Pandas data manipulation .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.6000</td>\n",
       "      <td>C2</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>155</td>\n",
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       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.3792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female  22.0      1   \n",
       "152                                   Meo, Mr. Alfonzo    male  55.5      0   \n",
       "153                    van Billiard, Mr. Austin Blyler    male  40.5      0   \n",
       "154                              Olsen, Mr. Ole Martin    male   NaN      0   \n",
       "155                        Williams, Mr. Charles Duane    male  51.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "151      0            113776  66.6000    C2        S  \n",
       "152      0        A.5. 11206   8.0500   NaN        S  \n",
       "153      2          A/5. 851  14.5000   NaN        S  \n",
       "154      0         Fa 265302   7.3125   NaN        S  \n",
       "155      1          PC 17597  61.3792   NaN        C  \n",
       "\n",
       "[156 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train = pd.read_csv(\"https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/ff414a1bcfcba32481e4d4e8db578e55872a2ca1/titanic.csv\",\n",
    "                           sep='\\t')   \n",
    "titanic_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.6000</td>\n",
       "      <td>C2</td>\n",
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       "      <td>Meo, Mr. Alfonzo</td>\n",
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       "      <td>Olsen, Mr. Ole Martin</td>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "146          147         1       3   \n",
       "147          148         0       3   \n",
       "148          149         0       2   \n",
       "149          150         0       2   \n",
       "150          151         0       2   \n",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "146  Andersson, Mr. August Edvard (\"Wennerstrom\")    male  27.0      0      0   \n",
       "147              Ford, Miss. Robina Maggie \"Ruby\"  female   9.0      2      2   \n",
       "148      Navratil, Mr. Michel (\"Louis M Hoffman\")    male  36.5      0      2   \n",
       "149             Byles, Rev. Thomas Roussel Davids    male  42.0      0      0   \n",
       "150                    Bateman, Rev. Robert James    male  51.0      0      0   \n",
       "151             Pears, Mrs. Thomas (Edith Wearne)  female  22.0      1      0   \n",
       "152                              Meo, Mr. Alfonzo    male  55.5      0      0   \n",
       "153               van Billiard, Mr. Austin Blyler    male  40.5      0      2   \n",
       "154                         Olsen, Mr. Ole Martin    male   NaN      0      0   \n",
       "155                   Williams, Mr. Charles Duane    male  51.0      0      1   \n",
       "\n",
       "          Ticket     Fare Cabin Embarked  \n",
       "146       350043   7.7958   NaN        S  \n",
       "147   W./C. 6608  34.3750   NaN        S  \n",
       "148       230080  26.0000    F2        S  \n",
       "149       244310  13.0000   NaN        S  \n",
       "150  S.O.P. 1166  12.5250   NaN        S  \n",
       "151       113776  66.6000    C2        S  \n",
       "152   A.5. 11206   8.0500   NaN        S  \n",
       "153     A/5. 851  14.5000   NaN        S  \n",
       "154    Fa 265302   7.3125   NaN        S  \n",
       "155     PC 17597  61.3792   NaN        C  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>31</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
       "      <td>male</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Name   Sex   Ticket        Cabin Embarked\n",
       "count                        156   156      156           31      155\n",
       "unique                       156     2      145           28        3\n",
       "top     Moubarek, Master. Gerios  male  CA 2144  C23 C25 C27        S\n",
       "freq                           1   100        2            2      110"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.dtypes == \"object\"\n",
    "a = titanic_train.dtypes[titanic_train.dtypes == \"object\"].index\n",
    "a\n",
    "titanic_train[a].describe()\n"
   ]
  },
  {
   "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",
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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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>2.423077</td>\n",
       "      <td>28.141508</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.397436</td>\n",
       "      <td>28.109587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.177428</td>\n",
       "      <td>0.477275</td>\n",
       "      <td>0.795459</td>\n",
       "      <td>14.613880</td>\n",
       "      <td>1.056235</td>\n",
       "      <td>0.870146</td>\n",
       "      <td>39.401047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.003150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.371850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>263.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   156.000000  156.000000  156.000000  126.000000  156.000000   \n",
       "mean     78.500000    0.346154    2.423077   28.141508    0.615385   \n",
       "std      45.177428    0.477275    0.795459   14.613880    1.056235   \n",
       "min       1.000000    0.000000    1.000000    0.830000    0.000000   \n",
       "25%      39.750000    0.000000    2.000000   19.000000    0.000000   \n",
       "50%      78.500000    0.000000    3.000000   26.000000    0.000000   \n",
       "75%     117.250000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     156.000000    1.000000    3.000000   71.000000    5.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  156.000000  156.000000  \n",
       "mean     0.397436   28.109587  \n",
       "std      0.870146   39.401047  \n",
       "min      0.000000    6.750000  \n",
       "25%      0.000000    8.003150  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   30.371850  \n",
       "max      5.000000  263.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.dtypes[titanic_train.dtypes == \"object\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.dtypes[titanic_train.dtypes == \"object\"]\n",
    "\n",
    "\n",
    "titanic_train.dtypes[titanic_train.dtypes == \"object\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        object\n",
       "Sex         object\n",
       "Ticket      object\n",
       "Cabin       object\n",
       "Embarked    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.dtypes[titanic_train.dtypes == \"object\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "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",
       "        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>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>31</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>113803</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Name   Sex  Ticket Cabin Embarked\n",
       "count                            156   156     156    31      155\n",
       "unique                           156     2     145    28        3\n",
       "top     Williams, Mr. Charles Eugene  male  113803  C123        S\n",
       "freq                               1   100       2     2      110"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_col = titanic_train.dtypes[titanic_train.dtypes == \"object\"].index\n",
