data_science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science\n",
    "\n",
    "Data scientist need to have these skills:\n",
    "1. Basic Tools: Like python, R or SQL. You do not need to know everything. What you only need is to learn how to use python\n",
    "2. Basic Statistics: Like mean, median or standart deviation. If you know basic statistics, you can use python easily.\n",
    "3. Data Munging: Working with messy and difficult data. Like a inconsistent date and string formatting. As you guess, python\n",
    "helps us.\n",
    "4. Data Visualization: Title is actually explanatory. We will visualize the data with python like matplot and seaborn libraries.\n",
    "5. Machine Learning: You do not need to understand math behind the machine learning technique. You only need is\n",
    "understanding basics of machine learning and learning how to implement it while using python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>Content:</font>\n",
    "1. [Introduction to Python](#1):\n",
    "    1. [Matplotlib](#1.1)\n",
    "    2. [Dictionaries](#1.2)\n",
    "    3. [Pandas](#1.3)\n",
    "    4. [Logic, control flow and filtering](#1.4)\n",
    "    5. [Loop data structures](#1.5)\n",
    "2. [Python Data Science Toolbox](#2):\n",
    "    1. [User defined function](#2.1)\n",
    "    2. [Scope](#2.2)\n",
    "    3. [Nested function](#2.3)\n",
    "    4. [Default and flexible arguments](#2.4)\n",
    "    5. [Lambda function](#2.5)\n",
    "    6. [Anonymous function](#2.6)\n",
    "    7. [Iterators](#2.7)\n",
    "    8. [List comprehension](#2.8)\n",
    "3. [Cleaning Data](#3)\n",
    "    1. [Diagnose data for cleaning](#3.1)\n",
    "    2. [Exploratory data analysis](#3.2)\n",
    "    3. [Visual exploratory data analysis](#3.3)\n",
    "    4. [Tidy data](#3.4)\n",
    "    5. [Pivoting data](#3.5)\n",
    "    6. [Concatenating data](#3.6)\n",
    "    7. [Data types](#3.7)\n",
    "    8. [Missing data and testing with assert](#3.8)\n",
    "4. [Pandas Foundation](#4)\n",
    "    1. [Review of pandas](#4.1)\n",
    "    2. [Building data frames from scratch](#4.2)\n",
    "    3. [Visual exploratory data analysis](#4.3)\n",
    "    4. [Statistical explatory data analysis](#4.4)\n",
    "    5. [Indexing pandas time series](#4.5)\n",
    "    6. [Resampling pandas time series](#4.6)\n",
    "5. [Manipulating Data Frames with Pandas](#5)\n",
    "    1. [Indexing data frames](#5.1)\n",
    "    2. [Slicing data frames](#5.2)\n",
    "    3. [Filtering data frames](#5.3)\n",
    "    4. [Transforming data frames](#5.4)\n",
    "    5. [Index objects and labeled data](#5.5)\n",
    "    6. [Hierarchical indexing](#5.6)\n",
    "    7. [Pivoting data frames](#5.7)\n",
    "    8. [Stacking and unstacking data frames](#5.8)\n",
    "    9. [Melting data frames](#5.9)\n",
    "    10. [Categoricals and groupby](#5.10)\n",
    "6. [Data Visualization](#6)\n",
    "    1. [Seaborn](#6.1): https://github.com/ankur715/data_science/blob/master/data_visualization/dv_seaborn.ipynb\n",
    "    2. [Bokeh 1](#6.2): https://github.com/ankur715/data_science/blob/master/data_visualization/dv_boreh.ipynb\n",
    "    3. [Rare Visualization](#6.3): https://github.com/ankur715/data_science/blob/master/data_visualization/dv_rare_visualization.ipynb\n",
    "7. [Machine Learning](#7)\n",
    "    1. https://github.com/ankur715/data_science/tree/master/machine_learning\n",
    "8. [Deep Learning](#8)\n",
    "    1. https://github.com/ankur715/data_science/tree/master/deep_learning\n",
    "9. [Time Series Prediction](#9)\n",
    "    1. https://github.com/ankur715/data_science/tree/master/time_series_ww2\n",
    "10. [Statistics](#10)\n",
    "    1. https://github.com/ankur715/data_science/tree/master/statistics\n",
    "11. [Deep Learning with Pytorch](#11)\n",
    "    1. Artificial Neural Network: https://github.com/ankur715/data_science/blob/master/deep_learning_pytorch/ann.ipynb\n",
    "    2. Convolutional Neural Network: https://github.com/ankur715/data_science/blob/master/deep_learning_pytorch/cnn.ipynb\n",
    "    3. Recurrent Neural Network: https://github.com/ankur715/data_science/blob/master/deep_learning_pytorch/rnn.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python 3 environment for analytics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;37;40m Normal text\n",
      "\n",
      "\u001b[2;37;40m Underlined text\u001b[0;37;40m \n",
      "\n",
      "\u001b[1;37;40m Bright Colour\u001b[0;37;40m \n",
      "\n",
      "\u001b[3;37;40m Negative Colour\u001b[0;37;40m \n",
      "\n",
      "\u001b[5;37;40m Negative Colour\u001b[0;37;40m\n",
      "\n",
      "\u001b[1;37;40m \u001b[2;37:40m TextColour BlackBackground          TextColour GreyBackground                WhiteText ColouredBackground\u001b[0;37;40m\n",
      "\n",
      "\u001b[1;30;40m Dark Gray      \u001b[0m 1;30;40m            \u001b[0;30;47m Black      \u001b[0m 0;30;47m               \u001b[0;37;41m Black      \u001b[0m 0;37;41m\n",
      "\u001b[1;31;40m Bright Red     \u001b[0m 1;31;40m            \u001b[0;31;47m Red        \u001b[0m 0;31;47m               \u001b[0;37;42m Black      \u001b[0m 0;37;42m\n",
      "\u001b[1;32;40m Bright Green   \u001b[0m 1;32;40m            \u001b[0;32;47m Green      \u001b[0m 0;32;47m               \u001b[0;37;43m Black      \u001b[0m 0;37;43m\n",
      "\u001b[1;33;40m Yellow         \u001b[0m 1;33;40m            \u001b[0;33;47m Brown      \u001b[0m 0;33;47m               \u001b[0;37;44m Black      \u001b[0m 0;37;44m\n",
      "\u001b[1;34;40m Bright Blue    \u001b[0m 1;34;40m            \u001b[0;34;47m Blue       \u001b[0m 0;34;47m               \u001b[0;37;45m Black      \u001b[0m 0;37;45m\n",
      "\u001b[1;35;40m Bright Magenta \u001b[0m 1;35;40m            \u001b[0;35;47m Magenta    \u001b[0m 0;35;47m               \u001b[0;37;46m Black      \u001b[0m 0;37;46m\n",
      "\u001b[1;36;40m Bright Cyan    \u001b[0m 1;36;40m            \u001b[0;36;47m Cyan       \u001b[0m 0;36;47m               \u001b[0;37;47m Black      \u001b[0m 0;37;47m\n",
      "\u001b[1;37;40m White          \u001b[0m 1;37;40m            \u001b[0;37;40m Light Grey \u001b[0m 0;37;40m               \u001b[0;37;48m Black      \u001b[0m 0;37;48m\n"
     ]
    }
   ],
   "source": [
    "# options for distinguishing outputs\n",
    "print(\"\\033[0;37;40m Normal text\\n\")\n",
    "print(\"\\033[2;37;40m Underlined text\\033[0;37;40m \\n\")\n",
    "print(\"\\033[1;37;40m Bright Colour\\033[0;37;40m \\n\")\n",
    "print(\"\\033[3;37;40m Negative Colour\\033[0;37;40m \\n\")\n",
    "print(\"\\033[5;37;40m Negative Colour\\033[0;37;40m\\n\")\n",
    " \n",
    "print(\"\\033[1;37;40m \\033[2;37:40m TextColour BlackBackground          TextColour GreyBackground                WhiteText ColouredBackground\\033[0;37;40m\\n\")\n",
    "print(\"\\033[1;30;40m Dark Gray      \\033[0m 1;30;40m            \\033[0;30;47m Black      \\033[0m 0;30;47m               \\033[0;37;41m Black      \\033[0m 0;37;41m\")\n",
    "print(\"\\033[1;31;40m Bright Red     \\033[0m 1;31;40m            \\033[0;31;47m Red        \\033[0m 0;31;47m               \\033[0;37;42m Black      \\033[0m 0;37;42m\")\n",
    "print(\"\\033[1;32;40m Bright Green   \\033[0m 1;32;40m            \\033[0;32;47m Green      \\033[0m 0;32;47m               \\033[0;37;43m Black      \\033[0m 0;37;43m\")\n",
    "print(\"\\033[1;33;40m Yellow         \\033[0m 1;33;40m            \\033[0;33;47m Brown      \\033[0m 0;33;47m               \\033[0;37;44m Black      \\033[0m 0;37;44m\")\n",
    "print(\"\\033[1;34;40m Bright Blue    \\033[0m 1;34;40m            \\033[0;34;47m Blue       \\033[0m 0;34;47m               \\033[0;37;45m Black      \\033[0m 0;37;45m\")\n",
    "print(\"\\033[1;35;40m Bright Magenta \\033[0m 1;35;40m            \\033[0;35;47m Magenta    \\033[0m 0;35;47m               \\033[0;37;46m Black      \\033[0m 0;37;46m\")\n",
    "print(\"\\033[1;36;40m Bright Cyan    \\033[0m 1;36;40m            \\033[0;36;47m Cyan       \\033[0m 0;36;47m               \\033[0;37;47m Black      \\033[0m 0;37;47m\")\n",
    "print(\"\\033[1;37;40m White          \\033[0m 1;37;40m            \\033[0;37;40m Light Grey \\033[0m 0;37;40m               \\033[0;37;48m Black      \\033[0m 0;37;48m\")\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns # visualization tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob\n",
    "glob.glob(\"data/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name  Age\n",
       "0    A    1\n",
       "1    B    2\n",
       "2    C    3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Name\":[\"A\",\"B\",\"C\"],\n",
    "                   \"Age\":[1,2,3]})\n",
    "df.to_csv(\"data/test.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data\\\\test.csv']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob(\"data/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name Type 1  Type 2  Total  HP  Attack  Defense  Sp. Atk  \\\n",
       "0              Bulbasaur  Grass  Poison    318  45      49       49       65   \n",
       "1                Ivysaur  Grass  Poison    405  60      62       63       80   \n",
       "2               Venusaur  Grass  Poison    525  80      82       83      100   \n",
       "3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123      122   \n",
       "4             Charmander   Fire     NaN    309  39      52       43       60   \n",
       "\n",
       "   Sp. Def  Speed  Generation  Legendary  \n",
       "0       65     45           1      False  \n",
       "1       80     60           1      False  \n",
       "2      100     80           1      False  \n",
       "3      120     80           1      False  \n",
       "4       50     65           1      False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/Pokemon.csv')\n",
    "df = df.drop(columns=[\"#\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800 entries, 0 to 799\n",
      "Data columns (total 12 columns):\n",
      "Name          800 non-null object\n",
      "Type 1        800 non-null object\n",
      "Type 2        414 non-null object\n",
      "Total         800 non-null int64\n",
      "HP            800 non-null int64\n",
      "Attack        800 non-null int64\n",
      "Defense       800 non-null int64\n",
      "Sp. Atk       800 non-null int64\n",
      "Sp. Def       800 non-null int64\n",
      "Speed         800 non-null int64\n",
      "Generation    800 non-null int64\n",
      "Legendary     800 non-null bool\n",
      "dtypes: bool(1), int64(8), object(3)\n",
      "memory usage: 69.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800.00000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>435.10250</td>\n",
       "      <td>69.258750</td>\n",
       "      <td>79.001250</td>\n",
       "      <td>73.842500</td>\n",
       "      <td>72.820000</td>\n",
       "      <td>71.902500</td>\n",
       "      <td>68.277500</td>\n",
       "      <td>3.32375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>119.96304</td>\n",
       "      <td>25.534669</td>\n",
       "      <td>32.457366</td>\n",
       "      <td>31.183501</td>\n",
       "      <td>32.722294</td>\n",
       "      <td>27.828916</td>\n",
       "      <td>29.060474</td>\n",
       "      <td>1.66129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>180.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>330.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>450.00000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>515.00000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>780.00000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total          HP      Attack     Defense     Sp. Atk     Sp. Def  \\\n",
       "count  800.00000  800.000000  800.000000  800.000000  800.000000  800.000000   \n",
       "mean   435.10250   69.258750   79.001250   73.842500   72.820000   71.902500   \n",
       "std    119.96304   25.534669   32.457366   31.183501   32.722294   27.828916   \n",
       "min    180.00000    1.000000    5.000000    5.000000   10.000000   20.000000   \n",
       "25%    330.00000   50.000000   55.000000   50.000000   49.750000   50.000000   \n",
       "50%    450.00000   65.000000   75.000000   70.000000   65.000000   70.000000   \n",
       "75%    515.00000   80.000000  100.000000   90.000000   95.000000   90.000000   \n",
       "max    780.00000  255.000000  190.000000  230.000000  194.000000  230.000000   \n",
       "\n",
       "            Speed  Generation  \n",
       "count  800.000000   800.00000  \n",
       "mean    68.277500     3.32375  \n",
       "std     29.060474     1.66129  \n",
       "min      5.000000     1.00000  \n",
       "25%     45.000000     2.00000  \n",
       "50%     65.000000     3.00000  \n",
       "75%     90.000000     5.00000  \n",
       "max    180.000000     6.00000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.618748</td>\n",
       "      <td>0.736211</td>\n",
       "      <td>0.612787</td>\n",
       "      <td>0.747250</td>\n",
       "      <td>0.717609</td>\n",
       "      <td>0.575943</td>\n",
       "      <td>0.048384</td>\n",
       "      <td>0.501758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HP</th>\n",
       "      <td>0.618748</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.422386</td>\n",
       "      <td>0.239622</td>\n",
       "      <td>0.362380</td>\n",
       "      <td>0.378718</td>\n",
       "      <td>0.175952</td>\n",
       "      <td>0.058683</td>\n",
       "      <td>0.273620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attack</th>\n",
       "      <td>0.736211</td>\n",
       "      <td>0.422386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.438687</td>\n",
       "      <td>0.396362</td>\n",
       "      <td>0.263990</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.051451</td>\n",
       "      <td>0.345408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Defense</th>\n",
       "      <td>0.612787</td>\n",
       "      <td>0.239622</td>\n",
       "      <td>0.438687</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.223549</td>\n",
       "      <td>0.510747</td>\n",
       "      <td>0.015227</td>\n",
       "      <td>0.042419</td>\n",
       "      <td>0.246377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sp. Atk</th>\n",
       "      <td>0.747250</td>\n",
       "      <td>0.362380</td>\n",
       "      <td>0.396362</td>\n",
       "      <td>0.223549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506121</td>\n",
       "      <td>0.473018</td>\n",
       "      <td>0.036437</td>\n",
       "      <td>0.448907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sp. Def</th>\n",
       "      <td>0.717609</td>\n",
       "      <td>0.378718</td>\n",
       "      <td>0.263990</td>\n",
       "      <td>0.510747</td>\n",
       "      <td>0.506121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.259133</td>\n",
       "      <td>0.028486</td>\n",
       "      <td>0.363937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Speed</th>\n",
       "      <td>0.575943</td>\n",
       "      <td>0.175952</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.015227</td>\n",
       "      <td>0.473018</td>\n",
       "      <td>0.259133</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023121</td>\n",
       "      <td>0.326715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generation</th>\n",
       "      <td>0.048384</td>\n",
       "      <td>0.058683</td>\n",
       "      <td>0.051451</td>\n",
       "      <td>0.042419</td>\n",
       "      <td>0.036437</td>\n",
       "      <td>0.028486</td>\n",
       "      <td>-0.023121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.079794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legendary</th>\n",
       "      <td>0.501758</td>\n",
       "      <td>0.273620</td>\n",
       "      <td>0.345408</td>\n",
       "      <td>0.246377</td>\n",
       "      <td>0.448907</td>\n",
       "      <td>0.363937</td>\n",
       "      <td>0.326715</td>\n",
       "      <td>0.079794</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total        HP    Attack   Defense   Sp. Atk   Sp. Def  \\\n",
       "Total       1.000000  0.618748  0.736211  0.612787  0.747250  0.717609   \n",
       "HP          0.618748  1.000000  0.422386  0.239622  0.362380  0.378718   \n",
       "Attack      0.736211  0.422386  1.000000  0.438687  0.396362  0.263990   \n",
       "Defense     0.612787  0.239622  0.438687  1.000000  0.223549  0.510747   \n",
       "Sp. Atk     0.747250  0.362380  0.396362  0.223549  1.000000  0.506121   \n",
       "Sp. Def     0.717609  0.378718  0.263990  0.510747  0.506121  1.000000   \n",
       "Speed       0.575943  0.175952  0.381240  0.015227  0.473018  0.259133   \n",
       "Generation  0.048384  0.058683  0.051451  0.042419  0.036437  0.028486   \n",
       "Legendary   0.501758  0.273620  0.345408  0.246377  0.448907  0.363937   \n",
       "\n",
       "               Speed  Generation  Legendary  \n",
       "Total       0.575943    0.048384   0.501758  \n",
       "HP          0.175952    0.058683   0.273620  \n",
       "Attack      0.381240    0.051451   0.345408  \n",
       "Defense     0.015227    0.042419   0.246377  \n",
       "Sp. Atk     0.473018    0.036437   0.448907  \n",
       "Sp. Def     0.259133    0.028486   0.363937  \n",
       "Speed       1.000000   -0.023121   0.326715  \n",
       "Generation -0.023121    1.000000   0.079794  \n",
       "Legendary   0.326715    0.079794   1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation map\n",
    "f,ax = plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(df.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"1\"></a>Introduction to Python:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"1.1\"></a>Matplotlib:\n",
    "Matplot is a python library that help us to plot data. The easiest and basic plots are line, scatter and histogram plots.\n",
    "- Line plot is better when x axis is time.\n",
    "- Scatter is better when there is correlation between two variables\n",
    "- Histogram is better when we need to see distribution of numerical data.\n",
    "- Customization: Colors,labels,thickness of line, title, opacity, grid, figsize, ticks of axis and linestyle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "# Line Plot\n",
    "# color = color, label = label, linewidth = width of line, alpha = opacity, grid = grid, linestyle = sytle of line\n",
    "df.Speed.plot(kind = 'line', color = 'g',label = 'Speed',linewidth=1,alpha = 0.5,grid = True,linestyle = ':')\n",
    "df.Defense.plot(color = 'r',label = 'Defense',linewidth=1, alpha = 0.5,grid = True,linestyle = '-.')\n",
    "plt.legend(loc='upper right') # legend = puts label into plot\n",
    "plt.xlabel('x axis') # label = name of label\n",
    "plt.ylabel('y axis')\n",
    "plt.title('Line Plot') # title = title of plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Attack Defense Scatter Plot')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Scatter Plot\n",
    "# x = attack, y = defense\n",
    "df.plot(kind='scatter', x='Attack', y='Defense',alpha = 0.5,color = 'red')\n",
    "plt.xlabel('Attack') # label = name of label\n",
    "plt.ylabel('Defence')\n",
    "plt.title('Attack Defense Scatter Plot') # title = title of plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram\n",
    "# bins = number of bar in figure\n",
    "df.Speed.plot(kind = 'hist',bins = 50,figsize = (6,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# clf() = cleans it up again you can start a fresh\n",
    "df.Speed.plot(kind = 'hist',bins = 50)\n",
    "plt.clf()\n",
    "# We cannot see plot due to clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"1.2\"></a>Dictionary:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why we need dictionary?\n",
    "- It has 'key' and 'value'\n",
    "- Faster than lists\n",
    "\n",
    "What is key and value. Example:\n",
    "- dictionary = {'spain' : 'madrid'}\n",
    "- Key is spain.\n",
    "- Values is madrid.\n",
    "\n",
    "__It's that easy.__  \n",
    "\n",
    "Lets practice some other properties like keys(), values(), update, add, check, remove key, remove all entries and remove dicrionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['spain', 'usa'])\n",
      "dict_values(['madrid', 'vegas'])\n"
     ]
    }
   ],
   "source": [
    "#create dictionary and look its keys and values\n",
    "dictionary = {'spain' : 'madrid','usa' : 'vegas'}\n",
    "print(dictionary.keys())\n",
    "print(dictionary.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'spain': 'barcelona', 'usa': 'vegas'}\n",
      "{'spain': 'barcelona', 'usa': 'vegas', 'france': 'paris'}\n",
      "{'usa': 'vegas', 'france': 'paris'}\n",
      "True\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "# Keys have to be immutable objects like string, boolean, float, integer or tubles\n",
    "# List is not immutable\n",
    "# Keys are unique\n",
    "dictionary['spain'] = \"barcelona\" # update existing entry\n",
    "print(dictionary)\n",
