pandas-titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas on the Titanic dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple analysis of the [Titanic dataset](https://www.kaggle.com/c/titanic/data) with Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sibsp = nb of siblings / spouses\n",
    "# parch = nb of parents / children\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# info\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         2\n",
       "pclass           3\n",
       "sex              2\n",
       "age             88\n",
       "sibsp            7\n",
       "parch            7\n",
       "fare           248\n",
       "embarked         3\n",
       "class            3\n",
       "who              3\n",
       "adult_male       2\n",
       "deck             7\n",
       "embark_town      3\n",
       "alive            2\n",
       "alone            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived       0.383838\n",
       "pclass         2.308642\n",
       "age           29.699118\n",
       "sibsp          0.523008\n",
       "parch          0.381594\n",
       "fare          32.204208\n",
       "adult_male     0.602694\n",
       "alone          0.602694\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <th>sibsp</th>\n",
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       "      <th>803</th>\n",
       "      <td>1</td>\n",
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       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
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       "      <td>8.5167</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "803         1       3    male  0.42      0      1   8.5167        C   Third   \n",
       "755         1       2    male  0.67      1      1  14.5000        S  Second   \n",
       "644         1       3  female  0.75      2      1  19.2583        C   Third   \n",
       "469         1       3  female  0.75      2      1  19.2583        C   Third   \n",
       "78          1       2    male  0.83      0      2  29.0000        S  Second   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "859         0       3    male   NaN      0      0   7.2292        C   Third   \n",
       "863         0       3  female   NaN      8      2  69.5500        S   Third   \n",
       "868         0       3    male   NaN      0      0   9.5000        S   Third   \n",
       "878         0       3    male   NaN      0      0   7.8958        S   Third   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "803  child       False  NaN    Cherbourg   yes  False  \n",
       "755  child       False  NaN  Southampton   yes  False  \n",
       "644  child       False  NaN    Cherbourg   yes  False  \n",
       "469  child       False  NaN    Cherbourg   yes  False  \n",
       "78   child       False  NaN  Southampton   yes  False  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "859    man        True  NaN    Cherbourg    no   True  \n",
       "863  woman       False  NaN  Southampton    no  False  \n",
       "868    man        True  NaN  Southampton    no   True  \n",
       "878    man        True  NaN  Southampton    no   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(8,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('sex')['survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pclass         1         2         3\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table('survived', index='sex', columns='pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pclass         1         2         3       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table('survived', index='sex', columns='pclass', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_cat = pd.cut(df['age'], [0, 18, 25, 40, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (18.0, 25.0]\n",
       "1      (25.0, 40.0]\n",
       "2      (25.0, 40.0]\n",
       "3      (25.0, 40.0]\n",
       "4      (25.0, 40.0]\n",
       "           ...     \n",
       "886    (25.0, 40.0]\n",
       "887    (18.0, 25.0]\n",
       "888             NaN\n",
       "889    (25.0, 40.0]\n",
       "890    (25.0, 40.0]\n",
       "Name: age, Length: 891, dtype: category\n",
       "Categories (4, interval[int64]): [(0, 18] < (18, 25] < (25, 40] < (40, 100]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 18]      139\n",
       "(18, 25]     162\n",
       "(25, 40]     263\n",
       "(40, 100]    150\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_cat.value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          714\n",
       "unique           4\n",
       "top       (25, 40]\n",
       "freq           263\n",
       "Name: age, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_cat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_cat = age_cat.cat.add_categories(\"Unknown\")\n",
    "age_cat = age_cat.fillna(\"Unknown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          891\n",
       "unique           5\n",
       "top       (25, 40]\n",
       "freq           263\n",
       "Name: age, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_cat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 25]</th>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25, 40]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 100]</th>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.595238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 25]</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25, 40]</th>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.172043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 100]</th>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.064516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.095745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pclass                   1         2         3\n",
       "sex    age                                    \n",
       "female (0, 18]    0.909091  1.000000  0.511628\n",
       "       (18, 25]   0.941176  0.933333  0.500000\n",
       "       (25, 40]   1.000000  0.906250  0.464286\n",
       "       (40, 100]  0.961538  0.846154  0.111111\n",
       "       Unknown    1.000000  1.000000  0.595238\n",
       "male   (0, 18]    0.800000  0.600000  0.215686\n",
       "       (18, 25]   0.333333  0.047619  0.115385\n",
       "       (25, 40]   0.513514  0.071429  0.172043\n",
       "       (40, 100]  0.280000  0.095238  0.064516\n",
       "       Unknown    0.238095  0.222222  0.095745"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_survived = df.pivot_table('survived', index=['sex', age_cat], columns='pclass')\n",
    "df_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_survived.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">sum</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 25]</th>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25, 40]</th>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 100]</th>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 25]</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25, 40]</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 100]</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sum         len        \n",
       "pclass             1   2   3   1   2   3\n",
       "sex    age                              \n",
       "female (0, 18]    10  14  22  11  14  43\n",
       "       (18, 25]   16  14  11  17  15  22\n",
       "       (25, 40]   31  29  13  31  32  28\n",
       "       (40, 100]  25  11   1  26  13   9\n",
       "       Unknown     9   2  25   9   2  42\n",
       "male   (0, 18]     4   9  11   5  15  51\n",
       "       (18, 25]    3   1   9   9  21  78\n",
       "       (25, 40]   19   3  16  37  42  93\n",
       "       (40, 100]  14   2   2  50  21  31\n",
       "       Unknown     5   2   9  21   9  94"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table('survived', index=['sex', age_cat], columns='pclass', aggfunc=[np.sum, len])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived      1.000000\n",
       "pclass       -0.338481\n",
       "age          -0.077221\n",
       "sibsp        -0.035322\n",
       "parch         0.081629\n",
       "fare          0.257307\n",
       "adult_male   -0.557080\n",
       "alone        -0.203367\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corrwith(df['survived'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}