    "titanic_train[list_col].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>McMahon, Mr. Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Madsen, Mr. Fridtjof Arne</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Peter, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Ekstrom, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Drazenoic, Mr. Jozef</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Coelho, Mr. Domingos Fernandeo</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Robins, Mrs. Alexander A (Grace Charity Laury)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Weisz, Mrs. Leopold (Mathilde Francoise Pede)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Sobey, Mr. Samuel James Hayden</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Richard, Mr. Emile</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Newsom, Miss. Helen Monypeny</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Futrelle, Mr. Jacques Heath</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Osen, Mr. Olaf Elon</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Giglio, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Nysten, Miss. Anna Sofia</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ...</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Burke, Mr. Jeremiah</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Andrew, Mr. Edgardo Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Nicholls, Mr. Joseph Charles</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Andersson, Mr. August Edvard (\"Wennerstrom\")</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Ford, Miss. Robina Maggie \"Ruby\"</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Navratil, Mr. Michel (\"Louis M Hoffman\")</td>\n",
       "      <td>male</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Byles, Rev. Thomas Roussel Davids</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Bateman, Rev. Robert James</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Name     Sex   Age\n",
       "0                              Braund, Mr. Owen Harris    male  22.0\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0\n",
       "2                               Heikkinen, Miss. Laina  female  26.0\n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0\n",
       "4                             Allen, Mr. William Henry    male  35.0\n",
       "5                                     Moran, Mr. James    male   NaN\n",
       "6                              McCarthy, Mr. Timothy J    male  54.0\n",
       "7                       Palsson, Master. Gosta Leonard    male   2.0\n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0\n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0\n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0\n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0\n",
       "12                      Saundercock, Mr. William Henry    male  20.0\n",
       "13                         Andersson, Mr. Anders Johan    male  39.0\n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.0\n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0\n",
       "16                                Rice, Master. Eugene    male   2.0\n",
       "17                        Williams, Mr. Charles Eugene    male   NaN\n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0\n",
       "19                             Masselmani, Mrs. Fatima  female   NaN\n",
       "20                                Fynney, Mr. Joseph J    male  35.0\n",
       "21                               Beesley, Mr. Lawrence    male  34.0\n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.0\n",
       "23                        Sloper, Mr. William Thompson    male  28.0\n",
       "24                       Palsson, Miss. Torborg Danira  female   8.0\n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0\n",
       "26                             Emir, Mr. Farred Chehab    male   NaN\n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0\n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN\n",
       "29                                 Todoroff, Mr. Lalio    male   NaN\n",
       "..                                                 ...     ...   ...\n",
       "126                                McMahon, Mr. Martin    male   NaN\n",
       "127                          Madsen, Mr. Fridtjof Arne    male  24.0\n",
       "128                                  Peter, Miss. Anna  female   NaN\n",
       "129                                 Ekstrom, Mr. Johan    male  45.0\n",
       "130                               Drazenoic, Mr. Jozef    male  33.0\n",
       "131                     Coelho, Mr. Domingos Fernandeo    male  20.0\n",
       "132     Robins, Mrs. Alexander A (Grace Charity Laury)  female  47.0\n",
       "133      Weisz, Mrs. Leopold (Mathilde Francoise Pede)  female  29.0\n",
       "134                     Sobey, Mr. Samuel James Hayden    male  25.0\n",
       "135                                 Richard, Mr. Emile    male  23.0\n",
       "136                       Newsom, Miss. Helen Monypeny  female  19.0\n",
       "137                        Futrelle, Mr. Jacques Heath    male  37.0\n",
       "138                                Osen, Mr. Olaf Elon    male  16.0\n",
       "139                                 Giglio, Mr. Victor    male  24.0\n",
       "140                      Boulos, Mrs. Joseph (Sultana)  female   NaN\n",
       "141                           Nysten, Miss. Anna Sofia  female  22.0\n",
       "142  Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ...  female  24.0\n",
       "143                                Burke, Mr. Jeremiah    male  19.0\n",
       "144                         Andrew, Mr. Edgardo Samuel    male  18.0\n",
       "145                       Nicholls, Mr. Joseph Charles    male  19.0\n",
       "146       Andersson, Mr. August Edvard (\"Wennerstrom\")    male  27.0\n",
       "147                   Ford, Miss. Robina Maggie \"Ruby\"  female   9.0\n",
       "148           Navratil, Mr. Michel (\"Louis M Hoffman\")    male  36.5\n",
       "149                  Byles, Rev. Thomas Roussel Davids    male  42.0\n",
       "150                         Bateman, Rev. Robert James    male  51.0\n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female  22.0\n",
       "152                                   Meo, Mr. Alfonzo    male  55.5\n",
       "153                    van Billiard, Mr. Austin Blyler    male  40.5\n",
       "154                              Olsen, Mr. Ole Martin    male   NaN\n",
       "155                        Williams, Mr. Charles Duane    male  51.0\n",
       "\n",
       "[156 rows x 3 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[['Name','Sex','Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Braund, Mr. Owen Harris\n",
       "1      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                                 Heikkinen, Miss. Laina\n",
       "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                               Allen, Mr. William Henry\n",
       "                             ...                        \n",
       "151                    Pears, Mrs. Thomas (Edith Wearne)\n",
       "152                                     Meo, Mr. Alfonzo\n",
       "153                      van Billiard, Mr. Austin Blyler\n",
       "154                                Olsen, Mr. Ole Martin\n",
       "155                          Williams, Mr. Charles Duane\n",
       "Name: Name, Length: 156, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ahlin, Mrs. Johan (Johanna Persdotter Larsson)',\n",
       " 'Allen, Mr. William Henry',\n",
       " 'Andersson, Miss. Ellis Anna Maria',\n",
       " 'Andersson, Miss. Erna Alexandra',\n",
       " 'Andersson, Mr. Anders Johan',\n",
       " 'Andersson, Mr. August Edvard (\"Wennerstrom\")',\n",
       " 'Andreasson, Mr. Paul Edvin',\n",
       " 'Andrew, Mr. Edgardo Samuel',\n",
       " 'Arnold-Franchi, Mrs. Josef (Josefine Franchi)',\n",
       " 'Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)',\n",
       " 'Attalah, Miss. Malake',\n",
       " 'Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)',\n",
       " 'Barton, Mr. David John',\n",
       " 'Bateman, Rev. Robert James',\n",
       " 'Baxter, Mr. Quigg Edmond',\n",
       " 'Beesley, Mr. Lawrence',\n",
       " 'Bing, Mr. Lee',\n",
       " 'Bonnell, Miss. Elizabeth',\n",
       " 'Boulos, Mrs. Joseph (Sultana)',\n",
       " 'Braund, Mr. Owen Harris',\n",
       " 'Burke, Mr. Jeremiah',\n",
       " 'Byles, Rev. Thomas Roussel Davids',\n",