    "dictionary['france'] = \"paris\" # Add new entry\n",
    "print(dictionary)\n",
    "del dictionary['spain'] # remove entry with key 'spain'\n",
    "print(dictionary)\n",
    "print('france' in dictionary) # check include or not\n",
    "dictionary.clear() # remove all entries in dict\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    }
   ],
   "source": [
    "# In order to run all code you need to take comment this line\n",
    "# del dictionary # delete entire dictionary\n",
    "print(dictionary) # it gives error because dictionary is deleted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"1.3\"></a>Pandas:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Data/Pokemon.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "series = data['Defense'] # data['Defense'] = series\n",
    "print(type(series))\n",
    "data_frame = data[['Defense']] # data[['Defense']] = data frame\n",
    "print(type(data_frame))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before continue with pandas, we need to learn  <a name=\"1.4\"></a>__logic, control flow and filtering__.\n",
    "\n",
    "Comparison operator: ==, <, >, <=\n",
    "\n",
    "Boolean operators: and, or ,not \n",
    "\n",
    "Filtering pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Comparison operator\n",
    "print(3 > 2)\n",
    "print(3!=2)\n",
    "# Boolean operators\n",
    "print(True and False)\n",
    "print(True or False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>208</td>\n",
       "      <td>SteelixMega Steelix</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Ground</td>\n",
       "      <td>610</td>\n",
       "      <td>75</td>\n",
       "      <td>125</td>\n",
       "      <td>230</td>\n",
       "      <td>55</td>\n",
       "      <td>95</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>213</td>\n",
       "      <td>Shuckle</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Rock</td>\n",
       "      <td>505</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>306</td>\n",
       "      <td>AggronMega Aggron</td>\n",
       "      <td>Steel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>630</td>\n",
       "      <td>70</td>\n",
       "      <td>140</td>\n",
       "      <td>230</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                 Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "224  208  SteelixMega Steelix  Steel  Ground    610  75     125      230   \n",
       "230  213              Shuckle    Bug    Rock    505  20      10      230   \n",
       "333  306    AggronMega Aggron  Steel     NaN    630  70     140      230   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "224       55       95     30           2      False  \n",
       "230       10      230      5           2      False  \n",
       "333       60       80     50           3      False  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 - Filtering Pandas data frame\n",
    "x = data['Defense']>200 # There are only 3 pokemons who have higher defense value than 200\n",
    "data[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
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       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>208</td>\n",
       "      <td>SteelixMega Steelix</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Ground</td>\n",
       "      <td>610</td>\n",
       "      <td>75</td>\n",
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       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>306</td>\n",
       "      <td>AggronMega Aggron</td>\n",
       "      <td>Steel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>630</td>\n",
       "      <td>70</td>\n",
       "      <td>140</td>\n",
       "      <td>230</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                 Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "224  208  SteelixMega Steelix  Steel  Ground    610  75     125      230   \n",
       "333  306    AggronMega Aggron  Steel     NaN    630  70     140      230   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "224       55       95     30           2      False  \n",
       "333       60       80     50           3      False  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 - Filtering pandas with logical_and\n",
    "# There are only 2 pokemons who have higher defence value than 2oo and higher attack value than 100\n",
    "data[np.logical_and(data['Defense']>200, data['Attack']>100 )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
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       "      <th>224</th>\n",
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       "      <td>SteelixMega Steelix</td>\n",
       "      <td>Steel</td>\n",
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       "      <td>75</td>\n",
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       "      <th>333</th>\n",
       "      <td>306</td>\n",
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       "      <td>Steel</td>\n",
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       "      <td>630</td>\n",
       "      <td>70</td>\n",
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      "text/plain": [
       "       #                 Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "224  208  SteelixMega Steelix  Steel  Ground    610  75     125      230   \n",
       "333  306    AggronMega Aggron  Steel     NaN    630  70     140      230   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "224       55       95     30           2      False  \n",
       "333       60       80     50           3      False  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is also same with previous code line. Therefore we can also use '&' for filtering.\n",
    "data[(data['Defense']>200) & (data['Attack']>100)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"1.5\"></a>Loop data structure:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will learn most basic while and for loops\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i is:  0\n",
      "i is:  1\n",
      "i is:  2\n",
      "i is:  3\n",
      "i is:  4\n",
      "5  is equal to 5\n"
     ]
    }
   ],
   "source": [
    "# Stay in loop if condition( i is not equal 5) is true\n",
    "i = 0\n",
    "while i != 5 :\n",
    "    print('i is: ',i)\n",
    "    i += 1\n",
    "print(i,' is equal to 5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i is:  1\n",
      "i is:  2\n",
      "i is:  3\n",
      "i is:  4\n",
      "i is:  5\n",
      "\n",
      "0  :  1\n",
      "1  :  2\n",
      "2  :  3\n",
      "3  :  4\n",
      "4  :  5\n",
      "\n",
      "spain  :  madrid\n",
      "france  :  paris\n",
      "\n",
      "0  :  Attack    49\n",
      "Name: 0, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Stay in loop if condition( i is not equal 5) is true\n",
    "lis = [1,2,3,4,5]\n",
    "for i in lis:\n",
    "    print('i is: ',i)\n",
    "print('')\n",
    "\n",
    "# Enumerate index and value of list\n",
    "# index : value = 0:1, 1:2, 2:3, 3:4, 4:5\n",
    "for index, value in enumerate(lis):\n",
    "    print(index,\" : \",value)\n",
    "print('')\n",
    "\n",
    "# For dictionaries\n",
    "# We can use for loop to achive key and value of dictionary. We learnt key and value at dictionary part.\n",
    "dictionary = {'spain':'madrid','france':'paris'}\n",
    "for key,value in dictionary.items():\n",
    "    print(key,\" : \",value)\n",
    "print('')\n",
    "\n",
    "# For pandas we can achieve index and value\n",
    "for index,value in data[['Attack']][0:1].iterrows():\n",
    "    print(index,\" : \",value)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Summary:_\n",
    "- how to import csv file\n",
    "- plotting line,scatter and histogram\n",
    "- basic dictionary features\n",
    "- basic pandas features like filtering that is actually something always used and main for being data scientist\n",
    "- while and for loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"2\"></a>Python Data Science Tookbox:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"2.1\"></a>User defined function:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we need to know about functions:\n",
    "- docstrings: documentation for functions. Example:  \n",
    "\n",
    "    for f():\n",
    "        \"\"\"This is docstring for documentation of function f\"\"\"\n",
    "- tuble: sequence of immutable python objects.\n",
    "- cant modify values\n",
    "- tuble uses paranthesis like tuble = (1,2,3)\n",
    "- unpack tuble into several variables like a,b,c = tuble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3\n"
     ]
    }
   ],
   "source": [
    "# example of what we learn above\n",
    "def tuble_ex():\n",
    "    \"\"\" return defined t tuble\"\"\"\n",
    "    t = (1,2,3)\n",
    "    return t\n",
    "a,b,c = tuble_ex()\n",
    "print(a,b,c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"2.2\"></a>Scope:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we need to know about scope:\n",
    "- global: defined main body in script\n",
    "- local: defined in a function\n",
    "- built in scope: names in predefined built in scope module such as print, len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# guess print what\n",
    "x = 2\n",
    "def f():\n",
    "    x = 3\n",
    "    return x\n",
    "print(x) # x = 2 global scope\n",
    "print(f()) # x = 3 local scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# What if there is no local scope\n",
    "x = 5\n",
    "def f():\n",
    "    y = 2*x # there is no local scope x\n",
    "    return y\n",
    "print(f()) # it uses global scope x\n",
    "# First local scopesearched, then global scope searched, if two of them cannot be found lastly built in scope searched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ArithmeticError',\n",
       " 'AssertionError',\n",
       " 'AttributeError',\n",
       " 'BaseException',\n",
       " 'BlockingIOError',\n",
       " 'BrokenPipeError',\n",
       " 'BufferError',\n",
       " 'BytesWarning',\n",
       " 'ChildProcessError',\n",
       " 'ConnectionAbortedError',\n",
       " 'ConnectionError',\n",
       " 'ConnectionRefusedError',\n",
       " 'ConnectionResetError',\n",
       " 'DeprecationWarning',\n",
       " 'EOFError',\n",
       " 'Ellipsis',\n",
       " 'EnvironmentError',\n",
       " 'Exception',\n",
       " 'False',\n",
       " 'FileExistsError',\n",
       " 'FileNotFoundError',\n",
       " 'FloatingPointError',\n",
       " 'FutureWarning',\n",
       " 'GeneratorExit',\n",
       " 'IOError',\n",
       " 'ImportError',\n",
       " 'ImportWarning',\n",
       " 'IndentationError',\n",
       " 'IndexError',\n",
       " 'InterruptedError',\n",
       " 'IsADirectoryError',\n",
       " 'KeyError',\n",
       " 'KeyboardInterrupt',\n",
       " 'LookupError',\n",
       " 'MemoryError',\n",
       " 'ModuleNotFoundError',\n",
       " 'NameError',\n",
       " 'None',\n",
       " 'NotADirectoryError',\n",
       " 'NotImplemented',\n",
       " 'NotImplementedError',\n",
       " 'OSError',\n",
       " 'OverflowError',\n",
       " 'PendingDeprecationWarning',\n",
       " 'PermissionError',\n",
       " 'ProcessLookupError',\n",
       " 'RecursionError',\n",
       " 'ReferenceError',\n",
       " 'ResourceWarning',\n",
       " 'RuntimeError',\n",
       " 'RuntimeWarning',\n",
       " 'StopAsyncIteration',\n",
       " 'StopIteration',\n",
       " 'SyntaxError',\n",
       " 'SyntaxWarning',\n",
       " 'SystemError',\n",
       " 'SystemExit',\n",
       " 'TabError',\n",
       " 'TimeoutError',\n",
       " 'True',\n",
       " 'TypeError',\n",
       " 'UnboundLocalError',\n",
       " 'UnicodeDecodeError',\n",
       " 'UnicodeEncodeError',\n",
       " 'UnicodeError',\n",
       " 'UnicodeTranslateError',\n",
       " 'UnicodeWarning',\n",
       " 'UserWarning',\n",
       " 'ValueError',\n",
       " 'Warning',\n",
       " 'WindowsError',\n",
       " 'ZeroDivisionError',\n",
       " '__IPYTHON__',\n",
       " '__build_class__',\n",
       " '__debug__',\n",
       " '__doc__',\n",
       " '__import__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " 'abs',\n",
       " 'all',\n",
       " 'any',\n",
       " 'ascii',\n",
       " 'bin',\n",
       " 'bool',\n",
       " 'breakpoint',\n",
       " 'bytearray',\n",
       " 'bytes',\n",
       " 'callable',\n",
       " 'chr',\n",
       " 'classmethod',\n",
       " 'compile',\n",
       " 'complex',\n",
       " 'copyright',\n",
       " 'credits',\n",
       " 'delattr',\n",
       " 'dict',\n",
       " 'dir',\n",
       " 'display',\n",
       " 'divmod',\n",
       " 'enumerate',\n",
       " 'eval',\n",
       " 'exec',\n",
       " 'filter',\n",
       " 'float',\n",
       " 'format',\n",
       " 'frozenset',\n",
       " 'get_ipython',\n",
       " 'getattr',\n",
       " 'globals',\n",
       " 'hasattr',\n",
       " 'hash',\n",
       " 'help',\n",
       " 'hex',\n",
       " 'id',\n",
       " 'input',\n",
       " 'int',\n",
       " 'isinstance',\n",
       " 'issubclass',\n",
       " 'iter',\n",
       " 'len',\n",
       " 'license',\n",
       " 'list',\n",
       " 'locals',\n",
       " 'map',\n",
       " 'max',\n",
       " 'memoryview',\n",
       " 'min',\n",
       " 'next',\n",
       " 'object',\n",
       " 'oct',\n",
       " 'open',\n",
       " 'ord',\n",
       " 'pow',\n",
       " 'print',\n",
       " 'property',\n",
       " 'range',\n",
       " 'repr',\n",
       " 'reversed',\n",
       " 'round',\n",
       " 'set',\n",
       " 'setattr',\n",
       " 'slice',\n",
       " 'sorted',\n",
       " 'staticmethod',\n",
       " 'str',\n",
       " 'sum',\n",
       " 'super',\n",
       " 'tuple',\n",
       " 'type',\n",
       " 'vars',\n",
       " 'zip']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How can we learn what is built in scope\n",
    "import builtins\n",
    "dir(builtins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"2.3\"></a>Nested function:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- function inside function.\n",
    "- There is a LEGB rule that is search local scope, enclosing function, global and built in scopes, respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "#nested function\n",
    "def square():\n",
    "    \"\"\" return square of value \"\"\"\n",
    "    def add():\n",
    "        \"\"\" add two local variable \"\"\"\n",
    "        x = 2\n",
    "        y = 3\n",
    "        z = x + y\n",
    "        return z\n",
    "    return add()**2\n",
    "print(square())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"2.4\"></a>Default and flexible arguments:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Default argument example:\n",
    "\n",
    "    def f(a, b=1):\n",
    "\n",
    "    \"\"\" b = 1 is default argument\"\"\"\n",
    "- Flexible argument example:\n",
    "\n",
    "    def f(*args):\n",
    "\n",
    "    \"\"\" *args can be one or more\"\"\"\n",
    " \n",
    "    def f(** kwargs):  \n",
    "\n",
    "    \"\"\" **kwargs is a dictionary\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "# default arguments\n",
    "def f(a, b = 1, c = 2):\n",
    "    y = a + b + c\n",
    "    return y\n",
    "print(f(5))\n",
    "# what if we want to change default arguments\n",
    "print(f(5,4,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "country   spain\n",
      "capital   madrid\n",
      "population   123456\n"
     ]
    }
   ],
   "source": [
    "# flexible arguments *args\n",
    "def f(*args):\n",
    "    for i in args:\n",
    "        print(i)\n",
    "f(1)\n",
    "print(\"\")\n",
    "f(1,2,3,4)\n",
    "\n",
    "# flexible arguments **kwargs that is dictionary\n",
    "def f(**kwargs):\n",
    "    \"\"\" print key and value of dictionary\"\"\"\n",
    "    for key, value in kwargs.items(): # If you do not understand this part turn for loop part and look at dictionary in for loop\n",
    "        print(key, \" \", value)\n",
    "f(country = 'spain', capital = 'madrid', population = 123456)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"2.5\"></a>Lambda function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "# lambda function\n",
    "square = lambda x: x**2 # where x is name of argument\n",
    "print(square(4))\n",
    "tot = lambda x,y,z: x+y+z # where x,y,z are names of arguments\n",
    "print(tot(1,2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"2.6\"></a>Anonymous function:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like lambda function but it can take more than one arguments.\n",
    "- map(func,seq) : applies a function to all the items in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 9]\n"
     ]
    }
   ],
   "source": [
    "number_list = [1,2,3]\n",
    "y = map(lambda x:x**2,number_list)\n",
    "print(list(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"2.7\"></a>Iterators:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- iterable is an object that can return an iterator\n",
    "- iterable: an object with an associated iter() method  \n",
    "    example: list, strings and dictionaries\n",
    "- iterator: produces next value with next() method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r\n",
      "o n a l d o\n"
     ]
    }
   ],
   "source": [
    "# iteration example\n",
    "name = \"ronaldo\"\n",
    "it = iter(name)\n",
    "print(next(it)) # print next iteration\n",
    "print(*it) # print remaining iteration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<zip object at 0x0000022F188198C8>\n",
      "[(1, 5), (2, 6), (3, 7), (4, 8)]\n"
     ]
    }
   ],
   "source": [
    "# zip example\n",
    "list1 = [1,2,3,4]\n",
    "list2 = [5,6,7,8]\n",
    "z = zip(list1,list2)\n",
    "print(z)\n",
    "z_list = list(z)\n",
    "print(z_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4)\n",
      "(5, 6, 7, 8)\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "un_zip = zip(*z_list)\n",
    "un_list1,un_list2 = list(un_zip) # unzip returns tuble\n",
    "print(un_list1)\n",
    "print(un_list2)\n",
    "print(type(un_list2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"2.8\"></a>List comprehension:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__One of the most important topic of this kernel__  \n",
    "\n",
    "We use list comprehension for data analysis often.  \n",
    "\n",
    "list comprehension: collapse for loops for building lists into a single line  \n",
    "\n",
    "Ex: num1 = [1,2,3] and we want to make it num2 = [2,3,4]. This can be done with for loop. However it is unnecessarily long.  We can make it one line code that is\n",
    "list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "# Example of list comprehension\n",
    "num1 = [1,2,3]\n",
    "num2 = [i + 1 for i in num1 ]\n",
    "print(num2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[i + 1 for i in num1 ]: list of comprehension\n",
    " \n",
    " i +1: list comprehension syntax\n",
    " \n",
    " for i in num1: for loop syntax\n",
    " \n",
    " i: iterator\n",
    " \n",
    " num1: iterable object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 100, 20]\n"
     ]
    }
   ],
   "source": [
    "# Conditionals on iterable\n",
    "num1 = [5,10,15]\n",
    "num2 = [i**2 if i == 10 else i-5 if i < 7 else i+5 for i in num1]\n",
    "print(num2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>speed_level</th>\n",
       "      <th>Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>low</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>low</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>high</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>high</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>high</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>high</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>low</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>low</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   speed_level  Speed\n",
       "0          low     45\n",
       "1          low     60\n",
       "2         high     80\n",
       "3         high     80\n",
       "4          low     65\n",
       "5         high     80\n",
       "6         high    100\n",
       "7         high    100\n",
       "8         high    100\n",
       "9          low     43\n",
       "10         low     58"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets return pokemon csv and make one more list comprehension example\n",
    "# lets classify pokemons whether they have high or low speed. Our threshold is average speed.\n",
    "threshold = sum(data.Speed)/len(data.Speed)\n",
    "data[\"speed_level\"] = [\"high\" if i > threshold else \"low\" for i in data.Speed]\n",
    "data.loc[:10,[\"speed_level\",\"Speed\"]] # we will learn loc more detailed later\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Summary:_\n",
    "- User defined function\n",
    "- Scope\n",
    "- Nested function\n",
    "- Default and flexible arguments\n",
