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": 136,
   "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": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <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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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</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>113803</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
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       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
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       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
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       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>126</th>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>McMahon, Mr. Martin</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370372</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <th>127</th>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Madsen, Mr. Fridtjof Arne</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C 17369</td>\n",
       "      <td>7.1417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>F E69</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ekstrom, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347061</td>\n",
       "      <td>6.9750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Drazenoic, Mr. Jozef</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349241</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Coelho, Mr. Domingos Fernandeo</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101307</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Robins, Mrs. Alexander A (Grace Charity Laury)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 3337</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Weisz, Mrs. Leopold (Mathilde Francoise Pede)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>228414</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Sobey, Mr. Samuel James Hayden</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 29178</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Richard, Mr. Emile</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2133</td>\n",
       "      <td>15.0458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Newsom, Miss. Helen Monypeny</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11752</td>\n",
       "      <td>26.2833</td>\n",
       "      <td>D47</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mr. Jacques Heath</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0</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>138</th>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Osen, Mr. Olaf Elon</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.2167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Giglio, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17593</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>B86</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Nysten, Miss. Anna Sofia</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347081</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ...</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101279</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Burke, Mr. Jeremiah</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365222</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Andrew, Mr. Edgardo Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>231945</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Nicholls, Mr. Joseph Charles</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 33112</td>\n",
       "      <td>36.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. August Edvard (\"Wennerstrom\")</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350043</td>\n",
       "      <td>7.7958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ford, Miss. Robina Maggie \"Ruby\"</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6608</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Navratil, Mr. Michel (\"Louis M Hoffman\")</td>\n",
       "      <td>male</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>230080</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>F2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Byles, Rev. Thomas Roussel Davids</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244310</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Bateman, Rev. Robert James</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.O.P. 1166</td>\n",
       "      <td>12.5250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "      <td>A/5. 851</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.3125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <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",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "126          127         0       3   \n",
       "127          128         1       3   \n",
       "128          129         1       3   \n",
       "129          130         0       3   \n",
       "130          131         0       3   \n",
       "131          132         0       3   \n",
       "132          133         0       3   \n",
       "133          134         1       2   \n",
       "134          135         0       2   \n",
       "135          136         0       2   \n",
       "136          137         1       1   \n",
       "137          138         0       1   \n",
       "138          139         0       3   \n",
       "139          140         0       1   \n",
       "140          141         0       3   \n",
