n",
      "1                True                   NaN  \n",
      "3                True               Finance  \n",
      "4                True       Client Services  \n",
      "5               False                 Legal  \n",
      "..                ...                   ...  \n",
      "994              True             Marketing  \n",
      "996             False               Finance  \n",
      "997             False               Product  \n",
      "998             False  Business Development  \n",
      "999              True                 Sales  \n",
      "\n",
      "[424 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "for name,group in gr:\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To select individual group  \n",
    "# groupby.get_group()\n",
    "\n",
    "g=D.groupby(['Team'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>8/6/1993</td>\n",
       "      <td>12:42 PM</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>9/5/1995</td>\n",
       "      <td>2:12 AM</td>\n",
       "      <td>100612</td>\n",
       "      <td>13.645</td>\n",
       "      <td>False</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Craig</td>\n",
       "      <td>Male</td>\n",
       "      <td>2/27/2000</td>\n",
       "      <td>7:45 AM</td>\n",
       "      <td>37598</td>\n",
       "      <td>7.757</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Marilyn</td>\n",
       "      <td>Female</td>\n",
       "      <td>12/7/1980</td>\n",
       "      <td>3:16 AM</td>\n",
       "      <td>73524</td>\n",
       "      <td>5.207</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>6/12/2007</td>\n",
       "      <td>5:25 PM</td>\n",
       "      <td>58112</td>\n",
       "      <td>19.414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>Lori</td>\n",
       "      <td>Female</td>\n",
       "      <td>11/20/2015</td>\n",
       "      <td>1:15 PM</td>\n",
       "      <td>75498</td>\n",
       "      <td>6.537</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>7/30/2012</td>\n",
       "      <td>3:07 PM</td>\n",
       "      <td>107351</td>\n",
       "      <td>5.329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>11/26/1982</td>\n",
       "      <td>7:04 AM</td>\n",
       "      <td>82871</td>\n",
       "      <td>17.999</td>\n",
       "      <td>False</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>Rose</td>\n",
       "      <td>Female</td>\n",
       "      <td>8/25/2002</td>\n",
       "      <td>5:12 AM</td>\n",
       "      <td>134505</td>\n",
       "      <td>11.051</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>George</td>\n",
       "      <td>Male</td>\n",
       "      <td>6/21/2013</td>\n",
       "      <td>5:47 PM</td>\n",
       "      <td>98874</td>\n",
       "      <td>4.479</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender  Start Date Last Login Time  Salary  Bonus %  \\\n",
       "0      Douglas    Male    8/6/1993        12:42 PM   97308    6.945   \n",
       "21     Matthew    Male    9/5/1995         2:12 AM  100612   13.645   \n",
       "26       Craig    Male   2/27/2000         7:45 AM   37598    7.757   \n",
       "43     Marilyn  Female   12/7/1980         3:16 AM   73524    5.207   \n",
       "62         NaN  Female   6/12/2007         5:25 PM   58112   19.414   \n",
       "..         ...     ...         ...             ...     ...      ...   \n",
       "942       Lori  Female  11/20/2015         1:15 PM   75498    6.537   \n",
       "947        NaN    Male   7/30/2012         3:07 PM  107351    5.329   \n",
       "986      Donna  Female  11/26/1982         7:04 AM   82871   17.999   \n",
       "991       Rose  Female   8/25/2002         5:12 AM  134505   11.051   \n",
       "994     George    Male   6/21/2013         5:47 PM   98874    4.479   \n",
       "\n",
       "    Senior Management       Team  \n",
       "0                True  Marketing  \n",
       "21              False  Marketing  \n",
       "26               True  Marketing  \n",
       "43               True  Marketing  \n",
       "62                NaN  Marketing  \n",
       "..                ...        ...  \n",
       "942              True  Marketing  \n",
       "947               NaN  Marketing  \n",
       "986             False  Marketing  \n",
       "991              True  Marketing  \n",
       "994              True  Marketing  \n",
       "\n",
       "[98 rows x 8 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.get_group('Marketing')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up to now we split the data set into groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying functions \n",
    "# Aggregation : It is a process in which we compute a summary statistic of each group\n",
    "# Transformation : It is a process in which we perform some group-specific computations and return a like-indexed\n",
    "# Filtering : It is a process in which we discard some groups\n",
    "\n",
    "\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = {'Name':['Jai', 'Anuj', 'Jai', 'Princi',  \n",
    "                 'Gaurav', 'Anuj', 'Princi', 'Abhi'],  \n",
    "        'Age':[27, 24, 22, 32,  \n",
    "               33, 36, 27, 32],  \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj', \n",
    "                   'Jaunpur', 'Kanpur', 'Allahabad', 'Aligarh'],  \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd', \n",
    "                         'B.Tech', 'B.com', 'Msc', 'MA']} \n",
    "\n",
    "A=pd.DataFrame(data1)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Qualification</th>\n",
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       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jai</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Princi</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>33</td>\n",
       "      <td>Jaunpur</td>\n",
       "      <td>B.Tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>36</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>B.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Princi</td>\n",
       "      <td>27</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Abhi</td>\n",
       "      <td>32</td>\n",
       "      <td>Aligarh</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age    Address Qualification\n",
       "0     Jai   27     Nagpur           Msc\n",
       "1    Anuj   24     Kanpur            MA\n",
       "2     Jai   22  Allahabad           MCA\n",
       "3  Princi   32    Kannuaj           Phd\n",
       "4  Gaurav   33    Jaunpur        B.Tech\n",
       "5    Anuj   36     Kanpur         B.com\n",
       "6  Princi   27  Allahabad           Msc\n",
       "7    Abhi   32    Aligarh            MA"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "B=A.groupby(['Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Name</th>\n",
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       "      <th>1</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>36</td>\n",
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       "      <td>B.com</td>\n",
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      ],
      "text/plain": [
       "   Name  Age Address Qualification\n",
       "1  Anuj   24  Kanpur            MA\n",
       "5  Anuj   36  Kanpur         B.com"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.get_group('Anuj')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abhi</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gaurav</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age\n",
       "Name       \n",
       "Abhi     32\n",
       "Anuj     60\n",
       "Gaurav   33\n",
       "Jai      49\n",
       "Princi   59"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying aggregation functions \n",
    "# grouped df.agg(np.sum,np.mean,np.std)\n",
    "B.agg(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abhi</th>\n",
       "      <td>32</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>60</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.485281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gaurav</th>\n",
       "      <td>33</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>49</td>\n",
       "      <td>24.5</td>\n",
       "      <td>3.535534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>59</td>\n",
       "      <td>29.5</td>\n",
       "      <td>3.535534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age                \n",
       "       sum  mean       std\n",
       "Name                      \n",
       "Abhi    32  32.0       NaN\n",
       "Anuj    60  30.0  8.485281\n",
       "Gaurav  33  33.0       NaN\n",
       "Jai     49  24.5  3.535534\n",
       "Princi  59  29.5  3.535534"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.agg([np.sum,np.mean,np.std])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abhi</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anuj</th>\n",
       "      <td>8.485281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gaurav</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jai</th>\n",
       "      <td>3.535534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princi</th>\n",
       "      <td>3.535534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age\n",
       "Name            \n",
       "Abhi         NaN\n",
       "Anuj    8.485281\n",
       "Gaurav       NaN\n",
       "Jai     3.535534\n",
       "Princi  3.535534"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also aggregate function using dictionary\n",
    "\n",
    "B.agg({'Age':'std'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformation : transfer the column values using function \n",
    "# grouped df.transform(fuction)\n",
    "\n",
    "data1 = {'Name':['Jai', 'Anuj', 'Jai', 'Princi',  \n",
    "                 'Gaurav', 'Anuj', 'Princi', 'Abhi'],  \n",
    "        'Age':[27, 24, 22, 32,  \n",
    "               33, 36, 27, 32],  \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj', \n",
    "                   'Jaunpur', 'Kanpur', 'Allahabad', 'Aligarh'],  \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd', \n",
    "                         'B.Tech', 'B.com', 'Msc', 'MA'], \n",
    "        'Score': [23, 34, 35, 45, 47, 50, 52, 53]} \n",
    "\n",
    "C=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Msc</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jai</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Princi</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>33</td>\n",
       "      <td>Jaunpur</td>\n",
       "      <td>B.Tech</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>36</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>B.com</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Princi</td>\n",
       "      <td>27</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Msc</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Abhi</td>\n",
       "      <td>32</td>\n",
       "      <td>Aligarh</td>\n",
       "      <td>MA</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age    Address Qualification  Score\n",
       "0     Jai   27     Nagpur           Msc     23\n",
       "1    Anuj   24     Kanpur            MA     34\n",
       "2     Jai   22  Allahabad           MCA     35\n",
       "3  Princi   32    Kannuaj           Phd     45\n",
       "4  Gaurav   33    Jaunpur        B.Tech     47\n",
       "5    Anuj   36     Kanpur         B.com     50\n",
       "6  Princi   27  Allahabad           Msc     52\n",
       "7    Abhi   32    Aligarh            MA     53"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "D=C.groupby(['Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001599FB50CF8>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.071068</td>\n",
       "      <td>-7.071068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-7.071068</td>\n",
       "      <td>-7.071068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-7.071068</td>\n",
       "      <td>7.071068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.071068</td>\n",
       "      <td>-7.071068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.071068</td>\n",
       "      <td>7.071068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-7.071068</td>\n",
       "      <td>7.071068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age     Score\n",
       "0  7.071068 -7.071068\n",
       "1 -7.071068 -7.071068\n",
       "2 -7.071068  7.071068\n",
       "3  7.071068 -7.071068\n",
       "4       NaN       NaN\n",
       "5  7.071068  7.071068\n",
       "6 -7.071068  7.071068\n",
       "7       NaN       NaN"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = lambda x: (x - x.mean()) / x.std()*10  # transformation function\n",
    "D.transform(sc) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Msc</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jai</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Princi</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>36</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>B.com</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Princi</td>\n",
       "      <td>27</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Msc</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age    Address Qualification  Score\n",
       "0     Jai   27     Nagpur           Msc     23\n",
       "1    Anuj   24     Kanpur            MA     34\n",
       "2     Jai   22  Allahabad           MCA     35\n",
       "3  Princi   32    Kannuaj           Phd     45\n",
       "5    Anuj   36     Kanpur         B.com     50\n",
       "6  Princi   27  Allahabad           Msc     52"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Flitering : we can filter groups by using some function\n",
    "\n",
    "D.filter(lambda x: len(x) >= 2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "example = {'Team':['Arsenal', 'Manchester United', 'Arsenal', \n",
    "                   'Arsenal', 'Chelsea', 'Manchester United', \n",
    "                   'Manchester United', 'Chelsea', 'Chelsea', 'Chelsea'], \n",
    "                     \n",
    "           'Player':['Ozil', 'Pogba', 'Lucas', 'Aubameyang', \n",
    "                       'Hazard', 'Mata', 'Lukaku', 'Morata',  \n",
    "                                         'Giroud', 'Kante'], \n",
    "                                           \n",
    "           'Goals':[5, 3, 6, 4, 9, 2, 0, 5, 2, 3] } \n",
    "\n",
    "dd=pd.DataFrame(example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>Goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Ozil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Manchester United</td>\n",
       "      <td>Pogba</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Lucas</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Aubameyang</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Hazard</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manchester United</td>\n",