    "- Lambda function\n",
    "- Anonymous function\n",
    "- Iterators\n",
    "- List comprehension\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"3\"></a>Cleaning Data:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"3.1\"></a>Diagnose data for cleaning:    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to diagnose and clean data before exploring. \n",
    "\n",
    "Unclean data:  \n",
    "- Column name inconsistency like upper-lower case letter or space between words\n",
    "- missing data\n",
    "- different language\n",
    "\n",
    "We will use head, tail, columns, shape and info methods to diagnose data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #                   Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "0  1              Bulbasaur  Grass  Poison    318  45      49       49   \n",
       "1  2                Ivysaur  Grass  Poison    405  60      62       63   \n",
       "2  3               Venusaur  Grass  Poison    525  80      82       83   \n",
       "3  3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123   \n",
       "4  4             Charmander   Fire     NaN    309  39      52       43   \n",
       "\n",
       "   Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "0       65       65     45           1      False  \n",
       "1       80       80     60           1      False  \n",
       "2      100      100     80           1      False  \n",
       "3      122      120     80           1      False  \n",
       "4       60       50     65           1      False  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Data/Pokemon.csv')\n",
    "data.head() # head shows first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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",
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       "    }\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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>719</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>600</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>719</td>\n",
       "      <td>DiancieMega Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>700</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Confined</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Unbound</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Dark</td>\n",
       "      <td>680</td>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>170</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>721</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                 Name   Type 1 Type 2  Total  HP  Attack  Defense  \\\n",
       "795  719              Diancie     Rock  Fairy    600  50     100      150   \n",
       "796  719  DiancieMega Diancie     Rock  Fairy    700  50     160      110   \n",
       "797  720  HoopaHoopa Confined  Psychic  Ghost    600  80     110       60   \n",
       "798  720   HoopaHoopa Unbound  Psychic   Dark    680  80     160       60   \n",
       "799  721            Volcanion     Fire  Water    600  80     110      120   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "795      100      150     50           6       True  \n",
       "796      160      110    110           6       True  \n",
       "797      150      130     70           6       True  \n",
       "798      170      130     80           6       True  \n",
       "799      130       90     70           6       True  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tail shows last 5 rows\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['#', 'Name', 'Type 1', 'Type 2', 'Total', 'HP', 'Attack', 'Defense',\n",
       "       'Sp. Atk', 'Sp. Def', 'Speed', 'Generation', 'Legendary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns gives column names of features\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 13)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape gives number of rows and columns in a tuble\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800 entries, 0 to 799\n",
      "Data columns (total 13 columns):\n",
      "#             800 non-null int64\n",
      "Name          800 non-null object\n",
      "Type 1        800 non-null object\n",
      "Type 2        414 non-null object\n",
      "Total         800 non-null int64\n",
      "HP            800 non-null int64\n",
      "Attack        800 non-null int64\n",
      "Defense       800 non-null int64\n",
      "Sp. Atk       800 non-null int64\n",
      "Sp. Def       800 non-null int64\n",
      "Speed         800 non-null int64\n",
      "Generation    800 non-null int64\n",
      "Legendary     800 non-null bool\n",
      "dtypes: bool(1), int64(9), object(3)\n",
      "memory usage: 75.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# info gives data type like dataframe, number of sample or row, number of feature or column, feature types and memory usage\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"3.2\"></a>Exploratory data analysis:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "value_counts(): Frequency counts\n",
    "\n",
    "outliers: the value that is considerably higher or lower from rest of the data  \n",
    "- Lets say value at 75% is Q3 and value at 25% is Q1.  \n",
    "- Outlier are smaller than Q1 - 1.5(Q3-Q1) and bigger than Q3 + 1.5(Q3-Q1). (Q3-Q1) = IQR\n",
    "\n",
    "We will use describe() method. Describe method includes:  \n",
    "- count: number of entries\n",
    "- mean: average of entries\n",
    "- std: standart deviation\n",
    "- min: minimum entry\n",
    "- 25%: first quantile\n",
    "- 50%: median or second quantile\n",
    "- 75%: third quantile\n",
    "- max: maximum entry\n",
    "\n",
    "What is quantile?  \n",
    "- 1,4,5,6,8,9,11,12,13,14,15,16,17\n",
    "- The median is the number that is in middle of the sequence. In this case it would be 11.\n",
    "- The lower quartile is the median in between the smallest number and the median i.e. in between 1 and 11, which is 6.\n",
    "- The upper quartile, you find the median between the median and the largest number i.e. between 11 and 17, which will be 14 according to the question above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Water       112\n",
      "Normal       98\n",
      "Grass        70\n",
      "Bug          69\n",
      "Psychic      57\n",
      "Fire         52\n",
      "Electric     44\n",
      "Rock         44\n",
      "Ghost        32\n",
      "Ground       32\n",
      "Dragon       32\n",
      "Dark         31\n",
      "Poison       28\n",
      "Steel        27\n",
      "Fighting     27\n",
      "Ice          24\n",
      "Fairy        17\n",
      "Flying        4\n",
      "Name: Type 1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# For example lets look frequency of pokemom types\n",
    "print(data['Type 1'].value_counts(dropna =False)) # if there are nan values that also be counted\n",
    "# As it can be seen below there are 112 water pokemon or 70 grass pokemon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>#</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.00000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>362.813750</td>\n",
       "      <td>435.10250</td>\n",
       "      <td>69.258750</td>\n",
       "      <td>79.001250</td>\n",
       "      <td>73.842500</td>\n",
       "      <td>72.820000</td>\n",
       "      <td>71.902500</td>\n",
       "      <td>68.277500</td>\n",
       "      <td>3.32375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>208.343798</td>\n",
       "      <td>119.96304</td>\n",
       "      <td>25.534669</td>\n",
       "      <td>32.457366</td>\n",
       "      <td>31.183501</td>\n",
       "      <td>32.722294</td>\n",
       "      <td>27.828916</td>\n",
       "      <td>29.060474</td>\n",
       "      <td>1.66129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>180.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>184.750000</td>\n",
       "      <td>330.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>364.500000</td>\n",
       "      <td>450.00000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>539.250000</td>\n",
       "      <td>515.00000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>721.000000</td>\n",
       "      <td>780.00000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                #      Total          HP      Attack     Defense     Sp. Atk  \\\n",
       "count  800.000000  800.00000  800.000000  800.000000  800.000000  800.000000   \n",
       "mean   362.813750  435.10250   69.258750   79.001250   73.842500   72.820000   \n",
       "std    208.343798  119.96304   25.534669   32.457366   31.183501   32.722294   \n",
       "min      1.000000  180.00000    1.000000    5.000000    5.000000   10.000000   \n",
       "25%    184.750000  330.00000   50.000000   55.000000   50.000000   49.750000   \n",
       "50%    364.500000  450.00000   65.000000   75.000000   70.000000   65.000000   \n",
       "75%    539.250000  515.00000   80.000000  100.000000   90.000000   95.000000   \n",
       "max    721.000000  780.00000  255.000000  190.000000  230.000000  194.000000   \n",
       "\n",
       "          Sp. Def       Speed  Generation  \n",
       "count  800.000000  800.000000   800.00000  \n",
       "mean    71.902500   68.277500     3.32375  \n",
       "std     27.828916   29.060474     1.66129  \n",
       "min     20.000000    5.000000     1.00000  \n",
       "25%     50.000000   45.000000     2.00000  \n",
       "50%     70.000000   65.000000     3.00000  \n",
       "75%     90.000000   90.000000     5.00000  \n",
       "max    230.000000  180.000000     6.00000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example max HP is 255 or min defense is 5\n",
    "data.describe() #ignore null entries\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"3.3\"></a>Visual exploratory data analysis:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plots: visualize basic statistics like outliers, min/max or quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f184aaf28>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# For example: compare attack of pokemons that are legendary or not\n",
    "# Black line at top is max\n",
    "# Blue line at top is 75%\n",
    "# Red line is median (50%)\n",
    "# Blue line at bottom is 25%\n",
    "# Black line at bottom is min\n",
    "# There are no outliers\n",
    "data.boxplot(column='Attack',by = 'Legendary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"3.4\"></a>Tidy data:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We tidy data with melt().  \n",
    "Describing melt is confusing. Therefore lets make example to understand it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #                   Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "0  1              Bulbasaur  Grass  Poison    318  45      49       49   \n",
       "1  2                Ivysaur  Grass  Poison    405  60      62       63   \n",
       "2  3               Venusaur  Grass  Poison    525  80      82       83   \n",
       "3  3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123   \n",
       "4  4             Charmander   Fire     NaN    309  39      52       43   \n",
       "\n",
       "   Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "0       65       65     45           1      False  \n",
       "1       80       80     60           1      False  \n",
       "2      100      100     80           1      False  \n",
       "3      122      120     80           1      False  \n",
       "4       60       50     65           1      False  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Firstly I create new data from pokemons data to explain melt nore easily.\n",
    "data_new = data.head() # I only take 5 rows into new data\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Attack</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Attack</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Attack</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Attack</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Attack</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Defense</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Defense</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Defense</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Defense</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Defense</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name variable  value\n",
       "0              Bulbasaur   Attack     49\n",
       "1                Ivysaur   Attack     62\n",
       "2               Venusaur   Attack     82\n",
       "3  VenusaurMega Venusaur   Attack    100\n",
       "4             Charmander   Attack     52\n",
       "5              Bulbasaur  Defense     49\n",
       "6                Ivysaur  Defense     63\n",
       "7               Venusaur  Defense     83\n",
       "8  VenusaurMega Venusaur  Defense    123\n",
       "9             Charmander  Defense     43"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets melt\n",
    "# id_vars = what we do not wish to melt\n",
    "# value_vars = what we want to melt\n",
    "melted = pd.melt(frame=data_new,id_vars = 'Name', value_vars= ['Attack','Defense'])\n",
    "melted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"3.5\"></a>Pivoting data:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reverse of melting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>variable</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bulbasaur</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charmander</th>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivysaur</th>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venusaur</th>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VenusaurMega Venusaur</th>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "variable               Attack  Defense\n",
       "Name                                  \n",
       "Bulbasaur                  49       49\n",
       "Charmander                 52       43\n",
       "Ivysaur                    62       63\n",
       "Venusaur                   82       83\n",
       "VenusaurMega Venusaur     100      123"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index is name\n",
    "# I want to make that columns are variable\n",
    "# Finally values in columns are value\n",
    "melted.pivot(index = 'Name', columns = 'variable',values='value')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"3.6\"></a>Concatenating data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>719</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>600</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>719</td>\n",
       "      <td>DiancieMega Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>700</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Confined</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Unbound</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Dark</td>\n",
       "      <td>680</td>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>170</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>721</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     #                   Name   Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "0    1              Bulbasaur    Grass  Poison    318  45      49       49   \n",
       "1    2                Ivysaur    Grass  Poison    405  60      62       63   \n",
       "2    3               Venusaur    Grass  Poison    525  80      82       83   \n",
       "3    3  VenusaurMega Venusaur    Grass  Poison    625  80     100      123   \n",
       "4    4             Charmander     Fire     NaN    309  39      52       43   \n",
       "5  719                Diancie     Rock   Fairy    600  50     100      150   \n",
       "6  719    DiancieMega Diancie     Rock   Fairy    700  50     160      110   \n",
       "7  720    HoopaHoopa Confined  Psychic   Ghost    600  80     110       60   \n",
       "8  720     HoopaHoopa Unbound  Psychic    Dark    680  80     160       60   \n",
       "9  721              Volcanion     Fire   Water    600  80     110      120   \n",
       "\n",
       "   Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "0       65       65     45           1      False  \n",
       "1       80       80     60           1      False  \n",
       "2      100      100     80           1      False  \n",
       "3      122      120     80           1      False  \n",
       "4       60       50     65           1      False  \n",
       "5      100      150     50           6       True  \n",
       "6      160      110    110           6       True  \n",
       "7      150      130     70           6       True  \n",
       "8      170      130     80           6       True  \n",
       "9      130       90     70           6       True  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Firstly lets create 2 data frame\n",
    "data1 = data.head()\n",
    "data2= data.tail()\n",
    "conc_data_row = pd.concat([data1,data2],axis =0,ignore_index =True) # axis = 0 : adds dataframes in row\n",
    "conc_data_row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Attack</th>\n",
       "      <th>Defense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attack  Defense\n",
       "0      49       49\n",
       "1      62       63\n",
       "2      82       83\n",
       "3     100      123\n",
       "4      52       43"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = data['Attack'].head()\n",
    "data2= data['Defense'].head()\n",
    "conc_data_col = pd.concat([data1,data2],axis =1) # axis = 0 : adds dataframes in row\n",
    "conc_data_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"3.7\"></a>Data types:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 5 basic data types: object(string),booleab, integer, float and categorical.\n",
    "\n",
    "We can make conversion data types like from str to categorical or from int to float\n",
    "\n",
    "Why is category important:  \n",
    "- make dataframe smaller in memory  \n",
    "- can be utilized for anlaysis especially for sklear(we will learn later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#              int64\n",
       "Name          object\n",
       "Type 1        object\n",
       "Type 2        object\n",
       "Total          int64\n",
       "HP             int64\n",
       "Attack         int64\n",
       "Defense        int64\n",
       "Sp. Atk        int64\n",
       "Sp. Def        int64\n",
       "Speed          int64\n",
       "Generation     int64\n",
       "Legendary       bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets convert object(str) to categorical and int to float.\n",
    "data['Type 1'] = data['Type 1'].astype('category')\n",
    "data['Speed'] = data['Speed'].astype('float')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#                int64\n",
       "Name            object\n",
       "Type 1        category\n",
       "Type 2          object\n",
       "Total            int64\n",
       "HP               int64\n",
       "Attack           int64\n",
       "Defense          int64\n",
       "Sp. Atk          int64\n",
       "Sp. Def          int64\n",
       "Speed          float64\n",
       "Generation       int64\n",
       "Legendary         bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As you can see Type 1 is converted from object to categorical\n",
    "# And Speed ,s converted from int to float\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"3.8\"></a>Missing data and testing with assert:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we encounter with missing data, what we can do:\n",
    "\n",
    "- leave as is  \n",
    "- drop them with dropna()  \n",
    "- fill missing value with fillna()  \n",
    "- fill missing values with test statistics like mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800 entries, 0 to 799\n",
      "Data columns (total 13 columns):\n",
      "#             800 non-null int64\n",
      "Name          800 non-null object\n",
      "Type 1        800 non-null category\n",
      "Type 2        414 non-null object\n",
      "Total         800 non-null int64\n",
      "HP            800 non-null int64\n",
      "Attack        800 non-null int64\n",
      "Defense       800 non-null int64\n",
      "Sp. Atk       800 non-null int64\n",
      "Sp. Def       800 non-null int64\n",
      "Speed         800 non-null float64\n",
      "Generation    800 non-null int64\n",
      "Legendary     800 non-null bool\n",
      "dtypes: bool(1), category(1), float64(1), int64(8), object(2)\n",
      "memory usage: 71.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Lets look at does pokemon data have nan value\n",
    "# As you can see there are 800 entries. However Type 2 has 414 non-null object so it has 386 null object.\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN         386\n",
       "Flying       97\n",
       "Ground       35\n",
       "Poison       34\n",
       "Psychic      33\n",
