Data_scientist_01.ipynb

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    "# DATA SCIENTIST\n",
    "**In this tutorial, I only explain you what you need to be a data scientist neither more nor less.**\n",
    "\n",
    "Data scientist need to have these skills:\n",
    "\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",
    "1. Basic Statistics: Like mean, median or standart deviation. If you know basic statistics, you can use **python** easily. \n",
    "1. Data Munging: Working with messy and difficult data. Like a inconsistent date and string formatting. As you guess, **python** helps us.\n",
    "1. Data Visualization: Title is actually explanatory. We will visualize the data with **python** like matplot and seaborn libraries.\n",
    "1. Machine Learning: You do not need to understand math behind the machine learning technique. You only need is understanding basics of machine learning and learning how to implement it while using **python**.\n",
    "\n",
    "### As a summary we will learn python to be data scientist !!!\n",
    "\n",
    "**Content:**\n",
    "1. [Introduction to Python:](#1)\n",
    "    1. [Matplotlib](#2)\n",
    "    1. [Dictionaries ](#3)\n",
    "    1. [Pandas](#4)\n",
    "    1. [Logic, control flow and filtering](#5)\n",
    "    1. [Loop data structures](#6)\n",
    "1. [Python Data Science Toolbox:](#7)\n",
    "    1. [User defined function](#8)\n",
    "    1. [Scope](#9)\n",
    "    1. [Nested function](#10)\n",
    "    1. [Default and flexible arguments](#11)\n",
    "    1. [Lambda function](#12)\n",
    "    1. [Anonymous function](#13)\n",
    "    1. [Iterators](#14)\n",
    "    1. [List comprehension](#15)\n",
    "1. [Cleaning Data](#16)\n",
    "    1. [Diagnose data for cleaning](#17)\n",
    "    1. [Exploratory data analysis](#18)\n",
    "    1. [Visual exploratory data analysis](#19)\n",
    "    1. [Tidy data](#20)\n",
    "    1. [Pivoting data](#21)\n",
    "    1. [Concatenating data](#22)\n",
    "    1. [Data types](#23)\n",
    "    1. [Missing data and testing with assert](#24)\n",
    "1. [Pandas Foundation](#25)\n",
    "    1. [Review of pandas](#26)\n",
    "    1. [Building data frames from scratch](#27)\n",
    "    1. [Visual exploratory data analysis](#28)\n",
    "    1. [Statistical explatory data analysis](#29)\n",
    "    1. [Indexing pandas time series](#30)\n",
    "    1. [Resampling pandas time series](#31)\n",
    "1. [Manipulating Data Frames with Pandas](#32)\n",
    "    1. [Indexing data frames](#33)\n",
    "    1. [Slicing data frames](#34)\n",
    "    1. [Filtering data frames](#35)\n",
    "    1. [Transforming data frames](#36)\n",
    "    1. [Index objects and labeled data](#37)\n",
    "    1. [Hierarchical indexing](#38)\n",
    "    1. [Pivoting data frames](#39)\n",
    "    1. [Stacking and unstacking data frames](#40)\n",
    "    1. [Melting data frames](#41)\n",
    "    1. [Categoricals and groupby](#42)\n"
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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "\n",
    "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"
   ]
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   "source": [
    "data = pd.read_csv('/Users/garvitkhurana/Desktop/Projects/Data_Science/pokemon.csv')"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800 entries, 0 to 799\n",
      "Data columns (total 12 columns):\n",
      "#             800 non-null int64\n",
      "Name          799 non-null object\n",
      "Type 1        800 non-null object\n",
      "Type 2        414 non-null object\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"
     ]
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   "source": [
    "data.info()"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</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>#</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097712</td>\n",
       "      <td>0.102664</td>\n",
       "      <td>0.094691</td>\n",
       "      <td>0.089199</td>\n",
       "      <td>0.085596</td>\n",
       "      <td>0.012181</td>\n",
       "      <td>0.983428</td>\n",
       "      <td>0.154336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HP</th>\n",
       "      <td>0.097712</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>0.102664</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>0.094691</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>0.089199</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>0.085596</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>0.012181</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>0.983428</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>0.154336</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",
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       "                   #        HP    Attack   Defense   Sp. Atk   Sp. Def  \\\n",
       "#           1.000000  0.097712  0.102664  0.094691  0.089199  0.085596   \n",
       "HP          0.097712  1.000000  0.422386  0.239622  0.362380  0.378718   \n",
       "Attack      0.102664  0.422386  1.000000  0.438687  0.396362  0.263990   \n",
       "Defense     0.094691  0.239622  0.438687  1.000000  0.223549  0.510747   \n",
       "Sp. Atk     0.089199  0.362380  0.396362  0.223549  1.000000  0.506121   \n",
       "Sp. Def     0.085596  0.378718  0.263990  0.510747  0.506121  1.000000   \n",
       "Speed       0.012181  0.175952  0.381240  0.015227  0.473018  0.259133   \n",
       "Generation  0.983428  0.058683  0.051451  0.042419  0.036437  0.028486   \n",
       "Legendary   0.154336  0.273620  0.345408  0.246377  0.448907  0.363937   \n",
       "\n",
       "               Speed  Generation  Legendary  \n",
       "#           0.012181    0.983428   0.154336  \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  "
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "import seaborn as sns\n",