       " 'Caldwell, Master. Alden Gates',\n",
       " 'Cann, Mr. Ernest Charles',\n",
       " 'Carrau, Mr. Francisco M',\n",
       " 'Celotti, Mr. Francesco',\n",
       " 'Chaffee, Mr. Herbert Fuller',\n",
       " 'Christmann, Mr. Emil',\n",
       " 'Chronopoulos, Mr. Apostolos',\n",
       " 'Coelho, Mr. Domingos Fernandeo',\n",
       " 'Connors, Mr. Patrick',\n",
       " 'Coxon, Mr. Daniel',\n",
       " 'Crease, Mr. Ernest James',\n",
       " 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',\n",
       " 'Dean, Mr. Bertram Frank',\n",
       " 'Devaney, Miss. Margaret Delia',\n",
       " 'Doling, Mrs. John T (Ada Julia Bone)',\n",
       " 'Dowdell, Miss. Elizabeth',\n",
       " 'Drazenoic, Mr. Jozef',\n",
       " 'Ekstrom, Mr. Johan',\n",
       " 'Emir, Mr. Farred Chehab',\n",
       " 'Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)',\n",
       " 'Ford, Miss. Robina Maggie \"Ruby\"',\n",
       " 'Ford, Mr. William Neal',\n",
       " 'Fortune, Miss. Mabel Helen',\n",
       " 'Fortune, Mr. Charles Alexander',\n",
       " 'Futrelle, Mr. Jacques Heath',\n",
       " 'Futrelle, Mrs. Jacques Heath (Lily May Peel)',\n",
       " 'Fynney, Mr. Joseph J',\n",
       " 'Giglio, Mr. Victor',\n",
       " 'Glynn, Miss. Mary Agatha',\n",
       " 'Goldschmidt, Mr. George B',\n",
       " 'Goodwin, Master. William Frederick',\n",
       " 'Goodwin, Miss. Lillian Amy',\n",
       " 'Greenfield, Mr. William Bertram',\n",
       " 'Gustafsson, Mr. Anders Vilhelm',\n",
       " 'Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)',\n",
       " 'Harper, Mrs. Henry Sleeper (Myna Haxtun)',\n",
       " 'Harris, Mr. Henry Birkhardt',\n",
       " 'Heikkinen, Miss. Laina',\n",
       " 'Hewlett, Mrs. (Mary D Kingcome) ',\n",
       " 'Hickman, Mr. Stanley George',\n",
       " 'Holverson, Mr. Alexander Oskar',\n",
       " 'Hood, Mr. Ambrose Jr',\n",
       " 'Icard, Miss. Amelie',\n",
       " 'Ilett, Miss. Bertha',\n",
       " 'Jenkin, Mr. Stephen Curnow',\n",
       " 'Johansson, Mr. Gustaf Joel',\n",
       " 'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)',\n",
       " 'Jussila, Miss. Katriina',\n",
       " 'Kantor, Mr. Sinai',\n",
       " 'Kink, Mr. Vincenz',\n",
       " 'Kraeff, Mr. Theodor',\n",
       " 'Laroche, Miss. Simonne Marie Anne Andree',\n",
       " 'Lennon, Mr. Denis',\n",
       " 'Madsen, Mr. Fridtjof Arne',\n",
       " 'Mamee, Mr. Hanna',\n",
       " 'Masselmani, Mrs. Fatima',\n",
       " 'McCarthy, Mr. Timothy J',\n",
       " 'McDermott, Miss. Brigdet Delia',\n",
       " 'McGowan, Miss. Anna \"Annie\"',\n",
       " 'McMahon, Mr. Martin',\n",
       " 'Meo, Mr. Alfonzo',\n",
       " 'Meyer, Mr. Edgar Joseph',\n",
       " 'Mionoff, Mr. Stoytcho',\n",
       " 'Moen, Mr. Sigurd Hansen',\n",
       " 'Moore, Mr. Leonard Charles',\n",
       " 'Moran, Miss. Bertha',\n",
       " 'Moran, Mr. James',\n",
       " 'Moss, Mr. Albert Johan',\n",
       " 'Moubarek, Master. Gerios',\n",
       " 'Moutal, Mr. Rahamin Haim',\n",
       " 'Nasser, Mr. Nicholas',\n",
       " 'Nasser, Mrs. Nicholas (Adele Achem)',\n",
       " 'Navratil, Mr. Michel (\"Louis M Hoffman\")',\n",
       " 'Newsom, Miss. Helen Monypeny',\n",
       " 'Nicholls, Mr. Joseph Charles',\n",
       " 'Nicola-Yarred, Master. Elias',\n",
       " 'Nicola-Yarred, Miss. Jamila',\n",
       " 'Nosworthy, Mr. Richard Cater',\n",
       " 'Novel, Mr. Mansouer',\n",
       " 'Nye, Mrs. (Elizabeth Ramell)',\n",
       " 'Nysten, Miss. Anna Sofia',\n",
       " \"O'Driscoll, Miss. Bridget\",\n",
       " 'O\\'Dwyer, Miss. Ellen \"Nellie\"',\n",
       " 'Olsen, Mr. Ole Martin',\n",
       " 'Osen, Mr. Olaf Elon',\n",
       " 'Ostby, Mr. Engelhart Cornelius',\n",
       " 'Palsson, Master. Gosta Leonard',\n",
       " 'Palsson, Miss. Torborg Danira',\n",
       " 'Panula, Master. Juha Niilo',\n",
       " 'Pears, Mrs. Thomas (Edith Wearne)',\n",
       " 'Pekoniemi, Mr. Edvard',\n",
       " 'Peter, Miss. Anna',\n",
       " 'Petranec, Miss. Matilda',\n",
       " 'Petroff, Mr. Pastcho (\"Pentcho\")',\n",
       " 'Porter, Mr. Walter Chamberlain',\n",
       " 'Rekic, Mr. Tido',\n",
       " 'Rice, Master. Eugene',\n",
       " 'Richard, Mr. Emile',\n",
       " 'Robins, Mrs. Alexander A (Grace Charity Laury)',\n",
       " 'Rogers, Mr. William John',\n",
       " 'Rugg, Miss. Emily',\n",
       " 'Salkjelsvik, Miss. Anna Kristine',\n",
       " 'Samaan, Mr. Youssef',\n",
       " 'Sandstrom, Miss. Marguerite Rut',\n",
       " 'Saundercock, Mr. William Henry',\n",
       " 'Sheerlinck, Mr. Jan Baptist',\n",
       " 'Shorney, Mr. Charles Joseph',\n",
       " 'Sirayanian, Mr. Orsen',\n",
       " 'Skoog, Master. Harald',\n",
       " 'Slocovski, Mr. Selman Francis',\n",
       " 'Sloper, Mr. William Thompson',\n",
       " 'Sobey, Mr. Samuel James Hayden',\n",
       " 'Spencer, Mrs. William Augustus (Marie Eugenie)',\n",
       " 'Staneff, Mr. Ivan',\n",
       " 'Stewart, Mr. Albert A',\n",
       " 'Todoroff, Mr. Lalio',\n",
       " 'Turpin, Mr. William John Robert',\n",
       " 'Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)',\n",
       " 'Uruchurtu, Don. Manuel E',\n",
       " 'Vander Planke, Miss. Augusta Maria',\n",
       " 'Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)',\n",
       " 'Vestrom, Miss. Hulda Amanda Adolfina',\n",
       " 'Waelens, Mr. Achille',\n",
       " 'Webber, Miss. Susan',\n",
       " 'Weisz, Mrs. Leopold (Mathilde Francoise Pede)',\n",
       " 'West, Miss. Constance Mirium',\n",
       " 'Wheadon, Mr. Edward H',\n",
       " 'White, Mr. Percival Wayland',\n",
       " 'White, Mr. Richard Frasar',\n",
       " 'Williams, Mr. Charles Duane',\n",
       " 'Williams, Mr. Charles Eugene',\n",
       " 'Woolner, Mr. Hugh',\n",
       " 'Zabour, Miss. Hileni',\n",
       " 'van Billiard, Mr. Austin Blyler']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(titanic_train[\"Name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Andersson, Mr. August Edvard (\"Wennerstrom\")',\n",
       " 'Andrew, Mr. Edgardo Samuel',\n",
       " 'Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)',\n",
       " 'Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)',\n",
       " 'Bateman, Rev. Robert James',\n",
       " 'Beesley, Mr. Lawrence',\n",
       " 'Bonnell, Miss. Elizabeth',\n",
       " 'Braund, Mr. Owen Harris']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(titanic_train[\"Name\"])[5:20:2]# Check the first 15 sorted names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                              156\n",
       "unique                             156\n",
       "top       Williams, Mr. Charles Eugene\n",
       "freq                                 1\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Name\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>330877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>349909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>347742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticket\n",
       "4  373450\n",
       "5  330877\n",
       "6   17463\n",
       "7  349909\n",
       "8  347742"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[[\"Ticket\"]][4:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Ticket\"].describe()\n",
    "titanic_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_train[\"Name\"]=\"Hari\""
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374910</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17759</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>D10 D12</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>231919</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>244367</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Name     Sex   Age  SibSp  Parch  \\\n",
       "0             1         0       3  Hari    male  22.0      1      0   \n",
       "1             2         1       1  Hari  female  38.0      1      0   \n",
       "2             3         1       3  Hari  female  26.0      0      0   \n",
       "3             4         1       1  Hari  female  35.0      1      0   \n",