       "141          142         1       3   \n",
       "142          143         1       3   \n",
       "143          144         0       3   \n",
       "144          145         0       2   \n",
       "145          146         0       2   \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  \\\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",
       "5                                     Moran, Mr. James    male   NaN      0   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                       Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                      Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                                Rice, Master. Eugene    male   2.0      4   \n",
       "17                        Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                             Masselmani, Mrs. Fatima  female   NaN      0   \n",
       "20                                Fynney, Mr. Joseph J    male  35.0      0   \n",
       "21                               Beesley, Mr. Lawrence    male  34.0      0   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "24                       Palsson, Miss. Torborg Danira  female   8.0      3   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n",
       "26                             Emir, Mr. Farred Chehab    male   NaN      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN      0   \n",
       "29                                 Todoroff, Mr. Lalio    male   NaN      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "126                                McMahon, Mr. Martin    male   NaN      0   \n",
       "127                          Madsen, Mr. Fridtjof Arne    male  24.0      0   \n",
       "128                                  Peter, Miss. Anna  female   NaN      1   \n",
       "129                                 Ekstrom, Mr. Johan    male  45.0      0   \n",
       "130                               Drazenoic, Mr. Jozef    male  33.0      0   \n",
       "131                     Coelho, Mr. Domingos Fernandeo    male  20.0      0   \n",
       "132     Robins, Mrs. Alexander A (Grace Charity Laury)  female  47.0      1   \n",
       "133      Weisz, Mrs. Leopold (Mathilde Francoise Pede)  female  29.0      1   \n",
       "134                     Sobey, Mr. Samuel James Hayden    male  25.0      0   \n",
       "135                                 Richard, Mr. Emile    male  23.0      0   \n",
       "136                       Newsom, Miss. Helen Monypeny  female  19.0      0   \n",
       "137                        Futrelle, Mr. Jacques Heath    male  37.0      1   \n",
       "138                                Osen, Mr. Olaf Elon    male  16.0      0   \n",
       "139                                 Giglio, Mr. Victor    male  24.0      0   \n",
       "140                      Boulos, Mrs. Joseph (Sultana)  female   NaN      0   \n",
       "141                           Nysten, Miss. Anna Sofia  female  22.0      0   \n",
       "142  Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ...  female  24.0      1   \n",
       "143                                Burke, Mr. Jeremiah    male  19.0      0   \n",
       "144                         Andrew, Mr. Edgardo Samuel    male  18.0      0   \n",
       "145                       Nicholls, Mr. Joseph Charles    male  19.0      1   \n",
       "146       Andersson, Mr. August Edvard (\"Wennerstrom\")    male  27.0      0   \n",
       "147                   Ford, Miss. Robina Maggie \"Ruby\"  female   9.0      2   \n",
       "148           Navratil, Mr. Michel (\"Louis M Hoffman\")    male  36.5      0   \n",
       "149                  Byles, Rev. Thomas Roussel Davids    male  42.0      0   \n",
       "150                         Bateman, Rev. Robert James    male  51.0      0   \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",
       "5        0              330877    8.4583          NaN        Q  \n",
       "6        0               17463   51.8625          E46        S  \n",
       "7        1              349909   21.0750          NaN        S  \n",
       "8        2              347742   11.1333          NaN        S  \n",
       "9        0              237736   30.0708          NaN        C  \n",
       "10       1             PP 9549   16.7000           G6        S  \n",
       "11       0              113783   26.5500         C103        S  \n",
       "12       0           A/5. 2151    8.0500          NaN        S  \n",
       "13       5              347082   31.2750          NaN        S  \n",
       "14       0              350406    7.8542          NaN        S  \n",
       "15       0              248706   16.0000          NaN        S  \n",
       "16       1              382652   29.1250          NaN        Q  \n",
       "17       0              244373   13.0000          NaN        S  \n",
       "18       0              345763   18.0000          NaN        S  \n",
       "19       0                2649    7.2250          NaN        C  \n",
       "20       0              239865   26.0000          NaN        S  \n",
       "21       0              248698   13.0000          D56        S  \n",
       "22       0              330923    8.0292          NaN        Q  \n",
       "23       0              113788   35.5000           A6        S  \n",
       "24       1              349909   21.0750          NaN        S  \n",
       "25       5              347077   31.3875          NaN        S  \n",
       "26       0                2631    7.2250          NaN        C  \n",
       "27       2               19950  263.0000  C23 C25 C27        S  \n",
       "28       0              330959    7.8792          NaN        Q  \n",
