pandas_05_Apply_Map_ApplyMap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3085813a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb55f69",
   "metadata": {},
   "source": [
    "# Apply\n",
    "\n",
    "It applies a function to each element in a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f9f7027",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load this DataFrame from internet and save it to Data Directory\n",
    "#pd.read_csv('http://bit.ly/kaggletrain').to_csv('Data\\\\train.csv')\n",
    "train = pd.read_csv('Data\\\\train.csv', index_col=0)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c35b0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Name_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Name  Name_length\n",
       "0                             Braund, Mr. Owen Harris           23\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...           51\n",
       "2                              Heikkinen, Miss. Laina           22\n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)           44\n",
       "4                            Allen, Mr. William Henry           24\n",
       "5                                    Moran, Mr. James           16\n",
       "6                             McCarthy, Mr. Timothy J           23\n",
       "7                      Palsson, Master. Gosta Leonard           30\n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)           49\n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)           35\n",
       "10                    Sandstrom, Miss. Marguerite Rut           31"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply name method to the all the values of Name columns \n",
    "#and store the results in a new column\n",
    "train[\"Name_length\"] = train.Name.apply(len)\n",
    "train.loc[0:10, ['Name', 'Name_length']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10ac0a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Fare_Ciel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>72.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.4583</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>51.8625</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.1333</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30.0708</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.7000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fare  Fare_Ciel\n",
       "0    7.2500        8.0\n",
       "1   71.2833       72.0\n",
       "2    7.9250        8.0\n",
       "3   53.1000       54.0\n",
       "4    8.0500        9.0\n",
       "5    8.4583        9.0\n",
       "6   51.8625       52.0\n",
       "7   21.0750       22.0\n",
       "8   11.1333       12.0\n",
       "9   30.0708       31.0\n",
       "10  16.7000       17.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply numpy ciel function to Fare value\n",
    "\n",
    "train['Fare_Ciel'] = train.Fare.apply(np.ceil)\n",
    "train.loc[0:10, ['Fare', 'Fare_Ciel']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4df5f10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Braund\n",
       "1      Cumings\n",
       "2    Heikkinen\n",
       "3     Futrelle\n",
       "4        Allen\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the first name from name\n",
    "train.Name.str.split(',').apply(lambda x: x[0]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0270266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load this DataFrame from internet and save it to Data Directory\n",
    "#pd.read_csv('http://bit.ly/drinksbycountry').to_csv('Data\\\\drinksbycountry.csv')\n",
    "drinks = pd.read_csv('Data\\\\drinksbycountry.csv', index_col=0)\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f01b83a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_servings      376\n",
       "spirit_servings    438\n",
       "wine_servings      370\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get All rows with columns ranging between beer_servings to wine_servings\n",
    "#Then apply max function on it, axis 0 means column wise apply i.e. \n",
    "#max of each column\n",
    "drinks.loc[:, 'beer_servings':'wine_servings'].apply(max, axis=0) #Axis=0 means apply in verticaly direction i.e. column wise\n",
    "\n",
    "#max value of beer_servings column\n",
    "#max value of spirit_servings column\n",
    "#max value of wine_servings column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8933c75f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1      132\n",
       "2       25\n",
       "3      312\n",
       "4      217\n",
       "      ... \n",
       "188    333\n",
       "189    111\n",
       "190      6\n",
       "191     32\n",
       "192     64\n",
       "Length: 193, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get All rows with columns ranging between beer_servings to wine_servings\n",
    "#Then apply max function on it, axis 1 means row wise apply i.e. \n",
    "#max of each row\n",
    "drinks.loc[:, 'beer_servings':'wine_servings'].apply(max, axis=1) #Axis=1 means apply in horizontally direction i.e. row wise\n",
    "\n",
    "#max value of row 0 is 0\n",