       "4             5         0       3  Hari    male  35.0      0      0   \n",
       "..          ...       ...     ...   ...     ...   ...    ...    ...   \n",
       "95           96         0       3  Hari    male   NaN      0      0   \n",
       "96           97         0       1  Hari    male  71.0      0      0   \n",
       "97           98         1       1  Hari    male  23.0      0      1   \n",
       "98           99         1       2  Hari  female  34.0      0      1   \n",
       "99          100         0       2  Hari    male  34.0      1      0   \n",
       "\n",
       "              Ticket     Fare    Cabin Embarked  \n",
       "0          A/5 21171   7.2500      NaN        S  \n",
       "1           PC 17599  71.2833      C85        C  \n",
       "2   STON/O2. 3101282   7.9250      NaN        S  \n",
       "3             113803  53.1000     C123        S  \n",
       "4             373450   8.0500      NaN        S  \n",
       "..               ...      ...      ...      ...  \n",
       "95            374910   8.0500      NaN        S  \n",
       "96          PC 17754  34.6542       A5        C  \n",
       "97          PC 17759  63.3583  D10 D12        C  \n",
       "98            231919  23.0000      NaN        S  \n",
       "99            244367  26.0000      NaN        S  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "1      C85\n",
       "2      NaN\n",
       "3     C123\n",
       "4      NaN\n",
       "5      NaN\n",
       "6      E46\n",
       "7      NaN\n",
       "8      NaN\n",
       "9      NaN\n",
       "10      G6\n",
       "11    C103\n",
       "12     NaN\n",
       "13     NaN\n",
       "14     NaN\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Cabin\"][0:15]       # Check the first 15 tickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "test             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.dtypes# Check number of unique cabins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "5      0\n",
       "6      0\n",
       "7      0\n",
       "8      1\n",
       "9      1\n",
       "10     1\n",
       "11     1\n",
       "12     0\n",
       "13     0\n",
       "14     0\n",
       "15     1\n",
       "16     0\n",
       "17     1\n",
       "18     0\n",
       "19     1\n",
       "20     0\n",
       "21     1\n",
       "22     1\n",
       "23     1\n",
       "24     0\n",
       "25     1\n",
       "26     0\n",
       "27     0\n",
       "28     1\n",
       "29     0\n",
       "      ..\n",
       "126    0\n",
       "127    1\n",
       "128    1\n",
       "129    0\n",
       "130    0\n",
       "131    0\n",
       "132    0\n",
       "133    1\n",
       "134    0\n",
       "135    0\n",
       "136    1\n",
       "137    0\n",
       "138    0\n",
       "139    0\n",
       "140    0\n",
       "141    1\n",
       "142    1\n",
       "143    0\n",
       "144    0\n",
       "145    0\n",
       "146    1\n",
       "147    0\n",
       "148    0\n",
       "149    0\n",
       "150    0\n",
       "151    1\n",
       "152    0\n",
       "153    0\n",
       "154    0\n",
       "155    0\n",
       "Name: Survived, Length: 156, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'test'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.columns"
   ]
  },
  {
   "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",
       "    .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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>2.423077</td>\n",
       "      <td>28.141508</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.397436</td>\n",
       "      <td>28.109587</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.177428</td>\n",
       "      <td>0.477275</td>\n",
       "      <td>0.795459</td>\n",
       "      <td>14.613880</td>\n",
       "      <td>1.056235</td>\n",
       "      <td>0.870146</td>\n",
       "      <td>39.401047</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.003150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.371850</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   156.000000  156.000000  156.000000  126.000000  156.000000   \n",
       "mean     78.500000    0.346154    2.423077   28.141508    0.615385   \n",
       "std      45.177428    0.477275    0.795459   14.613880    1.056235   \n",
       "min       1.000000    0.000000    1.000000    0.830000    0.000000   \n",
       "25%      39.750000    0.000000    2.000000   19.000000    0.000000   \n",
       "50%      78.500000    0.000000    3.000000   26.000000    0.000000   \n",
       "75%     117.250000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     156.000000    1.000000    3.000000   71.000000    5.000000   \n",
       "\n",
       "            Parch        Fare   test  \n",
       "count  156.000000  156.000000  156.0  \n",
       "mean     0.397436   28.109587    0.0  \n",
       "std      0.870146   39.401047    0.0  \n",
       "min      0.000000    6.750000    0.0  \n",
       "25%      0.000000    8.003150    0.0  \n",
       "50%      0.000000   14.454200    0.0  \n",
       "75%      0.000000   30.371850    0.0  \n",
       "max      5.000000  263.000000    0.0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 1, 1, 0, ..., 1, 0, 0, 0, 0]\n",
       "Length: 156\n",
       "Categories (2, int64): [0, 1]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_Pclass = pd.Categorical(titanic_train[\"Survived\"])\n",
    "\n",
    "new_Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 1, 3, 1, 3, ..., 1, 3, 3, 3, 1]\n",
       "Length: 156\n",
       "Categories (3, int64): [1, 2, 3]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_Pclass = pd.Categorical(titanic_train[\"Pclass\"])\n",
    "new_Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
       "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
       "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
       "       'F E69', 'D47', 'B86', 'F2', 'C2'], dtype=object)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Cabin\"].unique()   # Check unique cabins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       nan\n",
       "1       C85\n",
       "2       nan\n",
       "3      C123\n",
       "4       nan\n",
       "       ... \n",
       "151      C2\n",
       "152     nan\n",
       "153     nan\n",
       "154     nan\n",
       "155     nan\n",
       "Name: Cabin, Length: 156, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "char_cabin = titanic_train[\"Cabin\"].astype(str) # Convert data to str\n",
    "char_cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[n, C, n, C, n, ..., C, n, n, n, n]\n",
       "Length: 156\n",
       "Categories (8, object): [A, B, C, D, E, F, G, n]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "char_cabin = titanic_train[\"Cabin\"].astype(str) # Convert data to str\n",
    "\n",
    "new_Cabin = [cabin[0] for cabin in char_cabin] # Take first letter\n",
    "\n",
    "new_Cabin = pd.Categorical(new_Cabin)\n",
    "#new_Cabin\n",
    "new_Cabin\n"
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Name     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3  Hari    male  22.0      1      0   \n",
       "1            2         1       1  Hari  female  38.0      1      0   \n",
       "2            3         1       3  Hari  female  26.0      0      0   \n",
       "3            4         1       1  Hari  female  35.0      1      0   \n",
       "4            5         0       3  Hari    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked Cabin1  \n",
       "0         A/5 21171   7.2500   NaN        S      n  \n",
       "1          PC 17599  71.2833   C85        C      C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S      n  \n",
       "3            113803  53.1000  C123        S      C  \n",
       "4            373450   8.0500   NaN        S      n  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Cabin1\"] = new_Cabin\n",
    "titanic_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "5       True\n",
       "6      False\n",