       "29       0              349216    7.8958          NaN        S  \n",
       "..     ...                 ...       ...          ...      ...  \n",
       "126      0              370372    7.7500          NaN        Q  \n",
       "127      0             C 17369    7.1417          NaN        S  \n",
       "128      1                2668   22.3583        F E69        C  \n",
       "129      0              347061    6.9750          NaN        S  \n",
       "130      0              349241    7.8958          NaN        C  \n",
       "131      0  SOTON/O.Q. 3101307    7.0500          NaN        S  \n",
       "132      0           A/5. 3337   14.5000          NaN        S  \n",
       "133      0              228414   26.0000          NaN        S  \n",
       "134      0          C.A. 29178   13.0000          NaN        S  \n",
       "135      0       SC/PARIS 2133   15.0458          NaN        C  \n",
       "136      2               11752   26.2833          D47        S  \n",
       "137      0              113803   53.1000         C123        S  \n",
       "138      0                7534    9.2167          NaN        S  \n",
       "139      0            PC 17593   79.2000          B86        C  \n",
       "140      2                2678   15.2458          NaN        C  \n",
       "141      0              347081    7.7500          NaN        S  \n",
       "142      0    STON/O2. 3101279   15.8500          NaN        S  \n",
       "143      0              365222    6.7500          NaN        Q  \n",
       "144      0              231945   11.5000          NaN        S  \n",
       "145      1          C.A. 33112   36.7500          NaN        S  \n",
       "146      0              350043    7.7958          NaN        S  \n",
       "147      2          W./C. 6608   34.3750          NaN        S  \n",
       "148      2              230080   26.0000           F2        S  \n",
       "149      0              244310   13.0000          NaN        S  \n",
       "150      0         S.O.P. 1166   12.5250          NaN        S  \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": 139,
     "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": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>Navratil, Mr. Michel (\"Louis M Hoffman\")</td>\n",
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       "      <td>230080</td>\n",
       "      <td>26.0000</td>\n",
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       "      <td>Byles, Rev. Thomas Roussel Davids</td>\n",
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       "      <td>Bateman, Rev. Robert James</td>\n",
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       "      <td>S.O.P. 1166</td>\n",
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       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
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       "      <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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       "      <th>152</th>\n",
       "      <td>153</td>\n",
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       "      <td>Meo, Mr. Alfonzo</td>\n",
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       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.5</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</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": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
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       "      <td>31</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
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       "      <th>unique</th>\n",
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       "      <td>2</td>\n",
       "      <td>145</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>freq</th>\n",
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       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
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       "</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": 163,
     "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": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>126.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
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       "    <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",
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       "      <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": 164,
     "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",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>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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       "    .dataframe thead th {\n",
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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>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": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Andersson, Mr. August Edvard (\"Wennerstrom\")',\n",
       " 'Andrew, Mr. Edgardo Samuel',\n",
       " 'Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)']"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(titanic_train[\"Name\"])[5:10:2]# 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": 171,
   "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>test</th>\n",
       "      <th>sudh</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>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",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>sdffs</td>\n",
       "    </tr>\n",
       "    <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",
