Pandas_03.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "44346908",
   "metadata": {},
   "source": [
    "## Gruplama İşlemleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd17c825",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d133b236",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'gruplar': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                    'veri': [10, 11, 45, 56, 78, 87]}, columns=['gruplar', 'veri'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7156026c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gruplar</th>\n",
       "      <th>veri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gruplar  veri\n",
       "0       A    10\n",
       "1       B    11\n",
       "2       C    45\n",
       "3       A    56\n",
       "4       B    78\n",
       "5       C    87"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40f73a70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fc5f28e8550>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gruplar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b4d3430",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>veri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         veri\n",
       "gruplar      \n",
       "A        33.0\n",
       "B        44.5\n",
       "C        66.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gruplar').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4007e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>veri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         veri\n",
       "gruplar      \n",
       "A          66\n",
       "B          89\n",
       "C         132"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gruplar').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "351bc47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = sns.load_dataset('planets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b49ec8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da377d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fc5f610e8b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('method')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c849f56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7fc5f610e670>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('method')['orbital_period']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91a19ed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method\n",
       "Astrometry                          631.180000\n",
       "Eclipse Timing Variations          4751.644444\n",
       "Imaging                          118247.737500\n",
       "Microlensing                       3153.571429\n",
       "Orbital Brightness Modulation         0.709307\n",
       "Pulsar Timing                      7343.021201\n",
       "Pulsation Timing Variations        1170.000000\n",
       "Radial Velocity                     823.354680\n",
       "Transit                              21.102073\n",
       "Transit Timing Variations            79.783500\n",
       "Name: orbital_period, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('method')['orbital_period'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dcb31066",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
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       "      <th>distance</th>\n",
       "      <th>year</th>\n",
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       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astrometry</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>631.180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.875000</td>\n",
       "      <td>2011.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>4751.644444</td>\n",
       "      <td>5.125000</td>\n",
       "      <td>315.360000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>1.315789</td>\n",
       "      <td>118247.737500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.715937</td>\n",
       "      <td>2009.131579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>1.173913</td>\n",
       "      <td>3153.571429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4144.000000</td>\n",
       "      <td>2009.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.709307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1180.000000</td>\n",
       "      <td>2011.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>7343.021201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>1998.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>1.721519</td>\n",
       "      <td>823.354680</td>\n",
       "      <td>2.630699</td>\n",
       "      <td>51.600208</td>\n",
       "      <td>2007.518987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>1.954660</td>\n",
       "      <td>21.102073</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>599.298080</td>\n",
       "      <td>2011.236776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>79.783500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1104.333333</td>\n",
       "      <td>2012.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 number  orbital_period      mass  \\\n",
       "method                                                              \n",
       "Astrometry                     1.000000      631.180000       NaN   \n",
       "Eclipse Timing Variations      1.666667     4751.644444  5.125000   \n",
       "Imaging                        1.315789   118247.737500       NaN   \n",
       "Microlensing                   1.173913     3153.571429       NaN   \n",
       "Orbital Brightness Modulation  1.666667        0.709307       NaN   \n",
       "Pulsar Timing                  2.200000     7343.021201       NaN   \n",
       "Pulsation Timing Variations    1.000000     1170.000000       NaN   \n",
       "Radial Velocity                1.721519      823.354680  2.630699   \n",
       "Transit                        1.954660       21.102073  1.470000   \n",
       "Transit Timing Variations      2.250000       79.783500       NaN   \n",
       "\n",
       "                                  distance         year  \n",
       "method                                                   \n",
       "Astrometry                       17.875000  2011.500000  \n",