       "7      False\n",
       "8      False\n",
       "9      False\n",
       "10     False\n",
       "11     False\n",
       "12     False\n",
       "13     False\n",
       "14     False\n",
       "15     False\n",
       "16     False\n",
       "17      True\n",
       "18     False\n",
       "19      True\n",
       "20     False\n",
       "21     False\n",
       "22     False\n",
       "23     False\n",
       "24     False\n",
       "25     False\n",
       "26      True\n",
       "27     False\n",
       "28      True\n",
       "29      True\n",
       "       ...  \n",
       "126     True\n",
       "127    False\n",
       "128     True\n",
       "129    False\n",
       "130    False\n",
       "131    False\n",
       "132    False\n",
       "133    False\n",
       "134    False\n",
       "135    False\n",
       "136    False\n",
       "137    False\n",
       "138    False\n",
       "139    False\n",
       "140     True\n",
       "141    False\n",
       "142    False\n",
       "143    False\n",
       "144    False\n",
       "145    False\n",
       "146    False\n",
       "147    False\n",
       "148    False\n",
       "149    False\n",
       "150    False\n",
       "151    False\n",
       "152    False\n",
       "153    False\n",
       "154     True\n",
       "155    False\n",
       "Name: Age, Length: 156, dtype: bool"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Age\"].isnull() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5,  17,  19,  26,  28,  29,  31,  32,  36,  42,  45,  46,  47,\n",
       "         48,  55,  64,  65,  76,  77,  82,  87,  95, 101, 107, 109, 121,\n",
       "        126, 128, 140, 154], dtype=int64),)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = np.where(titanic_train[\"Age\"].isnull() == True)\n",
    "missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "keyword can't be an expression (<ipython-input-37-5268125faf28>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-37-5268125faf28>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    np.where(titanic_train[\"Fare\"]=max(titanic_train[\"Fare\"]))\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m keyword can't be an expression\n"
     ]
    }
   ],
   "source": [
    "np.where(titanic_train[\"Fare\"]=max(titanic_train[\"Fare\"]))"
   ]
  },
  {
   "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",
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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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Name     Sex   Age  SibSp  Parch Ticket  \\\n",
       "27           28         0       1  Hari    male  19.0      3      2  19950   \n",
       "88           89         1       1  Hari  female  23.0      3      2  19950   \n",
       "\n",
       "     Fare        Cabin Embarked Cabin1  \n",
       "27  263.0  C23 C25 C27        S      C  \n",
       "88  263.0  C23 C25 C27        S      C  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.iloc[np.where(titanic_train[\"Fare\"]==max(titanic_train[\"Fare\"]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([27, 88]),)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(titanic_train[\"Fare\"]==max(titanic_train[\"Fare\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missing[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([59, 71], dtype=int64),)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Family\"] = titanic_train[\"SibSp\"] + titanic_train[\"Parch\"]\n",
    "titanic_train[\"Family\"]\n",
    "most_family = np.where(titanic_train[\"Family\"] == max(titanic_train[\"Family\"]))\n",
    "most_family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin1</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>male</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>46.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>n</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hari</td>\n",
       "      <td>female</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>46.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>n</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Name     Sex   Age  SibSp  Parch   Ticket  \\\n",
       "59           60         0       3  Hari    male  11.0      5      2  CA 2144   \n",
       "71           72         0       3  Hari  female  16.0      5      2  CA 2144   \n",
       "\n",
       "    Fare Cabin Embarked Cabin1  Family  \n",
       "59  46.9   NaN        S      n       7  \n",
       "71  46.9   NaN        S      n       7  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Family\"] = titanic_train[\"SibSp\"] + titanic_train[\"Parch\"]\n",
    "\n",
    "most_family = np.where(titanic_train[\"Family\"] == max(titanic_train[\"Family\"]))\n",
    "most_family\n",
    "titanic_train.iloc[most_family]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>kjhk</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>kjhk</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>kjhk</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a   b   c\n",
       "a  kjhk  20  30\n",
       "b  kjhk  20  30\n",
       "c  kjhk  20  30"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "d = {'a':\"kjhk\",'b':20,'c':30}\n",
    "d.items\n",
    "pd.DataFrame(d,index = ['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.841192</td>\n",
       "      <td>0.662334</td>\n",
       "      <td>0.616624</td>\n",
       "      <td>-0.730156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.245374</td>\n",
       "      <td>0.554750</td>\n",
       "      <td>1.397740</td>\n",
       "      <td>-1.202630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.106038</td>\n",
       "      <td>-1.422779</td>\n",
       "      <td>0.477050</td>\n",
       "      <td>-0.999455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.375482</td>\n",
       "      <td>-0.464899</td>\n",
       "      <td>0.968338</td>\n",
       "      <td>0.772466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>2.067285</td>\n",
       "      <td>-1.349534</td>\n",
       "      <td>-2.438901</td>\n",
       "      <td>0.321404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.841192  0.662334  0.616624 -0.730156\n",
       "B  0.245374  0.554750  1.397740 -1.202630\n",
       "C -2.106038 -1.422779  0.477050 -0.999455\n",
       "D  0.375482 -0.464899  0.968338  0.772466\n",
       "E  2.067285 -1.349534 -2.438901  0.321404"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.random import randn as rn\n",
    "#np.random.seed(101)\n",
    "matrix_data = rn(5,4)\n",
    "row_labels = ['A','B','C','D','E']\n",
    "column_headings = ['W','X','Y','Z']\n",
    "\n",
    "df = pd.DataFrame(matrix_data,row_labels,column_headings)\n",
    "#print(\"\\nThe data frame looks like\\n\",'-'*45, sep='')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W   -2.106038\n",
       "X   -1.422779\n",
       "Y    0.477050\n",
       "Z   -0.999455\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 'X' column accessed by DOT method (NOT recommended)\n",
      "-------------------------------------------------------\n",
      "A    0.662334\n",
      "B    0.554750\n",
      "C   -1.422779\n",
      "D   -0.464899\n",
      "E   -1.349534\n",
      "Name: X, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nThe 'X' column accessed by DOT method (NOT recommended)\\n\",'-'*55, sep='')\n",
    "print(df[\"X\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A column is created by assigning it in relation to an existing column\n",
      "---------------------------------------------------------------------------\n",
      "          W         X         Y         Z       New  New (Sum of X and Z)\n",
      "A  0.841192  0.662334  0.616624 -0.730156 -0.067822             -0.483608\n",
      "B  0.245374  0.554750  1.397740 -1.202630 -0.647881             -0.667159\n",
      "C -2.106038 -1.422779  0.477050 -0.999455 -2.422234              1.422003\n",
      "D  0.375482 -0.464899  0.968338  0.772466  0.307566             -0.359119\n",
