2022.12.09.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "df = sns.load_dataset('titanic')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "nan_deck = df.deck.value_counts(dropna=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "NaN    688\nC       59\nB       47\nD       33\nE       32\nA       15\nF       13\nG        4\nName: deck, dtype: int64"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_deck"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "   survived  pclass    sex    age  sibsp  parch   fare  embarked  class  \\\n0     False   False  False  False  False  False  False     False  False   \n1     False   False  False  False  False  False  False     False  False   \n2     False   False  False  False  False  False  False     False  False   \n3     False   False  False  False  False  False  False     False  False   \n4     False   False  False  False  False  False  False     False  False   \n\n     who  adult_male   deck  embark_town  alive  alone  \n0  False       False   True        False  False  False  \n1  False       False  False        False  False  False  \n2  False       False   True        False  False  False  \n3  False       False  False        False  False  False  \n4  False       False   True        False  False  False  ",
      "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>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>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().isnull()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "survived         0\npclass           0\nsex              0\nage            177\nsibsp            0\nparch            0\nfare             0\nembarked         2\nclass            0\nwho              0\nadult_male       0\ndeck           688\nembark_town      2\nalive            0\nalone            0\ndtype: int64"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis = 0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "df_thresh = df.dropna(axis = 1, thresh=500)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n       'embarked', 'class', 'who', 'adult_male', 'embark_town', 'alive',\n       'alone'],\n      dtype='object')"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_thresh.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n0           0       3    male  22.0      1      0   7.2500        S   Third   \n1           1       1  female  38.0      1      0  71.2833        C   First   \n2           1       3  female  26.0      0      0   7.9250        S   Third   \n3           1       1  female  35.0      1      0  53.1000        S   First   \n4           0       3    male  35.0      0      0   8.0500        S   Third   \n..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n885         0       3  female  39.0      0      5  29.1250        Q   Third   \n886         0       2    male  27.0      0      0  13.0000        S  Second   \n887         1       1  female  19.0      0      0  30.0000        S   First   \n889         1       1    male  26.0      0      0  30.0000        C   First   \n890         0       3    male  32.0      0      0   7.7500        Q   Third   \n\n       who  adult_male deck  embark_town alive  alone  \n0      man        True  NaN  Southampton    no  False  \n1    woman       False    C    Cherbourg   yes  False  \n2    woman       False  NaN  Southampton   yes   True  \n3    woman       False    C  Southampton   yes  False  \n4      man        True  NaN  Southampton    no   True  \n..     ...         ...  ...          ...   ...    ...  \n885  woman       False  NaN   Queenstown    no  False  \n886    man        True  NaN  Southampton    no   True  \n887  woman       False    B  Southampton   yes   True  \n889    man        True    C    Cherbourg   yes   True  \n890    man        True  NaN   Queenstown    no   True  \n\n[714 rows x 15 columns]",
      "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>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    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>885</th>\n      <td>0</td>\n      <td>3</td>\n      <td>female</td>\n      <td>39.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>29.1250</td>\n      <td>Q</td>\n      <td>Third</td>\n      <td>woman</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>Queenstown</td>\n      <td>no</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>886</th>\n      <td>0</td>\n      <td>2</td>\n      <td>male</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13.0000</td>\n      <td>S</td>\n      <td>Second</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>887</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>19.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30.0000</td>\n      <td>S</td>\n      <td>First</td>\n      <td>woman</td>\n      <td>False</td>\n      <td>B</td>\n      <td>Southampton</td>\n      <td>yes</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>1</td>\n      <td>1</td>\n      <td>male</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30.0000</td>\n      <td>C</td>\n      <td>First</td>\n      <td>man</td>\n      <td>True</td>\n      <td>C</td>\n      <td>Cherbourg</td>\n      <td>yes</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>32.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.7500</td>\n      <td>Q</td>\n      <td>Third</td>\n      <td>man</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>Queenstown</td>\n      <td>no</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>714 rows × 15 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(subset=['age'], how='any',axis = 0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "mean_age = df['age'].mean(axis = 0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "29.69911764705882"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_age"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "df.age.fillna(mean_age, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "0"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "825     Queenstown\n826    Southampton\n827      Cherbourg\n828     Queenstown\n829            NaN\nName: embark_town, dtype: object"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.embark_town[825:830]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "most_freq = df.embark_town.value_counts(dropna=True).idxmax()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "'Southampton'"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_freq"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "df.embark_town.fillna(most_freq, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "825     Queenstown\n826    Southampton\n827      Cherbourg\n828     Queenstown\n829    Southampton\nName: embark_town, dtype: object"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.embark_town[825:830]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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