       "Fighting     26\n",
       "Grass        25\n",
       "Fairy        23\n",
       "Steel        22\n",
       "Dark         20\n",
       "Dragon       18\n",
       "Rock         14\n",
       "Ice          14\n",
       "Water        14\n",
       "Ghost        14\n",
       "Fire         12\n",
       "Electric      6\n",
       "Normal        4\n",
       "Bug           3\n",
       "Name: Type 2, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets chech Type 2\n",
    "data[\"Type 2\"].value_counts(dropna =False)\n",
    "# As you can see, there are 386 NAN value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>405</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Charizard</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Flying</td>\n",
       "      <td>534</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>109</td>\n",
       "      <td>85</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>CharizardMega Charizard X</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>634</td>\n",
       "      <td>78</td>\n",
       "      <td>130</td>\n",
       "      <td>111</td>\n",
       "      <td>130</td>\n",
       "      <td>85</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>CharizardMega Charizard Y</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Flying</td>\n",
       "      <td>634</td>\n",
       "      <td>78</td>\n",
       "      <td>104</td>\n",
       "      <td>78</td>\n",
       "      <td>159</td>\n",
       "      <td>115</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>Squirtle</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>Wartortle</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>405</td>\n",
       "      <td>59</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "      <td>Blastoise</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>530</td>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "      <td>105</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>BlastoiseMega Blastoise</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>630</td>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "      <td>120</td>\n",
       "      <td>135</td>\n",
       "      <td>115</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Caterpie</td>\n",
       "      <td>Bug</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195</td>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11</td>\n",
       "      <td>Metapod</td>\n",
       "      <td>Bug</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12</td>\n",
       "      <td>Butterfree</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Flying</td>\n",
       "      <td>395</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13</td>\n",
       "      <td>Weedle</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Poison</td>\n",
       "      <td>195</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14</td>\n",
       "      <td>Kakuna</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Poison</td>\n",
       "      <td>205</td>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15</td>\n",
       "      <td>Beedrill</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Poison</td>\n",
       "      <td>395</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15</td>\n",
       "      <td>BeedrillMega Beedrill</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Poison</td>\n",
       "      <td>495</td>\n",
       "      <td>65</td>\n",
       "      <td>150</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>Pidgey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>251</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>17</td>\n",
       "      <td>Pidgeotto</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>349</td>\n",
       "      <td>63</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>18</td>\n",
       "      <td>Pidgeot</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>479</td>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18</td>\n",
       "      <td>PidgeotMega Pidgeot</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>579</td>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>135</td>\n",
       "      <td>80</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19</td>\n",
       "      <td>Rattata</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>253</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20</td>\n",
       "      <td>Raticate</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413</td>\n",
       "      <td>55</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>70</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21</td>\n",
       "      <td>Spearow</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>262</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>22</td>\n",
       "      <td>Fearow</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>442</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23</td>\n",
       "      <td>Ekans</td>\n",
       "      <td>Poison</td>\n",
       "      <td>NaN</td>\n",
       "      <td>288</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>24</td>\n",
       "      <td>Arbok</td>\n",
       "      <td>Poison</td>\n",
       "      <td>NaN</td>\n",
       "      <td>438</td>\n",
       "      <td>60</td>\n",
       "      <td>85</td>\n",
       "      <td>69</td>\n",
       "      <td>65</td>\n",
       "      <td>79</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>700</td>\n",
       "      <td>Sylveon</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>525</td>\n",
       "      <td>95</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>110</td>\n",
       "      <td>130</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>701</td>\n",
       "      <td>Hawlucha</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>Flying</td>\n",
       "      <td>500</td>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "      <td>63</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>702</td>\n",
       "      <td>Dedenne</td>\n",
       "      <td>Electric</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>431</td>\n",
       "      <td>67</td>\n",
       "      <td>58</td>\n",
       "      <td>57</td>\n",
       "      <td>81</td>\n",
       "      <td>67</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>703</td>\n",
       "      <td>Carbink</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>704</td>\n",
       "      <td>Goomy</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>35</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>705</td>\n",
       "      <td>Sliggoo</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>452</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>53</td>\n",
       "      <td>83</td>\n",
       "      <td>113</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>706</td>\n",
       "      <td>Goodra</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>110</td>\n",
       "      <td>150</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>707</td>\n",
       "      <td>Klefki</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>470</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "      <td>91</td>\n",
       "      <td>80</td>\n",
       "      <td>87</td>\n",
       "      <td>75.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>708</td>\n",
       "      <td>Phantump</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>309</td>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "      <td>48</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>709</td>\n",
       "      <td>Trevenant</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>474</td>\n",
       "      <td>85</td>\n",
       "      <td>110</td>\n",
       "      <td>76</td>\n",
       "      <td>65</td>\n",
       "      <td>82</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>710</td>\n",
       "      <td>PumpkabooAverage Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>335</td>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>710</td>\n",
       "      <td>PumpkabooSmall Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>335</td>\n",
       "      <td>44</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>710</td>\n",
       "      <td>PumpkabooLarge Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>335</td>\n",
       "      <td>54</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>710</td>\n",
       "      <td>PumpkabooSuper Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>335</td>\n",
       "      <td>59</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>711</td>\n",
       "      <td>GourgeistAverage Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>494</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>122</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
       "      <td>84.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>711</td>\n",
       "      <td>GourgeistSmall Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>494</td>\n",
       "      <td>55</td>\n",
       "      <td>85</td>\n",
       "      <td>122</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
       "      <td>99.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>711</td>\n",
       "      <td>GourgeistLarge Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>494</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "      <td>122</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>711</td>\n",
       "      <td>GourgeistSuper Size</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>Grass</td>\n",
       "      <td>494</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>122</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>712</td>\n",
       "      <td>Bergmite</td>\n",
       "      <td>Ice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>304</td>\n",
       "      <td>55</td>\n",
       "      <td>69</td>\n",
       "      <td>85</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>713</td>\n",
       "      <td>Avalugg</td>\n",
       "      <td>Ice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>514</td>\n",
       "      <td>95</td>\n",
       "      <td>117</td>\n",
       "      <td>184</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>714</td>\n",
       "      <td>Noibat</td>\n",
       "      <td>Flying</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>245</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>715</td>\n",
       "      <td>Noivern</td>\n",
       "      <td>Flying</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>535</td>\n",
       "      <td>85</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>80</td>\n",
       "      <td>123.0</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>716</td>\n",
       "      <td>Xerneas</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>680</td>\n",
       "      <td>126</td>\n",
       "      <td>131</td>\n",
       "      <td>95</td>\n",
       "      <td>131</td>\n",
       "      <td>98</td>\n",
       "      <td>99.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>717</td>\n",
       "      <td>Yveltal</td>\n",
       "      <td>Dark</td>\n",
       "      <td>Flying</td>\n",
       "      <td>680</td>\n",
       "      <td>126</td>\n",
       "      <td>131</td>\n",
       "      <td>95</td>\n",
       "      <td>131</td>\n",
       "      <td>98</td>\n",
       "      <td>99.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>718</td>\n",
       "      <td>Zygarde50% Forme</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>Ground</td>\n",
       "      <td>600</td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "      <td>121</td>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "      <td>95.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>719</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>600</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>719</td>\n",
       "      <td>DiancieMega Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>700</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>110.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Confined</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Unbound</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Dark</td>\n",
       "      <td>680</td>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>170</td>\n",
       "      <td>130</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>721</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                       Name    Type 1  Type 2  Total   HP  Attack  \\\n",
       "0      1                  Bulbasaur     Grass  Poison    318   45      49   \n",
       "1      2                    Ivysaur     Grass  Poison    405   60      62   \n",
       "2      3                   Venusaur     Grass  Poison    525   80      82   \n",
       "3      3      VenusaurMega Venusaur     Grass  Poison    625   80     100   \n",
       "4      4                 Charmander      Fire     NaN    309   39      52   \n",
       "5      5                 Charmeleon      Fire     NaN    405   58      64   \n",
       "6      6                  Charizard      Fire  Flying    534   78      84   \n",
       "7      6  CharizardMega Charizard X      Fire  Dragon    634   78     130   \n",
       "8      6  CharizardMega Charizard Y      Fire  Flying    634   78     104   \n",
       "9      7                   Squirtle     Water     NaN    314   44      48   \n",
       "10     8                  Wartortle     Water     NaN    405   59      63   \n",
       "11     9                  Blastoise     Water     NaN    530   79      83   \n",
       "12     9    BlastoiseMega Blastoise     Water     NaN    630   79     103   \n",
       "13    10                   Caterpie       Bug     NaN    195   45      30   \n",
       "14    11                    Metapod       Bug     NaN    205   50      20   \n",
       "15    12                 Butterfree       Bug  Flying    395   60      45   \n",
       "16    13                     Weedle       Bug  Poison    195   40      35   \n",
       "17    14                     Kakuna       Bug  Poison    205   45      25   \n",
       "18    15                   Beedrill       Bug  Poison    395   65      90   \n",
       "19    15      BeedrillMega Beedrill       Bug  Poison    495   65     150   \n",
       "20    16                     Pidgey    Normal  Flying    251   40      45   \n",
       "21    17                  Pidgeotto    Normal  Flying    349   63      60   \n",
       "22    18                    Pidgeot    Normal  Flying    479   83      80   \n",
       "23    18        PidgeotMega Pidgeot    Normal  Flying    579   83      80   \n",
       "24    19                    Rattata    Normal     NaN    253   30      56   \n",
       "25    20                   Raticate    Normal     NaN    413   55      81   \n",
       "26    21                    Spearow    Normal  Flying    262   40      60   \n",
       "27    22                     Fearow    Normal  Flying    442   65      90   \n",
       "28    23                      Ekans    Poison     NaN    288   35      60   \n",
       "29    24                      Arbok    Poison     NaN    438   60      85   \n",
       "..   ...                        ...       ...     ...    ...  ...     ...   \n",
       "770  700                    Sylveon     Fairy     NaN    525   95      65   \n",
       "771  701                   Hawlucha  Fighting  Flying    500   78      92   \n",
       "772  702                    Dedenne  Electric   Fairy    431   67      58   \n",
       "773  703                    Carbink      Rock   Fairy    500   50      50   \n",
       "774  704                      Goomy    Dragon     NaN    300   45      50   \n",
       "775  705                    Sliggoo    Dragon     NaN    452   68      75   \n",
       "776  706                     Goodra    Dragon     NaN    600   90     100   \n",
       "777  707                     Klefki     Steel   Fairy    470   57      80   \n",
       "778  708                   Phantump     Ghost   Grass    309   43      70   \n",
       "779  709                  Trevenant     Ghost   Grass    474   85     110   \n",
       "780  710      PumpkabooAverage Size     Ghost   Grass    335   49      66   \n",
       "781  710        PumpkabooSmall Size     Ghost   Grass    335   44      66   \n",
       "782  710        PumpkabooLarge Size     Ghost   Grass    335   54      66   \n",
       "783  710        PumpkabooSuper Size     Ghost   Grass    335   59      66   \n",
       "784  711      GourgeistAverage Size     Ghost   Grass    494   65      90   \n",
       "785  711        GourgeistSmall Size     Ghost   Grass    494   55      85   \n",
       "786  711        GourgeistLarge Size     Ghost   Grass    494   75      95   \n",
       "787  711        GourgeistSuper Size     Ghost   Grass    494   85     100   \n",
       "788  712                   Bergmite       Ice     NaN    304   55      69   \n",
       "789  713                    Avalugg       Ice     NaN    514   95     117   \n",
       "790  714                     Noibat    Flying  Dragon    245   40      30   \n",
       "791  715                    Noivern    Flying  Dragon    535   85      70   \n",
       "792  716                    Xerneas     Fairy     NaN    680  126     131   \n",
       "793  717                    Yveltal      Dark  Flying    680  126     131   \n",
       "794  718           Zygarde50% Forme    Dragon  Ground    600  108     100   \n",
       "795  719                    Diancie      Rock   Fairy    600   50     100   \n",
       "796  719        DiancieMega Diancie      Rock   Fairy    700   50     160   \n",
       "797  720        HoopaHoopa Confined   Psychic   Ghost    600   80     110   \n",
       "798  720         HoopaHoopa Unbound   Psychic    Dark    680   80     160   \n",
       "799  721                  Volcanion      Fire   Water    600   80     110   \n",
       "\n",
       "     Defense  Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "0         49       65       65   45.0           1      False  \n",
       "1         63       80       80   60.0           1      False  \n",
       "2         83      100      100   80.0           1      False  \n",
       "3        123      122      120   80.0           1      False  \n",
       "4         43       60       50   65.0           1      False  \n",
       "5         58       80       65   80.0           1      False  \n",
       "6         78      109       85  100.0           1      False  \n",
       "7        111      130       85  100.0           1      False  \n",
       "8         78      159      115  100.0           1      False  \n",
       "9         65       50       64   43.0           1      False  \n",
       "10        80       65       80   58.0           1      False  \n",
       "11       100       85      105   78.0           1      False  \n",
       "12       120      135      115   78.0           1      False  \n",
       "13        35       20       20   45.0           1      False  \n",
       "14        55       25       25   30.0           1      False  \n",
       "15        50       90       80   70.0           1      False  \n",
       "16        30       20       20   50.0           1      False  \n",
       "17        50       25       25   35.0           1      False  \n",
       "18        40       45       80   75.0           1      False  \n",
       "19        40       15       80  145.0           1      False  \n",
       "20        40       35       35   56.0           1      False  \n",
       "21        55       50       50   71.0           1      False  \n",
       "22        75       70       70  101.0           1      False  \n",
       "23        80      135       80  121.0           1      False  \n",
       "24        35       25       35   72.0           1      False  \n",
       "25        60       50       70   97.0           1      False  \n",
       "26        30       31       31   70.0           1      False  \n",
       "27        65       61       61  100.0           1      False  \n",
       "28        44       40       54   55.0           1      False  \n",
       "29        69       65       79   80.0           1      False  \n",
       "..       ...      ...      ...    ...         ...        ...  \n",
       "770       65      110      130   60.0           6      False  \n",
       "771       75       74       63  118.0           6      False  \n",
       "772       57       81       67  101.0           6      False  \n",
       "773      150       50      150   50.0           6      False  \n",
       "774       35       55       75   40.0           6      False  \n",
       "775       53       83      113   60.0           6      False  \n",
       "776       70      110      150   80.0           6      False  \n",
       "777       91       80       87   75.0           6      False  \n",
       "778       48       50       60   38.0           6      False  \n",
       "779       76       65       82   56.0           6      False  \n",
       "780       70       44       55   51.0           6      False  \n",