       "Eclipse Timing Variations       315.360000  2010.000000  \n",
       "Imaging                          67.715937  2009.131579  \n",
       "Microlensing                   4144.000000  2009.782609  \n",
       "Orbital Brightness Modulation  1180.000000  2011.666667  \n",
       "Pulsar Timing                  1200.000000  1998.400000  \n",
       "Pulsation Timing Variations            NaN  2007.000000  \n",
       "Radial Velocity                  51.600208  2007.518987  \n",
       "Transit                         599.298080  2011.236776  \n",
       "Transit Timing Variations      1104.333333  2012.500000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('method').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44a1116b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
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       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astrometry</th>\n",
       "      <td>2.0</td>\n",
       "      <td>631.180000</td>\n",
       "      <td>544.217663</td>\n",
       "      <td>246.360000</td>\n",
       "      <td>438.770000</td>\n",
       "      <td>631.180000</td>\n",
       "      <td>823.590000</td>\n",
       "      <td>1016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4751.644444</td>\n",
       "      <td>2499.130945</td>\n",
       "      <td>1916.250000</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>4343.500000</td>\n",
       "      <td>5767.000000</td>\n",
       "      <td>10220.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>12.0</td>\n",
       "      <td>118247.737500</td>\n",
       "      <td>213978.177277</td>\n",
       "      <td>4639.150000</td>\n",
       "      <td>8343.900000</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>94250.000000</td>\n",
       "      <td>730000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3153.571429</td>\n",
       "      <td>1113.166333</td>\n",
       "      <td>1825.000000</td>\n",
       "      <td>2375.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>3550.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.709307</td>\n",
       "      <td>0.725493</td>\n",
       "      <td>0.240104</td>\n",
       "      <td>0.291496</td>\n",
       "      <td>0.342887</td>\n",
       "      <td>0.943908</td>\n",
       "      <td>1.544929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7343.021201</td>\n",
       "      <td>16313.265573</td>\n",
       "      <td>0.090706</td>\n",
       "      <td>25.262000</td>\n",
       "      <td>66.541900</td>\n",
       "      <td>98.211400</td>\n",
       "      <td>36525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "      <td>1170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>553.0</td>\n",
       "      <td>823.354680</td>\n",
       "      <td>1454.926210</td>\n",
       "      <td>0.736540</td>\n",
       "      <td>38.021000</td>\n",
       "      <td>360.200000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>17337.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>397.0</td>\n",
       "      <td>21.102073</td>\n",
       "      <td>46.185893</td>\n",
       "      <td>0.355000</td>\n",
       "      <td>3.160630</td>\n",
       "      <td>5.714932</td>\n",
       "      <td>16.145700</td>\n",
       "      <td>331.600590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>3.0</td>\n",
       "      <td>79.783500</td>\n",
       "      <td>71.599884</td>\n",
       "      <td>22.339500</td>\n",
       "      <td>39.675250</td>\n",
       "      <td>57.011000</td>\n",
       "      <td>108.505500</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               count           mean            std  \\\n",
       "method                                                               \n",
       "Astrometry                       2.0     631.180000     544.217663   \n",
       "Eclipse Timing Variations        9.0    4751.644444    2499.130945   \n",
       "Imaging                         12.0  118247.737500  213978.177277   \n",
       "Microlensing                     7.0    3153.571429    1113.166333   \n",
       "Orbital Brightness Modulation    3.0       0.709307       0.725493   \n",
       "Pulsar Timing                    5.0    7343.021201   16313.265573   \n",
       "Pulsation Timing Variations      1.0    1170.000000            NaN   \n",
       "Radial Velocity                553.0     823.354680    1454.926210   \n",
       "Transit                        397.0      21.102073      46.185893   \n",
       "Transit Timing Variations        3.0      79.783500      71.599884   \n",
       "\n",
       "                                       min          25%           50%  \\\n",
       "method                                                                  \n",
       "Astrometry                      246.360000   438.770000    631.180000   \n",
       "Eclipse Timing Variations      1916.250000  2900.000000   4343.500000   \n",
       "Imaging                        4639.150000  8343.900000  27500.000000   \n",
       "Microlensing                   1825.000000  2375.000000   3300.000000   \n",
       "Orbital Brightness Modulation     0.240104     0.291496      0.342887   \n",
       "Pulsar Timing                     0.090706    25.262000     66.541900   \n",
       "Pulsation Timing Variations    1170.000000  1170.000000   1170.000000   \n",
       "Radial Velocity                   0.736540    38.021000    360.200000   \n",
       "Transit                           0.355000     3.160630      5.714932   \n",
       "Transit Timing Variations        22.339500    39.675250     57.011000   \n",
       "\n",
       "                                        75%            max  \n",
       "method                                                      \n",
       "Astrometry                       823.590000    1016.000000  \n",
       "Eclipse Timing Variations       5767.000000   10220.000000  \n",
       "Imaging                        94250.000000  730000.000000  \n",
       "Microlensing                    3550.000000    5100.000000  \n",
       "Orbital Brightness Modulation      0.943908       1.544929  \n",
       "Pulsar Timing                     98.211400   36525.000000  \n",