    "#max value of row 1 is 132\n",
    "#...\n",
    "#...\n",
    "#max value of row n is ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "277e7eeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      0\n",
       "3      2\n",
       "4      0\n",
       "      ..\n",
       "188    0\n",
       "189    0\n",
       "190    0\n",
       "191    0\n",
       "192    0\n",
       "Length: 193, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get All rows with columns ranging between beer_servings to wine_servings\n",
    "#Then apply np.argmax function on it, which instead of max value returns the \n",
    "#column index of max value. \n",
    "#axis 1 means column wise apply i.e. max of each row\n",
    "drinks.loc[:, 'beer_servings':'wine_servings'].apply(np.argmax, axis=1) #Axis=1 means apply in horizontally direction i.e. row wise\n",
    "\n",
    "#max of row 0 is at column index 0\n",
    "#max of row 1 is at column index 1\n",
    "#...\n",
    "#...\n",
    "#max of row n is at column index ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0865293b",
   "metadata": {},
   "source": [
    "# Map\n",
    "\n",
    "You can run the map function against the Pandas' Series i.e. specific column of a DataFrame. The map function takes 2 kind of arguments:\n",
    "\n",
    "1. A mapping function\n",
    "2. A dictionary\n",
    "\n",
    "If a mapping function is passed. The map will execute the passed mapping function against each row or in a DataFrame, you can then control what values you can modify in the map funciton.\n",
    "\n",
    "If a dictionary is passed, then each occurance of the key value in the dictionary will be replaced with the corresponding value in the dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac440313",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a new column in DataFrame where sex when female = 0, and male = 1\n",
    "train['sex_num'] = train.Sex.map({'female':0, 'male':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c10a236",
   "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>Sex</th>\n",
       "      <th>sex_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sex  sex_num\n",
       "0     male        1\n",
       "1   female        0\n",
       "2   female        0\n",
       "3   female        0\n",
       "4     male        1\n",
       "5     male        1\n",
       "6     male        1\n",
       "7     male        1\n",
       "8   female        0\n",
       "9   female        0\n",
       "10  female        0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read first 10 rows and columns Sex and Sex_Num\n",
    "train.loc[0:10, ['Sex', 'sex_num']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "765b9359",
   "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>power_level</th>\n",
       "      <th>uniform color</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Goku</th>\n",
       "      <td>25000</td>\n",
       "      <td>orange</td>\n",
       "      <td>saiyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vegeta</th>\n",
       "      <td>20000</td>\n",
       "      <td>blue</td>\n",
       "      <td>saiyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nappa</th>\n",
       "      <td>4000</td>\n",
       "      <td>black</td>\n",
       "      <td>saiyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gohan</th>\n",
       "      <td>1500</td>\n",
       "      <td>orange</td>\n",
       "      <td>half saiyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Piccolo</th>\n",
       "      <td>3000</td>\n",
       "      <td>purple</td>\n",
       "      <td>namak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tien</th>\n",
       "      <td>2000</td>\n",
       "      <td>green</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yamcha</th>\n",
       "      <td>1600</td>\n",
       "      <td>orange</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Krillin</th>\n",
       "      <td>2000</td>\n",
       "      <td>orange</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         power_level uniform color      species\n",
       "Goku           25000        orange       saiyan\n",
       "Vegeta         20000          blue       saiyan\n",
       "Nappa           4000         black       saiyan\n",
       "Gohan           1500        orange  half saiyan\n",
       "Piccolo         3000        purple        namak\n",
       "Tien            2000         green        human\n",
       "Yamcha          1600        orange        human\n",
       "Krillin         2000        orange        human"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({\"power_level\": [25000, 20000, 4000, 1500, 3000, 2000, 1600, 2000],\n",
    "         \"uniform color\": [\"orange\", \"blue\", \"black\", \"orange\", \"purple\", \"green\", \"orange\", \"orange\"],\n",
    "         \"species\": [\"saiyan\",\"saiyan\",\"saiyan\",\"half saiyan\", \"namak\",\"human\",\"human\",\"human\"]}, \n",
    "         index = [\"Goku\",\"Vegeta\", \"Nappa\",\"Gohan\", \"Piccolo\",\"Tien\",\"Yamcha\", \"Krillin\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "06bd87bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use .map() to apply a function to a pandas Series\n",