       "781       70       44       55   56.0           6      False  \n",
       "782       70       44       55   46.0           6      False  \n",
       "783       70       44       55   41.0           6      False  \n",
       "784      122       58       75   84.0           6      False  \n",
       "785      122       58       75   99.0           6      False  \n",
       "786      122       58       75   69.0           6      False  \n",
       "787      122       58       75   54.0           6      False  \n",
       "788       85       32       35   28.0           6      False  \n",
       "789      184       44       46   28.0           6      False  \n",
       "790       35       45       40   55.0           6      False  \n",
       "791       80       97       80  123.0           6      False  \n",
       "792       95      131       98   99.0           6       True  \n",
       "793       95      131       98   99.0           6       True  \n",
       "794      121       81       95   95.0           6       True  \n",
       "795      150      100      150   50.0           6       True  \n",
       "796      110      160      110  110.0           6       True  \n",
       "797       60      150      130   70.0           6       True  \n",
       "798       60      170      130   80.0           6       True  \n",
       "799      120      130       90   70.0           6       True  \n",
       "\n",
       "[800 rows x 13 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets drop nan values\n",
    "data1=data # also we will use data to fill missing value so I assign it to data1 variable\n",
    "data1[\"Type 2\"].dropna(inplace = True) # inplace = True means we do not assign it to new variable. Changes automatically assigned to\n",
    "data\n",
    "# So does it work ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets check with assert statement\n",
    "# Assert statement:\n",
    "assert 1==1 # return nothing because it is true\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Type 2\"].fillna('empty',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert data['Type 2'].notnull().all() # returns nothing because we do not have nan values\n",
    "\n",
    "# # With assert statement we can check a lot of thing. For example\n",
    "# assert data.columns[1] == 'Name'\n",
    "# assert data.Speed.dtypes == np.int\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Summary:_\n",
    "- Diagnose data for cleaning\n",
    "- Exploratory data analysis\n",
    "- Visual exploratory data analysis\n",
    "- Tidy data\n",
    "- Pivoting data\n",
    "- Concatenating data\n",
    "- Data types\n",
    "- Missing data and testing with assert"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"4\"></a>Pandas Foundation:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### REVİEW of PANDAS\n",
    "- As single column = series\n",
    "- NaN = not a number\n",
    "- dataframe.values = numpy\n",
    "\n",
    "### BUILDING DATA FRAMES FROM SCRATCH\n",
    "- We can build data frames from csv as we did earlier.\n",
    "- Also we can build dataframe from dictionaries\n",
    "    - zip() method: This function returns a list of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables.\n",
    "- Adding new column\n",
    "- Broadcasting: Create new column and assign a value to entire column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country population\n",
       "0   Spain         11\n",
       "1  France         12"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data frames from dictionary\n",
    "country = [\"Spain\",\"France\"]\n",
    "population = [\"11\",\"12\"]\n",
    "list_label = [\"country\",\"population\"]\n",
    "list_col = [country,population]\n",
    "zipped = list(zip(list_label,list_col))\n",
    "data_dict = dict(zipped)\n",
    "df = pd.DataFrame(data_dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>country</th>\n",
       "      <th>population</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>12</td>\n",
       "      <td>paris</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country population capital\n",
       "0   Spain         11  madrid\n",
       "1  France         12   paris"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add new columns\n",
    "df[\"capital\"] = [\"madrid\",\"paris\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>country</th>\n",
       "      <th>population</th>\n",
       "      <th>capital</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>madrid</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>12</td>\n",
       "      <td>paris</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country population capital  income\n",
       "0   Spain         11  madrid       0\n",
       "1  France         12   paris       0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Broadcasting\n",
    "df[\"income\"] = 0 #Broadcasting entire column\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"4.3\"></a>Visual exploratory data analysis:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot\n",
    "- Subplot\n",
    "- Histogram:\n",
    "    - bins: number of bins\n",
    "    - range(tuble): min and max values of bins\n",
    "    - normed(boolean): normalize or not\n",
    "    - cumulative(boolean): compute cumulative distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f17f53cf8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plotting all data\n",
    "data1 = data.loc[:,[\"Attack\",\"Defense\",\"Speed\"]]\n",
    "data1.plot()\n",
    "# it is confusing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplots\n",
    "data1.plot(subplots = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": [
    "# scatter plot\n",
    "data1.plot(kind = \"scatter\",x=\"Attack\",y = \"Defense\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankur\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py:1344: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f184614a8>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# hist plot\n",
    "data1.plot(kind = \"hist\",y = \"Defense\",bins = 50,range= (0,250),normed = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankur\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py:1344: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)\n",
      "C:\\Users\\ankur\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py:1344: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\ankur\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram subplot with non cumulative and cumulative\n",
    "fig, axes = plt.subplots(nrows=2,ncols=1)\n",
    "data1.plot(kind = \"hist\",y = \"Defense\",bins = 50,range= (0,250),normed = True,ax = axes[0])\n",
    "data1.plot(kind = \"hist\",y = \"Defense\",bins = 50,range= (0,250),normed = True,ax = axes[1],cumulative = True)\n",
    "plt.savefig('plots/graph.png')\n",
    "plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"4.4\"></a>Statistical explatory data analysis:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- count: number of entries\n",
    "- mean: average of entries\n",
    "- std: standart deviation\n",
    "- min: minimum entry\n",
    "- 25%: first quantile\n",
    "- 50%: median or second quantile\n",
    "- 75%: third quantile\n",
    "- max: maximum entry\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>#</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.00000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>362.813750</td>\n",
       "      <td>435.10250</td>\n",
       "      <td>69.258750</td>\n",
       "      <td>79.001250</td>\n",
       "      <td>73.842500</td>\n",
       "      <td>72.820000</td>\n",
       "      <td>71.902500</td>\n",
       "      <td>68.277500</td>\n",
       "      <td>3.32375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>208.343798</td>\n",
       "      <td>119.96304</td>\n",
       "      <td>25.534669</td>\n",
       "      <td>32.457366</td>\n",
       "      <td>31.183501</td>\n",
       "      <td>32.722294</td>\n",
       "      <td>27.828916</td>\n",
       "      <td>29.060474</td>\n",
       "      <td>1.66129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>180.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>184.750000</td>\n",
       "      <td>330.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>364.500000</td>\n",
       "      <td>450.00000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>539.250000</td>\n",
       "      <td>515.00000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>721.000000</td>\n",
       "      <td>780.00000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                #      Total          HP      Attack     Defense     Sp. Atk  \\\n",
       "count  800.000000  800.00000  800.000000  800.000000  800.000000  800.000000   \n",
       "mean   362.813750  435.10250   69.258750   79.001250   73.842500   72.820000   \n",
       "std    208.343798  119.96304   25.534669   32.457366   31.183501   32.722294   \n",
       "min      1.000000  180.00000    1.000000    5.000000    5.000000   10.000000   \n",
       "25%    184.750000  330.00000   50.000000   55.000000   50.000000   49.750000   \n",
       "50%    364.500000  450.00000   65.000000   75.000000   70.000000   65.000000   \n",
       "75%    539.250000  515.00000   80.000000  100.000000   90.000000   95.000000   \n",
       "max    721.000000  780.00000  255.000000  190.000000  230.000000  194.000000   \n",
       "\n",
       "          Sp. Def       Speed  Generation  \n",
       "count  800.000000  800.000000   800.00000  \n",
       "mean    71.902500   68.277500     3.32375  \n",
       "std     27.828916   29.060474     1.66129  \n",
       "min     20.000000    5.000000     1.00000  \n",
       "25%     50.000000   45.000000     2.00000  \n",
       "50%     70.000000   65.000000     3.00000  \n",
       "75%     90.000000   90.000000     5.00000  \n",
       "max    230.000000  180.000000     6.00000  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"4.5\"></a>Indexing pandas time series:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- datetime = object\n",
    "- parse_dates(boolean): Transform date to ISO 8601 (yyyy-mm-dd hh:mm:ss ) format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n"
     ]
    }
   ],
   "source": [
    "time_list = [\"1992-03-08\",\"1992-04-12\"]\n",
    "print(type(time_list[1])) # As you can see date is string\n",
    "# however we want it to be datetime object\n",
    "datetime_object = pd.to_datetime(time_list)\n",
    "print(type(datetime_object))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1992-01-10</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-10</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-10</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-15</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-16</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #                   Name Type 1  Type 2  Total  HP  Attack  \\\n",
       "date                                                                     \n",
       "1992-01-10  1              Bulbasaur  Grass  Poison    318  45      49   \n",
       "1992-02-10  2                Ivysaur  Grass  Poison    405  60      62   \n",
       "1992-03-10  3               Venusaur  Grass  Poison    525  80      82   \n",
       "1993-03-15  3  VenusaurMega Venusaur  Grass  Poison    625  80     100   \n",
       "1993-03-16  4             Charmander   Fire     NaN    309  39      52   \n",
       "\n",
       "            Defense  Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "date                                                                 \n",
       "1992-01-10       49       65       65   45.0           1      False  \n",
       "1992-02-10       63       80       80   60.0           1      False  \n",
       "1992-03-10       83      100      100   80.0           1      False  \n",
       "1993-03-15      123      122      120   80.0           1      False  \n",
       "1993-03-16       43       60       50   65.0           1      False  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# close warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# In order to practice lets take head of pokemon data and add it a time list\n",
    "data2 = data.head()\n",
    "date_list = [\"1992-01-10\",\"1992-02-10\",\"1992-03-10\",\"1993-03-15\",\"1993-03-16\"]\n",
    "datetime_object = pd.to_datetime(date_list)\n",
    "data2[\"date\"] = datetime_object\n",
    "# lets make date as index\n",
    "data2= data2.set_index(\"date\")\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#                      4\n",
      "Name          Charmander\n",
      "Type 1              Fire\n",
      "Type 2               NaN\n",
      "Total                309\n",
      "HP                    39\n",
      "Attack                52\n",
      "Defense               43\n",
      "Sp. Atk               60\n",
      "Sp. Def               50\n",
      "Speed                 65\n",
      "Generation             1\n",
      "Legendary          False\n",
      "Name: 1993-03-16 00:00:00, dtype: object\n",
      "            #                   Name Type 1  Type 2  Total  HP  Attack  \\\n",
      "date                                                                     \n",
      "1992-03-10  3               Venusaur  Grass  Poison    525  80      82   \n",
      "1993-03-15  3  VenusaurMega Venusaur  Grass  Poison    625  80     100   \n",
      "1993-03-16  4             Charmander   Fire     NaN    309  39      52   \n",
      "\n",
      "            Defense  Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
      "date                                                                 \n",
      "1992-03-10       83      100      100   80.0           1      False  \n",
      "1993-03-15      123      122      120   80.0           1      False  \n",
      "1993-03-16       43       60       50   65.0           1      False  \n"
     ]
    }
   ],
   "source": [
    "# Now we can select according to our date index\n",
    "print(data2.loc[\"1993-03-16\"])\n",
    "print(data2.loc[\"1992-03-10\":\"1993-03-16\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"4.6\"></a>Resampling pandas time series:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Resampling: statistical method over different time intervals\n",
    "    - Needs string to specify frequency like \"M\" = month or \"A\" = year\n",
    "- Downsampling: reduce date time rows to slower frequency like from daily to weekly\n",
    "- Upsampling: increase date time rows to faster frequency like from daily to hourly\n",
    "- Interpolate: Interpolate values according to different methods like ‘linear’, ‘time’ or index’\n",
    "    -  https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.interpolate.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>#</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1992-12-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>64.333333</td>\n",
       "      <td>65.0</td>\n",
       "      <td>81.666667</td>\n",
       "      <td>81.666667</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-31</th>\n",
       "      <td>3.5</td>\n",
       "      <td>467.0</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              #  Total         HP     Attack  Defense    Sp. Atk    Sp. Def  \\\n",
       "date                                                                          \n",
       "1992-12-31  2.0  416.0  61.666667  64.333333     65.0  81.666667  81.666667   \n",
       "1993-12-31  3.5  467.0  59.500000  76.000000     83.0  91.000000  85.000000   \n",
       "\n",
       "                Speed  Generation  Legendary  \n",
       "date                                          \n",
       "1992-12-31  61.666667         1.0      False  \n",
       "1993-12-31  72.500000         1.0      False  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will use data2 that we create at previous part\n",
    "data2.resample(\"A\").mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1992-01-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-29</th>\n",
       "      <td>2.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-07-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-08-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-10-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-11-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-31</th>\n",
       "      <td>3.5</td>\n",
       "      <td>467.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>76.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>72.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              #  Total    HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  \\\n",
       "date                                                                     \n",
       "1992-01-31  1.0  318.0  45.0    49.0     49.0     65.0     65.0   45.0   \n",
       "1992-02-29  2.0  405.0  60.0    62.0     63.0     80.0     80.0   60.0   \n",
       "1992-03-31  3.0  525.0  80.0    82.0     83.0    100.0    100.0   80.0   \n",
       "1992-04-30  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1992-05-31  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1992-06-30  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1992-07-31  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1992-08-31  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1992-09-30  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1992-10-31  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1992-11-30  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1992-12-31  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1993-01-31  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1993-02-28  NaN    NaN   NaN     NaN      NaN      NaN      NaN    NaN   \n",
       "1993-03-31  3.5  467.0  59.5    76.0     83.0     91.0     85.0   72.5   \n",
       "\n",
       "            Generation  Legendary  \n",
       "date                               \n",
       "1992-01-31         1.0        0.0  \n",
       "1992-02-29         1.0        0.0  \n",
       "1992-03-31         1.0        0.0  \n",
       "1992-04-30         NaN        NaN  \n",
       "1992-05-31         NaN        NaN  \n",
       "1992-06-30         NaN        NaN  \n",
       "1992-07-31         NaN        NaN  \n",
       "1992-08-31         NaN        NaN  \n",
       "1992-09-30         NaN        NaN  \n",
       "1992-10-31         NaN        NaN  \n",
       "1992-11-30         NaN        NaN  \n",
       "1992-12-31         NaN        NaN  \n",
       "1993-01-31         NaN        NaN  \n",
       "1993-02-28         NaN        NaN  \n",
       "1993-03-31         1.0        0.0  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets resample with month\n",
    "data2.resample(\"M\").mean()\n",
    "# As you can see there are a lot of nan because data2 does not include all months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
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       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318.000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-29</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>1992-04-30</th>\n",
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       "      <td>533.333333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>86.333333</td>\n",
       "      <td>101.833333</td>\n",
       "      <td>101.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1992-05-31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>541.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>103.666667</td>\n",
       "      <td>103.333333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-06-30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>105.500000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1992-07-31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>558.333333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>96.333333</td>\n",
       "      <td>107.333333</td>\n",
       "      <td>106.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-08-31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>566.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>99.666667</td>\n",
       "      <td>109.166667</td>\n",
       "      <td>108.333333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1992-09-30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1992-10-31</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>583.333333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>92.5</td>\n",
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       "      <td>111.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1992-11-30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>591.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>109.666667</td>\n",
       "      <td>114.666667</td>\n",
       "      <td>113.333333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-12-31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>608.333333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>116.333333</td>\n",