       "      <td>Mata</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Manchester United</td>\n",
       "      <td>Lukaku</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Morata</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Giroud</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Kante</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Team      Player  Goals\n",
       "0            Arsenal        Ozil      5\n",
       "1  Manchester United       Pogba      3\n",
       "2            Arsenal       Lucas      6\n",
       "3            Arsenal  Aubameyang      4\n",
       "4            Chelsea      Hazard      9\n",
       "5  Manchester United        Mata      2\n",
       "6  Manchester United      Lukaku      0\n",
       "7            Chelsea      Morata      5\n",
       "8            Chelsea      Giroud      2\n",
       "9            Chelsea       Kante      3"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To get mean of individual team\n",
    "men=dd['Goals'].groupby(dd['Team'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Team\n",
       "Arsenal              5.000000\n",
       "Chelsea              4.750000\n",
       "Manchester United    1.666667\n",
       "Name: Goals, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "men.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID         Movies  Week_1_Viewers  Week_2_Viewers  Week_3_Viewers\n",
      "0   1  The Godfather              30              60              40\n",
      "1   2     Fight Club              30              40              20\n",
      "2   3     Casablanca              40              80              20\n"
     ]
    }
   ],
   "source": [
    "# Finally combinig columns with groupby function by creating dictionary \n",
    "# we create extra dictionary for how to combine the groups \n",
    "\n",
    "dict = { \n",
    "    \"ID\":[1, 2, 3], \n",
    "    \"Movies\":[\"The Godfather\", \"Fight Club\", \"Casablanca\"], \n",
    "    \"Week_1_Viewers\":[30, 30, 40], \n",
    "    \"Week_2_Viewers\":[60, 40, 80], \n",
    "    \"Week_3_Viewers\":[40, 20, 20] }; \n",
    "  \n",
    "\n",
    "q = pd.DataFrame(dict)\n",
    "print(q) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Total_Viewers\n",
      "Movies                      \n",
      "The Godfather            130\n",
      "Fight Club                90\n",
      "Casablanca               140\n"
     ]
    }
   ],
   "source": [
    "groupby_dict = {\n",
    "             \n",
    "            \"Week_1_Viewers\":\"Total_Viewers\", \n",
    "           \"Week_2_Viewers\":\"Total_Viewers\", \n",
    "           \"Week_3_Viewers\":\"Total_Viewers\", \n",
    "           \"Movies\":\"Movies\" }                     # Creating extra dictonary how to combine the columns \n",
    "  \n",
    "q = q.set_index('Movies')\n",
    "q = q.groupby(groupby_dict, axis = 1).sum()   # we combine the columns using sum \n",
    "print(q) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging , Concatenating , Joining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Robert</td>\n",
       "      <td>55</td>\n",
       "      <td>53</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0   1   2   3\n",
       "0     Ram  30  32  40\n",
       "1   Rahim  40  41  42\n",
       "2  Robert  55  53  51"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can merge two data frames using .merge()\n",
    "# We can join two data frames using .join()\n",
    "# We can concate two data frames using .concat()\n",
    "\n",
    "# cancatenating \n",
    "# pd.concat(objs: Union[Iterable[‘DataFrame’], Mapping[Label, ‘DataFrame’]], axis='0', join: str = \"'outer'\", ignore_index: bool = 'False', keys='None', levels='None', names='None', verify_integrity: bool = 'False', sort: bool = 'False', copy: bool = 'True')\n",
    "\n",
    "Data1= [('Ram',30,32,40),('Rahim',40,41,42),('Robert',55,53,51)]\n",
    "df1=pd.DataFrame(Data1)\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jani</td>\n",
       "      <td>75</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basha</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shaik</td>\n",
       "      <td>72</td>\n",
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       "      <td>77</td>\n",
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      "text/plain": [
       "       0   1   2   3\n",
       "0   jani  75  77  73\n",
       "1  Basha  83  92  90\n",
       "2  Shaik  72  70  77"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2= [('jani',75,77,73),('Basha',83,92,90),('Shaik',72,70,77)]\n",
    "df2=pd.DataFrame(Data2)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Robert</td>\n",
       "      <td>55</td>\n",
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       "      <td>51</td>\n",
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       "      <th>0</th>\n",
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       "      <td>73</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basha</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shaik</td>\n",
       "      <td>72</td>\n",
       "      <td>70</td>\n",
       "      <td>77</td>\n",
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       "  </tbody>\n",
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       "        0   1   2   3\n",
       "0     Ram  30  32  40\n",
       "1   Rahim  40  41  42\n",
       "2  Robert  55  53  51\n",
       "0    jani  75  77  73\n",
       "1   Basha  83  92  90\n",
       "2   Shaik  72  70  77"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>jani</td>\n",
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       "      <td>73</td>\n",
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       "      <td>Basha</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
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       "    <tr>\n",
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       "      <td>Shaik</td>\n",
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       "        0   1   2   3\n",
       "0     Ram  30  32  40\n",
       "1   Rahim  40  41  42\n",
       "2  Robert  55  53  51\n",
       "3    jani  75  77  73\n",
       "4   Basha  83  92  90\n",
       "5   Shaik  72  70  77"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>77</td>\n",
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      "text/plain": [
       "        0   1   2   3      4   5   6   7\n",
       "0     Ram  30  32  40   jani  75  77  73\n",
       "1   Rahim  40  41  42  Basha  83  92  90\n",
       "2  Robert  55  53  51  Shaik  72  70  77"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2],ignore_index=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     Name  Maths  Science  English\n",
       "0     Ram     30       32       40\n",
       "1   Rahim     40       41       42\n",
       "2  Robert     55       53       51"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data1= [('Ram',30,32,40),('Rahim',40,41,42),('Robert',55,53,51)]\n",
    "df1=pd.DataFrame(Data1,columns=['Name','Maths','Science','English'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jani</td>\n",
       "      <td>75</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basha</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shaik</td>\n",
       "      <td>72</td>\n",