      "E  2.067285 -1.349534 -2.438901  0.321404 -1.028130             -0.433745\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nA column is created by assigning it in relation to an existing column\\n\",'-'*75, sep='')\n",
    "df['New'] = df['X']+df['Z']\n",
    "df['New (Sum of X and Z)'] = df['X']*df['Z']\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>New</th>\n",
       "      <th>New (Sum of X and Z)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.841192</td>\n",
       "      <td>0.662334</td>\n",
       "      <td>0.616624</td>\n",
       "      <td>-0.730156</td>\n",
       "      <td>-0.067822</td>\n",
       "      <td>-0.483608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.245374</td>\n",
       "      <td>0.554750</td>\n",
       "      <td>1.397740</td>\n",
       "      <td>-1.202630</td>\n",
       "      <td>-0.647881</td>\n",
       "      <td>-0.667159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.106038</td>\n",
       "      <td>-1.422779</td>\n",
       "      <td>0.477050</td>\n",
       "      <td>-0.999455</td>\n",
       "      <td>-2.422234</td>\n",
       "      <td>1.422003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.375482</td>\n",
       "      <td>-0.464899</td>\n",
       "      <td>0.968338</td>\n",
       "      <td>0.772466</td>\n",
       "      <td>0.307566</td>\n",
       "      <td>-0.359119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>2.067285</td>\n",
       "      <td>-1.349534</td>\n",
       "      <td>-2.438901</td>\n",
       "      <td>0.321404</td>\n",
       "      <td>-1.028130</td>\n",
       "      <td>-0.433745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z       New  New (Sum of X and Z)\n",
       "A  0.841192  0.662334  0.616624 -0.730156 -0.067822             -0.483608\n",
       "B  0.245374  0.554750  1.397740 -1.202630 -0.647881             -0.667159\n",
       "C -2.106038 -1.422779  0.477050 -0.999455 -2.422234              1.422003\n",
       "D  0.375482 -0.464899  0.968338  0.772466  0.307566             -0.359119\n",
       "E  2.067285 -1.349534 -2.438901  0.321404 -1.028130             -0.433745"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A column is dropped by using df.drop() method\n",
      " -------------------------------------------------------\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"['W'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-914ae46838f9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\nA column is dropped by using df.drop() method\\n\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'-'\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m55\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"W\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Notice the axis=1 option, axis = 0 is default, so one has to change it to 1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3995\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3996\u001b[0m             \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3997\u001b[1;33m             \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3998\u001b[0m         )\n\u001b[0;32m   3999\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3934\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3935\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3936\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3938\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3968\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3969\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3970\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3971\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5016\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5017\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5018\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5019\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5020\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['W'] not found in axis\""
     ]
    }
   ],
   "source": [
    "print(\"\\nA column is dropped by using df.drop() method\\n\",'-'*55, sep=' ')\n",
    "df.drop(\"W\",axis=1) # Notice the axis=1 option, axis = 0 is default, so one has to change it to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>42</td>\n",
       "      <td>70</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>30</td>\n",
       "      <td>62</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>35</td>\n",
       "      <td>68</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Height  Weight\n",
       "B   42      70     148\n",
       "C   30      62     125\n",
       "D   35      68     160\n",
       "E   25      62     152"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['A'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-866d2ce47bab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'A'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\nA row (index) is dropped by using df.drop() method and axis=0\\n\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'-'\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m65\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3995\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3996\u001b[0m             \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3997\u001b[1;33m             \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3998\u001b[0m         )\n\u001b[0;32m   3999\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3934\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3935\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3936\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3938\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3968\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3969\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3970\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3971\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5016\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5017\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5018\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5019\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5020\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['A'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df1=df.drop('A')\n",
    "print(\"\\nA row (index) is dropped by using df.drop() method and axis=0\\n\",'-'*65, sep='')\n",
    "print(df1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nAn in-place change can be done by making inplace=True in the drop method\\n\",'-'*75, sep='')\n",
    "df.drop('New (Sum of X and Z)', axis=1, inplace=True)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Selecting/indexing Rows\n",
    "#* Label-based 'loc' method\n",
    "#* Index (numeric) 'iloc' method\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"\\nLabel-based 'loc' method can be used for selecting row(s)\\n\",'-'*60, sep='')\n",
    "print(\"\\nSingle row\\n\")\n",
    "print(df.iloc[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nMultiple rows\\n\")\n",
    "df.loc[['A']]  ---Colum index\n",
    "print(df[\"W\"]) ---Row Index\n",
    "#print(df.loc[['B','W']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nIndex position based 'iloc' method can be used for selecting row(s)\\n\",'-'*70, sep='')\n",
    "print(\"\\nSingle row\\n\")\n",
    "print(df.iloc[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nMultiple rows\\n\")\n",
    "print(df.iloc[[1,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Subsetting DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "matrix_data = rn(5,4)\n",
    "row_labels = ['A','B','C','D','E']\n",
    "column_headings = ['W','X','Y','Z']\n",
    "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nThe DatFrame\\n\",'-'*45, sep='')\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nElement at row 'B' and column 'Y' is\\n\") \n",
    "print(df.loc[['B','C'],['Y','W']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nSubset comprising of rows B and D, and columns W and Y, is\\n\")\n",