       "      <td>118.333333</td>\n",
       "      <td>116.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1993-02-28</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>616.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>119.666667</td>\n",
       "      <td>120.166667</td>\n",
       "      <td>118.333333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              #                   Name Type 1  Type 2       Total    HP  \\\n",
       "date                                                                      \n",
       "1992-01-31  1.0              Bulbasaur  Grass  Poison  318.000000  45.0   \n",
       "1992-02-29  2.0                Ivysaur  Grass  Poison  405.000000  60.0   \n",
       "1992-03-31  3.0               Venusaur  Grass  Poison  525.000000  80.0   \n",
       "1992-04-30  3.0                    NaN    NaN     NaN  533.333333  80.0   \n",
       "1992-05-31  3.0                    NaN    NaN     NaN  541.666667  80.0   \n",
       "1992-06-30  3.0                    NaN    NaN     NaN  550.000000  80.0   \n",
       "1992-07-31  3.0                    NaN    NaN     NaN  558.333333  80.0   \n",
       "1992-08-31  3.0                    NaN    NaN     NaN  566.666667  80.0   \n",
       "1992-09-30  3.0                    NaN    NaN     NaN  575.000000  80.0   \n",
       "1992-10-31  3.0                    NaN    NaN     NaN  583.333333  80.0   \n",
       "1992-11-30  3.0                    NaN    NaN     NaN  591.666667  80.0   \n",
       "1992-12-31  3.0                    NaN    NaN     NaN  600.000000  80.0   \n",
       "1993-01-31  3.0                    NaN    NaN     NaN  608.333333  80.0   \n",
       "1993-02-28  3.0                    NaN    NaN     NaN  616.666667  80.0   \n",
       "1993-03-31  3.0  VenusaurMega Venusaur  Grass  Poison  625.000000  80.0   \n",
       "\n",
       "            Attack     Defense     Sp. Atk     Sp. Def  Speed  Generation  \\\n",
       "date                                                                        \n",
       "1992-01-31    49.0   49.000000   65.000000   65.000000   45.0         1.0   \n",
       "1992-02-29    62.0   63.000000   80.000000   80.000000   60.0         1.0   \n",
       "1992-03-31    82.0   83.000000  100.000000  100.000000   80.0         1.0   \n",
       "1992-04-30    83.5   86.333333  101.833333  101.666667   80.0         1.0   \n",
       "1992-05-31    85.0   89.666667  103.666667  103.333333   80.0         1.0   \n",
       "1992-06-30    86.5   93.000000  105.500000  105.000000   80.0         1.0   \n",
       "1992-07-31    88.0   96.333333  107.333333  106.666667   80.0         1.0   \n",
       "1992-08-31    89.5   99.666667  109.166667  108.333333   80.0         1.0   \n",
       "1992-09-30    91.0  103.000000  111.000000  110.000000   80.0         1.0   \n",
       "1992-10-31    92.5  106.333333  112.833333  111.666667   80.0         1.0   \n",
       "1992-11-30    94.0  109.666667  114.666667  113.333333   80.0         1.0   \n",
       "1992-12-31    95.5  113.000000  116.500000  115.000000   80.0         1.0   \n",
       "1993-01-31    97.0  116.333333  118.333333  116.666667   80.0         1.0   \n",
       "1993-02-28    98.5  119.666667  120.166667  118.333333   80.0         1.0   \n",
       "1993-03-31   100.0  123.000000  122.000000  120.000000   80.0         1.0   \n",
       "\n",
       "           Legendary  \n",
       "date                  \n",
       "1992-01-31     False  \n",
       "1992-02-29     False  \n",
       "1992-03-31     False  \n",
       "1992-04-30       NaN  \n",
       "1992-05-31       NaN  \n",
       "1992-06-30       NaN  \n",
       "1992-07-31       NaN  \n",
       "1992-08-31       NaN  \n",
       "1992-09-30       NaN  \n",
       "1992-10-31       NaN  \n",
       "1992-11-30       NaN  \n",
       "1992-12-31       NaN  \n",
       "1993-01-31       NaN  \n",
       "1993-02-28       NaN  \n",
       "1993-03-31     False  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In real life (data is real. Not created from us like data2) we can solve this problem with interpolate\n",
    "# We can interpolete from first value\n",
    "data2.resample(\"M\").first().interpolate(\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1992-02-29</th>\n",
       "      <td>2.000000</td>\n",
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       "      <td>62.0</td>\n",
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       "    <tr>\n",
       "      <th>1992-03-31</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>525.000000</td>\n",
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       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>80.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-30</th>\n",
       "      <td>3.041667</td>\n",
       "      <td>520.166667</td>\n",
       "      <td>78.291667</td>\n",
       "      <td>81.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>99.25</td>\n",
       "      <td>98.75</td>\n",
       "      <td>79.375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-31</th>\n",
       "      <td>3.083333</td>\n",
       "      <td>515.333333</td>\n",
       "      <td>76.583333</td>\n",
       "      <td>81.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>98.50</td>\n",
       "      <td>97.50</td>\n",
       "      <td>78.750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-06-30</th>\n",
       "      <td>3.125000</td>\n",
       "      <td>510.500000</td>\n",
       "      <td>74.875000</td>\n",
       "      <td>80.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>97.75</td>\n",
       "      <td>96.25</td>\n",
       "      <td>78.125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-07-31</th>\n",
       "      <td>3.166667</td>\n",
       "      <td>505.666667</td>\n",
       "      <td>73.166667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>97.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>77.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-08-31</th>\n",
       "      <td>3.208333</td>\n",
       "      <td>500.833333</td>\n",
       "      <td>71.458333</td>\n",
       "      <td>79.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>96.25</td>\n",
       "      <td>93.75</td>\n",
       "      <td>76.875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-09-30</th>\n",
       "      <td>3.250000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>69.750000</td>\n",
       "      <td>79.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>95.50</td>\n",
       "      <td>92.50</td>\n",
       "      <td>76.250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-10-31</th>\n",
       "      <td>3.291667</td>\n",
       "      <td>491.166667</td>\n",
       "      <td>68.041667</td>\n",
       "      <td>78.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>94.75</td>\n",
       "      <td>91.25</td>\n",
       "      <td>75.625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-11-30</th>\n",
       "      <td>3.333333</td>\n",
       "      <td>486.333333</td>\n",
       "      <td>66.333333</td>\n",
       "      <td>78.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>75.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-12-31</th>\n",
       "      <td>3.375000</td>\n",
       "      <td>481.500000</td>\n",
       "      <td>64.625000</td>\n",
       "      <td>77.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>93.25</td>\n",
       "      <td>88.75</td>\n",
       "      <td>74.375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-31</th>\n",
       "      <td>3.416667</td>\n",
       "      <td>476.666667</td>\n",
       "      <td>62.916667</td>\n",
       "      <td>77.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>92.50</td>\n",
       "      <td>87.50</td>\n",
       "      <td>73.750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-02-28</th>\n",
       "      <td>3.458333</td>\n",
       "      <td>471.833333</td>\n",
       "      <td>61.208333</td>\n",
       "      <td>76.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>91.75</td>\n",
       "      <td>86.25</td>\n",
       "      <td>73.125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-31</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>76.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>85.00</td>\n",
       "      <td>72.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   #       Total         HP  Attack  Defense  Sp. Atk  \\\n",
       "date                                                                    \n",
       "1992-01-31  1.000000  318.000000  45.000000    49.0     49.0    65.00   \n",
       "1992-02-29  2.000000  405.000000  60.000000    62.0     63.0    80.00   \n",
       "1992-03-31  3.000000  525.000000  80.000000    82.0     83.0   100.00   \n",
       "1992-04-30  3.041667  520.166667  78.291667    81.5     83.0    99.25   \n",
       "1992-05-31  3.083333  515.333333  76.583333    81.0     83.0    98.50   \n",
       "1992-06-30  3.125000  510.500000  74.875000    80.5     83.0    97.75   \n",
       "1992-07-31  3.166667  505.666667  73.166667    80.0     83.0    97.00   \n",
       "1992-08-31  3.208333  500.833333  71.458333    79.5     83.0    96.25   \n",
       "1992-09-30  3.250000  496.000000  69.750000    79.0     83.0    95.50   \n",
       "1992-10-31  3.291667  491.166667  68.041667    78.5     83.0    94.75   \n",
       "1992-11-30  3.333333  486.333333  66.333333    78.0     83.0    94.00   \n",
       "1992-12-31  3.375000  481.500000  64.625000    77.5     83.0    93.25   \n",
       "1993-01-31  3.416667  476.666667  62.916667    77.0     83.0    92.50   \n",
       "1993-02-28  3.458333  471.833333  61.208333    76.5     83.0    91.75   \n",
       "1993-03-31  3.500000  467.000000  59.500000    76.0     83.0    91.00   \n",
       "\n",
       "            Sp. Def   Speed  Generation  Legendary  \n",
       "date                                                \n",
       "1992-01-31    65.00  45.000         1.0        0.0  \n",
       "1992-02-29    80.00  60.000         1.0        0.0  \n",
       "1992-03-31   100.00  80.000         1.0        0.0  \n",
       "1992-04-30    98.75  79.375         1.0        0.0  \n",
       "1992-05-31    97.50  78.750         1.0        0.0  \n",
       "1992-06-30    96.25  78.125         1.0        0.0  \n",
       "1992-07-31    95.00  77.500         1.0        0.0  \n",
       "1992-08-31    93.75  76.875         1.0        0.0  \n",
       "1992-09-30    92.50  76.250         1.0        0.0  \n",
       "1992-10-31    91.25  75.625         1.0        0.0  \n",
       "1992-11-30    90.00  75.000         1.0        0.0  \n",
       "1992-12-31    88.75  74.375         1.0        0.0  \n",
       "1993-01-31    87.50  73.750         1.0        0.0  \n",
       "1993-02-28    86.25  73.125         1.0        0.0  \n",
       "1993-03-31    85.00  72.500         1.0        0.0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or we can interpolate with mean()\n",
    "data2.resample(\"M\").mean().interpolate(\"linear\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"5\"></a>Manipulating Data Frames with Pandas:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.1\"></a>Indexing data frames:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Indexing using square brackets\n",
    "- Using column attribute and row label\n",
    "- Using loc accessor\n",
    "- Selecting only some columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name Type 1  Type 2  Total  HP  Attack  Defense  Sp. Atk  \\\n",
       "#                                                                              \n",
       "1              Bulbasaur  Grass  Poison    318  45      49       49       65   \n",
       "2                Ivysaur  Grass  Poison    405  60      62       63       80   \n",
       "3               Venusaur  Grass  Poison    525  80      82       83      100   \n",
       "3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123      122   \n",
       "4             Charmander   Fire     NaN    309  39      52       43       60   \n",
       "\n",
       "   Sp. Def  Speed  Generation  Legendary  \n",
       "#                                         \n",
       "1       65     45           1      False  \n",
       "2       80     60           1      False  \n",
       "3      100     80           1      False  \n",
       "3      120     80           1      False  \n",
       "4       50     65           1      False  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data\n",
    "data = pd.read_csv('data/Pokemon.csv')\n",
    "data= data.set_index(\"#\")\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing using square brackets\n",
    "data[\"HP\"][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using column attribute and row label\n",
    "data.HP[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HP    45\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using loc accessor\n",
    "data.loc[1,[\"HP\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
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       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>3</th>\n",
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       "      <th>3</th>\n",
       "      <td>80</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>78</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>78</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>59</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>65</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>63</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>55</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>60</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>95</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>67</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>85</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>44</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>54</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>59</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>55</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>55</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>95</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>85</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>126</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>126</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      HP  Attack\n",
       "#               \n",
       "1     45      49\n",
       "2     60      62\n",
       "3     80      82\n",
       "3     80     100\n",
       "4     39      52\n",
       "5     58      64\n",
       "6     78      84\n",
       "6     78     130\n",
       "6     78     104\n",
       "7     44      48\n",
       "8     59      63\n",
       "9     79      83\n",
       "9     79     103\n",
       "10    45      30\n",
       "11    50      20\n",
       "12    60      45\n",
       "13    40      35\n",
       "14    45      25\n",
       "15    65      90\n",
       "15    65     150\n",
       "16    40      45\n",
       "17    63      60\n",
       "18    83      80\n",
       "18    83      80\n",
       "19    30      56\n",
       "20    55      81\n",
       "21    40      60\n",
       "22    65      90\n",
       "23    35      60\n",
       "24    60      85\n",
       "..   ...     ...\n",
       "700   95      65\n",
       "701   78      92\n",
       "702   67      58\n",
       "703   50      50\n",
       "704   45      50\n",
       "705   68      75\n",
       "706   90     100\n",
       "707   57      80\n",
       "708   43      70\n",
       "709   85     110\n",
       "710   49      66\n",
       "710   44      66\n",
       "710   54      66\n",
       "710   59      66\n",
       "711   65      90\n",
       "711   55      85\n",
       "711   75      95\n",
       "711   85     100\n",
       "712   55      69\n",
       "713   95     117\n",
       "714   40      30\n",
       "715   85      70\n",
       "716  126     131\n",
       "717  126     131\n",
       "718  108     100\n",
       "719   50     100\n",
       "719   50     160\n",
       "720   80     110\n",
       "720   80     160\n",
       "721   80     110\n",
       "\n",
       "[800 rows x 2 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting only some columns\n",
    "data[[\"HP\",\"Attack\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.2\"></a>Slicing data frames:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Difference between selecting columns\n",
    "- Series and data frames\n",
    "- Slicing and indexing series\n",
    "- Reverse slicing\n",
    "- From something to end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# Difference between selecting columns: series and dataframes\n",
    "print(type(data[\"HP\"])) # series\n",
    "print(type(data[[\"HP\"]])) # data frames\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <td>78</td>\n",
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       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>59</td>\n",
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       "      <th>10</th>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HP  Attack  Defense\n",
       "#                      \n",
       "1   45      49       49\n",
       "2   60      62       63\n",
       "3   80      82       83\n",
       "3   80     100      123\n",
       "4   39      52       43\n",
       "5   58      64       58\n",
       "6   78      84       78\n",
       "6   78     130      111\n",
       "6   78     104       78\n",
       "7   44      48       65\n",
       "8   59      63       80\n",
       "9   79      83      100\n",
       "9   79     103      120\n",
       "10  45      30       35"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing and indexing series\n",
    "data.loc[1:10,\"HP\":\"Defense\"] # 10 and \"Defense\" are inclusive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>58</td>\n",
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       "      <td>43</td>\n",
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       "      <td>63</td>\n",
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      ],
      "text/plain": [
       "    HP  Attack  Defense\n",
       "#                      \n",
       "10  45      30       35\n",
       "9   79     103      120\n",
       "9   79      83      100\n",
       "8   59      63       80\n",
       "7   44      48       65\n",
       "6   78     104       78\n",
       "6   78     130      111\n",
       "6   78      84       78\n",
       "5   58      64       58\n",
       "4   39      52       43\n",
       "3   80     100      123\n",
       "3   80      82       83\n",
       "2   60      62       63\n",
       "1   45      49       49"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reverse slicing\n",
    "data.loc[10:1:-1,\"HP\":\"Defense\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Speed  Generation  Legendary\n",
       "#                               \n",
       "1      45           1      False\n",
       "2      60           1      False\n",
       "3      80           1      False\n",
       "3      80           1      False\n",
       "4      65           1      False\n",
       "5      80           1      False\n",
       "6     100           1      False\n",
       "6     100           1      False\n",
       "6     100           1      False\n",
       "7      43           1      False\n",
       "8      58           1      False\n",
       "9      78           1      False\n",
       "9      78           1      False\n",
       "10     45           1      False"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From something to end\n",
    "data.loc[1:10,\"Speed\":]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.3\"></a>Filtering data frames:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Creating boolean series  \n",
    "- Combining filters  \n",
    "- Filtering column based others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Chansey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450</td>\n",
       "      <td>250</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>105</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Blissey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>540</td>\n",
       "      <td>255</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>135</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Type 1 Type 2  Total   HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "#                                                                            \n",
       "113  Chansey  Normal    NaN    450  250       5        5       35      105   \n",
       "242  Blissey  Normal    NaN    540  255      10       10       75      135   \n",
       "\n",
       "     Speed  Generation  Legendary  \n",
       "#                                  \n",
       "113     50           1      False  \n",
       "242     55           2      False  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating boolean series\n",