    "corr = data.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values,annot=True,fmt= '.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "24c409ea-b8dc-4ed4-b5e1-da317255b014",
    "_uuid": "469700ec5671d7c2a3d56d9504ef6bebcf0d4869"
   },
   "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>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>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>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>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>4</td>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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>6</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Charizard</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Flying</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>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Mega Charizard X</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Dragon</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>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Mega Charizard Y</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Flying</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>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Squirtle</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #              Name Type 1  Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "0   1         Bulbasaur  Grass  Poison  45      49       49       65       65   \n",
       "1   2           Ivysaur  Grass  Poison  60      62       63       80       80   \n",
       "2   3          Venusaur  Grass  Poison  80      82       83      100      100   \n",
       "3   4     Mega Venusaur  Grass  Poison  80     100      123      122      120   \n",
       "4   5        Charmander   Fire     NaN  39      52       43       60       50   \n",
       "5   6        Charmeleon   Fire     NaN  58      64       58       80       65   \n",
       "6   7         Charizard   Fire  Flying  78      84       78      109       85   \n",
       "7   8  Mega Charizard X   Fire  Dragon  78     130      111      130       85   \n",
       "8   9  Mega Charizard Y   Fire  Flying  78     104       78      159      115   \n",
       "9  10          Squirtle  Water     NaN  44      48       65       50       64   \n",
       "\n",
       "   Speed  Generation  Legendary  \n",
       "0     45           1      False  \n",
       "1     60           1      False  \n",
       "2     80           1      False  \n",
       "3     80           1      False  \n",
       "4     65           1      False  \n",
       "5     80           1      False  \n",
       "6    100           1      False  \n",
       "7    100           1      False  \n",
       "8    100           1      False  \n",
       "9     43           1      False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "a1e23ca9-f572-4988-a34d-cb2d9d99ed1b",
    "_uuid": "f52e8dcb6b33c0b424690b0bb4c4230b9bbaa3dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['#', 'Name', 'Type 1', 'Type 2', 'HP', 'Attack', 'Defense', 'Sp. Atk',\n",
       "       'Sp. Def', 'Speed', 'Generation', 'Legendary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "979f5e3d-5460-4b34-81b2-b0dbea0dc1bc",
    "_uuid": "cd298bcb292e631434664a8519176189f0f8e9fa"
   },
   "source": [
    "<a id=\"1\"></a> <br>\n",
    "# 1. INTRODUCTION TO PYTHON"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f8cb1163-a0d7-4ac8-ab55-a44e44fe03ec",
    "_uuid": "7e085789db5a003ce5c448c7246a07a0a42b1cff"
   },
   "source": [
    "<a id=\"2\"></a> <br>\n",
    "### 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": 8,
   "metadata": {
    "_cell_guid": "187608a9-21ff-4f29-b866-f6d3c046c774",
    "_uuid": "23b40c9685022b5c08d255cdfd41e9ec6d3f0a49"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y691mNLOl8ctQH0TXSFfygxSKaUYpiJnkRz+CyAU/HnwQDh+eOXneCmRoFNMjRx9h2Jt5i9Io3tooBTHTjG40fL6ZkWOyKBPTpOoOaAE0mZmjG8VbF6UgFG8tMtgU5g64Z1oERSajfBCKWUsGN7xRaFpGmpgALEJ9jorMQr2RM83oXkGmmmaSMZtmUmcgeVl5FOYUzrQYCkUUSkEopoZMVXQZ6oNwB9z4g/5pFEahSI5SEIr0kKE98xgy1MRUbCueRkEUitRQCkKRXjJdUWSofEPeITwBz0yLoVBEoRTETHOt+SAyndkip0KRASgFMVNcaw2VWpN60nUrM5NiTFSY61uQ2dKwXitkqA8CSD5RrqUFjh5No0AKxdgoBTFTXKsjiEy/rgyVrzS3FF8wySz6ri5obp4egRQKlIKYOWZLg3qtkaEmpgHPAEGZJFGjSlWumGaUgpgprjUFMVtMZTNpYkpCUhNThsqtuHZRCiLTyNSGNVUyvRHL0BEEQEF2waTOV1zjKCf1W4hrfQSRqWSogrAIC7YsW/LzZ8t9VlwTKAUxUyT60GfrCGK2NFwZ2shqUmPQMzj2QRkot+LaRimImWKMEcTcnTujFxKaTWTAehDegJcfH/1x/J2ZtKLcnj1w6hS0tLB67zkkSRRAhio3xbVL1kwL8JZljA89t6MD2tpg1appFGiSZFDDNeIfoXW4Nf7OTDIxvfYazJkDy5cz91InMplsGXSPFTOA8kG8BZkt0T+zCJvVRn52fvydmbbkqMViPvMca07y85WSUEwjSkHMFMk+9NmmKDJoyVFPwIPL74q/M5NMTAAWizlyKMktGftYpRwU04xSEDOFimKaMrKt2RTlFMXfmUkmJogaQaTkpM6g+6y49lEKYqZQH/qU4Q14GfYlcPJnWiNrsSCEID87P3mqjUySW/GWYMoUhBDiUSFElxCiPmLbDiFEqxDihPFzb8S+vxNCNAohLggh7pkquTKGRCOI2WZaCjFbGq8MHEFoUsPld6koJkXGMZUjiJ8B74qz/d+llJuMnxcBhBBrgI8Aa41zfiSEsE6hbIqpIgN8EIU5heRm5cbfOZM+iAQKIuUmXykHxTQzZQpCSrkH6Evx8PcDv5JSeqWUl4FG4Mapki0jSOaDmG0jiQxqvDwBT+LV2TKtF27RP0GBoKqgauxjM0luxfTzFglz/ZIQ4pRhgiozti0AIvMYtxjbZj3+oJ8ddTtid0yTYtjVtIufn/h5WsucUnbsmHRDKISgxJYgIigTTEx795qbNOCX9Y8jkYkjryLPV0pi9rJjh/4zFg8/DPv2Jd7f1QW//W06pRqT6Z4o9xDwz4A0fn8f+Mx4ChBCfB74PEB1dTV1dXUTEsTpdE743PHg03w4rjpi6rI6nSx0OGjdtw9/ma4n7Q4HvUePUuTzcejQIdytCSZ7jYNdHbvo8naxeGDxpMsa654VXL5MpcNB75tvMsfhIGiz0Tze+ysldoeDK6+9hrSmbmEcLdegf5CTrSep88bWv6ixEYTg6jQ8+9FyLb50CREM4vn97+kIBrE7HLj8fqSQDAwMsHP3TspzyhOWV3r8ONkDA3RPUvbpevfHy7Uul93hAMAxRln2N97A09RERyAQc24gP5+eV1+l9PhxOsrLp+V+TauCkFJ2hv4WQvwYeN74txVYGHFojbEtXhmPAI8AbNmyRdbW1k5Ilrq6OiZ67nhw+V0c2H8gtq7BQXjzTezbtkFVVUgo7Js34zh8mJtuuglWrJh0/VlXs3D73dQuq016bDLGvGdz5sCVK9g3b4bOTigoYNl476+mweuvY7/tNrAlSVw3hlwdzg6Ov3k8vqz794PFwtJpePYx92vPHv0aFy1idW0t1NURXLGcN9uvUlpaynW3bqO6sDpxgcEgdHfDJGWfrnd/vFzzchmNuf3OOxNbCurqYPFi/f0Yvb2oiOVbt8LICKtra6flfk2riUkIMS/i3w8CoQin54CPCCFsQoglwArg8HTKNu1MUxRTZX4lRbYEcwIyjZADeZKO5ITmJZhZM02c6wqgmc88pSgmxexnos9RiGlfz2Qqw1yfAN4AVgkhWoQQnwX+VQhxWghxCngb8DUAKeUZ4NfAWWAn8EUpky2vNXUcbDmYPCY9RWxWGxYR5zZP00zqhr4GznafTUtZY5KOiX+hc4NB+tx91HfVj318AsZceCfDGllpEViNgL2y3LIkB0+zctu3L7Nmnnd1wblzaS3ycOvhxAENM8VY3/40K4gpMzFJKT8aZ/NPxjj+W8C3pkqe8bCzcSeluaWsrlg96bJcflf8BmuaZlLXd9Vn3geQiFBjFAxysP0gh1sPs65q3biLcQfcY9cxDv/GVCMtFnOp0SxLks9xupXbrl2wbh2Ulk5vvYn4/e/1NbmTOXrHwYsNL5JjzWHT3E1pKzMpk3mO14qCmO0kTNUwTgJaIP6OaYpiSrqMZbpIx0sbMYJYUrqEIe/QhIuKO2oL1ZFBo4icrFyKjRxMXSNdzCual/jgmZA9kOD9nQmmIMyzuqCauYVz017ulKEUxMzz8Q0fpyK/Ii1lSSSluWP0wKZYUWyau4nyvMSRMWkjHcn6QudqGt2ubq4MXJmQKEEtOPaobaaJvDcWS/IsriFmQn6/f/rrnEaslhkYTU52BDGNZj+ViykOj516bFK910g0qTHgGYjdkYJi8AV9iUcgKbKweGFUA+QNeAlqaXbvSAmeFMxYbvfYH0eEiakwp3DCJr6CnARrO6f6YbnHMFGlGY/mpdvVA6TopFYjiLQipZy+UXa40omddy05qWc7fe5UJ4GPzZy8OfF3pOCD+OHBH/Jk/ZOTqt/ld0X1xP/vvv/L8xefH+OMCXDwIOzcmfy4734Xjh9PvD/CxNQ61MrxjjGOHYPcrFzT8Ru3/GQf2He/q9u6pwEpwr6HhGtYmAfPwAgikxTEFNDubKfX1TvTYqSOUhDXFqHZsTGrhaXwkJeXLx/bJp0CLzW+xOmu01HbJjsqiWEwIk11MhOT05m4nIgRRL+nf8Li9Ln7TMdvFOP5sFIZEaUBiTCfx5imSJjeiKLQvbrGTUwwBd9DMpSTevaTsl04CQlNVSn0Zj0BD/7g5D/QSIft+1a9b+x5Aulikj6ILfO3JF+CM1HViPg+JCnN3EfJC0mzOSPBtWRbs5FGVV0jXWPnY5oJE1MmKYgpykWULn9j2kgW5qp8EDPLu5a/K20vjSY1FhQtQKT6ckccJxDkZedNqv75RfO5deGt5v9dI10U5hROqswYEi2EM9FygkEGPAMTNvNpUqPHsOtH7zAmpaXSyKa7MUrwUedm2Vg9R/e1JO0MTKdyCMnrS898oEyluqAaW1bqs/bTwiwaQSgFEYfGvsbEYZLjRJMarcOtsY6wFLK5+jX/pB1oqytWs6R0ifn/wZaDnOo8NakyU2IiDWyEickX9LGqYtWEqs62Zof/CQTgBz/Q/57JEUSkgogoe9jn5HzfBWDyTur/Ofk/tA+3T0rMqLrgmh9BZFuz8Qa8aS93TJSCmN009jXS4exIS1mVBZXxd6TwkJv6myY8mzhEsa2YEx0noraNOZFssiTzQYz1kUeMIJw+J90j3RMSoSy3DIFRj8cDQ0Ph8iPq/86+70zfJMJg/MixoAyaTuq4jvVIkrwzl/ovcWVwYqHBCevKpJnUU0DLUAsj/pGZFiN1lILIDOI6OSdA6KNP6KQeIxdTdUE184vmT6r+nY07aXdG9yqTztgdL/Fe2En6INqG27g8cHlC4gx6B+P3xjVNH0EY9Yzp40l3b3W0gjBkkFKaTtKkAQlJRhBLSpckX1MiVaZppv+EeO21tBbn9k9fWDMw+TDXTPJBCCG2CSEKjL8/LoT4gRBi8rmjMxyzBzpJrg5enfC5K+asoDI/wQgkRTwBT1ReqTsW35E85086mKSJaU3lmgn7SrwBb/yGctQIYkymSUFYhDBV2aBnkDFJ0rAEZTBtwRUZqRhCz+TqOL+pvrF9WdOezPIaMzE9BLiEEBuBrwOXgF9MqVQzzLqqdRTbitNSliY1lpcvj56x2dwcbjBGPeyAFmAkoA9567vqJzT8HfIO0e8Oh4kuLVsKwIhvhIAW4Kaam+Ke5/K7JmzWMUmTiamqoGrCgQISSddIlz5qi6wvjg8i4Uza8SqIq1djP1xNg5YW3dHb2horC1CSU2zmm0o6OTPJCMLpc07cdxaSv7sbXK7YEcTAQNhUNxkCAf1eTIbxBED4fPDIIwl3l+eVT0+HKV1koIIISN0+8n7gQSnlfwKzJH/0xJhbODdt6Sk0qdHY1xjtbP7JT+DSpbjHd7m6aXG3gBAsKFpATXHNuOt85twzPHbqMQAWlyzmxgX66q0NfQ0caD5A61D8D7S+q56XL7087vrSZmKKSPd9suMk2ZbssY9PhUjZIhSElJK1lWsTT04br/yPPgod0X6rgqYm+O//htdfhyefDCcJjIikGvE6qe8+o8s0yXTffe4+LvXFf69Skr+1Ff7zP+Gpp2IVxA9/CD/+8cTKjuTQoYmXE3om43k2wWBC/w/o65ePuZLfmTOIdDvqr7ERxLAQ4u+AjwMvCCEsQBq+3MxlV9Ou+GGSE6AsT++dJExvMephewNefJpuEjrTfYaDLQfHXefSsqWsqVwDwO2LbzfTfYeU1KX++I1IUAumzfcyWR9EXnaeOfIZL9UF+qI7MQ1uRJirEIL87PzEjcNk5A8VEWqYQrORI3u+EX6QcZU/lY2Dy7gXPl98H4Q3DdE+6Whsx/Nsktyz1qHWsVP7/+Y3FFyJdfy3D7dzqOVQ6nKMh7FG35nmgwA+DHiBz0opO9BXe/u3KZUqA3D6xpjxOw6qCqriO4VHD5ONl7jP1as30sb/E7Epn+0+y7H2YwAcaD5Ay1ALgNkjTxQ6e6H3Ak39TeOuL4p0JOuTkqb+Jv5w6Q8TEiFhlFbECCKgBTjSdiRx4zAR+UedE1NCZJpx00mtmc8laVbRJMphWdkys0MyIUIKIrKXGllnOvwyk2ncJjKCSNLjDspgaqa9UVweuMyeK3tSlyNJeSmTaSMIKWWHlPIHUsq9xv9XpZTXtA8inTT0NhDQArG92VBjMepDtEUohDsW38HikvHHAywpW4K91A7oobIhZbe+ej13Lbkr4QzlCdtix2NiSjZL1ChvXuHEU4wMeAawCqseaDDa7m/8H1KSCeeZTEVjGNkpMPZJTeKTfkO8yZmYIIV8TmMRSlIoZdSzSESfu4+H33x4fHVE3pMdOyY2okjjCAImFtVXYiuJG3U24BkY22QVkmmiZIqCEELsM34PCyGGIn6GhRDpSXWaoWRbsqMnW00Cd8BNXlZe7EuYwNE2N7+aPGseSDlhJ7UZgbVjB1m+ALlZuYAe852blcsN826Ie96y8mUTWqAnvhCTT7URUnJjsaNuBx2e2DkrMaayJ56ABx6A/n6Q4QyeE03nkRKjy44zgijLLWFFub72eNK5N0kau25Xd+LkkAnQpEZDb4P+T0hBtLfrq7dFyBmP+w/dHxNCnbzCUUozlZnaJ0/CP/1T+P/xOKmTNKglthIWliwcu4w455/pPkNjX2PM9t2Xd4fv51SQKQpCSnmb8btISlkc8VMkpUxPiM9MMxg/rPBP1v7JpOcfhNCkxryieeFGe7QJZtQIYsgblqk8r9xsPMbDsfZjnDEcnxVZxbxv1fsAaBtuo3mwOaHZqt/dP/kstpPtHRllHG0/mvIsclcguscWavQ1GfExXbgQc0xuVm5sMEIaPz4xuqxQwxbhpA4GAwz5hlMrMIlsQ96hcZsIfUEfvzz9yziFpd4HHJeSnYiJqbU17D+CtJqYCnIKks+DiHP+srL4namTnSdjJqamUl7KZIqCCCGEeEecbZ+cGnGmmX//dxiO/TgfP/142mbY5lhzaOpvCtu6E4S3hv4PBP3kWnNBShr7GrnQG92wpcLWBVt5m/1tAGxbuM2MWpJScrrrNPuu7ot7Xl523sRMO5HXkqwBSCXMVdPQpMZNC+KH40ayrGwZc2zRveZQoz9WVFBedh4fuu5DeIOjHK/psJGHSKQgIvYNeAboHOnUN6VhPYjx+s6iMpmOWsgoUs54FGQXxJaRjNH3N5XGfozJpCmfm+DaCEaMAAAgAElEQVQ6bFbbhDILjPhH9O+qrQ00jUHPIMPe9Cj6KJxOOKErHE/QSyDgyzgn9T8KIR4SQhQIIaqFEL8H3jvVgk0bCW52p7MzLcWvqVyDzWoLf/yhiJYENt6+kV76feE5DBORo9PZaU7QG/Y5zSimkAyJeuaNfY0cbT867vqimEzvJuJj7nB28ErTK0lPWVq2NGZSY9QIKYE8voCXx08/Hjs5LY0ziGNGEHGd1NL0GyRN951Eptys3IlNlBvLh5SgTk1qrK9ezwdWf2B85tgUfBsJmegIYgwuD1xO6qT2B31c7r8ctS2gBfSU9M8+C11d/PDgD/nJ8Z+Qm5WbvtnsAPX18NJLgG6+auptzKwRBHAn+uS4E8A+4HEp5R9PqVTTSehla2iImpvQ7ermTNeZSRd/dfBqdC81pCASjCCCWnj/bYtuC9vhfT54/HHdHhuPV181HX4WYWHIaPhed9SZH8D6qvXctui2hD3VtOTFH90A7N0LI6P8KK2tECd0MPJcb8AbfyW+UbzS9EqUQgVMu7hFWBJ+TKERYsIkiunAKGvYO6z3NkO98qam8HoTmsaI4dRMSUGMId+6qnXjdrjmWHMQ8YqMpyC8Xjh1CnbtQnv+eQ62HCRr3wGcA12pV5iO+5vGEYS+a2yZOr2d/OLkL+DAAdPisKxsGYtKFpkmnzWVa1hatpRaey3Ly5enJlOq8heHLfp+v9GWHDhAdpIZ4ukgFQVRBtyIriS8wGKRcu7qWcQvf6k7Mg0ONB/gN2d/M64i9lzZEzN/om24DYhIxBYyMY1uSI3f11WsJsei9wKbB5vDERHNzXDxIjzzTPzK9+41HYvLypexqEifYCeMmH/QG8PqguqEzt8t87ewas4EMqiOZWJ69VU4fz56W1OTfi2JytE0PrzuwywsTuI8NPBq0WYiKSU1xTVjzio2ndTx5kpEypIKiY41ymroa6ChryF6BOFwAFCUU2iGpiZNy5JEppMdJ1lfvT4lkUNkWbK4cf7W2PITjSB++1vYtw+OHAag5/lf4z49jpX/IiZDxi0/HmOZ6lKtb4x6kjmp8y15LCheAC+/rCtI9JDwq4NXTQWxsGQhNcU1dDo746f2T3adP/tZ/Hkmmhb13sjQ9TQ0kDUNS+OmcqcPAjullO8CtgLzgf1TKtV0EvkwjQfx4bUfnlCqjdcuvxYz6ghoAbYt3BYehicZQTi9w7rJREq8Qa854S0ljGvZe2Uvx9t0U1G2yOLuZXcDcKrzFB3OjoRlTmYNBpNkH2TIQRtv6B9x7vme85Oai9Iy1KL7fRKYT0IfWkxo70R6uAl6qaE3a27hXL2eOA1bQXY+S4wJgUnXCk8ygvBr/uSRUE89RWGEcvYEPNR3nIqVP8lzjOx1Byfig5iIKW8iJqYk9VTkVyRNT9LmaTPnEoXqzrJk6algjOvpdfUS1IIc7zjOhZ4kfsN4sjgcXHGcjO0kSBmlIIqyjDBmTUvmsUoLqSiId0gpHwWQUrqllF8B/vfUijUNjI4mAvMDfrHhRYpy0pNNJKAF2N+8P+z0TuaDcPVSkq2v+DbgGQibWVL5kIxrqSmuYWOl3pO8oXoTNqu+IIomNc52n+XVplfjn45gSdmSuPtSJtUeYTwFETGCaB1q5f2r35+0qGxLNtW51VHbCnIKkp5XmFPIxzd8PHaxmImMIBKdY2z3B411PeL4INqH281JjZNNtQERoxAp46eYqK+nsCEchimlxON36w3+OBREZA4rLZiagpBSho9NZzDAWCS5jtLc0qRrUttETtivYNRts9r0kbgxgjjRccJMzR/XNBp5vQlkcfqcjPhGEuYPe/eKd7M4NKqOjOqaQlKZKHdVCFEmhLhRCHGHEOKOKZdqprBYkFIy7BumJLeElXNWjruI0cPLUB4k09adZATh8o7Q5e2CYJCgczj+ehBJFpLPy86jOFvPhFpTtIDXr7yuV4HEr/kZThBW2TXSxdG2BE7qsdaSjsT4ELQRJy5v7DnegJdgMBD1wWhSY2S4LzzElpIh7xDnus8lre6+lfeRJaLt7uV55eRYc2IbvQgswsLvL/w+3DMMMYkRRCDgiw6ZjAi3zc3KjTuCcPtcZtbakCJPVk+IyHTlMXb0vXvhn/85/P8Yzy/b44stf4z8RSHuW3Ef5XnllNuS+E4Mdjbu5HfnnomuazwmptCkOuMbS2lJ3tC5caIVQQ/MSCVicVHJoqj/e1w9vNn2pqkg/JrfXEc97gp18a5z1DO5MnDFjGiLOs/oWLzStAtHnxHGHAxmhoIQQnwO2AP8Afim8XvH1Io1DcTrWVgsZi9uXuE83rPyPeMq8qYFN4WziBqEGgxzWxIfhC/k0G5o4NbTA/FTJ/zud4A+OglogZiX7+rgVQ5ePaAfeva3ph/DXmpnefnyhE65Ac9A4lxMP/4x1kQ2zzg9z8tXTvLkf3055tCXL71MfefpKAVxtvss+//PJ8L+FSnp9/RzqDV5rptXLr1Cry+6B9jh7MAX9OnKOoGJqdfVw6B3cPwr/cXDOHZX4yt8d/93w9UY27tGuvQPP06Y69G2N01T2oLiBcnriZDr+we+z5NnnuRM1xm6Rrq4fdHt4evpHNXQfO970Kvfp8hmxWqxcvPTh6JkAuJ+H5f7L+s9XHRF/0LDCywtW4o1xWVl2obbCAb8+II+hj0RiziNYkfdDpoHm2MLaDIaR+M+fu/A93jyzJNjVxq6jvvvT5j2O1lwhk3k6MoAzEb51oW36qn4jfIr8iuozK/kxgU3xvedRV5n6O/vfS8crACMBOLMwI4YQfg1P8PuwfB1ZYKCAP4K3fdwRUr5NuB6IHl4SYZj3tpRCiLErqZd/MfB/xhXmbctuo36rvqoRieUbM8cWSQaQRgsLTFMPD4fVbZycxZ0FP16T+Vf9vwLPz3+05iP+daFt7KuQvczRIZaLipZxOqK1QlNMGur1iaOpAkEwsnnxsKQxZZlozIYK3tZXjm5VluUgijMKaTSFzEK0DTuXXFvSrOCR/wjeILRPUBvwMuikkVjhnwmnEk9CRPT8OhwSaMMr2b0cuOYmAQgjVejayRJNNAomZaWLaWmuIaXL73Mhd4LnOs5x4bqDfrOeI1HnJFnblYuuVm5esco3ggiYlvnSKfZ2w51pN5se5O2wVGjsATcu+Jebq25mWPtx/jRoQfjXlOIqMi/0ccY12YRluSr8EWadhKMvOfkj/2eLczXAx4i08e3DbfR7eo2RxAbqzcyv2g+pbml8ZNMxlMQEDVSm1c0P3YNlFE+iEgnf6b4IDxSSg+AEMImpTwPTGyx4EwiwQjCIizcvuh2fEHfuDObfv+N7+tFRjy6gBbgfaveF86Rk8gHYfwuzi7UTSbBIOe6z4YXDErwgjl9zpjIkLPdZ8NDUSnNGZ/7ru6j19VrOq1HY7Pa4iukUNnjMAdYhZXc7LyY3ed7z9My2Bz14bYNtzEYMYMcKSnKKZrUyndXB68mdlITVgwxynISJqZ3LbuHv9jyFzHb7SWLEpqYttVsY9O86wGST7QaNYIIvZ+D3kGuDFyhx9WDN+DlSOuRuAqiy9Wjr/cxaoRbYiuJDQmO8314Ah7TPBmpWAfc/ckds+imzwJrHmW5ZaxOEi035oJdxrXdt+I+bll4y9iVJugAhsjPzo/tiAQCuonOwDHiQJOa/l0bdZumSeO7cPqc2Evt7L68O/5KiCm8V3nZeebkw6jzIuS2hu5LBo0gWoQQpcDvgFeEEM8CaVr4dgYJPbCLF+GNN/S/LRaCWhBv0Ju2VBtBGeRQy6HwwuhJZlKf6z5Lpa0SgkGKsgvjL0MZca4kwhlp/Hb6nBQa0Q5z86vYWL0R0NMqtA63JkwF0OPqSbyC3VhphuM0LNnWbLISTKDKy8qNKiumF6hpvNjwIjfX3By/vlHMyYn+wENRKQmjgoQgPyuPu5fdHTupaSImptAIwjMUFQUWOXrLsebEHUE09V8yn8d4ndSluaXmvQspiyfPPMkLDS/EbTxevrxLT8ESUU5AC9A50hmdliTimhLdh0jlfabzNE/UPxH3uEh+c+Y3vHZpF3nZeVSEGuVR5e+/uj+m/BiMa/MGvckj3SLf2TgKoqa4xgxFN+nq0sOzDQb9A+Rn50eNIPKz86m115oK4nzPeZr6m/Br/vjpThKNICKeU1VhtT5CG+2kNt6b2xbfzsqy5eHrygQFIaX8oJRyQEq5A/gH4CfAB6ZasKnGvLWvvAJ/MNJKWyxoUuNw6+EoR5NjwMGzT30LziV3mgJRYXPrqtbROdIZTrWRJIrJH/DpK8ppGk7vMK9dfBm+9a3oF934u8RWoi8oFAxyuf8yR5p1W/KCogVsrNKjmG5feBsvNrxoVCXpH6O3N5YDGym54nTwjOFkfO65f6Pzv+OY4ELOdr8rbsjlwuKF4QlGBiW5JdGrx0mJRVjC/pfubqiriyvW3cvupjQn2iy2sGRheCSUoIEryS1hxDcSa+uOpwR//euxn71Rx17H6/z6zK9jtg95hijNLWWX4zU6nZ0cbj3MzsadAHQMjyPZ3agRRKezk2HfMHML55oBFWZ0TRz5KouqybJkYevtNfOQhZTSaIe+1+fGE/CgRSjZyN62LcvGWsOMWTi615tIfCRCkwgEbu9IlGwhXml6hZVzVrK4dHGM/CF+d+FZBj2DvNjwoj5aGrPS+I1xiIu9F2NTvscJBvjIuo/okVtGGSP+Eeocdeb7Mugd5GKvHj4cd/STQoej190bPZIOnWcoiF5XL/0jvZzvOY/P780MBRGJlPJ1KeVzUsoUUjBmOPEaaKvV/GA2Vm9k+63bAWPWbUdHOMPlGIweeaycszLarjhaQYQw5PAE3AwFhiAYRAB5QaFHb8QJk/vaLV/jT9f+KQSDXBm8guPwy7B7N419jey5XAdAXdNrZqNfbCumJLckYU+109mJY8AR/8I0jaaRS5zs1Gdye44fIXA2ToSVIeeQd4gAsT34q0PNtA62RF2Py+8yJxie6TrDxZ4LDHoH+VX9r/QD+vv1iYJxaBtu48pI9IB2wDOQNDKlfbid/c37Y1MzxxtBnD2rpzxIhAw1stHPNDSCGPYN0z7cTo+njw5nBxZh4baabVHnWoQFe6kdKWXCIILRjUzzUDNXB69yc83NLCldwt3L7qbd2a4/w8iJbsa9vmf5Pdy26DZ9e0+PsXtUz3bePLBYaBto5lTnKZ47/6y5Oy8rz/TdDHuHGfE5uXXhrVTYUlt9UUqJkJIeVw8NRmMar+G82Hsxasnc0Tj9I7r9nzHW/ggR+d0kaFDHXDAI8ATdvNr0qh5wYpQRGpVHml6zrdmmLyKG0fc54j1r7G3A7XfHX+kxwsR0ruccbUMtDHoG8fs9GeODuDaJ1xAYYa6gh+TtatoF6HHsXr87aS/gH+/8R9qG26JC7544/UT0MDhJFFN1fpV53Pqq9eGJMZEOYuPYX5/5Nc9ffB40jUUli1iTPR/6+9m2aBsLC/WImD5Xr/lRb12wlU1zNyVsgMY0q2kai3Jr2DR3E6BP/soL+RhC5YVWvIKEvoxNczeRn5UX9eGuKF9hOpS7Xd009TayumJ1OOwzGEx47+u76gnIaOdjv7ufuYVzdRkSRDGFGoWEM6njnJOQROYY43+nfwS/5qesoILyvHIKsgvoNnJsCUCiO809AQ//duDfEi/7OqqXn5eVx+KSxTT1N9HU30Sns5Nbam6hxFYSlvfZZ+E/9JHenit7ON5uzHo2Gp2S3BKsItwxQkrIz6d54Aouvwu3e1hP/40e8RRSvEEZ5Eq/g4MtB/EFvCnNet84dyPzC+fh9DkZDCmABM81ai7BqGNs2XnmSCaZn+pc17mwAkhQ1+h3td/dr48O0M21d1beiTfgMaOdOpwdDHmH9BQlxjO5Z9k9LC5ZTE1xTWpO6ojv/njbMYZ9w/G/mdFO6qCGO+DG63Nl3gjiWiImkRqAxUKONYcFRQsY9g1zvEP/mFqGWvD5PUkVxD+9ruesj2x0PAEP9664N7GTOoRR9srylbpdORjkbNcZCq15sccbx57tPqubi4JBqguqWVWyFDSNIe8QbiMcUUhpfkyhCKv7Vt4XV/5l5cti4r0j61yQO5/N8zYDumkkxv4boSDys/Pj5qQ533Ned8ZGXE+/pz/cYAtAk9Taa8MfzBgKIq6o6Mn+IlfmG03IdBJjDkjkgxgrvYNx7JZ5m9m2cFtkJQDMNZZAvXvFu6gprmFVxSpzhLekdIm5KFT7UBu+oC9x2OUomeyldhaXLuZU5ylOdZ7CMeBgSdkS/bmEGo+rV83U3Zd6G8MmDON6vAEvC4oX6O9cqOGyWAj69ecxr7ED/ud/wGaja6TL7LFLKdEflUaZrYSPrv9olGz+oD9mwtjNNTdz47wtFOYUsqAwfmcklQy+d9jvJD87ny3zt3D7otvHPPbg1YhJqnHehdys3BiTXGQEVX1XPd6gB5vINt+nkx0n9TQ8Ee+w0+dkadlSDrUeIqAF6HP3RT/HBJ0HpORs1xn63H0UZhfEKtpRTmpznlEwkBkKQgjxZSHEJNYxzFASjCA0qXHH4juinLULihewpHhxyo1UZA89oAX0ELkUs7me7DhOeU45BIPkWHNYO+c6fUecEQTo5ouA38uRtiNc6roImsax9mN0Den2/7L2AVMhtAy10OHsSLjiVVN/U+zoYmhIN61JSdNwI4+degzQnXt5WaOilELzDoTAKqz0x5lR6gl48PhdUdc/fDbsNJcWCzmWLJ44/QQr5qyIvfY4FGTFt4GbgQFxsFqsbKjewKoKPZpGk5ruXEz0jCM/0suX4/qEagoXsG1RWEHkdutmkLmFcym2FfPLs7+isa+RtuE2pDGjeE5uuZmqIquljTUlK8ZU0vj9eqOPbhoJ+VA0qZlmuUTRd5H+hND1uPv0zL9mmKvRYy2zlYSP3bgR/uzPzHpAD9sWmn6vTrWf4F/3/2tUXSc7T/L46cejtj126jGeO/c7im3FrApNQh11v6+rvM4sH9DfP+M+hvhD08t0jXSxvmp9bFgowMCAOefD5XUmfN+7R7rJy8rjdNfpqAmOVkc43UVAC5CFlaLQO6ZpZmfmniXvNK+hoa8BiW4+O9J2hPsP3R+dVn90By/i/Q8p2kUli2JyuUWOIJaULWNZqR4Gn2vJyQwFAVQDR4QQvxZCvOuaSdSXQEH4gj6eqH8iytHc5+7jSv/llBVEZBqCuYVzef7i8+YEIwIBNKnROTTKOWmUPegepDCrEIJBPD4XJ1qNmc1jTNXfeeEFAIZd/aBprChfwd1L9GU87uso4nDrYeM0Sftwe0ITRsgOHsWvfgU/+hEAvd4es3fVMtQS61ALjSCMcOF4zCmoYFP1xvD1uN1UPP2SGU20Yf71rKm4Dk/AE7aXjzGCqCmuYX5udG80ylSW4LxFJYtYW7lWD/tEX7z+Fyd/kdhcFPna//znUZl/Q8e+2PBCVEOZbTiCHQMOSnNLkULg8rvodfUiNH1Vu6Ntb9I6rNue83fvY+7QGMpQSr3he/RRQF/vPDTKCkW7ZVmy9LDmOJ9plDnNUBA5j+tO9aAWDDdcFgvz8quZkzdHH91YLObxobU2SnNLw87pOPe4ILsgJnx00DOI0PT05k53/Ilyvz7za7IsWeF1Sf7nf/Tsv1HXoTt0f3rip+a7HcXDD+urBxrlxyzWZfCfR/6Tfk8/Lza8yEuNL5nb817fbxwuCWpBsixZ/PHqD+pzhGR4NcIjRt2XehvpGunidYeesUAgyLZk66Y+gyMth6PDmCPan8KsfMpyy7BZcmJ9KhEjCHuZHYsEizUrHO46xaQSxfQNYAV69NKngAYhxLeFEMvGOk8I8agQoksIUR+xrVwI8YoQosH4XWZsF0KI+4UQjUKIU0KI+GtiphGRQEGEPqIt87fwJ2v+BNBfdqHJMRVEqOdtFdaoCVrvXfXe6MR/wSAjvhHTxDBaUXkDbi6PXDad