    "df.iloc[[1,2,3],[0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.loc[['B','C'],])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-0adef0ec0e77>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'A'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'B'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'C'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1765\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1766\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1767\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1768\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1769\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1951\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1952\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1953\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1954\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1955\u001b[0m             \u001b[1;31m# nested tuple slicing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1592\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1593\u001b[0m             \u001b[1;31m# A collection of keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1594\u001b[1;33m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1595\u001b[0m             return self.obj._reindex_with_indexers(\n\u001b[0;32m   1596\u001b[0m                 \u001b[1;33m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1551\u001b[0m         self._validate_read_indexer(\n\u001b[1;32m-> 1552\u001b[1;33m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1553\u001b[0m         )\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1652\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_categorical\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1653\u001b[0m                 raise KeyError(\n\u001b[1;32m-> 1654\u001b[1;33m                     \u001b[1;34m\"Passing list-likes to .loc or [] with any missing labels \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1655\u001b[0m                     \u001b[1;34m\"is no longer supported, see \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1656\u001b[0m                     \u001b[1;34m\"https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\"\u001b[0m  \u001b[1;31m# noqa:E501\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "print(df.loc[['A','B','C']]>0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataFrame indexed by boolean dataframe\n",
      "---------------------------------------------\n",
      "          W        X         Y         Z       New  New (Sum of X and Z)\n",
      "B  0.245374  0.55475  1.397740       NaN       NaN                   NaN\n",
      "C       NaN      NaN  0.477050       NaN       NaN              1.422003\n",
      "D  0.375482      NaN  0.968338  0.772466  0.307566                   NaN\n",
      "E  2.067285      NaN       NaN  0.321404       NaN                   NaN\n"
     ]
    }
   ],
   "source": [
    "booldf = df>0\n",
    "print(\"\\nDataFrame indexed by boolean dataframe\\n\",'-'*45, sep='')\n",
    "print(df[booldf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 22,  66, 140],\n",
       "        [ 42,  70, 148],\n",
       "        [ 30,  62, 125],\n",
       "        [ 35,  68, 160],\n",
       "        [ 25,  62, 152]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_data = np.matrix('22,66,140;42,70,148;30,62,125;35,68,160;25,62,152')\n",
    "row_labels = ['A','B','C','D','E']\n",
    "column_headings = ['Age', 'Height', 'Weight']\n",
    "matrix_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A new DataFrame\n",
      "-------------------------\n",
      "   Age  Height  Weight\n",
      "A   22      66     140\n",
      "B   42      70     148\n",
      "C   30      62     125\n",
      "D   35      68     160\n",
      "E   25      62     152\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
    "print(\"\\nA new DataFrame\\n\",'-'*25, sep='')\n",
    "print(df)\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>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>22</td>\n",
       "      <td>66</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>42</td>\n",
       "      <td>70</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>35</td>\n",
       "      <td>68</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Height  Weight\n",
       "A   22      66     140\n",
       "B   42      70     148\n",
       "D   35      68     160"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Height']>65]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rows with Height > 65 inch\n",
      "----------------------------------------------------------------------------------------------------\n",
      "   Age  Height  Weight\n",
      "A   22      66     140\n",
      "B   42      70     148\n",
      "D   35      68     160\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nRows with Height > 65 inch\\n\",'-'*100, sep='')\n",
    "print(df[df['Height']>65])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     True\n",
       "B     True\n",
       "C    False\n",
       "D     True\n",
       "E    False\n",
       "Name: Height, dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Height']>65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "booldf1 = df['Height']>65\n",
    "booldf2 = df['Weight']>145\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rows with Height > 65 inch and Weight >145 lbs\n",
      "-------------------------------------------------------\n",
      "   Age  Height  Weight\n",
      "B   42      70     148\n",
      "D   35      68     160\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nRows with Height > 65 inch and Weight >145 lbs\\n\",'-'*55, sep='')\n",
    "print(df[(booldf1) & (booldf2)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataFrame with only Age and Weight columns whose Height > 65 inch\n",
      "--------------------------------------------------------------------\n",
      "   Age  Weight\n",
      "A   22     140\n",
      "B   42     148\n",
      "D   35     160\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nDataFrame with only Age and Weight columns whose Height > 65 inch\\n\",'-'*68, sep='')\n",
    "print(df[booldf1][['Age','Weight']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix_data = np.matrix('22,66,140;42,70,148;30,62,125;35,68,160;25,62,152')\n",
    "row_labels = ['A','B','C','D','E']\n",
    "column_headings = ['Age', 'Height', 'Weight']\n"
   ]
  },
  {
   "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",
       "        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>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>22</td>\n",
       "      <td>66</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>42</td>\n",
       "      <td>70</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>30</td>\n",
       "      <td>62</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>35</td>\n",
       "      <td>68</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Height  Weight\n",
       "A   22      66     140\n",
       "B   42      70     148\n",
       "C   30      62     125\n",
       "D   35      68     160\n",
       "E   25      62     152"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After resetting index\n",
      "-----------------------------------\n",
      "  index  Age  Height  Weight\n",
      "0     A   22      66     140\n",
      "1     B   42      70     148\n",
      "2     C   30      62     125\n",
      "3     D   35      68     160\n",
      "4     E   25      62     152\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nAfter resetting index\\n\",'-'*35, sep='')\n",
    "print(df.reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After resetting index with 'drop' option TRUE\n",
      "---------------------------------------------\n",
      "   Age  Height  Weight\n",
      "0   22      66     140\n",
      "1   42      70     148\n",
      "2   30      62     125\n",
      "3   35      68     160\n",