       "      <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>0</td>\n",
       "      <td>sdffs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</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>0</td>\n",
       "      <td>sdffs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "                                                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",
       "   Parch            Ticket     Fare Cabin Embarked  test   sudh  \n",
       "0      0         A/5 21171   7.2500   NaN        S     0  sdffs  \n",
       "1      0          PC 17599  71.2833   C85        C     0  sdffs  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S     0  sdffs  \n",
       "3      0            113803  53.1000  C123        S     0  sdffs  \n",
       "4      0            373450   8.0500   NaN        S     0  sdffs  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"sudh\"]=\"sdffs\"\n",
    "titanic_train.head()"
   ]
  },
  {
   "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",
       "    }\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>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": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[90, dfsf, 90, dfsf, 90, ..., dfsf, 90, 90, 90, dfsf]\n",
       "Length: 156\n",
       "Categories (3, object): [dfsf, 9, 90]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_Pclass = pd.Categorical(titanic_train[\"Pclass\"])\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": 184,
   "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": 184,
     "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": 186,
   "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>test</th>\n",
       "      <th>sudh</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>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",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</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>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>C</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</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>Heikkinen, Miss. Laina</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>n</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</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>Futrelle, Mrs. Jacques Heath (Lily May Peel)</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>C</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</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>Allen, Mr. William Henry</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>n</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "                                                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",
       "   Parch            Ticket     Fare Cabin Embarked  test   sudh Cabin1  \n",
       "0      0         A/5 21171   7.2500     n        S     0  sdffs      n  \n",
       "1      0          PC 17599  71.2833     C        C     0  sdffs      C  \n",
       "2      0  STON/O2. 3101282   7.9250     n        S     0  sdffs      n  \n",
       "3      0            113803  53.1000     C        S     0  sdffs      C  \n",
       "4      0            373450   8.0500     n        S     0  sdffs      n  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train[\"Cabin\"] = 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": 189,
   "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]),)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = np.where(titanic_train[\"Age\"].isnull() == True)\n",
    "missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([27, 88]),)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(titanic_train[\"Fare\"]==max(titanic_train[\"Fare\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "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>test</th>\n",
       "      <th>sudh</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>dfsf</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</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>C</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>dfsf</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</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>C</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>sdffs</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived Pclass                            Name     Sex  \\\n",
       "27           28         0   dfsf  Fortune, Mr. Charles Alexander    male   \n",
       "88           89         1   dfsf      Fortune, Miss. Mabel Helen  female   \n",
       "\n",
       "     Age  SibSp  Parch Ticket   Fare Cabin Embarked  test   sudh Cabin1  \n",
       "27  19.0      3      2  19950  263.0     C        S     0  sdffs      C  \n",
       "88  23.0      3      2  19950  263.0     C        S     0  sdffs      C  "
      ]
     },
     "execution_count": 191,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([59, 71]),)"
      ]
     },
     "execution_count": 194,
     "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": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>test</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>Goodwin, Master. William Frederick</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>0</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>Goodwin, Miss. Lillian Amy</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>0</td>\n",
       "      <td>n</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                                Name     Sex  \\\n",
       "59           60         0       3  Goodwin, Master. William Frederick    male   \n",