1ANOY5ZwIHa4+uUb9ZnKF7r0CBtrUA9F7Rrp4kL3OaTUU1aEoiskEqvFmtAH0TrUyt6re6M3RiimSLOckBHRWqHtFovZwLQ72+lxx+a46XX10jvSE9MQh2aguzQfNksO3qA3HBE0xhyMDdUbaHVHNyChBiFh2KgQdDu7eKL+CTPSymqx6o1SKiOIkEwhQs/OF8dhmpWFN+gjqAWR1vD7dfvCbZiRQ8b5xdZ8zvecT5xwb5Rsb7S8QWNfI3ctuYut87dyw7wb8AV9enqWOAriE+s/HjaBGftlICJVRcQIwuUZJj87X1/yVgiwWCjKKTL9Qk6fkzVzVrN53maWlthj6qrvqudcT3Tkl0R3Ug/7hukMXeOoa3L5XdGmmXiT24Qwo8BietyjzhEyvN7GWN9vpC8jPzuftZVrkUjspXasWLjQedY0jW6ZvwXA9KM8f/F5QyxBeV45RbYibph3Q9QEvFebdoXTaIzyQSwpsZOblcupzjip/CNGEK81vUpD9wVybQX4/d7McVJLvUXpMH4C6CnAnxJC/OsYp/0MeNeobf8beFVKuQJ4lXDSv3ejK6EVwOeBh1KUf+Ik8EGEqO+qN3tL1YXVVObOGfMFE0Lw97f/PRX5FeHRAvBSw0u4/K4oE5Mmtfg+ECCLcFrwDVXrKRA55v+jZQ9Fclg0SZYlC6uhxBaWLMRppDKIzA75jqXvYPO8zQkbzrhOsggHWbG1iLmFcwGoLKiMzYRqsZg9nkQjiNUVqxlw90c1sKH7rEmN830XONaqpzUwbdgJRhCa1OdLOAPRvpDQJCab1ZbwmYXmK4R6g33uPn0diVR9EJH74406Ihz3/Z5+WodbkULo6y8IwaKCBXqDGVFkrshmyDMYm48nXp1795I/MEKxrZigFqR1uJXqgurwTPgIBTHoGaS+q56WweZwg2pcj83ozESZmCwWup2dNA8Zx1ssIATFtmJ6DaXv8rt4s/UIHc4ORuLMRSiyxSa7LM8rR0jdydsaCi9O8HxMJZnAYJFoxn/7cHvUKowrypYnXNBoR+0OAN636n3msryg+0/63H1YhMUMt23qadCjATWNyoJKvnzjlxGaHnEWSjVSkV/BO5a+A3upnXZnu9kR2391vz5SCJnDRikICwJv0Bs/ZckoH4TUggwFRvR8ZplgYhJC/JUQ4ijwr+hpvtdLKb8AbAb+KNF5Uso9wOjkJ+8Hfm78/XPC8yneD/xC6hwESoUQE1j7MnVCDbSmBcOpMSwWsi36y3Rl8AoPH30YgEMth+gdNQN1NEEtyE+O/yQ86cjgXM85ts7fGm58QwpCG9UQGb/fufQdWNB74o4Bh2mrjjeCCIWBCk1j87zNrC5ZBprG0rKllNliw1l7Xb3kWHPMHtBobqq5KXbJ0YiXc1XhCv54jb5WVPdId+ycidAIwmhMrquKTSsekEGk1OI2qprU0CwCb8DDF7d+MaxkEvggQhPhNKId/hLJ4pLFeq8wkZN61HoQ5jyMVKOY4iiDmqIFYbNChMwhU8wnb/g0q+asYnXFap458xRBLYgIauaCPV1DHQhJrG8nXp1XrrCccrbM38LrV17nUMshTnWe4qPrPqqb6yIVhHeQHlcPuy69Em48jbIsWFhUsijspDbWHxiJNIcYJqbW4Vazdx8a/bQOt1KWU8LfbvvbKFGrDcf8jrod7KjbAcDH1n+M+5a/myxLFjkifqMdMjUmtGRXVXHvinvNTsX1xiz0ECc7T0YlYBxyDyaMYnqpQTcrneo8xfme8JolnoCHdmc7fe4+PXmllGRj4crAFXZefJHfnvstDxx+wGxDPnv9Z7hryV0sKlnEq02vUp5XztXBq+bCVS1DLXxg1fvNzlWULFJS33EKl9+FVVjMgAWTUetBoOnfSDDonxYFkUoug3LgQ1LKqGBzKaUmhBhfNjuollKGjO8d6P4NgAVAZKB7i7EtZhaREOLz6KMMqqurqUswgSoZPqcTh8NBo7MRd9DN+pL1uDSNrr37Wetdywvtul3/Uz/7FJW2SjYPBDl54gT9OUaPy3jAoRfZp/k4eFXPu7QnsMd0nF5svsh1ruvMnEwnXv4+7+qrojevG4fPQduhAzxy5N/4XPmHqHE4qA92kityuXTxIleDV1jeasXhcDBw9CilxiIzAG2//S2OIf3/qs4h/nCmiWWVG8jq6eff5V7K2npY5yhkYHCAG/7jZXauvsCxJfnc/ds3qLBV4AB21MI3XgvQc+978Mydy/mh85wZOkOdM3xP5zY0kNuh9+Y6K7P5p2f+iQ8t+BCWYQvdHd001dVRcfo0hQ4Hwbw8/L29ZA8M0D/UxamLHipac+h5802cw8PYHQ72aD0s9xZS4nfSXleHHBlm8LR+ry9rl+kJDlDY2sl/P/YtBktyqXPVUXLqFHnNzfzkie3cWH6jPtMcfWJfc9Ml7t55gL9zNZNtyeauqrsY+OFXOfG2VbzUW03pkIf5EffNW1GB7Ovm+y9+j6KCCtqD7QycH2DQP0hzWzNvdB5gnsNB58GDXL50lBxLDjc4HAzl59OXr5sq7A4HnkcfxdbXx5VPfIKc3l7mOxzkLl7M9fmr+dTPPsX7K9/NGr8fR0MD1QEXzW4vP3jlh9Re1si15JI30sSVYj8bRTk9LZ20uAdovtyEr7AEzxUPr3S8gia1qAyvCy5eJNvISur2+bhY3kYLv8cx4KBF6I3ikTNH8Gk+busYotjhwN/by0hvH9IvabrcRPXAAL58Hwf278dXUcGcC/W0dF3AMmjBmZVNbmcngfx8urUOcgb0EdyxEycYcbsZGBjg7NBZnqGZOzrzWfvwGwxh4cTZvTz3TJC3V73dlPXEwImoOTV1dXWcGDjBdcdeId8ZpGywFIfjHG379+Mr1xt7p9PJvOEyDvce5g3PGzTlNrGgsZHsoXCOK9/QEM9feQGv7zauDF+hw99BXUP4fT3Wc4xV/QM4Ag4cdXWcbDjO8mQdC0EAACAASURBVKtZlGSX0LpvH/6y8Kj3SYee6M+BgysNV/jkC5fpetvbyG9pZsA5wO49uyns7aHI7ebimfMs6uvFu/8IlS/sYed7lrFMVuJwtPLYS9/j0hwLH1zwQY62HsXX5sMx6GB//348jR5ecrzEHqeHT19wUmGroG3/fgJFRSxyOGjZu5crHQ5a2tvpvtiOs6UBd2ENXUa7NufUKdwFuVQ5HAxpQ1SKPHp8A3Q6O3GOjEy4/UuVpApCSvn/jbEvtanF8c+VQsRd7DDZeY8AjwBs2bJF1tbWTqj+/c88g91up9BVSH1XPXa7HdauZeEtm3nw8IOsXbFWt7+i94YWtfrZuG4DGPXtqNvBu5a/y0wH4fK7OLBfz6B6xy13mEPsg/sO0pjdyCc2foISWwkd+6pYVbqK1cvuhaYm5l5/PfbBVm5bdyscO8bxgmZWFtlZZrdT39dOzYq52C/bYd06c/ZrnaOONfYq7EN2AO5dvALXuR9jKdS4btUq7tyUw8oayRJ3A5ZBvRdu8baybvV7KZpTz0BggE1zN7F6eS6LLw2zfPVq2LQJeVmyWqzWUwiEaG6GXH3083SglfIFVdTW1vKb1x6kvKqczbW1uuNUSigqgtJSGBhgoDuP3pos7NmF2Ldsgc2boa6OeXOzeWfxTVQN+llVW4u7v5tDvymlILuA5fOXY7PVcPniYfKLJH/3ke+wo24Hn7OvoCY/n9WrBDctu8mM8vEGvJwI7Gd+oZWFyxZSmFNI7cZa6n5WyorSCm67/TaK+0bMVcD0B7cD1ze/weLF8Jmbv0B5Xjl52XkcaD7AwuyF3GK/Cc6dw37TTXTK85TnlevvxqZN5rOnrk7vvRUUsKS2Vl+4/vRpLs8Z5kqpA7vdzoYVaxHPvIK9pobL/ZdZoi2hZW4urasXYD9wljm2OVTNraah6zxzSubgHHSyeP4Cbl2/nrL119Pj6uFQyyG2b9selv3kSTNRI0uW4Jy/CufyRfRd6aPYVmwuLXvjghvZ0ICeKbS8nJ48yUDXAPbFiyktvUwOOdx6660wfz7OQ/vo9VxiXs08bGvXQmEhlJdTqi3kQm83ZXllbNiyBW64gbonS7m0eSmD6xex8bUA67R19Lh6cFaOMFBlJfJbXNi3kIFT4Si22tpaTh48icwPsrR0KZaVduwON/ZbbtEn5wF1u3dT8ZufseSP13LL5ltYULQA6+nTZkQSAPPmUeHs4ba7PszJjpNkZ2VTuyJcb+/ZXkrLSrHX2LHX1nLhwuPMc2ZRVVCF/dZboTq8dkhk47q+ej32MxL73LkM1iykp6OHOZtW4ptTgSU3l0/c9af0HG2kx91LdkU+drudzQUrsTsuUFrVh33hHORciT3bzrK5yxjuGObmpTezbdE26urqKOgfobyqE3uZXb/msjI4eBD7tm3sOXII+wIbd9z1Iep2PcXq61azxriX3r5uHmvbyWftduyVW+lua2TA6WVl8Ur6Cwu5bYLtX6pM9zyIzpDpyPgdmprcCkQGANcY26aM0PCwxFYSHtYZTmpv0Mv18643QznzsvNoG2qJGaJGLjQSaVaKtL/+9S1/TVDqZixNalikbu/+3dnfAvrsyxJbiWnaCAT9UU7qph5jcZdRZha/DPD2JXqP7VAoY2wwaGR5lEgtiBCCElsJjgEHTs8Qwu9HCGGaZsz4cMOMNOAZMCcImUQOb0eZrKIybobKCQbBaqVtuI1OV+zMc4GIysUUGv5nWbKQUlKUq5toglkWMy+P1+cCKWkeao7Km5NjzeGDqz+IO+hmadlSM93E4pLFWFNYj2ZX0y5zvY0sSxZb52+NMn0daz8WnliWgompfagNx4CD2xfdTnVehXlfR/wjlOWWIa26j0YIoZu0hCAog6YvJ4csnN5h+tx9tA61mh0Uk0jzl8/H4ZZDdLu6WVa2jDsX38myMj1u5EzXmSh5C3MKsVlt3GV/W3iNE9PEEWFqi3BSBwN+VlWsojK/kgAReX+E4NaFtxI0TLNVBVXhvEoRXOrXo7wi82yFUm1YLVb6Qt/OqPvY4+phQV4Vjx5/lP3N+yN2SQY9g+Y9/UPjHzjZedKMAPMEPHgDXjbN3cTayrXmeULKuOtstCdKceJ269mF8yt5+uxTus8IPUler7uXEZ/T9H+cbj8ZNdcmFKWUbclmTeUa87gSW0mszzHiPZufP5fS3FKOGr63yM8sqAXQLPq9P997gXNdZwgKYwZ5Jvgg0sxzwCeNvz8JPBux/X8Z0Uw3A4MRpqipwXhgR9uPhm2DETOp3X43mtRnLa6cs5IyW2mMgoi0k+Zl5fHHa/6Y1RWro+KuL/RcMKNqrBYrc3LL8Wt+0wfhD/jCK1MBmgziDrohGKSmaAGWoFHnKAVhtWSxdYG+lrD58hkKIqgFOdb6JgEtYKbGEJpk45w1po08ykQWmjQ1usE37kmInIgBZ2luafi+xYliSpS+oCSvVF8PwjgnlJ5i0DuIy+/iUIce1qtZLRxtP2rIHm4YI2epB2WQVy+/ypBfz3UU0AJIKXU7uaYl9EFYrVkI4PLAZdMH4fK7ONJ2JMY3Yj7jsSbKmefov9dVrSNP5CAtFs51n6PH1cOZ7jPmhw6wYs4KsrFiERZzwmFJVj4Xey7QNtwWu05FhEwA+HwIKfEFfWxdsJXCnEJzcaoR/0hYpmBQz/Ia9LK0dInpGwiVFbXYUkSYa9dQO1cHr3Kh9wJ+GQSLheqCaqTQG8BuVzctQy0JAx4GPfpoN3JOhv7OSS72XqSxtyHmHHP9jIHW8PHG/e/39OsTV41AiNH357v7vsuPjvyImuIaciIUQnluWdwopoePPsyKcn2ezdrKtdy5+E59h1v/7kNhw2sq14CUNPZcNCfD2UvtfGD1B2IafVuWjXcufSeluaVYhMX8PhaXLjZzUJlyRPggaooX4Pa7cRnBLZH3NNdqY3ON/p03DzaTI7IIWow1LGazghBCPAG8AawSQrQIIT4LfAd4pxCiAXiH8T/Ai0AT0Aj8GPjLqZLLxGyQtfDCNBaLGe52tP2o2Vs91n6MQVdfTGNjvngYvSME53vOc7rzND8++mN6Th7klT0/o7qwGquwEtSC9I50687JUAP5st4TCimAty96m75d0+3PlpBiCEQ4B4G8l3fz3X3G4jRSsqJ8hf5CSsnti29nfn51VDhrQVYeNUcuUGQrojCnEE3qDWiWJcts/G6uuTnqmp678BzBiJdwYV4Nn9z4SXY27mQwXr6jiBFEYU4hy+LMpN6cv5zS8w79/p88SUmr3pMsyilCIgkYCuCWRdvCocNSv8ZFJYui5jh4A156XD1IJI4BB+3D7QSlHtFTklUYdS1RYka89iHnpBmzH6GMaoprwr3RFGZS51l1U9xDbz5E62Az0mqNikj6wJo/4raF25hXOI8LPRfw+t0U5RSZSmjQ1U8obj8qeeGoegDw+SjJLuLOxXfyatOrvNjwIhd6L/DpTZ/WHbihYzXNvI8Pv/kwe6/upcXVgseYGJZrzaEiv0JvvK5exTfUz4B/mCH3gNnR0ZC4gh76Pf1IIXj9yutIYyTU6+qlyBXkf8mNUaIuLIlNvfHZGz7LLfN1k6zZuL78cnhltdDz9gd5z8r3xE/lLQTrKteaje9KbyE0NiKReAIenj73NMc7jpmHl9tKw3MLIu6f+b2gP2cz0sntxuV3carzFEIaUYBSkm1EFzb1N3Gw5SDD3mFW7TuPlJIPrf4gdy6+k2JbMV0jXdww7wbqu+rNiK9BzyB/tOoD0cv5RigIR/9l/n/23jxIkuu+7/xkZt1nV993z30fmAMAAQxAEIQI8QBoSaRISZZkrSzLjrWkteXYtf9QBMIRinDEmg5bS68pSrIpBS2JoEiAACEQBIhjcA7mvnqme/q+u6vrvqsy8+0fL/NVVfeQdmwIDu+C7x9guruqsjLf+93f77dqVhG2TU+op20CsG7WMDyy7xnKFAnoPtmktv8nUZT7f7uEEL8khBgQQniFEMNCiD8TQqSEEJ8UQuwVQjwuhEg7fyuEEP+rEGK3EOKoEOLCh3Vd7nJvrXt43Ggl7Avz5L4n2/52s7wpJTxbNtjv3Pc7bbQAyVJSwu+Bl6ZeYrmwzNKl10kkC3z5yJeJB+Iqc/AY3uYUk9N0FJaFEAKPZqAjS10zqSmOJA7Iv7MsiMfRNI0zo2eIlU0VacR8UZYLy9Iw2DZvL7zdBsQbjg2zK76D269/W27s7v3SGNumjMhdxSqr0RahX1q9RN1uZgIJI86VtSu8v/Q+OnfJElwHod9FyEXIkcCZV78tr9u24dlnufWfpZMr1AvOdJc00D5nmuzpR59mLDIMQtJnNOzm9bVGkbc3bzObnVXXX6zmZGR+lwzCLd2MxEZ4YvcTgCRV9Bm+toN7oPtAU+XtJx1G5zWf2fNpfu/+35O3whYI5772R/oJeoJsVFNoSIGaYr2IR/dwsOegmnCrW3Ui3jCnB0/z4MiD/OaJ37zr5wDkiikKtTyFeoFkOUmuliPx7A+4du0VOcLbohGiaRpD0SHc2kWqnlKKbuVGGQ0N+/AhLk68wUJugVdmf0SxklNCRrYGby28LZ+3cx9sYaNrOpqmUV+aJzI513apbgbRulLlFKZZIxFI8PCIA4Kcm2sqxTn33DAtbmzcIFPJcG75PKW6LNE9PPqwGqF2ndeJWqek7HfWVvqXmfR0E1fRcv/upO8ozM/L0y/z2uxr8heWpc6VJgTJchJNCLUfQfYkX5t6hXgyj67pLOeXeHP+TT6x4xNcXb+q6L5d8r2VwgrP3vyO6hFtzSBSxSRVs4rf48dreNupeuoV3ls5hxACgaD/wL0ITUPTtP95cBD/v1zOA+oJt+sfFOtFlgvLbcIdYW+Y3fGdbRvs1ZlXVYQAtNWLjzpU27oAA53XZl+jWC8q/EN3tI8n927hQ7Jlj+KVmVc41nGsmSmIJi6C/n4aVoPr69fbwER/b99TlBtlWU+3bcqNMmPREVXr3pXYxa21Gwgh+yvT6WkVpdiiOUY3l51rp4rwBNpq+bfy41xdv0oikCDgDTQzlK0lJsOgK9RFdosIkEDgrTVkzd0xYO68u67p+A0//UH5PC4sn+dXjv0K37jyDfKVLAhBR6CjDXvhHqQOr6z1RnwR5bQ0y76rHKv8paQEOTN6Rv39ueVz8v9bMoiqWWXdIdX77ykxzWZmmM/Nq88Xzn2tmTUCngBnl97m7fm3GE+Oo2ka2WqWlcJKG2fRcGwYj+5hIjWh6vit99Bdy7lFNFu0MYAad6bYuPpuW8kSITEya8U1GmaToNB1rpZlkiwnSYY1CvUCS/klbF1jKNyPcIxxwBfmVlqOx7pOIx6IS1wDGma9ytvzZ9su1aXMaF3fvfVdLi1fIOKL4Gk1PR6ZtWtCIpd105KTWfklNstJivUiFbPC1fWr2EJwfe0aHxv+GE/tf0r26IQEtO1K7NpWenIR61vXVhzF3dDWraPoXYGE6vF0h7rxV+pyPFg3FJrbBQa6gkHuGWvYDcViqz6j9ewI2Ze5f+g+1ovrbTomtrAQmoZAcHrwND17j6PpugwG/7+cQfxPv5wDtK9rnypv4NR0L61eaitleA0v11avtG2eW5u3GE+Oq3+3guNcBOVqbpmHxx7m5sZNya0vbAK6n2w9z1uzEpbvMqIKp+nnQSdZS0pnAlyalw1oTBMMg4bdUPV6t/m21MLHg20T98fZ17kHj+5hJDbC2fmzygFZtkW6km4nqWvpQdxpqQ1XzXaCwroj7ZmpZijXS82IqPV9nBp2upK+K8pV13RZ33XuvzvH5rLD7nEkV8v1EnWrzlx2jnxZTu5slDbaBHkCngCf2/tZ2RTsPigJ/px7olvN0srWJYRgNDbKSHxE8QWV6iU5kdZiWN9eeJvZtGOk/zsyiEvLF3nuttQLD+qyBxHzx8jVcozER+RBd97f0AwKtQLFerGNkiHsDTGXnePq2tXtAwMt38cQGpqQZZXecC+nBk4R8ASwDb1NK9myTPoj/eiazmhsRJXSKo1yW7ZYpxlwCF3DsGFH5y6O9h5FN4yms9eaNBIdgQ4ivgi61YLrcZb77F1D7OJmNCGI+WPMZWabf+yV0flkfoI76TsMOAJQL0y+AMh9XW6Uydfy2LrM/q+uX+X5iedZzS2DEPyDe/4BXzryJQYiA21nV6OlUd5y/7byM6lek2gir1UZzLbp8MWUIw/7wgQKFRZyCzSshtrDLmMBSIR/qxPSt9yfVgfRFexkIDLATHoaSzgZTEM+G8s2EZrcs5VGhXNrF7ERbdTjH+b6yDoI99ZeWbvCiYET25C/B3sOEvXJaaSHRh6SCm1bm9RoKpXuDfdyov8Eo/FR1cc40LmP4eiQTAeFIOAJEPdHyZpF8s7rGlaDR8YeUZGOoeksV5bRNI2gEUBzNopb23ejC4HgH578h3SHuplOTcmfOU3GpfwSr9x5mYbVUJs2WKwS9oXxe/xqUsv9PvmGTMtL9ZKaCnGNq7elVOQeBJfIMFVJtZGctfYgMpWMijadN0Q4zcU7qTstkbp805XCihztnH9XveRbN+Scut5Cc9IabXt1L7lqjvXqOoPRQfK1PF50Tg2ckrQjzuduXT7Dz0J+gTfm3mij/W4rzTirWitjC5tS7Scol21pUj/96NN01uV9c43NbGYWoWvqeo73H5eAMcOHLWy8upeecA+XVy6Rq+X+m01qty9TMSs8PPowx/uPszuxG9vQ25rtr8/8iDupOzTsBk/sfoKjfTK7Tfg7+MO3/lBloprRbFYLXWchM4tmeLi1eYvNaob+FmXDz+77LJOpSRZzi4R9YTr9HduYgeZmJQGj+7xc3i7NFszn5llp1T7wekEIVjdkwJXKt1CNaFpTD9r5N8C047iFJbPEb1z5Bs/eepan9j/Fga4D6uUepJNuC3ayWSgU0Jw9olk2e73NgQuv7uXM6BkChQrH+o6hAenSJqlKiuN9x9E1nVNlafxbtbw1NB7b+RhBT7AZCAGnBk4p1LV6ji3XMhjup2pWWcg4ULNyGf6TJJPo8HcQcah6bmzcwDTkPSg3fkr3/eEux2Blq1mm09NKctF9iM9PPK+ijInUxF31INaKa3ztwtcASZb2+QOfp2pWlY7EewvvKCMHMnreLGxgimaTumpWOTt/FmGZGLrB3s69aMhDsa9rH3qjpUltGE0EsBC8u/gug9FB+sK9xPwxCeUXgi8d+RJ9oR5p2JzSl6duMhofbZti6vInMHSD/3xZkr+pEUia9X23LAEQMyJ86fCXFMUH0F7G0TTlIHyGr52szUmjj/YelU1Dx4ANRmWNvzvUjWmbZMYkkrY/3NesBVvy2eia3sbemalmeNupIxvobJQ2WMrMc3H1It3eeFORbssK+8NoAuWwLFsy506mJrewbGqMRAZZLazyTMtz3Lac17jZwdNvPE35m/8FfzrNelEi6wv1AjsSOxm573H1HZJHdxH3xwl5Q+iaLp2tc02tfF7u8xAtzqs70IVmS73xpfwSL06+SKlR4r6xB2V51NXlMAJqD3xn/G/U2K7umPSwJyg/X/cQ9obpDnXj9fjRLJs7uRnqVp2qXeO+oftUj+a5288plPBmeZOqWW2n7y6VOPz9c23X72asO2JjFOtFzJZ9ha7D0hJHfyilf42GRVewi5/Z9TMYutHUHXH+tivQqVh4Q4bU/JjLzjGRmuDVmVd5be419ee7O3ZRs2q8v/Q+mbKTff77f8+TL0zyq0Wp2/BblYMc/Iu/Vc89W83y9sLbnH7homQEtm1CeoBorAf/voMwMMDDi5I2RQjBI2OPqLMjhODkwEluJW8pVoZkOcmJ3uPtU3/us3zmGYJGQNoaIfAZPgzThpqcKLQtk8/tf1JyqCHQDI8qOf2PWB9dB+HgBTS0Jm+KEHSFulTz2R3Rm0pPbSOM++KhL3Kk94hKI9+ce5P3Ft9rK91otiQn+417fkPW5Kuyli70JlWy+6BNsy6blo6Ye82scWP9Ov2uWpdjeKP+KAe6DxDyhphKT/H4rsfVpMruTkm1MZ2eplQrYJ4+qUYr93XtY6G8hmmbnBo4hS1ssqUUpm2qBtzOxE71fdzrsloO8oHoftUEdMn6gt7g9h6EruM1vO16EEI2xafSUyrTARiNDgOygSmEwNM3gH90F6cd3Ykndj9Bl9N3GIoOtfHqyJ6OvNYfTr0sm4OmfGaZcnpbicxdKadMpWkaT+yRTWoXUyJaxokP9Rxif+c+vIaXnXchpFPLnUDb+ZhqerdmemFvGEMzeHz3pzh06OOM7pATP2d++w9J/OpvEfVHifljsgQD7BddfCZ2ik/t/pT6iH/95r9uYwONecNoQjAcG+bc8jnWS+tUGhVi4U7pEJzvXTMrmLYsM5WSy+RqOQoRP/lanr2de2lYDfrCfYx17iARTBD2hvH5grKH4kSoZavK5dXL1K06MV+TeNIty+iaztHeI8370WhsK6m4GfrO+BhBT5CIN9xW4mqlktEtmzOjZ9iZ2MlIbISgJ0hHoIO9nXvxeHzc03+PykxO9Bxri+BbKTMA5p0JIWjHZKQrad6ZkIHc6ze+z6xb8nLsgrsmU5Notk3A8HHq/r9H6Df/Mfz6rwNytF3TNMLeEKZt8jO7f4bX515nJjNDzaopjE25UUZYZpuMsWrMAxPXX6diVhRFjJsdgizN/eXNv1KZXlesT2XmP21Sf4hLQ3p7r+FFo+mRK40Ko/HRbcR19w/cKxGzV5oC8zWrpiKpulXHEpZqDJ8ZPdOc6y5t0LAaqkm9o2u3MoAq7bRsSvUSb82dZVd4FwJBQPezIzzEamGVXCmlSkwhb0iVJnRNJ+HvoFAvSAPp6EFolkAYOkHDz6M7HpUsqsUNqqYcV/ToHjTblpNLjhN4f+l9VT7y6B5ZYmsxmEulBV688yJRX5SxjjH8hl+JG8mbqqkppq5gF7dbSNPcyLhiVqSRdxyEO2LsMs0ORofYkdjJ+aUPODN6hsHooMogFvOL7U1q596FDKdmjIZwG3xC/FgdAHeUNu6P87Hhj2HoRtOwtGQQOzp2kC5vUqgVmMvO3vW95BvKw+vXfRzoPqC+j7s6g534DB9fv/wnPHf7OeYzc83XbikTdAU62fHMD9G+9rXtuhAtZaeN0gaagLfmZQal2YJSo8RLMy/LSH9Lj0cIwcG3ZBM15I2gI0F6lm0ym50lW8+zlF9iPjdPrlFAdzh/AGKBuOI3OtRzUO0RtwdWapR4+c4Pmtdp26o86y43ej47f5aAL0ipXlQNfYSQYl26j/5IP5oQfG/ie3z94tclyNMJSkLeEA3b5LWZH3Gk9wiP7XyMTCWljKnX8G4rzTUaMhKP+CJtk0gCGai5n9/K19Tau5LgPlsFPoBqqp8YOIHP8PGDOy8xk5lRQYHbO3Ed6GZ5k+trV5vUI0LAS0168QPvyHNy78BpFnIL1GpNvRTbwTvYwmZ3Yjf7+w6DpslA7qclpg9x2TaGbvDgyIPNnwlBsV7k2+PfbpuhD3gCss6fTMJzsgn5N+N/w9W1Jj2vm408OPIgA5EB3l54G80WjMSGeWnqJbLVLEJIJGldWFgOzbL7OcK2sIRF3azh031OU6rEzaXLTKQmmNucBsOgUC9wafUS6Uoa27bk9I8uyxEZhyW1N9zLmZEHCQdiHO05wvtL75OqpLDNBg27wVR6Cr/HT7dXAnpcZymEUA2+htWQZHwtDmKjKpHRhXqB+ew8FVPOjCuj6upBONreNbt9DFY4KfShnkPbav3DsWF6w708OPYQK8VVkqUNTvSf4L9c+S+kihvKCLQaXp/h497B0yR8CbqDXXxq96cIOuy37ne6WwZhIzjcc5iHRh/iaxe+RqosheAHo4Ntf39u+Rwr2SWC3uC2JmzbchzED6Ze4j+c+w9tv/LqXhbzi+zp3INwJpcKrSOgWw75nsQeVnNLzGfn+dNLf9r+OS3X1soIbGgGEXzyWQpk5uHcX78mM7lUJYVwhJyKdplao8pMZkY5y7VKs2wodA3dEiTC3RzoPkCuXlD3/dzSOQKeAA+OPEhPuEeVwtq+hWUR2hJgucZSIBhxgGNt7Ka6jl/3s1Zca7vXQkMFNVfXr2Ihg4VcNcdrs68xn5IiT//yzL/kH5/+x+iaTsiZQHRV70CWQqstWgtBT1Dde63l+hCiPdJ3vo97jfKGy0xkPjvf1qR+ZeaVtpe1Irjb9s+lS2xdA5EBTLtBzaqpvgqAbVkI57sMxYa4lZlE4GRkP3UQH+JyRuqW8kvcN3SfYrN0D0IrxuHUwCk5aglqCgjktFK3LwF//dfE/XFi/hjH+o6p0srjOx7jkRZJRL/Hz1BkgJXyGtecWrChGzw08hB6S5N6ojihIqFapchgdJCENyoNr7Nxrm9c53fXxgieu6QMjosvyFQzXF+9SkOT2hCtgDa3BFaoFciWmmN3mWqGJWeeG8D+sz+lezlDoIV108UoDBoJupbkiO9X3vsK2DbrxXWWiiuMb9zkVnqSGxs3mg3sF16AmzcRyEhtpbBCo1FrG+dbL66TKqd4Y/4sSwU5mfJH5/5I/rKF7ttVUAM5ajkYGWC9us5obARDN+gNdkm22taG4Jblqdbp+L++zrX1a5TqJa6sXcEWNh8b/libTshmeZNcJUPEF2HQCsG/+TfwzDPb3s91orbz36cffVppgLjspPlaXkWsbcbCOeRuf+jiynlMx5Fvu+4t2BLNFpxfOc+R3iP8yoFfZDg2jCaE1CdwrqVu1Ug64MwHRh/i4bGHsTWN5yckiYHpMuI6tmZl34Ccs7dtRrt20h/pZ6hjhAeGJWgt7A1xpPcIk6lJstUsMX9MflfXGT/9NNg2s+lmxtW5nCb71a80yyaVVJsULnfu8OYf/BpVZ0pu77k7BIpbQJjOcsteLvmladZBCC6vXmb1je/zm9MxhSh3qT3i/jhVs9rWT6uY3luMDgAAIABJREFUFUK5Mg88I/sebr8AIR2XC5Acjg1vzyCca1gtrm6Th21lY3WnGU8PnkZrzUxu3Gh7TUegA03TFNWGMJv7vSMg9cUFgrPzZ8mZJYSuySmxnzqID29pyKhiIbdAqVFSjsF9iC9NvaQUzQSS7hdk9Py92/JwFetFssUk3LnD/cP3c7TvKE+/8TTP3n4WgMvLF3hj7g1Vwor5Y6RKm9SFqTZMtpplvbSOV5O0C1FvRPIVeYPc03cco1IlVUlTrZVUZO6u8vl34YMPONR9kBMDJ1Vt/2PDH6NULVC2qm16EEf7jtIV6kIIgSUsAnjUxNWl1UtqggmAhQUSi5uSVthZQtjcN3Qfuc0mnbItbJ4b/y7lRplirSjFijLbaRS4cwe/4edo71HWimtMpCbacCQdAVkmWy6uoOkyq1HYErPpSFqFXZbzy7w89TJ1u87OxE6urV9jLbfMhZULeNC3axI4spku9mUxt8h9Q/dRrpewTZPv3vouZqM96wnqfhZyC9RXFiX53fg425aTQbjO5asffJVqIsrKZz+rkNTzuXnQNLqCXe3RtmN0Ap4AHt0jZUHF9hHdpx99mlALDbihGcScMo6hG7w6+RKGZig093M3v+NcE6yX1hEI5vOLkh6jZZoq7EzcuPlcYmCXnByyBRfXLjOVniIeSrC/ez9xf5ygN0TIG5L0HWaNUr1E1azKKLqF3sPbMhXYtZjCWF5BEzKqXs4vodmiObI8MYFA0BDNvRbKlWXQpmntfadgAA2UToNuScf08vTL3PnRM1SvXuT6+nX196OxkSbmoyUg8epe7tNH8JdrfGIz2kQ5O2dyubCslO22OYgtqy/ci1eX5a37h+9npyML6upL56o5dFsQD8Tv+vq4Py553ZxnorufB3T44/zDU7+F71d+TQ4qeOQ9kcC5D399ZB1Ea5P6VvKWklzsCHSoGqurKfvu4rsq+sjX8szn5hmNj9IV7GJnZARsm6trTQSlu3LlDGv5VT6+ownDb5g1rJYmtS1sptJTVOplYv4YpwakmF6lUWEpt0igbhPo6KbDEwHDIOwNq9T1+sZ1lUbHgh2cGjwFQshJFM2AlkN6qPcI5YYc2dzbtRdb2ITccoxAEeO5AEGBQGhQrzcjuWPRI5wePN02vurVvWiWzUphhYXCIoPRQY71yybsw65kKEAwiCUsCvUCwonOU+WUYo5t2A05VaRBItjJmZEzGJpB1Bcl7JGN8IPdB9vS/3QlTb1RRSB4/vb3yFQyVGol5/FaTY0DdzlU7a0o30M9h7hvVefJZ2RG19rDGIoO8cDQ/QQ8AfY7vD13XY6DONi5n85gJ5vlTcq1IsLna4sw/7cH/zk/d/DnOOAMIsibL2/mnfQdGRUKQdATZHfXnjYB+6ffeFpx9YAUsfqFAz8HSOe+kluialbVz9z9FffH0TWduD/OUnGZmcwMtiafeUegA1c1bWfXbg52H2Q0sYMdnbvQbBthOGh5XSfoCUq51FJS8XtZwqJu1Ql5Qzy573NNB2GabdmOu180ITg1eEr2v4RoBgBiO6HeU/ue4g8+/gcc6ztO3B8n7A3j1b0EEz18Zs+nidXlmx7tOdyWLd5K3iJVkZmxrulYVkPhdfwtk2Exf4ybDrBtLjvXxOw4fblsNYtpm1xZu9J0EFsidjfw+/Tun+UTOz/B4Z7DfGf8O3xm72eAJo1Ltprliwd+QWWTW9d8bl7xiO3v2o9f96rvNLM5xQt3vk/DPcoejyw5IX6aQXyYS3OAY+50hTvP7NE9fP7A57f9/UNDMsV2o5EvHvoiI/ERFtOzIES7WhfS8MiIUhq2oCdIzayh2YKB6CDdAZl+KqoPy5TN0MwsXb4uaVhqNfb3HGC1nmIttwKGHPm7p/8e+R3Q5AMUAlOXNdFSrcAzN58hX8kidI2QN8T9Q/dTtmXNueZQHWhoFMtZNV7nxiNuc95n+OQ4XUvTr2HX+b/PS33qgcgAYW9YoUQbdoOyVWV3YreamZ9NtzjMoPz+rRoBtrBVH0fhGzSNnZ27qDTK7OjYwe8/+PtEHQzKrc1bbaCyUqNE2BdGR8eyTHnIzCatQiuozn1voA3glwgm6C410ba21TTox/qOUajmqVt1MqXt8qmVRkUCJC3Ji3Oi5yi/euxX5S8tS1Ft7OjYgc/w8e/O/XvOzp+lUPnxPQhNSE3tSCDGLx/95bbfmS3XlqvmeG+hiRlBSG6kv7z6TQBVGszVcpi2yVBsCOFgCqK+Dj6585PcN3QfptUgWUri8waYyczw3so5ZrKzaIImuaCu4/f4nWmloypCNm1TIcIvLV9ocxCBLQY/6JWTUZOZaUKe4DbyOjdLcMtO35t8nqffeJpr61dp2A3CvjAPjT6EHQhw/tpL7PvGC3QEOiQi251i2hJS1606q/km+2+8Rfo3VUmpkl/Nqsn+nXMtrbF5w24oEsytGcSxvmMEPAH+/Oqf88PpH3Kw5yCWsPiP5/8j0MyAk+Uk37n5beazbZI6bUsg2J3YxWZ5k3q9OVJv2g1Wi2sU60USgQT3jT4Amib7Nz91EB/icsjwDvUcamNlXS+t860b38JbqRNfbx7ksjNi6Kp9feW9r3Bj4wa6A7YJLq2hoalRushqCm9Fjq7+8cU/Jnn5Had8IOiM9nC85wiapsnSQt1Em5ml3CiTLG4wGByUHDeFLCt2DjSNUjkHhsFGaUNN/miaJrmc6nXqwmQ+N8/M5jTdoW4+PvowIX+EsfgYyXKS5eIqNbNG3apzbvkciWCCPn8XmiaPaXeoG1+5xhmjhVDMbTo7azx/U/18tbhKqVEiUKyi2YLOYCcj8VHeWniL15w+RhsfTyCAQEbHh3qk0lzEFyFbzTIWH2M4NsxIfIRfvefXZJaWnePJ/U/yR//1d0mml1SU2arzWzNrnOo/yXBoGA04OXAS36QEDSpj0ZpBtDRKo74ojzdGuLl+g/cW32U6PY2hGar0N5eZI7cwydriOIZmyEb5lnVu+ZxsTJomBIO8ePM5/uKDP2F4fAnqdeUg5rPzDEQGEMD19eustVJNbznkpwZPsVHa4MbmOM9PPO98BTdCbj6LQr3A9GazlKfZwrl+eHTHo0qJMOAJsKdzD5OpSYQmnXLOyvOjmVf54fQPsSyTQr3A9eRN2SDVtOZ90jQZ2es6yVKSmlVjInmbZDnJwe6D9IX7lJFfza808SCNxjb5zKAnSPdiivVqUoJHW5vBznBI1BNtlh01jUi6iL9UJVvNslHa4I25NyiaJWqlPDF/jKHoELfXb4IQ/NKRX+JYz5E2426vrhAsyD5YZ7CT/K0rUK+TKqcwNINQfruGeK6c2aYJblQqMDu7zUG4Wa+7x771E7AymmXfnS3ZWYlAgp0dOyg3yhQquSYwUwhVUjref5y16iZC19p4sT7M9ZF1EKV6nqn0FLqmsyuxq60cUagX+OJciBMvybLD7sRuxtdlYynmj3HSKQPF/XH8DgX20LM/Qtd0fv/B3+fM6BlC3/wWDwT28Pl9T8mewrefxbOxyUCoj4n0Hd6YfQ1b2ET9UR6YaWBMTGIJCw86dwp3qFt1TNtkpZHmWGgnnU6TunWMT+EMrl0jGIiwr2sfBzr30jDrZMtpvF4/fo+fmcwMZVFTGZNlW0ynp0kV1jE0A5/mZX/Xfg6fvU3uq/8WkNGx0MDXSgdtudoBzXX8B1c40n2IoCdINCAjNJc9s022kqahs4TFSGyEnnCPrI3n5inWi9StOrdSE0ylp0mVNnnu9nPsPj8tka/ISG9iszk6+/EdH2dvYg+ZeoZEIMHhnkMMX5vjSO8RPJrRhnIF1AF3ezCNZ/4Ko24inHLjl498GZ/mkeOid/4W39f/jMR3/pbh2DAhfbumAMDhWlw6iECAUjmLvbrC52f9RE0DoesMRaX2tP9nP0exK4otbJa++AT8o38k38A55IlAgqA3SK6SpVgvUraqitvHvnqFULa0rS/hHl5d07lv8F78Hj+aELwx94