    "boolean = data.HP > 200\n",
    "data[boolean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Chansey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450</td>\n",
       "      <td>250</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>105</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Blissey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>540</td>\n",
       "      <td>255</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>135</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Wailord</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500</td>\n",
       "      <td>170</td>\n",
       "      <td>90</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>Alomomola</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>470</td>\n",
       "      <td>165</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name  Type 1 Type 2  Total   HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "#                                                                              \n",
       "113    Chansey  Normal    NaN    450  250       5        5       35      105   \n",
       "242    Blissey  Normal    NaN    540  255      10       10       75      135   \n",
       "321    Wailord   Water    NaN    500  170      90       45       90       45   \n",
       "594  Alomomola   Water    NaN    470  165      75       80       40       45   \n",
       "\n",
       "     Speed  Generation  Legendary  \n",
       "#                                  \n",
       "113     50           1      False  \n",
       "242     55           2      False  \n",
       "321     60           3      False  \n",
       "594     65           5      False  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combining filters\n",
    "first_filter = data.HP > 150\n",
    "second_filter = data.Speed > 35\n",
    "data[first_filter & second_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#\n",
       "213     20\n",
       "328     45\n",
       "438     50\n",
       "446    135\n",
       "597     44\n",
       "Name: HP, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering column based others\n",
    "data.HP[data.Speed<15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.4\"></a>Transforming data frames:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plain python functions\n",
    "- Lambda function: to apply arbitrary python function to every element\n",
    "- Defining column using other columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#\n",
       "1      22.5\n",
       "2      30.0\n",
       "3      40.0\n",
       "3      40.0\n",
       "4      19.5\n",
       "5      29.0\n",
       "6      39.0\n",
       "6      39.0\n",
       "6      39.0\n",
       "7      22.0\n",
       "8      29.5\n",
       "9      39.5\n",
       "9      39.5\n",
       "10     22.5\n",
       "11     25.0\n",
       "12     30.0\n",
       "13     20.0\n",
       "14     22.5\n",
       "15     32.5\n",
       "15     32.5\n",
       "16     20.0\n",
       "17     31.5\n",
       "18     41.5\n",
       "18     41.5\n",
       "19     15.0\n",
       "20     27.5\n",
       "21     20.0\n",
       "22     32.5\n",
       "23     17.5\n",
       "24     30.0\n",
       "       ... \n",
       "700    47.5\n",
       "701    39.0\n",
       "702    33.5\n",
       "703    25.0\n",
       "704    22.5\n",
       "705    34.0\n",
       "706    45.0\n",
       "707    28.5\n",
       "708    21.5\n",
       "709    42.5\n",
       "710    24.5\n",
       "710    22.0\n",
       "710    27.0\n",
       "710    29.5\n",
       "711    32.5\n",
       "711    27.5\n",
       "711    37.5\n",
       "711    42.5\n",
       "712    27.5\n",
       "713    47.5\n",
       "714    20.0\n",
       "715    42.5\n",
       "716    63.0\n",
       "717    63.0\n",
       "718    54.0\n",
       "719    25.0\n",
       "719    25.0\n",
       "720    40.0\n",
       "720    40.0\n",
       "721    40.0\n",
       "Name: HP, Length: 800, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plain python functions\n",
    "def div(n):\n",
    "    return n/2\n",
    "data.HP.apply(div)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#\n",
       "1      22.5\n",
       "2      30.0\n",
       "3      40.0\n",
       "3      40.0\n",
       "4      19.5\n",
       "5      29.0\n",
       "6      39.0\n",
       "6      39.0\n",
       "6      39.0\n",
       "7      22.0\n",
       "8      29.5\n",
       "9      39.5\n",
       "9      39.5\n",
       "10     22.5\n",
       "11     25.0\n",
       "12     30.0\n",
       "13     20.0\n",
       "14     22.5\n",
       "15     32.5\n",
       "15     32.5\n",
       "16     20.0\n",
       "17     31.5\n",
       "18     41.5\n",
       "18     41.5\n",
       "19     15.0\n",
       "20     27.5\n",
       "21     20.0\n",
       "22     32.5\n",
       "23     17.5\n",
       "24     30.0\n",
       "       ... \n",
       "700    47.5\n",
       "701    39.0\n",
       "702    33.5\n",
       "703    25.0\n",
       "704    22.5\n",
       "705    34.0\n",
       "706    45.0\n",
       "707    28.5\n",
       "708    21.5\n",
       "709    42.5\n",
       "710    24.5\n",
       "710    22.0\n",
       "710    27.0\n",
       "710    29.5\n",
       "711    32.5\n",
       "711    27.5\n",
       "711    37.5\n",
       "711    42.5\n",
       "712    27.5\n",
       "713    47.5\n",
       "714    20.0\n",
       "715    42.5\n",
       "716    63.0\n",
       "717    63.0\n",
       "718    54.0\n",
       "719    25.0\n",
       "719    25.0\n",
       "720    40.0\n",
       "720    40.0\n",
       "721    40.0\n",
       "Name: HP, Length: 800, dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or we can use lambda function\n",
    "data.HP.apply(lambda n : n/2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
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       "      <th>Legendary</th>\n",
       "      <th>total_power</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name Type 1  Type 2  Total  HP  Attack  Defense  Sp. Atk  \\\n",
       "#                                                                              \n",
       "1              Bulbasaur  Grass  Poison    318  45      49       49       65   \n",
       "2                Ivysaur  Grass  Poison    405  60      62       63       80   \n",
       "3               Venusaur  Grass  Poison    525  80      82       83      100   \n",
       "3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123      122   \n",
       "4             Charmander   Fire     NaN    309  39      52       43       60   \n",
       "\n",
       "   Sp. Def  Speed  Generation  Legendary  total_power  \n",
       "#                                                      \n",
       "1       65     45           1      False           98  \n",
       "2       80     60           1      False          125  \n",
       "3      100     80           1      False          165  \n",
       "3      120     80           1      False          223  \n",
       "4       50     65           1      False           95  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining column using other columns\n",
    "data[\"total_power\"] = data.Attack + data.Defense\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.5\"></a>Index objects and labeled data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
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       "    <tr>\n",
       "      <th>index_name</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "index_name                                                                     \n",
       "1                       Bulbasaur  Grass  Poison    318  45      49       49   \n",
       "2                         Ivysaur  Grass  Poison    405  60      62       63   \n",
       "3                        Venusaur  Grass  Poison    525  80      82       83   \n",
       "3           VenusaurMega Venusaur  Grass  Poison    625  80     100      123   \n",
       "4                      Charmander   Fire     NaN    309  39      52       43   \n",
       "\n",
       "            Sp. Atk  Sp. Def  Speed  Generation  Legendary  total_power  \n",
       "index_name                                                               \n",
       "1                65       65     45           1      False           98  \n",
       "2                80       80     60           1      False          125  \n",
       "3               100      100     80           1      False          165  \n",
       "3               122      120     80           1      False          223  \n",
       "4                60       50     65           1      False           95  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our index name is this:\n",
    "print(data.index.name)\n",
    "# lets change it\n",
    "data.index.name = \"index_name\"\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
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       "      <th>102</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "100              Bulbasaur  Grass  Poison    318  45      49       49   \n",
       "101                Ivysaur  Grass  Poison    405  60      62       63   \n",
       "102               Venusaur  Grass  Poison    525  80      82       83   \n",
       "103  VenusaurMega Venusaur  Grass  Poison    625  80     100      123   \n",
       "104             Charmander   Fire     NaN    309  39      52       43   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  total_power  \n",
       "100       65       65     45           1      False           98  \n",
       "101       80       80     60           1      False          125  \n",
       "102      100      100     80           1      False          165  \n",
       "103      122      120     80           1      False          223  \n",
       "104       60       50     65           1      False           95  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overwrite index\n",
    "# if we want to modify index we need to change all of them.\n",
    "data.head()\n",
    "# first copy of our data to data3 then change index\n",
    "data3 = data.copy()\n",
    "# lets make index start from 100. It is not remarkable change but it is just example\n",
    "data3.index = range(100,900,1)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.6\"></a>Hierarchical indexing:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #                   Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "0  1              Bulbasaur  Grass  Poison    318  45      49       49   \n",
       "1  2                Ivysaur  Grass  Poison    405  60      62       63   \n",
       "2  3               Venusaur  Grass  Poison    525  80      82       83   \n",
       "3  3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123   \n",
       "4  4             Charmander   Fire     NaN    309  39      52       43   \n",
       "\n",
       "   Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "0       65       65     45           1      False  \n",
       "1       80       80     60           1      False  \n",
       "2      100      100     80           1      False  \n",
       "3      122      120     80           1      False  \n",
       "4       60       50     65           1      False  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets read data frame one more time to start from beginning\n",
    "data = pd.read_csv('data/Pokemon.csv')\n",
    "data.head()\n",
    "# As you can see there is index. However we want to set one or more column to be index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Grass</th>\n",
       "      <th>Poison</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Fire</th>\n",
       "      <th>NaN</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>405</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>6</td>\n",
       "      <td>Charizard</td>\n",
       "      <td>534</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>109</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dragon</th>\n",
       "      <td>6</td>\n",
       "      <td>CharizardMega Charizard X</td>\n",
       "      <td>634</td>\n",
       "      <td>78</td>\n",
       "      <td>130</td>\n",
       "      <td>111</td>\n",
       "      <td>130</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>6</td>\n",
       "      <td>CharizardMega Charizard Y</td>\n",
       "      <td>634</td>\n",
       "      <td>78</td>\n",
       "      <td>104</td>\n",
       "      <td>78</td>\n",
       "      <td>159</td>\n",
       "      <td>115</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Water</th>\n",
       "      <th>NaN</th>\n",
       "      <td>7</td>\n",
       "      <td>Squirtle</td>\n",
       "      <td>314</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>8</td>\n",
       "      <td>Wartortle</td>\n",
       "      <td>405</td>\n",
       "      <td>59</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>9</td>\n",
       "      <td>Blastoise</td>\n",
       "      <td>530</td>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>9</td>\n",
       "      <td>BlastoiseMega Blastoise</td>\n",
       "      <td>630</td>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "      <td>120</td>\n",
       "      <td>135</td>\n",
       "      <td>115</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Bug</th>\n",
       "      <th>NaN</th>\n",
       "      <td>10</td>\n",
       "      <td>Caterpie</td>\n",
       "      <td>195</td>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>11</td>\n",
       "      <td>Metapod</td>\n",
       "      <td>205</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>12</td>\n",
       "      <td>Butterfree</td>\n",
       "      <td>395</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>13</td>\n",
       "      <td>Weedle</td>\n",
       "      <td>195</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>14</td>\n",
       "      <td>Kakuna</td>\n",
       "      <td>205</td>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>15</td>\n",
       "      <td>Beedrill</td>\n",
       "      <td>395</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>15</td>\n",
       "      <td>BeedrillMega Beedrill</td>\n",
       "      <td>495</td>\n",
       "      <td>65</td>\n",
       "      <td>150</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Normal</th>\n",
       "      <th>Flying</th>\n",
       "      <td>16</td>\n",
       "      <td>Pidgey</td>\n",
       "      <td>251</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>17</td>\n",
       "      <td>Pidgeotto</td>\n",
       "      <td>349</td>\n",
       "      <td>63</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>18</td>\n",
       "      <td>Pidgeot</td>\n",
       "      <td>479</td>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>18</td>\n",
       "      <td>PidgeotMega Pidgeot</td>\n",
       "      <td>579</td>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>135</td>\n",
       "      <td>80</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>19</td>\n",
       "      <td>Rattata</td>\n",
       "      <td>253</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>20</td>\n",
       "      <td>Raticate</td>\n",
       "      <td>413</td>\n",
       "      <td>55</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>70</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>21</td>\n",
       "      <td>Spearow</td>\n",
       "      <td>262</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>22</td>\n",
       "      <td>Fearow</td>\n",
       "      <td>442</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Poison</th>\n",
       "      <th>NaN</th>\n",
       "      <td>23</td>\n",
       "      <td>Ekans</td>\n",
       "      <td>288</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>24</td>\n",
       "      <td>Arbok</td>\n",
       "      <td>438</td>\n",
       "      <td>60</td>\n",
       "      <td>85</td>\n",
       "      <td>69</td>\n",
       "      <td>65</td>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Psychic</th>\n",
       "      <th>NaN</th>\n",
       "      <td>65</td>\n",
       "      <td>Alakazam</td>\n",
       "      <td>500</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>135</td>\n",
       "      <td>95</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>65</td>\n",
       "      <td>AlakazamMega Alakazam</td>\n",
       "      <td>590</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>175</td>\n",
       "      <td>95</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Fighting</th>\n",
       "      <th>NaN</th>\n",
       "      <td>66</td>\n",
       "      <td>Machop</td>\n",
       "      <td>305</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>67</td>\n",
       "      <td>Machoke</td>\n",
       "      <td>405</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>68</td>\n",
       "      <td>Machamp</td>\n",
       "      <td>505</td>\n",
       "      <td>90</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>85</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Grass</th>\n",
       "      <th>Poison</th>\n",
       "      <td>69</td>\n",
       "      <td>Bellsprout</td>\n",
       "      <td>300</td>\n",
       "      <td>50</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "      <td>70</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>70</td>\n",
       "      <td>Weepinbell</td>\n",
       "      <td>390</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>85</td>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>71</td>\n",
       "      <td>Victreebel</td>\n",
       "      <td>490</td>\n",
       "      <td>80</td>\n",
       "      <td>105</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Water</th>\n",
       "      <th>Poison</th>\n",
       "      <td>72</td>\n",
       "      <td>Tentacool</td>\n",
       "      <td>335</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>73</td>\n",
       "      <td>Tentacruel</td>\n",
       "      <td>515</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>120</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Rock</th>\n",
       "      <th>Ground</th>\n",
       "      <td>74</td>\n",
       "      <td>Geodude</td>\n",
       "      <td>300</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ground</th>\n",
       "      <td>75</td>\n",
       "      <td>Graveler</td>\n",
       "      <td>390</td>\n",
       "      <td>55</td>\n",
       "      <td>95</td>\n",
       "      <td>115</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ground</th>\n",
       "      <td>76</td>\n",
       "      <td>Golem</td>\n",
       "      <td>495</td>\n",
       "      <td>80</td>\n",
       "      <td>120</td>\n",
       "      <td>130</td>\n",
       "      <td>55</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Fire</th>\n",
       "      <th>NaN</th>\n",
       "      <td>77</td>\n",
       "      <td>Ponyta</td>\n",
       "      <td>410</td>\n",
       "      <td>50</td>\n",
       "      <td>85</td>\n",
       "      <td>55</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>78</td>\n",
       "      <td>Rapidash</td>\n",
       "      <td>500</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Water</th>\n",
       "      <th>Psychic</th>\n",
       "      <td>79</td>\n",
       "      <td>Slowpoke</td>\n",
       "      <td>315</td>\n",
       "      <td>90</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychic</th>\n",
       "      <td>80</td>\n",
       "      <td>Slowbro</td>\n",
       "      <td>490</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "      <td>110</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychic</th>\n",
       "      <td>80</td>\n",
       "      <td>SlowbroMega Slowbro</td>\n",
       "      <td>590</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "      <td>180</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Electric</th>\n",
       "      <th>Steel</th>\n",
       "      <td>81</td>\n",
       "      <td>Magnemite</td>\n",
       "      <td>325</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>70</td>\n",
       "      <td>95</td>\n",
       "      <td>55</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel</th>\n",
       "      <td>82</td>\n",
       "      <td>Magneton</td>\n",
       "      <td>465</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>95</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Normal</th>\n",
       "      <th>Flying</th>\n",
       "      <td>83</td>\n",
       "      <td>Farfetch'd</td>\n",
       "      <td>352</td>\n",
       "      <td>52</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>58</td>\n",
       "      <td>62</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>84</td>\n",
       "      <td>Doduo</td>\n",
       "      <td>310</td>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>85</td>\n",
       "      <td>Dodrio</td>\n",
       "      <td>460</td>\n",