       "      <td>70</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Maths  Science  English\n",
       "0   jani     75       77       73\n",
       "1  Basha     83       92       90\n",
       "2  Shaik     72       70       77"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2= [('jani',75,77,73),('Basha',83,92,90),('Shaik',72,70,77)]\n",
    "df2=pd.DataFrame(Data2,columns=['Name','Maths','Science','English'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>Name</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>jani</td>\n",
       "      <td>75</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>Basha</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Robert</td>\n",
       "      <td>55</td>\n",
       "      <td>53</td>\n",
       "      <td>51</td>\n",
       "      <td>Shaik</td>\n",
       "      <td>72</td>\n",
       "      <td>70</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Maths  Science  English   Name  Maths  Science  English\n",
       "0     Ram     30       32       40   jani     75       77       73\n",
       "1   Rahim     40       41       42  Basha     83       92       90\n",
       "2  Robert     55       53       51  Shaik     72       70       77"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Mobile No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Msc</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age    Address Qualification  Mobile No\n",
       "0     Jai   27     Nagpur           Msc         97\n",
       "1  Princi   24     Kanpur            MA         91\n",
       "2  Gaurav   22  Allahabad           MCA         58\n",
       "3    Anuj   32    Kannuaj           Phd         76"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can conctate the dataframes with union of df's ,intersection of df's, specific index\n",
    "# union====> join=outer\n",
    "# intersection ====> join = inner\n",
    "# Specific index=====> df.index\n",
    "\n",
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd'],\n",
    "        'Mobile No': [97, 91, 58, 76]} \n",
    "df1=pd.DataFrame(data1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>12</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hitesh</td>\n",
       "      <td>52</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age    Address Qualification  Salary\n",
       "2  Gaurav   22  Allahabad           MCA    1000\n",
       "3    Anuj   32    Kannuaj           Phd    2000\n",
       "6  Dhiraj   12  Allahabad          Bcom    3000\n",
       "7  Hitesh   52    Kannuaj        B.hons    4000"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = {'Name':['Gaurav', 'Anuj', 'Dhiraj', 'Hitesh'], \n",
    "        'Age':[22, 32, 12, 52], \n",
    "        'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons'],\n",
    "        'Salary':[1000, 2000, 3000, 4000]} \n",
    "\n",
    "df2=pd.DataFrame(data2,index=[2,3,6,7])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Mobile No</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Msc</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "      <td>91.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hitesh</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age    Address Qualification  Mobile No    Name   Age    Address  \\\n",
       "0     Jai  27.0     Nagpur           Msc       97.0     NaN   NaN        NaN   \n",
       "1  Princi  24.0     Kanpur            MA       91.0     NaN   NaN        NaN   \n",
       "2  Gaurav  22.0  Allahabad           MCA       58.0  Gaurav  22.0  Allahabad   \n",
       "3    Anuj  32.0    Kannuaj           Phd       76.0    Anuj  32.0    Kannuaj   \n",
       "6     NaN   NaN        NaN           NaN        NaN  Dhiraj  12.0  Allahabad   \n",
       "7     NaN   NaN        NaN           NaN        NaN  Hitesh  52.0    Kannuaj   \n",
       "\n",
       "  Qualification  Salary  \n",
       "0           NaN     NaN  \n",
       "1           NaN     NaN  \n",
       "2           MCA  1000.0  \n",
       "3           Phd  2000.0  \n",
       "6          Bcom  3000.0  \n",
       "7        B.hons  4000.0  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat by union operation ====> join=outer\n",
    "pd.concat([df1,df2],axis=1,join='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Mobile No</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>58</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>76</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age    Address Qualification  Mobile No    Name  Age    Address  \\\n",
       "2  Gaurav   22  Allahabad           MCA         58  Gaurav   22  Allahabad   \n",
       "3    Anuj   32    Kannuaj           Phd         76    Anuj   32    Kannuaj   \n",
       "\n",
       "  Qualification  Salary  \n",
       "2           MCA    1000  \n",
       "3           Phd    2000  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat by intersection ====>join=inner\n",
    "pd.concat([df1,df2],axis=1,join='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you give the same index to the both data frame inner and outer join gives same result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Mobile No</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Msc</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "      <td>91.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>12</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hitesh</td>\n",
       "      <td>52</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age    Address Qualification  Mobile No  Salary\n",
       "0     Jai   27     Nagpur           Msc       97.0     NaN\n",
       "1  Princi   24     Kanpur            MA       91.0     NaN\n",
       "2  Gaurav   22  Allahabad           MCA       58.0     NaN\n",
       "3    Anuj   32    Kannuaj           Phd       76.0     NaN\n",
       "4  Gaurav   22  Allahabad           MCA        NaN  1000.0\n",
       "5    Anuj   32    Kannuaj           Phd        NaN  2000.0\n",
       "6  Dhiraj   12  Allahabad          Bcom        NaN  3000.0\n",
       "7  Hitesh   52    Kannuaj        B.hons        NaN  4000.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We concat two df's using .append() also\n",
    "# df.append(df/series, ignore_index=False, verify_integrity=False, sort=False)\n",
    "\n",
    "df1.append(df2,ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Mobile No</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">A</th>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Msc</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "      <td>91.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">B</th>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>12</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hitesh</td>\n",
       "      <td>52</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Age    Address Qualification  Mobile No  Salary\n",
       "A 0     Jai   27     Nagpur           Msc       97.0     NaN\n",
       "  1  Princi   24     Kanpur            MA       91.0     NaN\n",
       "  2  Gaurav   22  Allahabad           MCA       58.0     NaN\n",
       "  3    Anuj   32    Kannuaj           Phd       76.0     NaN\n",
       "B 2  Gaurav   22  Allahabad           MCA        NaN  1000.0\n",
       "  3    Anuj   32    Kannuaj           Phd        NaN  2000.0\n",
       "  6  Dhiraj   12  Allahabad          Bcom        NaN  3000.0\n",