    "# Data Frame columns are Series\n",
    "\n",
    "def my_map_function(x):\n",
    "    if x > 10000:\n",
    "        return \"High\"\n",
    "    if x > 2000:\n",
    "        return \"Medium\"\n",
    "    else:\n",
    "        return \"Low\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8cf39923",
   "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>power_level</th>\n",
       "      <th>powers_category</th>\n",
       "      <th>uniform color</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Goku</th>\n",
       "      <td>25000</td>\n",
       "      <td>High</td>\n",
       "      <td>orange</td>\n",
       "      <td>saiyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vegeta</th>\n",
       "      <td>20000</td>\n",
       "      <td>High</td>\n",
       "      <td>blue</td>\n",
       "      <td>saiyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nappa</th>\n",
       "      <td>4000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>black</td>\n",
       "      <td>saiyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gohan</th>\n",
       "      <td>1500</td>\n",
       "      <td>Low</td>\n",
       "      <td>orange</td>\n",
       "      <td>half saiyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Piccolo</th>\n",
       "      <td>3000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>purple</td>\n",
       "      <td>namak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tien</th>\n",
       "      <td>2000</td>\n",
       "      <td>Low</td>\n",
       "      <td>green</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yamcha</th>\n",
       "      <td>1600</td>\n",
       "      <td>Low</td>\n",
       "      <td>orange</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Krillin</th>\n",
       "      <td>2000</td>\n",
       "      <td>Low</td>\n",
       "      <td>orange</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         power_level powers_category uniform color      species\n",
       "Goku           25000            High        orange       saiyan\n",
       "Vegeta         20000            High          blue       saiyan\n",
       "Nappa           4000          Medium         black       saiyan\n",
       "Gohan           1500             Low        orange  half saiyan\n",
       "Piccolo         3000          Medium        purple        namak\n",
       "Tien            2000             Low         green        human\n",
       "Yamcha          1600             Low        orange        human\n",
       "Krillin         2000             Low        orange        human"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the generating series back to DataFrame\n",
    "data.insert(1, \"powers_category\", data.power_level.map(my_map_function, na_action='ignore')) #na_action = NAN Action\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e75ca3c4",
   "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>power_level</th>\n",
       "      <th>powers_category</th>\n",
       "      <th>uniform color</th>\n",
       "      <th>species</th>\n",
       "      <th>species_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Goku</th>\n",
       "      <td>25000</td>\n",
       "      <td>High</td>\n",
       "      <td>orange</td>\n",
       "      <td>saiyan</td>\n",
       "      <td>alien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vegeta</th>\n",
       "      <td>20000</td>\n",
       "      <td>High</td>\n",
       "      <td>blue</td>\n",
       "      <td>saiyan</td>\n",
       "      <td>alien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nappa</th>\n",
       "      <td>4000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>black</td>\n",
       "      <td>saiyan</td>\n",
       "      <td>alien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gohan</th>\n",
       "      <td>1500</td>\n",
       "      <td>Low</td>\n",
       "      <td>orange</td>\n",
       "      <td>half saiyan</td>\n",
       "      <td>earthling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Piccolo</th>\n",
       "      <td>3000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>purple</td>\n",
       "      <td>namak</td>\n",
       "      <td>alien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tien</th>\n",
       "      <td>2000</td>\n",
       "      <td>Low</td>\n",
       "      <td>green</td>\n",
       "      <td>human</td>\n",
       "      <td>earthling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yamcha</th>\n",
       "      <td>1600</td>\n",
       "      <td>Low</td>\n",
       "      <td>orange</td>\n",
       "      <td>human</td>\n",
       "      <td>earthling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Krillin</th>\n",
       "      <td>2000</td>\n",
       "      <td>Low</td>\n",
       "      <td>orange</td>\n",
       "      <td>human</td>\n",
       "      <td>earthling</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         power_level powers_category uniform color      species species_type\n",