      "4   25      62     152\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Student', 'Teacher', 'Engineer', 'Doctor', 'Nurse']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nAfter resetting index with 'drop' option TRUE\\n\",'-'*45, sep='')\n",
    "print(df.reset_index(drop=True))\n",
    "\"Student Teacher Engineer Doctor Nurse\".split()"
   ]
  },
  {
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>New</th>\n",
       "      <th>New (Sum of X and Z)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.353145</td>\n",
       "      <td>-1.094828</td>\n",
       "      <td>3.705136</td>\n",
       "      <td>-0.148606</td>\n",
       "      <td>-1.243434</td>\n",
       "      <td>0.162698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.249747</td>\n",
       "      <td>-0.147924</td>\n",
       "      <td>1.190617</td>\n",
       "      <td>0.195905</td>\n",
       "      <td>0.047981</td>\n",
       "      <td>-0.028979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.605244</td>\n",
       "      <td>0.872743</td>\n",
       "      <td>0.777672</td>\n",
       "      <td>-0.333161</td>\n",
       "      <td>0.539582</td>\n",
       "      <td>-0.290764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.709008</td>\n",
       "      <td>0.225959</td>\n",
       "      <td>0.135471</td>\n",
       "      <td>0.150424</td>\n",
       "      <td>0.376383</td>\n",
       "      <td>0.033990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z       New  New (Sum of X and Z)\n",
       "B -0.353145 -1.094828  3.705136 -0.148606 -1.243434              0.162698\n",
       "C -2.249747 -0.147924  1.190617  0.195905  0.047981             -0.028979\n",
       "D -0.605244  0.872743  0.777672 -0.333161  0.539582             -0.290764\n",
       "E -0.709008  0.225959  0.135471  0.150424  0.376383              0.033990"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Adding a new column 'Profession'\n",
      "---------------------------------------------\n",
      "          W         X         Y         Z       New  New (Sum of X and Z)  \\\n",
      "B -0.353145 -1.094828  3.705136 -0.148606 -1.243434              0.162698   \n",
      "C -2.249747 -0.147924  1.190617  0.195905  0.047981             -0.028979   \n",
      "D -0.605244  0.872743  0.777672 -0.333161  0.539582             -0.290764   \n",
      "E -0.709008  0.225959  0.135471  0.150424  0.376383              0.033990   \n",
      "\n",
      "  Profession  \n",
      "B    Student  \n",
      "C    Teacher  \n",
      "D   Engineer  \n",
      "E     Doctor  \n"
     ]
    }
   ],
   "source": [
    "print(\"\\nAdding a new column 'Profession'\\n\",'-'*45, sep='')\n",
    "df['Profession'] = \"Student Teacher Engineer Doctor\".split()\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Setting 'Profession' column as index\n",
      "---------------------------------------------\n",
      "                   W         X         Y         Z       New  \\\n",
      "Profession                                                     \n",
      "Student    -0.353145 -1.094828  3.705136 -0.148606 -1.243434   \n",
      "Teacher    -2.249747 -0.147924  1.190617  0.195905  0.047981   \n",
      "Engineer   -0.605244  0.872743  0.777672 -0.333161  0.539582   \n",
      "Doctor     -0.709008  0.225959  0.135471  0.150424  0.376383   \n",
      "\n",
      "            New (Sum of X and Z)  \n",
      "Profession                        \n",
      "Student                 0.162698  \n",
      "Teacher                -0.028979  \n",
      "Engineer               -0.290764  \n",
      "Doctor                  0.033990  \n"
     ]
    }
   ],
   "source": [
    "print(\"\\nSetting 'Profession' column as index\\n\",'-'*45, sep='')\n",
    "print (df.set_index('Profession'))"
   ]
  },
  {
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>New</th>\n",
       "      <th>New (Sum of X and Z)</th>\n",
       "      <th>Profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.353145</td>\n",
       "      <td>-1.094828</td>\n",
       "      <td>3.705136</td>\n",
       "      <td>-0.148606</td>\n",
       "      <td>-1.243434</td>\n",
       "      <td>0.162698</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.249747</td>\n",
       "      <td>-0.147924</td>\n",
       "      <td>1.190617</td>\n",
       "      <td>0.195905</td>\n",
       "      <td>0.047981</td>\n",
       "      <td>-0.028979</td>\n",
       "      <td>Teacher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.605244</td>\n",
       "      <td>0.872743</td>\n",
       "      <td>0.777672</td>\n",
       "      <td>-0.333161</td>\n",
       "      <td>0.539582</td>\n",
       "      <td>-0.290764</td>\n",
       "      <td>Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.709008</td>\n",
       "      <td>0.225959</td>\n",
       "      <td>0.135471</td>\n",
       "      <td>0.150424</td>\n",
       "      <td>0.376383</td>\n",
       "      <td>0.033990</td>\n",
       "      <td>Doctor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z       New  New (Sum of X and Z)  \\\n",
       "B -0.353145 -1.094828  3.705136 -0.148606 -1.243434              0.162698   \n",
       "C -2.249747 -0.147924  1.190617  0.195905  0.047981             -0.028979   \n",
       "D -0.605244  0.872743  0.777672 -0.333161  0.539582             -0.290764   \n",
       "E -0.709008  0.225959  0.135471  0.150424  0.376383              0.033990   \n",
       "\n",
       "  Profession  \n",
       "B    Student  \n",
       "C    Teacher  \n",
       "D   Engineer  \n",
       "E     Doctor  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#multi-indexing\n",
    "# Index Levels\n",
    "outside = ['G1','G1','G1','G2','G2','G2']\n",
    "inside = [1,2,3,1,2,3]\n",
    "hier_index = list(zip(outside,inside))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tuple pairs after the zip and list command\n",
      "---------------------------------------------\n",
      "[('G1', 1), ('G1', 2), ('G1', 3), ('G2', 1), ('G2', 2), ('G2', 3)]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nTuple pairs after the zip and list command\\n\",'-'*45, sep='')\n",
    "print(hier_index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Index hierarchy\n",
      "-------------------------\n",
      "MultiIndex([('G1', 1),\n",
      "            ('G1', 2),\n",
      "            ('G1', 3),\n",
      "            ('G2', 1),\n",
      "            ('G2', 2),\n",
      "            ('G2', 3)],\n",
      "           )\n"
     ]
    }
   ],
   "source": [
    "hier_index = pd.MultiIndex.from_tuples(hier_index)\n",
    "print(\"\\nIndex hierarchy\\n\",'-'*25, sep='')\n",
    "print(hier_index)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Index hierarchy type\n",
      "-------------------------\n",
      "<class 'pandas.core.indexes.multi.MultiIndex'>\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nIndex hierarchy type\\n\",'-'*25, sep='')\n",
    "print(type(hier_index))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Creating DataFrame with multi-index\n",
      "-------------------------------------\n",
      "        A    B    C\n",
      "G1 1  3.0  1.0  1.0\n",
      "   2  1.0  1.0 -0.0\n",
      "   3 -1.0  1.0 -2.0\n",
      "G2 1  1.0  1.0 -1.0\n",
      "   2  0.0 -1.0 -1.0\n",
      "   3  1.0  0.0  2.0\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nCreating DataFrame with multi-index\\n\",'-'*37, sep='')\n",
    "np.random.seed(101)\n",
    "df1 = pd.DataFrame(data=np.round(rn(6,3)), index= hier_index, columns= ['A','B','C'])\n",
    "print(df1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cross tabulation like pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Grabbing a cross-section from outer level\n",
      "---------------------------------------------\n",
      "     A    B    C\n",
      "1 -0.0  1.0 -0.0\n",
      "2  2.0 -1.0  0.0\n",
      "3 -0.0 -0.0  1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nGrabbing a cross-section from outer level\\n\",'-'*45, sep='')\n",
    "print(df1.xs('G1'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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