       "  7  Hitesh   52    Kannuaj        B.hons        NaN  4000.0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2],keys=['A','B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K0</td>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K1</td>\n",
       "      <td>Princi</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K3</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key    Name  Age\n",
       "0  K0     Jai   27\n",
       "1  K1  Princi   24\n",
       "2  K2  Gaurav   22\n",
       "3  K3    Anuj   32"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging \n",
    "# We can merge two data frames with .merge()\n",
    "# df.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes='_x', '_y', copy=True, indicator=False, validate=None)\n",
    "# We need to give which column basis we need to merge the dataframes\n",
    "\n",
    "data1 = {'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "         'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32],} \n",
    "   \n",
    "\n",
    "df = pd.DataFrame(data1)\n",
    "\n",
    "df\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K5</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Btech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>B.A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K3</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key    Address Qualification\n",
       "0  K5     Nagpur         Btech\n",
       "1  k6     Kanpur           B.A\n",
       "2  K2  Allahabad          Bcom\n",
       "3  K3    Kannuaj        B.hons"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a dictionary containing employee data \n",
    "data2 = {'key': ['K5', 'k6', 'K2', 'K3'],\n",
    "         'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']} \n",
    "\n",
    "\n",
    "df1 = pd.DataFrame(data2) \n",
    "\n",
    "df1\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K2</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K3</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key    Name  Age    Address Qualification\n",
       "0  K2  Gaurav   22  Allahabad          Bcom\n",
       "1  K3    Anuj   32    Kannuaj        B.hons"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.merge(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K2</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K3</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key    Name  Age    Address Qualification\n",
       "0  K2  Gaurav   22  Allahabad          Bcom\n",
       "1  K3    Anuj   32    Kannuaj        B.hons"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can merge the data frames in four ways inner,outer,left,right\n",
    "# inner= intersection of df's\n",
    "# outer = union of df's\n",
    "# right = right df and common rows of left df \n",
    "# left = left df and common rows of right df\n",
    "\n",
    "df.merge(df1,how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K2</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K3</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Btech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>B.A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key    Name   Age    Address Qualification\n",
       "0  K2  Gaurav  22.0  Allahabad          Bcom\n",
       "1  K3    Anuj  32.0    Kannuaj        B.hons\n",
       "2  K5     NaN   NaN     Nagpur         Btech\n",
       "3  k6     NaN   NaN     Kanpur           B.A"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.merge(df1,how='right',on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age\n",
       "K0     Jai   27\n",
       "K1  Princi   24\n",
       "K2  Gaurav   22\n",
       "K3    Anuj   32"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similary we can join the two df using .join()\n",
    "# 4 ways to join the 2 df's inner,outer,right,left\n",
    "# df.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)\n",
    "\n",
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32]} \n",
    "\n",
    "df = pd.DataFrame(data1,index=['K0', 'K1', 'K2', 'K3'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Address Qualification\n",
       "K0  Allahabad           MCA\n",
       "K1    Kannuaj           Phd\n",
       "K2  Allahabad          Bcom\n",
       "K3    Kannuaj        B.hons"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = {'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons']} \n",
    "df1=pd.DataFrame(data2,index=['K0', 'K1', 'K2', 'K3'])\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>24</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age    Address Qualification\n",
       "K0     Jai   27  Allahabad           MCA\n",
       "K1  Princi   24    Kannuaj           Phd\n",
       "K2  Gaurav   22  Allahabad          Bcom\n",
       "K3    Anuj   32    Kannuaj        B.hons"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.join([df1],how='outer',)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For joining index should be similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Join\n",
      "       Last   First\n",
      "0  Gaitonde  Ganesh\n",
      "1     Singh  Sartaj\n",
      "2    Mathur  Anjali \n",
      "\n",
      "After join\n",
      "       Last   First               Name\n",
      "0  Gaitonde  Ganesh  Ganesh - Gaitonde\n",
      "1     Singh  Sartaj     Sartaj - Singh\n",
      "2    Mathur  Anjali    Anjali - Mathur\n"
     ]
    }
   ],
   "source": [
    "# Joining two text coloumns into single column\n",
    "\n",
    "# series.str.cat(joining series,sep='')\n",
    "df = pd.DataFrame({'Last': ['Gaitonde', 'Singh', 'Mathur'], \n",
    "                   'First': ['Ganesh', 'Sartaj', 'Anjali']}) \n",
    "  \n",
    "print('Before Join') \n",
    "print(df, '\\n') \n",
    "  \n",
    "print('After join') \n",
    "df['Name'] = df['First'].str.cat(df['Last'], sep =\" - \") \n",
    "print(df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Text Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>535</td>\n",
       "      <td>BTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robert</td>\n",
       "      <td>540</td>\n",
       "      <td>BCom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>555</td>\n",
       "      <td>BSc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks Degree\n",
       "0     Ram    535  BTech\n",
       "1  Robert    540   BCom\n",
       "2   Rahim    555    BSc"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convrting lowecase \n",
    "# str.lower()\n",
    "\n",
    "sen={'Names':['Ram','Robert','Rahim'],'Marks':[535,540,555],'Degree':['BTech','BCom','BSc']}\n",
    "df=pd.DataFrame(sen)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Names']=df['Names'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ram</td>\n",
       "      <td>535</td>\n",
       "      <td>BTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>robert</td>\n",
       "      <td>540</td>\n",
       "      <td>BCom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rahim</td>\n",
       "      <td>555</td>\n",
       "      <td>BSc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks Degree\n",
       "0     ram    535  BTech\n",
       "1  robert    540   BCom\n",
       "2   rahim    555    BSc"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Degree']=df['Degree'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ram</td>\n",
       "      <td>535</td>\n",
       "      <td>btech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>robert</td>\n",
       "      <td>540</td>\n",