       "Pulsation Timing Variations     1170.000000    1170.000000  \n",
       "Radial Velocity                  982.000000   17337.500000  \n",
       "Transit                           16.145700     331.600590  \n",
       "Transit Timing Variations        108.505500     160.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('method')['orbital_period'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "25298758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>method</th>\n",
       "      <th>Astrometry</th>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <th>Imaging</th>\n",
       "      <th>Microlensing</th>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <th>Radial Velocity</th>\n",
       "      <th>Transit</th>\n",
       "      <th>Transit Timing Variations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>553.00000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>631.180000</td>\n",
       "      <td>4751.644444</td>\n",
       "      <td>118247.737500</td>\n",
       "      <td>3153.571429</td>\n",
       "      <td>0.709307</td>\n",
       "      <td>7343.021201</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>823.35468</td>\n",
       "      <td>21.102073</td>\n",
       "      <td>79.783500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>544.217663</td>\n",
       "      <td>2499.130945</td>\n",
       "      <td>213978.177277</td>\n",
       "      <td>1113.166333</td>\n",
       "      <td>0.725493</td>\n",
       "      <td>16313.265573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1454.92621</td>\n",
       "      <td>46.185893</td>\n",
       "      <td>71.599884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>246.360000</td>\n",
       "      <td>1916.250000</td>\n",
       "      <td>4639.150000</td>\n",
       "      <td>1825.000000</td>\n",
       "      <td>0.240104</td>\n",
       "      <td>0.090706</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>0.73654</td>\n",
       "      <td>0.355000</td>\n",
       "      <td>22.339500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>438.770000</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>8343.900000</td>\n",
       "      <td>2375.000000</td>\n",
       "      <td>0.291496</td>\n",
       "      <td>25.262000</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>38.02100</td>\n",
       "      <td>3.160630</td>\n",
       "      <td>39.675250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>631.180000</td>\n",
       "      <td>4343.500000</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>0.342887</td>\n",
       "      <td>66.541900</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>360.20000</td>\n",
       "      <td>5.714932</td>\n",
       "      <td>57.011000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>823.590000</td>\n",
       "      <td>5767.000000</td>\n",
       "      <td>94250.000000</td>\n",
       "      <td>3550.000000</td>\n",
       "      <td>0.943908</td>\n",
       "      <td>98.211400</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>982.00000</td>\n",
       "      <td>16.145700</td>\n",
       "      <td>108.505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1016.000000</td>\n",
       "      <td>10220.000000</td>\n",
       "      <td>730000.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>1.544929</td>\n",
       "      <td>36525.000000</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>17337.50000</td>\n",
       "      <td>331.600590</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "method   Astrometry  Eclipse Timing Variations        Imaging  Microlensing  \\\n",
       "count      2.000000                   9.000000      12.000000      7.000000   \n",
       "mean     631.180000                4751.644444  118247.737500   3153.571429   \n",
       "std      544.217663                2499.130945  213978.177277   1113.166333   \n",
       "min      246.360000                1916.250000    4639.150000   1825.000000   \n",
       "25%      438.770000                2900.000000    8343.900000   2375.000000   \n",
       "50%      631.180000                4343.500000   27500.000000   3300.000000   \n",
       "75%      823.590000                5767.000000   94250.000000   3550.000000   \n",
       "max     1016.000000               10220.000000  730000.000000   5100.000000   \n",
       "\n",
       "method  Orbital Brightness Modulation  Pulsar Timing  \\\n",
       "count                        3.000000       5.000000   \n",
       "mean                         0.709307    7343.021201   \n",
       "std                          0.725493   16313.265573   \n",
       "min                          0.240104       0.090706   \n",
       "25%                          0.291496      25.262000   \n",
       "50%                          0.342887      66.541900   \n",
       "75%                          0.943908      98.211400   \n",
       "max                          1.544929   36525.000000   \n",
       "\n",
       "method  Pulsation Timing Variations  Radial Velocity     Transit  \\\n",
       "count                           1.0        553.00000  397.000000   \n",
       "mean                         1170.0        823.35468   21.102073   \n",
       "std                             NaN       1454.92621   46.185893   \n",
       "min                          1170.0          0.73654    0.355000   \n",
       "25%                          1170.0         38.02100    3.160630   \n",
       "50%                          1170.0        360.20000    5.714932   \n",
       "75%                          1170.0        982.00000   16.145700   \n",
       "max                          1170.0      17337.50000  331.600590   \n",
       "\n",
       "method  Transit Timing Variations  \n",
       "count                    3.000000  \n",
       "mean                    79.783500  \n",
       "std                     71.599884  \n",
       "min                     22.339500  \n",
       "25%                     39.675250  \n",
       "50%                     57.011000  \n",
       "75%                    108.505500  \n",
       "max                    160.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('method')['orbital_period'].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fde352f",