       "71           72         0       3          Goodwin, Miss. Lillian Amy  female   \n",
       "\n",
       "     Age  SibSp  Parch   Ticket  Fare Cabin Embarked  test Cabin1  Family  \n",
       "59  11.0      5      2  CA 2144  46.9   NaN        S     0      n       7  \n",
       "71  16.0      5      2  CA 2144  46.9   NaN        S     0      n       7  "
      ]
     },
     "execution_count": 135,
     "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": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: ['a', 'b', 'c']\n",
      "My data: [10, 20, 30]\n",
      "Dictionary: {'a': 10, 'b': 20, 'c': 30}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "labels = ['a','b','c']\n",
    "my_data = [10,20,30]\n",
    "arr = np.array(my_data)\n",
    "d = {'a':10,'b':20,'c':30}\n",
    "\n",
    "print (\"Labels:\", labels)\n",
    "print(\"My data:\", my_data)\n",
    "print(\"Dictionary:\", d)\n",
    "pd.Series(my_data, index=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(my_data, index=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Holding numerical data\n",
      "-------------------------\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nHolding numerical data\\n\",'-'*25, sep='')\n",
    "print(pd.Series(arr)[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Holding text labels\n",
      "--------------------\n",
      "0    a\n",
      "1    b\n",
      "2    c\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nHolding text labels\\n\",'-'*20, sep='')\n",
    "print(pd.Series(labels))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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>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": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d = {'a':\"kjhk\",'b':20,'c':30}\n",
    "d.items\n",
    "pd.DataFrame(d,index = ['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Holding objects from a dictionary\n",
      "----------------------------------------\n",
      "0             <class 'type'>\n",
      "1    <built-in function sum>\n",
      "2    <built-in function max>\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nHolding objects from a dictionary\\n\",'-'*40, sep='')\n",
    "print(pd.Series([type, sum, max]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "4    2\n",
       "6    3\n",
       "8    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series([1,2,3,4],index = [2,4,6,8])\n",
    "ser2 = pd.Series([1,2,5,4],['CA', 'OR', 'NV', 'AZ'])\n",
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "6    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1[0:3:2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Indexing by number (positional value in the series)\n",
      "----------------------------------------------------\n",
      "Value for CA in ser1: 1\n",
      "Value for AZ in ser1: 4\n",
      "Value for NV in ser2: 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CA    1\n",
       "OR    2\n",
       "CO    3\n",
       "AZ    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"\\nIndexing by number (positional value in the series)\\n\",'-'*52, sep='')\n",
    "print(\"Value for CA in ser1:\", ser1[0])\n",
    "print(\"Value for AZ in ser1:\", ser1[3])\n",
    "print(\"Value for NV in ser2:\", ser2[2])\n",
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Indexing by a range\n",
      "-------------------------\n",
      "Value for OR, CO, and AZ in ser1:\n",
      "100    1\n",
      "CO     3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nIndexing by a range\\n\",'-'*25, sep='')\n",
    "print (\"Value for OR, CO, and AZ in ser1:\\n\", ser1[0:3:2], sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AZ    NaN\n",
       "CA    2.0\n",
       "CA    5.0\n",
       "CO    NaN\n",
       "NV    NaN\n",
       "OR    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series([1,2,3,4],['CA', 'OR', 'CO', 'CA'])\n",
    "ser2 = pd.Series([1,2,5,4],['CA', 'NV', 'AZ','OR'])\n",
    "ser3 = ser1+ser2\n",
    "ser3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After adding the two series, the result looks like this...\n",
      "-----------------------------------------------------------\n",
      "AZ    NaN\n",
      "CA    2.0\n",
      "CA    5.0\n",
      "CO    NaN\n",
      "NV    NaN\n",
      "OR    6.0\n",
      "dtype: float64\n",
      "\n",
      "Python tries to add values where it finds common index name, and puts NaN where indices are missing\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nAfter adding the two series, the result looks like this...\\n\",'-'*59, sep='')\n",
    "print(ser3)\n",
    "print(\"\\nPython tries to add values where it finds common index name, and puts NaN where indices are missing\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The idea works even for multiplication...\n",
      "-------------------------------------------\n",
      "AZ    NaN\n",
      "CA    1.0\n",
      "CA    4.0\n",
      "CO    NaN\n",
      "NV    NaN\n",
      "OR    8.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (\"\\nThe idea works even for multiplication...\\n\",'-'*43, sep='')\n",
    "print (ser1*ser2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>0.150705</td>\n",
       "      <td>-0.446910</td>\n",
       "      <td>1.669238</td>\n",
       "      <td>-1.138071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.462929</td>\n",
       "      <td>0.129871</td>\n",
       "      <td>0.128628</td>\n",
       "      <td>1.865870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.783021</td>\n",
       "      <td>0.561695</td>\n",
       "      <td>-1.608215</td>\n",