       "      <td>bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rahim</td>\n",
       "      <td>555</td>\n",
       "      <td>bsc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks Degree\n",
       "0     ram    535  btech\n",
       "1  robert    540   bcom\n",
       "2   rahim    555    bsc"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For upper case we use str.upper()\n",
    "\n",
    "df['Degree']=df['Degree'].str.upper()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ram</td>\n",
       "      <td>535</td>\n",
       "      <td>BTECH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>robert</td>\n",
       "      <td>540</td>\n",
       "      <td>BCOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rahim</td>\n",
       "      <td>555</td>\n",
       "      <td>BSC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks Degree\n",
       "0     ram    535  BTECH\n",
       "1  robert    540   BCOM\n",
       "2   rahim    555    BSC"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Names']=df['Names'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RAM</td>\n",
       "      <td>535</td>\n",
       "      <td>BTECH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ROBERT</td>\n",
       "      <td>540</td>\n",
       "      <td>BCOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAHIM</td>\n",
       "      <td>555</td>\n",
       "      <td>BSC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks Degree\n",
       "0     RAM    535  BTECH\n",
       "1  ROBERT    540   BCOM\n",
       "2   RAHIM    555    BSC"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R</td>\n",
       "      <td>535</td>\n",
       "      <td>BTECH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ROBERT</td>\n",
       "      <td>540</td>\n",
       "      <td>BCOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R</td>\n",
       "      <td>555</td>\n",
       "      <td>BSC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks Degree\n",
       "0       R    535  BTECH\n",
       "1  ROBERT    540   BCOM\n",
       "2       R    555    BSC"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spliting the string's using str.split()\n",
    "\n",
    "df['Names']=df['Names'].str.split('A',n=-1,expand=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>r</td>\n",
       "      <td>535</td>\n",
       "      <td>BTECH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ROBERT</td>\n",
       "      <td>540</td>\n",
       "      <td>BCOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>r</td>\n",
       "      <td>555</td>\n",
       "      <td>BSC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks Degree\n",
       "0       r    535  BTECH\n",
       "1  ROBERT    540   BCOM\n",
       "2       r    555    BSC"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing the string using str.replace('what to replace','with to replace')\n",
    "# series.str.replace(pat, repl, n=-1, case=None, regex=True)\n",
    "\n",
    "df['Names']=df['Names'].replace('R','r')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     jani\n",
       "1    basha\n",
       "2    shaik\n",
       "dtype: object"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the series values with series.repalce()\n",
    "# series.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method=’pad’)\n",
    "\n",
    "seri=pd.Series(['jani','basha','shaik'])\n",
    "seri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    johnny\n",
       "1     Basha\n",
       "2     shaik\n",
       "dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seri.replace(to_replace=(['jani','basha']),value=['johnny','Basha'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concate the two series or indexs we use str.cat(what to cat,sep=,np_nan=,join=)\n",
    "\n",
    "A=df['Names'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Degree']=df['Degree'].str.cat(A,sep='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>r</td>\n",
       "      <td>535</td>\n",
       "      <td>BTECH-r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ROBERT</td>\n",
       "      <td>540</td>\n",
       "      <td>BCOM-ROBERT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>r</td>\n",
       "      <td>555</td>\n",
       "      <td>BSC-r</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks       Degree\n",
       "0       r    535      BTECH-r\n",
       "1  ROBERT    540  BCOM-ROBERT\n",
       "2       r    555        BSC-r"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>535</td>\n",
       "      <td>BTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robert</td>\n",
       "      <td>540</td>\n",
       "      <td>BCom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>555</td>\n",
       "      <td>BSc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Names  Marks Degree\n",
       "0       Ram    535  BTech\n",
       "1  Robert      540   BCom\n",
       "2    Rahim     555    BSc"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To removing white spces in text we use str.strip()\n",
    "# str.lstrip(): remove the white spaces left side of string\n",
    "# str.rstrip(): remove the white spaces right side of string\n",
    "# str.strip(): remove both side of white spaces\n",
    "\n",
    "sen={'Names':['  Ram','Robert  ','Rahim '],'Marks':[535,540,555],'Degree':['BTech','BCom','BSc']}\n",
    "df=pd.DataFrame(sen)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Names']=df['Names'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram</td>\n",
       "      <td>535</td>\n",
       "      <td>BTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robert</td>\n",
       "      <td>540</td>\n",
       "      <td>BCom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahim</td>\n",
       "      <td>555</td>\n",
       "      <td>BSc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Names  Marks Degree\n",
       "0     Ram    535  BTech\n",
       "1  Robert    540   BCom\n",
       "2   Rahim    555    BSc"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df['Names'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "Name: Names, dtype: bool"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the white space removed or not\n",
    "\n",
    "X.str.lstrip()=='Ram'\n",
    "X.str.rstrip()=='Robert'\n",
    "X.str.strip()=='Rahim'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "False means all white spaces removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For extracting data we use str.extract(r'regular expession with capturing groups')\n",
    "\n",
    "s = pd.Series(['a1', 'b2', 'c3'])\n",
    "n= s.str.extract(r'([ab])(\\d)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1\n",
       "0    a    1\n",
       "1    b    2\n",
       "2  NaN  NaN"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if capturing groups doesnt match return NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "n1 = s.str.extract(r'(?P<Column1>[ab])(?P<column2>\\d)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column1</th>\n",
       "      <th>column2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Column1 column2\n",
       "0       a       1\n",
       "1       b       2\n",
       "2     NaN     NaN"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For each regualr expression group it create one column \n",
    "\n",
    "For two groups two column have been created\n",