   "metadata": {},
   "source": [
    "## İleri Toplulaştırma İşlemleri (Aggregate, filter, transform, apply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e501e6fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame({'gruplar': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                   'degisken1': [10, 43, 65, 56, 34, 23],\n",
    "                   'degisken2': [100, 542, 345, 676, 563, 342]},\n",
    "                   columns = ['gruplar', 'degisken1', 'degisken2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "569f1513",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gruplar</th>\n",
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       "      <th>0</th>\n",
       "      <td>A</td>\n",
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       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>43</td>\n",
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       "      <th>2</th>\n",
       "      <td>C</td>\n",
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       "      <td>345</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>56</td>\n",
       "      <td>676</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>34</td>\n",
       "      <td>563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gruplar  degisken1  degisken2\n",
       "0       A         10        100\n",
       "1       B         43        542\n",
       "2       C         65        345\n",
       "3       A         56        676\n",
       "4       B         34        563\n",
       "5       C         23        342"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "22393597",
   "metadata": {},
   "outputs": [],
   "source": [
    "#aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f01b0176",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>33.0</td>\n",
       "      <td>388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>38.5</td>\n",
       "      <td>552.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>44.0</td>\n",
       "      <td>343.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         degisken1  degisken2\n",
       "gruplar                      \n",
       "A             33.0      388.0\n",
       "B             38.5      552.5\n",
       "C             44.0      343.5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby('gruplar').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fc0e0cf9",
   "metadata": {},
   "outputs": [
    {
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       "      <th colspan=\"3\" halign=\"left\">degisken2</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>33.0</td>\n",
       "      <td>56</td>\n",
       "      <td>100</td>\n",
       "      <td>388.0</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>34</td>\n",
       "      <td>38.5</td>\n",
       "      <td>43</td>\n",
       "      <td>542</td>\n",
       "      <td>552.5</td>\n",
       "      <td>563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>23</td>\n",
       "      <td>44.0</td>\n",
       "      <td>65</td>\n",
       "      <td>342</td>\n",
       "      <td>343.5</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        degisken1            degisken2            \n",
       "              min median max       min median  max\n",
       "gruplar                                           \n",
       "A              10   33.0  56       100  388.0  676\n",
       "B              34   38.5  43       542  552.5  563\n",
       "C              23   44.0  65       342  343.5  345"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby('gruplar').aggregate(['min', np.median, max])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4073468a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gruplar</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>676</td>\n",
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       "      <th>B</th>\n",
       "      <td>34</td>\n",
       "      <td>563</td>\n",
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       "      <th>C</th>\n",
       "      <td>23</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         degisken1  degisken2\n",
       "gruplar                      \n",
       "A               10        676\n",
       "B               34        563\n",
       "C               23        345"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby('gruplar').aggregate({'degisken1': min, 'degisken2': max})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0c8ea64c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e52041e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_func(x):\n",
    "    return x['degisken1'].std() > 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "16b2d132",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>gruplar</th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
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       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>65</td>\n",
       "      <td>345</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>56</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gruplar  degisken1  degisken2\n",
       "0       A         10        100\n",
       "2       C         65        345\n",
       "3       A         56        676\n",
       "5       C         23        342"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby('gruplar').filter(filter_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1eb7cf00",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "84b17cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     90\n",
       "1    387\n",
       "2    585\n",
       "3    504\n",
       "4    306\n",
       "5    207\n",
       "Name: degisken1, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['degisken1']*9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f122e243",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f7/0d9ymj8529q_b5n4c0ddsl780000gn/T/ipykernel_51713/4173768990.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df3.transform(lambda x: x-x.mean())\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "      <th>gruplar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-28.5</td>\n",