       "      <td>-0.997146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.124639</td>\n",
       "      <td>0.626070</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.075665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.133687</td>\n",
       "      <td>1.988570</td>\n",
       "      <td>0.416920</td>\n",
       "      <td>-0.729497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.150705 -0.446910  1.669238 -1.138071\n",
       "B  0.462929  0.129871  0.128628  1.865870\n",
       "C  0.783021  0.561695 -1.608215 -0.997146\n",
       "D  1.124639  0.626070 -0.129314 -0.075665\n",
       "E  0.133687  1.988570  0.416920 -0.729497"
      ]
     },
     "execution_count": 47,
     "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W    0.783021\n",
       "X    0.561695\n",
       "Y   -1.608215\n",
       "Z   -0.997146\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Type of the pair of columns: <class 'pandas.core.frame.DataFrame'>\n",
      "\n",
      "So, for more than one column, the object turns into a DataFrame\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nType of the pair of columns: \", type(df[['X','Z']]), sep='')\n",
    "print (\"\\nSo, for more than one column, the object turns into a DataFrame\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 'X' column accessed by DOT method (NOT recommended)\n",
      "-------------------------------------------------------\n",
      "A   -0.446910\n",
      "B    0.129871\n",
      "C    0.561695\n",
      "D    0.626070\n",
      "E    1.988570\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": 51,
   "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  0.150705 -0.446910  1.669238 -1.138071 -1.584981             -1.584981\n",
      "B  0.462929  0.129871  0.128628  1.865870  1.995741              1.995741\n",
      "C  0.783021  0.561695 -1.608215 -0.997146 -0.435451             -0.435451\n",
      "D  1.124639  0.626070 -0.129314 -0.075665  0.550405              0.550405\n",
      "E  0.133687  1.988570  0.416920 -0.729497  1.259073              1.259073\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": 52,
   "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>0.150705</td>\n",
       "      <td>-0.446910</td>\n",
       "      <td>1.669238</td>\n",
       "      <td>-1.138071</td>\n",
       "      <td>-1.584981</td>\n",
       "      <td>-1.584981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.462929</td>\n",
       "      <td>0.129871</td>\n",
       "      <td>0.128628</td>\n",
       "      <td>1.865870</td>\n",
       "      <td>1.995741</td>\n",
       "      <td>1.995741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.783021</td>\n",
       "      <td>0.561695</td>\n",
       "      <td>-1.608215</td>\n",
       "      <td>-0.997146</td>\n",
       "      <td>-0.435451</td>\n",
       "      <td>-0.435451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.124639</td>\n",
       "      <td>0.626070</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.075665</td>\n",
       "      <td>0.550405</td>\n",
       "      <td>0.550405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.133687</td>\n",
       "      <td>1.988570</td>\n",
       "      <td>0.416920</td>\n",
       "      <td>-0.729497</td>\n",
       "      <td>1.259073</td>\n",
       "      <td>1.259073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z       New  New (Sum of X and Z)\n",
       "A  0.150705 -0.446910  1.669238 -1.138071 -1.584981             -1.584981\n",
       "B  0.462929  0.129871  0.128628  1.865870  1.995741              1.995741\n",
       "C  0.783021  0.561695 -1.608215 -0.997146 -0.435451             -0.435451\n",
       "D  1.124639  0.626070 -0.129314 -0.075665  0.550405              0.550405\n",
       "E  0.133687  1.988570  0.416920 -0.729497  1.259073              1.259073"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A column is dropped by using df.drop() method\n",
      " -------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nA column is dropped by using df.drop() method\\n\",'-'*55, sep=' ')\n",
    "df.drop(\"C\",inplace=True) # Notice the axis=1 option, axis = 0 is default, so one has to change it to 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>0.150705</td>\n",
       "      <td>-0.446910</td>\n",
       "      <td>1.669238</td>\n",
       "      <td>-1.138071</td>\n",
       "      <td>-1.584981</td>\n",
       "      <td>-1.584981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.462929</td>\n",
       "      <td>0.129871</td>\n",
       "      <td>0.128628</td>\n",
       "      <td>1.865870</td>\n",
       "      <td>1.995741</td>\n",
       "      <td>1.995741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.124639</td>\n",
       "      <td>0.626070</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.075665</td>\n",
       "      <td>0.550405</td>\n",
       "      <td>0.550405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.133687</td>\n",
       "      <td>1.988570</td>\n",
       "      <td>0.416920</td>\n",
       "      <td>-0.729497</td>\n",
       "      <td>1.259073</td>\n",
       "      <td>1.259073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z       New  New (Sum of X and Z)\n",
       "A  0.150705 -0.446910  1.669238 -1.138071 -1.584981             -1.584981\n",
       "B  0.462929  0.129871  0.128628  1.865870  1.995741              1.995741\n",
       "D  1.124639  0.626070 -0.129314 -0.075665  0.550405              0.550405\n",
       "E  0.133687  1.988570  0.416920 -0.729497  1.259073              1.259073"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A row (index) is dropped by using df.drop() method and axis=0\n",