    "similarly For 3 groups 3 column creates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    " df=pd.read_csv('list.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Country</th>\n",
       "      <th>Firstname</th>\n",
       "      <th>Lastname</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Category</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>ARVID KALLE</td>\n",
       "      <td>AADLI</td>\n",
       "      <td>M</td>\n",
       "      <td>62</td>\n",
       "      <td>P</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>LEA</td>\n",
       "      <td>AALISTE</td>\n",
       "      <td>F</td>\n",
       "      <td>22</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>AIRI</td>\n",
       "      <td>AAVASTE</td>\n",
       "      <td>F</td>\n",
       "      <td>21</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>JURI</td>\n",
       "      <td>AAVIK</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>BRITTA ELISABET</td>\n",
       "      <td>AHLSTROM</td>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>P</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>985</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>ANNA INGRID BIRGITTA</td>\n",
       "      <td>OSTROM</td>\n",
       "      <td>F</td>\n",
       "      <td>60</td>\n",
       "      <td>P</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>986</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>ELMAR MIKAEL</td>\n",
       "      <td>OUN</td>\n",
       "      <td>M</td>\n",
       "      <td>34</td>\n",
       "      <td>P</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>987</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>ENN</td>\n",
       "      <td>QUNAPUU</td>\n",
       "      <td>M</td>\n",
       "      <td>77</td>\n",
       "      <td>P</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>988</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>LY</td>\n",
       "      <td>GUNAPUU</td>\n",
       "      <td>F</td>\n",
       "      <td>87</td>\n",
       "      <td>P</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>989</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>CARL</td>\n",
       "      <td>OVBERG</td>\n",
       "      <td>M</td>\n",
       "      <td>42</td>\n",
       "      <td>P</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "<p>989 rows × 8 columns</p>\n",
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       "     PassengerId  Country             Firstname  Lastname Sex  Age Category  \\\n",
       "0              1   Sweden           ARVID KALLE     AADLI   M   62        P   \n",
       "1              2  Estonia                   LEA   AALISTE   F   22        C   \n",
       "2              3  Estonia                  AIRI   AAVASTE   F   21        C   \n",
       "3              4   Sweden                  JURI     AAVIK   M   53        C   \n",
       "4              5   Sweden       BRITTA ELISABET  AHLSTROM   F   55        P   \n",
       "..           ...      ...                   ...       ...  ..  ...      ...   \n",
       "984          985   Sweden  ANNA INGRID BIRGITTA    OSTROM   F   60        P   \n",
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       "\n",
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       "3           0  \n",
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       "984         0  \n",
       "985         1  \n",
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       "988         1  \n",
       "\n",
       "[989 rows x 8 columns]"
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     "execution_count": 151,
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    "df"
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       "       1   Sweden           ARVID KALLE     AADLI  M  62  P  0\n",
       "       2  Estonia                   LEA   AALISTE  F  22  C  0\n",
       "0      3  Estonia                  AIRI   AAVASTE  F  21  C  0\n",
       "1      4   Sweden                  JURI     AAVIK  M  53  C  0\n",
       "2      5   Sweden       BRITTA ELISABET  AHLSTROM  F  55  P  0\n",
       "3      6   Sweden    GERD INGA MAGNHILD  AHLSTROM  F  71  P  0\n",
       "4      7   Sweden               HJALMAR  AHLSTROM  M  60  P  0\n",
       "..   ...      ...                   ...       ... ..  .. .. ..\n",
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       "983  986   Sweden          ELMAR MIKAEL       OUN  M  34  P  1\n",
       "984  987   Sweden                   ENN   QUNAPUU  M  77  P  0\n",
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       "986  989   Sweden                  CARL    OVBERG  M  42  P  1\n",
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     "execution_count": 152,
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   "source": [
    "pd.read_csv(\"list.csv\", header =[1, 2]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     PassengerId  Country             Firstname  Lastname Sex  Age Category  \\\n",
       "0              5   Sweden       BRITTA ELISABET  AHLSTROM   F   55        P   \n",
       "1              6   Sweden    GERD INGA MAGNHILD  AHLSTROM   F   71        P   \n",
       "2              7   Sweden               HJALMAR  AHLSTROM   M   60        P   \n",
       "3              8  Estonia                 PILLE     AHMAN   F   18        P   \n",
       "4              9  Estonia                 TORMI   AINSALU   M   30        C   \n",
       "..           ...      ...                   ...       ...  ..  ...      ...   \n",
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       "982          987   Sweden                   ENN   QUNAPUU   M   77        P   \n",
       "983          988   Sweden                    LY   GUNAPUU   F   87        P   \n",
       "984          989   Sweden                  CARL    OVBERG   M   42        P   \n",
       "\n",
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       "3           0  \n",
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       "981         1  \n",
       "982         0  \n",
       "983         0  \n",
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     "execution_count": 153,
     "metadata": {},
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   "source": [
    "pd.read_csv(\"list.csv\", skiprows = [1, 2, 3, 4]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'C:\\Users\\Unify\\Desktop\\janibasha\\Pandas practice\\list.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
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       "      <td>F</td>\n",
       "      <td>87</td>\n",
       "      <td>P</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>989</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>CARL</td>\n",
       "      <td>OVBERG</td>\n",
       "      <td>M</td>\n",
       "      <td>42</td>\n",
       "      <td>P</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>989 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Country             Firstname  Lastname Sex  Age Category  \\\n",
       "0              1   Sweden           ARVID KALLE     AADLI   M   62        P   \n",
       "1              2  Estonia                   LEA   AALISTE   F   22        C   \n",
       "2              3  Estonia                  AIRI   AAVASTE   F   21        C   \n",
       "3              4   Sweden                  JURI     AAVIK   M   53        C   \n",
       "4              5   Sweden       BRITTA ELISABET  AHLSTROM   F   55        P   \n",