       "Goku           25000            High        orange       saiyan        alien\n",
       "Vegeta         20000            High          blue       saiyan        alien\n",
       "Nappa           4000          Medium         black       saiyan        alien\n",
       "Gohan           1500             Low        orange  half saiyan    earthling\n",
       "Piccolo         3000          Medium        purple        namak        alien\n",
       "Tien            2000             Low         green        human    earthling\n",
       "Yamcha          1600             Low        orange        human    earthling\n",
       "Krillin         2000             Low        orange        human    earthling"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It also maps the series to a key value pair mapping dictionary\n",
    "# It substitute each occurance of a key with it's value in the series\n",
    "#To map Series values based on key: value correspondence\n",
    "# Pass a mapping dictionary to .map()\n",
    "# Add the generating series back to DataFrame\n",
    "\n",
    "correspondance = {\n",
    "    \"saiyan\":\"alien\",\n",
    "    \"namak\":\"alien\",\n",
    "    \"human\":\"earthling\",\n",
    "    \"half saiyan\":\"earthling\"\n",
    "}\n",
    "\n",
    "data.insert(4, \"species_type\", data.species.map(correspondance))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5211454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'> 0    [6, 6, 15, 11, 16, 17, 19, 3, 2, 12]\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    [6, 6, 15, 11, 16, 17, 19, 3, 2, 12]\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myseries = pd.Series([np.random.randint(1, 20, size=10)])\n",
    "print(type(myseries), myseries)\n",
    "myseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "107bd3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [3.0, 3.0, 7.5, 5.5, 8.0, 8.5, 9.5, 1.5, 1.0, ...\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myseries = myseries.map(lambda x: x*0.5)\n",
    "myseries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2180087a",
   "metadata": {},
   "source": [
    "# ApplyMap\n",
    "\n",
    "This Dataframe method apply the map function to whole dataframe and returns a new dataframe. Map function is applied to Series in a DataFrame, while ApplyMap is applied on the whole DataFrame and returns a DataFrame as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4b956fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.172364</td>\n",
       "      <td>0.652402</td>\n",
       "      <td>0.071145</td>\n",
       "      <td>-1.054513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.242564</td>\n",
       "      <td>-2.504063</td>\n",
       "      <td>1.429891</td>\n",
       "      <td>-0.715450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.884968</td>\n",
       "      <td>1.793186</td>\n",
       "      <td>0.978052</td>\n",
       "      <td>-0.051947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.434943</td>\n",
       "      <td>1.237595</td>\n",
       "      <td>-1.218560</td>\n",
       "      <td>-2.201063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.172364  0.652402  0.071145 -1.054513\n",
       "1  1.242564 -2.504063  1.429891 -0.715450\n",
       "2 -0.884968  1.793186  0.978052 -0.051947\n",
       "3 -1.434943  1.237595 -1.218560 -2.201063"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(4,4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c8444e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
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       "\n",
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      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.827636  1.652402  1.071145 -0.054513\n",
       "1  2.242564 -1.504063  2.429891  0.284550\n",
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      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add1(x):\n",
    "    return x + 1\n",
    "\n",
    "new_df = df.applymap(add1)\n",
    "print(type(new_df))\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0e2df741",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "  </thead>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.242564</td>\n",
       "      <td>-1.504063</td>\n",
       "      <td>2.429891</td>\n",
       "      <td>0.284550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.115032</td>\n",
       "      <td>2.793186</td>\n",
       "      <td>1.978052</td>\n",
       "      <td>0.948053</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>-0.434943</td>\n",
       "      <td>2.237595</td>\n",
       "      <td>-0.218560</td>\n",
       "      <td>-1.201063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.827636  1.652402  1.071145 -0.054513\n",
       "1  2.242564 -1.504063  2.429891  0.284550\n",
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       "3 -0.434943  2.237595 -0.218560 -1.201063"
      ]
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same as above, but using anonymous functions.\n",
    "\n",
    "new_df = df.applymap(lambda x: x+1)\n",
    "new_df"
   ]
  }
 ],
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