      "-----------------------------------------------------------------\n",
      "          W         X         Y         Z       New  New (Sum of X and Z)\n",
      "B  0.462929  0.129871  0.128628  1.865870  1.995741              1.995741\n",
      "D  1.124639  0.626070 -0.129314 -0.075665  0.550405              0.550405\n",
      "E  0.133687  1.988570  0.416920 -0.729497  1.259073              1.259073\n"
     ]
    }
   ],
   "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": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "An in-place change can be done by making inplace=True in the drop method\n",
      "---------------------------------------------------------------------------\n",
      "          W         X         Y         Z       New\n",
      "A  0.150705 -0.446910  1.669238 -1.138071 -1.584981\n",
      "B  0.462929  0.129871  0.128628  1.865870  1.995741\n",
      "D  1.124639  0.626070 -0.129314 -0.075665  0.550405\n",
      "E  0.133687  1.988570  0.416920 -0.729497  1.259073\n"
     ]
    }
   ],
   "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": 57,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.150705</td>\n",
       "      <td>-0.446910</td>\n",
       "      <td>1.669238</td>\n",
       "      <td>-1.138071</td>\n",
       "      <td>-1.584981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.462929</td>\n",
       "      <td>0.129871</td>\n",
       "      <td>0.128628</td>\n",
       "      <td>1.865870</td>\n",
       "      <td>1.995741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.124639</td>\n",
       "      <td>0.626070</td>\n",
       "      <td>-0.129314</td>\n",
       "      <td>-0.075665</td>\n",
       "      <td>0.550405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.133687</td>\n",
       "      <td>1.988570</td>\n",
       "      <td>0.416920</td>\n",
       "      <td>-0.729497</td>\n",
       "      <td>1.259073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z       New\n",
       "A  0.150705 -0.446910  1.669238 -1.138071 -1.584981\n",
       "B  0.462929  0.129871  0.128628  1.865870  1.995741\n",
       "D  1.124639  0.626070 -0.129314 -0.075665  0.550405\n",
       "E  0.133687  1.988570  0.416920 -0.729497  1.259073"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Selecting/indexing Rows\n",
    "#* Label-based 'loc' method\n",
    "#* Index (numeric) 'iloc' method\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Label-based 'loc' method can be used for selecting row(s)\n",
      "------------------------------------------------------------\n",
      "\n",
      "Single row\n",
      "\n",
      "W      0.133687\n",
      "X      1.988570\n",
      "Y      0.416920\n",
      "Z     -0.729497\n",
      "New    1.259073\n",
      "Name: E, dtype: float64\n"
     ]
    }
   ],
   "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])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Multiple rows\n",
      "\n",
      "          W         X         Y        Z       New\n",
      "B  0.462929  0.129871  0.128628  1.86587  1.995741\n",
      "C       NaN       NaN       NaN      NaN       NaN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sudhanshukumar/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "print(\"\\nMultiple rows\\n\")\n",
    "print(df.loc[['B','C']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Index position based 'iloc' method can be used for selecting row(s)\n",
      "----------------------------------------------------------------------\n",
      "\n",
      "Single row\n",
      "\n",
      "W      1.124639\n",
      "X      0.626070\n",
      "Y     -0.129314\n",
      "Z     -0.075665\n",
      "New    0.550405\n",
      "Name: D, dtype: float64\n"
     ]
    }
   ],
   "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])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Multiple rows\n",
      "\n",
      "          W         X         Y         Z       New\n",
      "B  0.462929  0.129871  0.128628  1.865870  1.995741\n",
      "D  1.124639  0.626070 -0.129314 -0.075665  0.550405\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nMultiple rows\\n\")\n",
    "print(df.iloc[[1,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Subsetting DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The DatFrame\n",
      "---------------------------------------------\n",
      "          W         X         Y         Z\n",
      "A -0.320697  1.586737 -0.962361  0.984044\n",
      "B -0.236481 -0.520044 -0.137304 -1.719868\n",
      "C -0.415743 -0.280577  1.293790 -0.495861\n",
      "D -0.564725 -1.331318 -1.098884  0.385147\n",
      "E  1.079266  1.101438  1.097766  0.452282\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nThe DatFrame\\n\",'-'*45, sep='')\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Element at row 'B' and column 'Y' is\n",
      "\n",
      "          Y         W\n",
      "B -0.137304 -0.236481\n",
      "C  1.293790 -0.415743\n"
     ]
    }
   ],
   "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": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Subset comprising of rows B and D, and columns W and Y, is\n",
      "\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.236481</td>\n",
       "      <td>-0.520044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.415743</td>\n",
       "      <td>-0.280577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.564725</td>\n",
       "      <td>-1.331318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X\n",
       "B -0.236481 -0.520044\n",
       "C -0.415743 -0.280577\n",