       "..           ...      ...                   ...       ...  ..  ...      ...   \n",
       "984          985   Sweden  ANNA INGRID BIRGITTA    OSTROM   F   60        P   \n",
       "985          986   Sweden          ELMAR MIKAEL       OUN   M   34        P   \n",
       "986          987   Sweden                   ENN   QUNAPUU   M   77        P   \n",
       "987          988   Sweden                    LY   GUNAPUU   F   87        P   \n",
       "988          989   Sweden                  CARL    OVBERG   M   42        P   \n",
       "\n",
       "     Survived  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "..        ...  \n",
       "984         0  \n",
       "985         1  \n",
       "986         0  \n",
       "987         0  \n",
       "988         1  \n",
       "\n",
       "[989 rows x 8 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Operations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        50.12\n",
       "1        54.10\n",
       "2        54.65\n",
       "3        52.38\n",
       "4        52.95\n",
       "         ...  \n",
       "3007    772.88\n",
       "3008    771.07\n",
       "3009    773.18\n",
       "3010    771.61\n",
       "3011    782.22\n",
       "Name: Stock Price, Length: 3012, dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.apply(func, convert_dtype=True, args=())\n",
    "# function may be lambda or user defined\n",
    "\n",
    "S=pd.read_csv('stock.csv',squeeze = True)\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        55.12\n",
       "1        59.10\n",
       "2        59.65\n",
       "3        57.38\n",
       "4        57.95\n",
       "         ...  \n",
       "3007    777.88\n",
       "3008    776.07\n",
       "3009    778.18\n",
       "3010    776.61\n",
       "3011    787.22\n",
       "Name: Stock Price, Length: 3012, dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.apply(lambda x : x+5)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame:\n",
      "    A  B  C\n",
      "0  1  4  7\n",
      "1  2  5  8\n",
      "2  3  6  9\n",
      "\n",
      "After Applying Function: \n",
      "   A  B  C  add\n",
      "0  1  4  7   12\n",
      "1  2  5  8   15\n",
      "2  3  6  9   18\n"
     ]
    }
   ],
   "source": [
    "# .apply() on each row of data frame\n",
    "\n",
    "def main(): \n",
    "      \n",
    "    \n",
    "    data = { \n",
    "            'A':[1, 2, 3],  \n",
    "            'B':[4, 5, 6],  \n",
    "            'C':[7, 8, 9] } \n",
    "      \n",
    "      \n",
    "    df = pd.DataFrame(data) \n",
    "    print(\"Original DataFrame:\\n\", df) \n",
    "      \n",
    "    \n",
    "    df['add'] = df.apply(np.sum, axis = 1) \n",
    "   \n",
    "    print('\\nAfter Applying Function: ') \n",
    "   \n",
    "    print(df) \n",
    "    \n",
    "if __name__ == '__main__': \n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame:\n",
      "    X   Y\n",
      "0  1  45\n",
      "1  2  65\n",
      "2  3  89\n",
      "\n",
      "Normalized:\n",
      "     X   Y\n",
      "0 -0.5  45\n",
      "1 -0.5  65\n",
      "2 -0.5  89\n"
     ]
    }
   ],
   "source": [
    "def normalize(x, y): \n",
    "    x_new = ((x - np.mean([x, y])) /\n",
    "             (max(x, y) - min(x, y))) \n",
    "      \n",
    "     \n",
    "    return x_new \n",
    "  \n",
    "def main(): \n",
    "      \n",
    "     \n",
    "    data = { \n",
    "        'X':[1, 2, 3],  \n",
    "        'Y':[45, 65, 89] } \n",
    "      \n",
    "      \n",
    "    df = pd.DataFrame(data) \n",
    "    print(\"Original DataFrame:\\n\", df) \n",
    "      \n",
    "    df['X'] = df.apply(lambda row : normalize(row['X'], \n",
    "                                  row['Y']), axis = 1) \n",
    "   \n",
    "    print('\\nNormalized:') \n",
    "    print(df) \n",
    "    \n",
    "if __name__ == '__main__': \n",
    "    main() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        50.12\n",
       "1        54.10\n",
       "2        54.65\n",
       "3        52.38\n",
       "4        52.95\n",
       "         ...  \n",
       "3007    772.88\n",
       "3008    771.07\n",
       "3009    773.18\n",
       "3010    771.61\n",
       "3011    782.22\n",
       "Name: Stock Price, Length: 3012, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean values of requested dataframe \n",
    "\n",
    "S\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "334.31009296148744"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Maths</th>\n",
       "      <th>Physics</th>\n",
       "      <th>English</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Maths  Physics  English\n",
       "0     99       92       99\n",
       "1     92       90       93\n",
       "2     93       93       92\n",
       "3     35       92       73\n",
       "4     98       45       45"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)\n",
    "\n",
    "men={'Maths':[99,92,93,35,98],\n",
    "      'Physics':[92,90,93,92,45],\n",
    "      'English':[99,93,92,73,45]}\n",
    "Da=pd.DataFrame(men)\n",
    "Da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Maths      83.4\n",
       "Physics    82.4\n",
       "English    80.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Da.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    96.666667\n",
       "1    91.666667\n",
       "2    92.666667\n",
       "3    66.666667\n",
       "4    62.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Da.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Maths      19.36\n",
       "Physics    14.96\n",
       "English    17.12\n",
       "dtype: float64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# appling df.mad()\n",
    "# mad : mean absolute deviation = median(x-median)\n",
    "\n",
    "Da.mad(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3.111111\n",
       "1     1.111111\n",
       "2     0.444444\n",
       "3    21.111111\n",
       "4    23.555556\n",
       "dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Da.mad(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Maths      12.176206\n",
       "Physics     9.362692\n",
       "English     9.867117\n",
       "dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe.sem() function return unbiased standard error of the mean over requested axis\n",
    "# df.sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)\n",
    "\n",
    "Da.sem(axis=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.333333\n",
       "1     0.881917\n",
       "2     0.333333\n",
       "3    16.756425\n",
       "4    17.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Da.sem(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "291.21    5\n",
       "288.47    3\n",
       "194.80    3\n",
       "307.94    3\n",
       "243.26    3\n",
       "         ..\n",
       "294.94    1\n",
       "548.34    1\n",
       "261.09    1\n",
       "348.91    1\n",
       "92.50     1\n",
       "Name: Stock Price, Length: 2886, dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.value_counts() function return a Series containing counts of unique values.\n",
    "# df.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)\n",
    "\n",
    "S.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 50.12,  54.1 ,  54.65, ..., 773.18, 771.61, 782.22])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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