       "      <td>-328.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.5</td>\n",
       "      <td>-83.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.5</td>\n",
       "      <td>248.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-4.5</td>\n",
       "      <td>135.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-15.5</td>\n",
       "      <td>-86.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   degisken1  degisken2 gruplar\n",
       "0      -28.5     -328.0     NaN\n",
       "1        4.5      114.0     NaN\n",
       "2       26.5      -83.0     NaN\n",
       "3       17.5      248.0     NaN\n",
       "4       -4.5      135.0     NaN\n",
       "5      -15.5      -86.0     NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.transform(lambda x: x-x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "87edb72a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f7/0d9ymj8529q_b5n4c0ddsl780000gn/T/ipykernel_51713/3588867921.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df3.transform(lambda x: x-x.mean() / x.std())\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>degisken1</th>\n",
       "      <th>degisken2</th>\n",
       "      <th>gruplar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.122063</td>\n",
       "      <td>97.934049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41.122063</td>\n",
       "      <td>539.934049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.122063</td>\n",
       "      <td>342.934049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.122063</td>\n",
       "      <td>673.934049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.122063</td>\n",
       "      <td>560.934049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.122063</td>\n",
       "      <td>339.934049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   degisken1   degisken2 gruplar\n",
       "0   8.122063   97.934049     NaN\n",
       "1  41.122063  539.934049     NaN\n",
       "2  63.122063  342.934049     NaN\n",
       "3  54.122063  673.934049     NaN\n",
       "4  32.122063  560.934049     NaN\n",
       "5  21.122063  339.934049     NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.transform(lambda x: x-x.mean() / x.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4ab98569",
   "metadata": {},
   "outputs": [],
   "source": [
    "#apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "22379f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.DataFrame({'degisken1': [10, 43, 65, 56, 34, 23],\n",
    "                   'degisken2': [100, 542, 345, 676, 563, 342]},\n",
    "                   columns = ['degisken1', 'degisken2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b27afd46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "degisken1     231\n",
       "degisken2    2568\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.apply(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "523957c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "degisken1     38.5\n",
       "degisken2    428.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c2978d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1595f419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9233dc42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('sex')['survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bdc361f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>First</th>\n",
       "      <td>0.968085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>0.921053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>First</th>\n",
       "      <td>0.368852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>0.157407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               survived\n",
       "sex    class           \n",
       "female First   0.968085\n",
       "       Second  0.921053\n",
       "       Third   0.500000\n",
       "male   First   0.368852\n",
       "       Second  0.157407\n",
       "       Third   0.135447"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['sex', 'class'])[['survived']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6b095258",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead tr th {\n",
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       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived                    \n",
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['sex', 'class'])[['survived']].aggregate('mean').unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fc3f45ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1a6156ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index = 'sex', columns = 'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d82a848b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a19a901e",
   "metadata": {},
   "outputs": [],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "86a2c886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (18.0, 90.0]\n",
       "1      (18.0, 90.0]\n",
       "2      (18.0, 90.0]\n",
       "3      (18.0, 90.0]\n",
       "4      (18.0, 90.0]\n",
       "           ...     \n",
       "886    (18.0, 90.0]\n",
       "887    (18.0, 90.0]\n",
       "888             NaN\n",
       "889    (18.0, 90.0]\n",
       "890    (18.0, 90.0]\n",
       "Name: age, Length: 891, dtype: category\n",
       "Categories (2, interval[int64, right]): [(0, 18] < (18, 90]]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4a6419a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
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       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 90]</th>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 90]</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class               First    Second     Third\n",
       "sex    age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 90]  0.972973  0.900000  0.423729\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 90]  0.375000  0.071429  0.133663"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', ['sex', age], 'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca744877",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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