       "D -0.564725 -1.331318"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          W         X         Y         Z\n",
      "B -0.236481 -0.520044 -0.137304 -1.719868\n",
      "C -0.415743 -0.280577  1.293790 -0.495861\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[['B','C'],])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The DataFrame\n",
      "---------------------------------------------\n",
      "          W         X         Y         Z\n",
      "A -0.320697  1.586737 -0.962361  0.984044\n",
      "B -0.236481 -0.520044 -0.137304 -1.719868\n",
      "C -0.415743 -0.280577  1.293790 -0.495861\n",
      "D -0.564725 -1.331318 -1.098884  0.385147\n",
      "E  1.079266  1.101438  1.097766  0.452282\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nThe DataFrame\\n\",'-'*45, sep='')\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Boolean DataFrame(s) where we are checking if the values are greater than 0\n",
      "---------------------------------------------------------------------------\n",
      "       W      X      Y      Z\n",
      "A  False   True  False   True\n",
      "B  False  False  False  False\n",
      "C  False  False   True  False\n",
      "D  False  False  False   True\n",
      "E   True   True   True   True\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nBoolean DataFrame(s) where we are checking if the values are greater than 0\\n\",'-'*75, sep='')\n",
    "print(df>0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "       W      X      Y      Z\n",
      "A  False   True  False   True\n",
      "B  False  False  False  False\n",
      "C  False  False   True  False\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "print(df.loc[['A','B','C']]>0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataFrame indexed by boolean dataframe\n",
      "---------------------------------------------\n",
      "          W         X         Y         Z\n",
      "A       NaN  1.586737       NaN  0.984044\n",
      "B       NaN       NaN       NaN       NaN\n",
      "C       NaN       NaN  1.293790       NaN\n",
      "D       NaN       NaN       NaN  0.385147\n",
      "E  1.079266  1.101438  1.097766  0.452282\n"
     ]
    }
   ],
   "source": [
    "booldf = df>0\n",
    "print(\"\\nDataFrame indexed by boolean dataframe\\n\",'-'*45, sep='')\n",
    "print(df[booldf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "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": 73,
   "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": 74,
   "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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Height']>65]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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\",'-'*35, sep='')\n",
    "print(df[df['Height']>65])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Height']>65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "booldf1 = df['Height']>65\n",
    "booldf2 = df['Weight']>145\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": 79,
   "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": 80,
   "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": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 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(\"\\nThe DataFrame\\n\",'-'*25, sep='')\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 83,
     "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": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Adding a new column 'Profession'\n",
      "---------------------------------------------\n",
      "   Age  Height  Weight Profession\n",
      "A   22      66     140    Student\n",
      "B   42      70     148    Teacher\n",
      "C   30      62     125   Engineer\n",
      "D   35      68     160     Doctor\n",
      "E   25      62     152      Nurse\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nAdding a new column 'Profession'\\n\",'-'*45, sep='')\n",
    "df['Profession'] = \"Student Teacher Engineer Doctor Nurse\".split()\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Setting 'Profession' column as index\n",
      "---------------------------------------------\n",
      "            Age  Height  Weight\n",
      "Profession                     \n",
      "Student      22      66     140\n",
      "Teacher      42      70     148\n",
      "Engineer     30      62     125\n",
      "Doctor       35      68     160\n",
      "Nurse        25      62     152\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nSetting 'Profession' column as index\\n\",'-'*45, sep='')\n",
    "print (df.set_index('Profession'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": 87,
   "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": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Index hierarchy\n",
      "-------------------------\n",
      "MultiIndex(levels=[['G1', 'G2'], [1, 2, 3]],\n",
      "           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])\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": 89,
   "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": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Creating DataFrame with multi-index\n",
      "-------------------------------------\n",
      "        A    B    C\n",
      "G1 1 -0.0  1.0 -0.0\n",
      "   2  2.0 -1.0  0.0\n",
      "   3 -0.0 -0.0  1.0\n",
      "G2 1  0.0 -1.0  0.0\n",
      "   2  1.0  0.0  1.0\n",
      "   3  0.0 -1.0 -0.0\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": 91,
   "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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