       "      <td>60</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Water</th>\n",
       "      <th>NaN</th>\n",
       "      <td>86</td>\n",
       "      <td>Seel</td>\n",
       "      <td>325</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice</th>\n",
       "      <td>87</td>\n",
       "      <td>Dewgong</td>\n",
       "      <td>475</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>95</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Poison</th>\n",
       "      <th>NaN</th>\n",
       "      <td>88</td>\n",
       "      <td>Grimer</td>\n",
       "      <td>325</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>89</td>\n",
       "      <td>Muk</td>\n",
       "      <td>500</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Water</th>\n",
       "      <th>NaN</th>\n",
       "      <td>90</td>\n",
       "      <td>Shellder</td>\n",
       "      <td>305</td>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice</th>\n",
       "      <td>91</td>\n",
       "      <td>Cloyster</td>\n",
       "      <td>525</td>\n",
       "      <td>50</td>\n",
       "      <td>95</td>\n",
       "      <td>180</td>\n",
       "      <td>85</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ghost</th>\n",
       "      <th>Poison</th>\n",
       "      <td>92</td>\n",
       "      <td>Gastly</td>\n",
       "      <td>310</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>35</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   #                       Name  Total   HP  Attack  Defense  \\\n",
       "Type 1   Type 2                                                                \n",
       "Grass    Poison    1                  Bulbasaur    318   45      49       49   \n",
       "         Poison    2                    Ivysaur    405   60      62       63   \n",
       "         Poison    3                   Venusaur    525   80      82       83   \n",
       "         Poison    3      VenusaurMega Venusaur    625   80     100      123   \n",
       "Fire     NaN       4                 Charmander    309   39      52       43   \n",
       "         NaN       5                 Charmeleon    405   58      64       58   \n",
       "         Flying    6                  Charizard    534   78      84       78   \n",
       "         Dragon    6  CharizardMega Charizard X    634   78     130      111   \n",
       "         Flying    6  CharizardMega Charizard Y    634   78     104       78   \n",
       "Water    NaN       7                   Squirtle    314   44      48       65   \n",
       "         NaN       8                  Wartortle    405   59      63       80   \n",
       "         NaN       9                  Blastoise    530   79      83      100   \n",
       "         NaN       9    BlastoiseMega Blastoise    630   79     103      120   \n",
       "Bug      NaN      10                   Caterpie    195   45      30       35   \n",
       "         NaN      11                    Metapod    205   50      20       55   \n",
       "         Flying   12                 Butterfree    395   60      45       50   \n",
       "         Poison   13                     Weedle    195   40      35       30   \n",
       "         Poison   14                     Kakuna    205   45      25       50   \n",
       "         Poison   15                   Beedrill    395   65      90       40   \n",
       "         Poison   15      BeedrillMega Beedrill    495   65     150       40   \n",
       "Normal   Flying   16                     Pidgey    251   40      45       40   \n",
       "         Flying   17                  Pidgeotto    349   63      60       55   \n",
       "         Flying   18                    Pidgeot    479   83      80       75   \n",
       "         Flying   18        PidgeotMega Pidgeot    579   83      80       80   \n",
       "         NaN      19                    Rattata    253   30      56       35   \n",
       "         NaN      20                   Raticate    413   55      81       60   \n",
       "         Flying   21                    Spearow    262   40      60       30   \n",
       "         Flying   22                     Fearow    442   65      90       65   \n",
       "Poison   NaN      23                      Ekans    288   35      60       44   \n",
       "         NaN      24                      Arbok    438   60      85       69   \n",
       "...               ..                        ...    ...  ...     ...      ...   \n",
       "Psychic  NaN      65                   Alakazam    500   55      50       45   \n",
       "         NaN      65      AlakazamMega Alakazam    590   55      50       65   \n",
       "Fighting NaN      66                     Machop    305   70      80       50   \n",
       "         NaN      67                    Machoke    405   80     100       70   \n",
       "         NaN      68                    Machamp    505   90     130       80   \n",
       "Grass    Poison   69                 Bellsprout    300   50      75       35   \n",
       "         Poison   70                 Weepinbell    390   65      90       50   \n",
       "         Poison   71                 Victreebel    490   80     105       65   \n",
       "Water    Poison   72                  Tentacool    335   40      40       35   \n",
       "         Poison   73                 Tentacruel    515   80      70       65   \n",
       "Rock     Ground   74                    Geodude    300   40      80      100   \n",
       "         Ground   75                   Graveler    390   55      95      115   \n",
       "         Ground   76                      Golem    495   80     120      130   \n",
       "Fire     NaN      77                     Ponyta    410   50      85       55   \n",
       "         NaN      78                   Rapidash    500   65     100       70   \n",
       "Water    Psychic  79                   Slowpoke    315   90      65       65   \n",
       "         Psychic  80                    Slowbro    490   95      75      110   \n",
       "         Psychic  80        SlowbroMega Slowbro    590   95      75      180   \n",
       "Electric Steel    81                  Magnemite    325   25      35       70   \n",
       "         Steel    82                   Magneton    465   50      60       95   \n",
       "Normal   Flying   83                 Farfetch'd    352   52      65       55   \n",
       "         Flying   84                      Doduo    310   35      85       45   \n",
       "         Flying   85                     Dodrio    460   60     110       70   \n",
       "Water    NaN      86                       Seel    325   65      45       55   \n",
       "         Ice      87                    Dewgong    475   90      70       80   \n",
       "Poison   NaN      88                     Grimer    325   80      80       50   \n",
       "         NaN      89                        Muk    500  105     105       75   \n",
       "Water    NaN      90                   Shellder    305   30      65      100   \n",
       "         Ice      91                   Cloyster    525   50      95      180   \n",
       "Ghost    Poison   92                     Gastly    310   30      35       30   \n",
       "\n",
       "                  Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "Type 1   Type 2                                                   \n",
       "Grass    Poison        65       65     45           1      False  \n",
       "         Poison        80       80     60           1      False  \n",
       "         Poison       100      100     80           1      False  \n",
       "         Poison       122      120     80           1      False  \n",
       "Fire     NaN           60       50     65           1      False  \n",
       "         NaN           80       65     80           1      False  \n",
       "         Flying       109       85    100           1      False  \n",
       "         Dragon       130       85    100           1      False  \n",
       "         Flying       159      115    100           1      False  \n",
       "Water    NaN           50       64     43           1      False  \n",
       "         NaN           65       80     58           1      False  \n",
       "         NaN           85      105     78           1      False  \n",
       "         NaN          135      115     78           1      False  \n",
       "Bug      NaN           20       20     45           1      False  \n",
       "         NaN           25       25     30           1      False  \n",
       "         Flying        90       80     70           1      False  \n",
       "         Poison        20       20     50           1      False  \n",
       "         Poison        25       25     35           1      False  \n",
       "         Poison        45       80     75           1      False  \n",
       "         Poison        15       80    145           1      False  \n",
       "Normal   Flying        35       35     56           1      False  \n",
       "         Flying        50       50     71           1      False  \n",
       "         Flying        70       70    101           1      False  \n",
       "         Flying       135       80    121           1      False  \n",
       "         NaN           25       35     72           1      False  \n",
       "         NaN           50       70     97           1      False  \n",
       "         Flying        31       31     70           1      False  \n",
       "         Flying        61       61    100           1      False  \n",
       "Poison   NaN           40       54     55           1      False  \n",
       "         NaN           65       79     80           1      False  \n",
       "...                   ...      ...    ...         ...        ...  \n",
       "Psychic  NaN          135       95    120           1      False  \n",
       "         NaN          175       95    150           1      False  \n",
       "Fighting NaN           35       35     35           1      False  \n",
       "         NaN           50       60     45           1      False  \n",
       "         NaN           65       85     55           1      False  \n",
       "Grass    Poison        70       30     40           1      False  \n",
       "         Poison        85       45     55           1      False  \n",
       "         Poison       100       70     70           1      False  \n",
       "Water    Poison        50      100     70           1      False  \n",
       "         Poison        80      120    100           1      False  \n",
       "Rock     Ground        30       30     20           1      False  \n",
       "         Ground        45       45     35           1      False  \n",
       "         Ground        55       65     45           1      False  \n",
       "Fire     NaN           65       65     90           1      False  \n",
       "         NaN           80       80    105           1      False  \n",
       "Water    Psychic       40       40     15           1      False  \n",
       "         Psychic      100       80     30           1      False  \n",
       "         Psychic      130       80     30           1      False  \n",
       "Electric Steel         95       55     45           1      False  \n",
       "         Steel        120       70     70           1      False  \n",
       "Normal   Flying        58       62     60           1      False  \n",
       "         Flying        35       35     75           1      False  \n",
       "         Flying        60       60    100           1      False  \n",
       "Water    NaN           45       70     45           1      False  \n",
       "         Ice           70       95     70           1      False  \n",
       "Poison   NaN           40       50     25           1      False  \n",
       "         NaN           65      100     50           1      False  \n",
       "Water    NaN           45       25     40           1      False  \n",
       "         Ice           85       45     70           1      False  \n",
       "Ghost    Poison       100       35     80           1      False  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting index : type 1 is outer type 2 is inner index\n",
    "data1 = data.set_index([\"Type 1\",\"Type 2\"])\n",
    "data1.head(100)\n",
    "# data1.loc[\"Fire\",\"Flying\"] # howw to use indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.7\"></a>Pivoting data frames:\n",
    "\n",
    "- pivoting: reshape tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>treatment</th>\n",
       "      <th>gender</th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment gender  response  age\n",
       "0         A      F        10   15\n",
       "1         A      M        45    4\n",
       "2         B      F         5   72\n",
       "3         B      M         9   65"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {\"treatment\":[\"A\",\"A\",\"B\",\"B\"],\"gender\":[\"F\",\"M\",\"F\",\"M\"],\"response\":[10,45,5,9],\"age\":[15,4,72,65]}\n",
    "df = pd.DataFrame(dic)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender      F   M\n",
       "treatment        \n",
       "A          10  45\n",
       "B           5   9"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivoting\n",
    "df.pivot(index=\"treatment\",columns = \"gender\",values=\"response\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.8\"></a>Stacking and unstacking data frames:\n",
    "- deal with multi label indexes\n",
    "- level: position of unstacked index\n",
    "- swaplevel: change inner and outer level index position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>F</th>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>F</th>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  response  age\n",
       "treatment gender               \n",
       "A         F             10   15\n",
       "          M             45    4\n",
       "B         F              5   72\n",
       "          M              9   65"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.set_index([\"treatment\",\"gender\"])\n",
    "df1\n",
    "# lets unstack it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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",
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       "        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 colspan=\"2\" halign=\"left\">response</th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          response    age    \n",
       "treatment        A  B   A   B\n",
       "gender                       \n",
       "F               10  5  15  72\n",
       "M               45  9   4  65"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# level determines indexes\n",
    "df1.unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">response</th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          response     age    \n",
       "gender           F   M   F   M\n",
       "treatment                     \n",
       "A               10  45  15   4\n",
       "B                5   9  72  65"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.unstack(level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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",
       "\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></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>A</th>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>B</th>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  response  age\n",
       "gender treatment               \n",
       "F      A                10   15\n",
       "M      A                45    4\n",
       "F      B                 5   72\n",
       "M      B                 9   65"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change inner and outer level index position\n",
    "df2 = df1.swaplevel(0,1)\n",
    "df2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.9\"></a>Melting data frames:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment</th>\n",
       "      <th>gender</th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment gender  response  age\n",
       "0         A      F        10   15\n",
       "1         A      M        45    4\n",
       "2         B      F         5   72\n",
       "3         B      M         9   65"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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>treatment</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>age</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>age</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>age</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>age</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>response</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>response</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>response</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>response</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment  variable  value\n",
       "0         A       age     15\n",
       "1         A       age      4\n",
       "2         B       age     72\n",
       "3         B       age     65\n",
       "4         A  response     10\n",
       "5         A  response     45\n",
       "6         B  response      5\n",
       "7         B  response      9"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.pivot(index=\"treatment\",columns = \"gender\",values=\"response\")\n",
    "pd.melt(df,id_vars=\"treatment\",value_vars=[\"age\",\"response\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"5.10\"></a>Categoricals and groupby:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment</th>\n",
       "      <th>gender</th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment gender  response  age\n",
       "0         A      F        10   15\n",
       "1         A      M        45    4\n",
       "2         B      F         5   72\n",
       "3         B      M         9   65"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will use df\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
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       "    <tr>\n",
       "      <th>treatment</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>27.5</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>7.0</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           response   age\n",
       "treatment                \n",
       "A              27.5   9.5\n",
       "B               7.0  68.5"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# according to treatment take means of other features\n",
    "df.groupby(\"treatment\").mean() # mean is aggregation / reduction method\n",
    "# there are other methods like sum, std,max or min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "treatment\n",
       "A    15\n",
       "B    72\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can only choose one of the feature\n",
    "df.groupby(\"treatment\").age.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>age</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  response\n",
       "treatment               \n",
       "A            4        10\n",
       "B           65         5"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or we can choose multiple features\n",
    "df.groupby(\"treatment\")[[\"age\",\"response\"]].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      "treatment    4 non-null object\n",
      "gender       4 non-null object\n",
      "response     4 non-null int64\n",
      "age          4 non-null int64\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 208.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# as you can see gender is object\n",
    "# However if we use groupby, we can convert it categorical data.\n",
    "# Because categorical data uses less memory, speed up operations like groupby\n",
    "#df[\"gender\"] = df[\"gender\"].astype(\"category\")\n",
    "#df[\"treatment\"] = df[\"treatment\"].astype(\"category\")\n",
    "#df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Rest in separate notebooks:_\n",
    "\n",
    "## <a name=\"6\"></a>Data Visualization:\n",
    "- Seaborn\n",
    "- Bokeh 1\n",
    "- Rare Visualization\n",
    "\n",
    "## <a name=\"7\"></a>Machine Learning\n",
    "\n",
    "## <a name=\"8\"></a>Deep Learning\n",
    "\n",
    "## <a name=\"9\"></a>Time Series Prediction\n",
    "\n",
    "## <a name=\"10\"></a>Statistics\n",
    "\n",
    "##    Deep Learning with Pytorch:\n",
    "- Artificial Neural Network: https://www.kaggle.com/kanncaa1/pytorch-tutorial-for-deep-learning-lovers\n",
    "- Convolutional Neural Network: https://www.kaggle.com/kanncaa1/pytorch-tutorial-for-deep-learning-lovers\n",
    "- Recurrent Neural Network: https://www.kaggle.com/kanncaa1/recurrent-neural-network-with-pytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "    <img width=\"1000\" height=\"500\" src=\"https://i.ytimg.com/vi/UMZ1DxfaVUw/maxresdefault.jpg\">\n",
    "</p>\n",
    "\n"
   ]
  }
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