bcl7pO1azi0T14dS/7ew5yuOewbEI7r3WNlqVJ5z3cMYqta5waOMXfO/wLElujaexM7ORQzyHqtgx6+n77n+N//Ali/pjMOluwLflyhknnObkB02Z5k4Nv3VL8TpotVClXnTuzfb+cfv4C3lqTxXjhyAgC6QwnU5PcTN6kYUpGgK5QFz7dS8wf4+Njj8rP/uM/oXM5TcgbkvQyb78DFy5wM3mzjYa8dZ1feI90Ja0mrCK+CL5cTopObXEQtzdvN/svyIyllZHB/e594b4fq0HvPr+OQAevz7xGxawwnhzn+vo1EIKA4UdoUqr35sZNlgorCCSq+qc9iA9x5apSpP7a+jX6I/1MpadYya+oQ7iwPM5wTIK4RuOj4NR0i/WimniomBVMp0a/p3MPx/uP887l50n+pRxPLNVLLLY0iYc6x6g2KpTsmtpUqXKK7mCXwjV4bKjZNaK+KHs692B7PBTym9LotuhVAE39aE1DNzx0h7qpmBX6w32Mr91gs5LmypqD29B1dnfuJuKLYAmLxfwix169zu3N2zScja03Wvh4EEQCsXZSNQQjsRGiLWhm3RbU6mX8Hj81Z5bcdhHQrTfctgl4AvRH+rmVvEXYFyZZSqpDZNomq4VV3ll8FxNHLCkzg2FabYa+1UFOp6e5k5ok08jwmT2f5sVbz1NpVOTUjtXYru3sGOPB6CDlRpnlzCKGpmPbkkn3hckXqJs1NfoL0pmdXzl/V13sQz2H2P3qJfm7YFByEjmlHkM3QNdJVVJUEzG+HryJ0DU8uodGdwIGB9uuaSA6QMgbYjotqavtluOvP/scv1s60tbkFEKgO3/yseGPETICDrZF/rBaK6l5/fHkOBWzQsDv4GfkDDCAGpG2NVkGemjsYSyvLGVeT41LskKHBr833Mvujl3yPOzdC5GIYgTWQN2jIF7cMMISsvzmTmUJTePmxg10WzQ1ze9iPFv7XLom2Y2TO3pl31C08CU5GJmvfvBV3l98T1KvOBmA5uAF3EzFrdu7e8g9P6PxUYU5cSf1Qt4QhmYoXIu8kHZz2YqfAljML0raHGf33Dt0L5gm9752W2V4d1u7ErtYyi+pPmcimJDXJgSjsRGe3P8UEV+EzfImmmE0M4efZhAf4mo58O8vvc9yeY2or6khUC9kFTjo9bnX0ZxNVWqUMP+TU2Osl9jrkMuVGiWK9SIL537IwJTcoNOZaS6uXGRHfEyqaNXz5CsZdfiFA9C5vn6NhtWgP9LP/sQeNAfE1LAa2B6DqlmV43MOhYW7lI6FpoFhOKWZec6MPETEG8bWmwjWfb0H6Qx2NhXskAfERd1e37hOVA+ozS2EoFAvUmzJAsYCI/zi4V9s+xkIZlPTpCtpMrUshmYw0iEPW1sz1hHw6Qx2IhDc3rxNrpbj4bGHZfbhECbaOgzHRznRJ/ssumkp4Z8jvUfappiub1xno7iOEILzSx9QrZUoNUqsl9bx6V45xXSXEpPiHxI2MW+E7mAX/ZF+8rU8ZqOOz/DxwNDHFNmhoRkc7Gz5Ls5qWA3mkndUBpEwImi24MbGDXLVHMKJ4E3kXvu9+3+PLx/5suohtV7TTGZGNakBBuPDbYzCN9dvSNoHZ/VF+nhq35P0hft4d/Fdzs69iVf38qvH/r68b7YAj7zfIKe/rqxc4rnuTZlBOLfF7/EzGh+lLzZAf6QfoWvs6z9M3aozkZkiU820Gcb10jrJUlJdu0tTfZ+jxpev5fnmpW9s6we4gc3xoZPOvRdNgkvb3takfmznYzwy9giHew4T9UWdnhgEvSGO9x5Ve3gkMtTWpLaExe3UbfWdvbq3TfnRvd8BT0A5KNNuUud/YuQRQAIRLWG1TQH+uCmmXz7yS/zsnp/l1AsXuXj+eX73/t8FkPajXKYxeZsvHvh5mY3dZS3ll9Q17Oncg2VbkhFWCFbyy9gIAj4Z7NTtBkJznNtPHcSHt0TLdIkQElVZN2skggm+dPhLeGrtc+h7OqSXn8vOEU0X+T8e/N850nOY2U1Z817OLzOXndtGWoYQPDH2mJPmptAEREOSzkJzOVWEpKGoWXUadQmUy9fy5Go5jo2clk3SqmyidYW6ONF/AmgaOhxVODeief7WcxSrORkVeoIMRAYIBaJcWLmAZVuKN8ZFgYNsElcqTUCUeyBbHelQcJBvj39blQeCbrPRGQ1MVdI8PPYw9zpz6PVas8YrTJNcLce19aY+dN2qs5RfomJW1LXYCHoiPUS8YQKGn0+NPKoEcG4lb7Uh3C1bOg+B4E5qEtOsK0PUMOvthsG9T9AmWzoQ6edwzyH6In3SSTnTT+eWz0lUr9PY7fA2R2ZdUOGd9B3qlQKi0UAEAnxi6Ayf3yu1RCRSVx6vAz1ySOHy2mWy1awy2q3X5PaHArqfXYld7Onex6/f8+vyewppMBQy3KlJZ8tpNUarOZ/5zSt/AUhuKjwe1ag/1HNIMtz29BD3dfCp3Z/it55fplYrU26U2dW1h95wLyulNSyPjHSFrsmAwd803l84+AU+sfMT6jo0hzjOQNKsZCoZTNeRtThnN7CxdY2wJyTJ69zou1ZTv3cDmqtrVzk7f5abyZsEvUE6g538iwf/BUFfiHq9ohrAA6FehcFo54aUZypZTqrSaiu7atWsKoT2SmFFBYMTG7fatsxGqaX31Oog/tW/YiQ+gs/w8cyNb3F++Twd6TKJtSx/fPGPATkdJWzZe7jw1/+u6Vi3LBen1OGL0bAajMZHZfb7R39EvpLj+3deVLTu+3sOqB7gjyuT/V2uj6yD6PV10xvulUAY55Au5hZYK64xnhzHX29B+gpBX7CnLUU8/9ufo/H+u6pJDbTVH91GrAa8cutFOWLqKMnt7t7LozsedUoZzWhuubzGRmGFvdG9qoxlenUqZkVu4ECAVDnFYn7Ree+WVNOQOApb2FTrZQ507iPgC9ET7mFXYhcz2VmJvtU0xfDqM3wc7jmsDpZu28p5+D1+OkKdbYf8/c33WMgtoCH7NBWzgiZkTdhn+OgMdTGeHOfP1iWNgPHBefXa2Rf+gpnMDD7Dp0p3GhqzmVlGYiNS2zjUzWf2fhbLthFvvcU/fSnF5YUP1NSRJaw2fidb2IzEhhkOyvcbCDbHCF2cy90yCLfE0BvuZb2wxuWVS1xbvyYjSlsSHAqnJLZR2qAz2Ml7C0195Dfn32wCCs0Gb02/zlx1jZsrV5hqoXx2QV2rhVV6wj2cnT/Ld259hzfn3mxek95es97ZsYNSvcSFtUtqKs5thCqDquusFdd4v4X51mUL1YRs1vsxwOvFq3sZiAywlF9Ct2wsTZBt5Pjh1MusFlclgWR5k3Mr57mVvEXZqnIzK7+DmmIKNJ2yRzea+3ywSdD3/uJ7khY70KHORGsW0R3q5kT/CWby8xILIppTTKJWo1Av0OXrUo5i5Lkm4d56cZ1sNcvXL3ydjdIGk8nb5Gt5vnDoC9xYuwaTk3z5OxMMO8SPbqDk4h5cbfZKowI/+IECMbYu91paFQC3/1GLC/L76Y/049E9WMIiVUkpg+0a/FdnXuVvJ74PSI311cB28KMb0Pg9fu6/kSFVSclgzaxALieLjZq0LQ+PPYzP8KuiVl1sf7+/6/WRdRBeobO3cy/DsWE6Ah2MRGUpoWbWuL5xXRHEAcR9Md5ZfEdFLUFPUBqZ5IY6DC6m4rN75Az0eHKcnR07uX/wPlbSzpyzbdMX7GGpuML55fNK+zbqlBIsBB6hsV5dp27VydfyXN68wVh8DK2rG8JhKmZFGQ5Vk3YcRMgbYjg2jF/zMhIZojPcTXeom3cW3yHjjJHqmk6+lpcUxGjcTMopkJHYCB67iVTO1/Jkqlk8LU5PgeaEaEFnCyKeEIlAgtH4KBulDdb29DN1b3s67TMkJsBreBmMDhLwBNjXtQ9N01jKL+EzfFIQKNzF+OY4uekbrBXXlPaFy2TqOkeAR8YeYTQqsQI6Gk/sfJx4IC4lNFsjVHURPvWs9nTuIe6PU6tXSJdTNKwGv3HPbxAyAsqBuMSC+7r2baMGcZdH9+DHQIRCzCYnub0xLuvehh9Nl7rE2VpOkQFuo/F2jI7X8EonG0iwVlwjW8/zN+N/o65X3nbn+zhUD3pLzDwYGZATaQ7GZCDUCx4PDVuWLmtmjZgRYqxrF2jSgRbrRVVmKZplaeB0Hctj4Df8PDT2sASQ+e/eoKe/H89v/xP5NQBsm7AvzBM7Hse5YPWn6UqadCWN0HX6In1olq0CBXfEOdPIbJexdb5/pVEhX8+rJjXIspztUNYc6D7AaGyEkwMnOdZ3vK0/4DN8bSp023RM/nvXlhLT5dXL8r1+QgN6JSv3q95Cfrj1u2loDMeGWVq+RbFebJ+ismXfxBQWyVKS2+lmAHI3tP3f9frIOoil4jzvLL7DpdVL7O/az2JhUU2zgKxRhp1m7COOPKJbA97RsQOAiC+qppgGo4MMx4axbFNF4b3hXnrCPRgOZfdodBhD01mrJCk1SpJRtZrlnh45T21qAo+tkaqnCHgC9IX7sL0eIr4IhuFVpQV3KXpup8Tk9/jpCnZRN6u8Nv0qq6V1Bd6zNdl4cw/OxdWLaoJFE857maaaQLJsi77YAMHW6R2Hhron0Bw11QQc6txP0Bv8iRxD68V1yo0yQU+QCysXONp7lEw1o65nrbjGZGqSb1z9cyyHpdVN+wGFx2jFNgS9QfLVHAvlBT6952d5Z+4shmZIQJKQ9OBth9cRpukOdTOVnmI6My2b1EI+96n0VLNM5byuala5vXm77b53BDoIeoLKya2m5khSwjCl4NM9/fcQ9oUdMjzZpHXlaN0sbut92tO5h7AvzHuL7zogRU1F6oZmsDexp4kM12XT1m1Sn+g/ocgfNVuwkFtgIT2rmtQ3Nm5QapQ42HWAk8P3IoD4Wla9t7s/NDS6Ij3YHpklRIygzMKMlubqFmPo9wSa99mSRuzVSUnc535LgTTK2WoWocGN5E10uwmUc4F3d9XAaF3O/ncngor1onLc7yy8w3sL7zoTSM3eTcATIOwNS9yDGp7Qtv13KzW8Sy54rO9Y84dbHITtIN5jmwW8VWmsXSR7bCPHk6M/w8/ueFwSTrbgSioNOalUqBXQ0BiMDrJR2lCO0qN72O/07/Z27uX08H14DS+TqUl8Xn+zlybaaT4+jPWRdRDYLldNXZVcBqMD6lAu5ZdkvQ94c/Z1QDYGAcVCuZBflKk8cvy1I9DBy1Mvq/c7t3yOH9x5CY9pO3Vrm/X8KrUWz5+pZJhKyRHXgfgQca90SiFviK5QF5ZHZyo9RX9UbtjWqFgZUCEkkV+twMXVi/zy4S8T9gSp2DXFhzTSMcrR3qNtxkkpbwnBTGaGgN2CfkWwWlxrE8qJeaL80/v+KakWQKBmCy4un5dkaI1m9PfA8ANt5SD3usc6xmhYDWazs6TKKY70HiHii9AT6pFIaiSQsBUQB9IRdYe62wz1925/j9XCCgKp5T2blo3e8eQ4UV9EOQq1nAPu9gBsYStltrA3zFsLb1GplzB0Q6HBO4OdFOtFesO96rMNzSARTDAWH2M5v4zRsLCCfnTLxrYt3ph7Q5EzutoZxXqRT+z4BE/sfqJN8cw97MlSknwtT9ei7JsEfWHVkNc1nf3dB5olTsOgI9DB8d6jxPwxrm9c5/u3n0dD4+Ojj0jn6DSpu0PdKtK8sHKBZyefB01jaEL2YSK+CJ3BTvw+qQvRGe5mT/8halaNVydeanPSd1vZuozGx6KjYEuZV/0uYk22sAl6g+zq2kOpUUZz+hU/bnUGOznSe4SeUA9evanT7PX6GAzL6aOgEZAgSpqynhoa7y+9jyUs/Iaf3nAvOecaFSjVweO4k0sHew6qPeHKBbtRfOtgwN0m2Qzd4HPlYU5X5etj8+v8i72/zs+/m6Hz4k0iRpB0Jc2xvmPs75UyrJOpSTZKG6oHYuhywEQIQV+4D4/uUaWuUr3AYec+AJSd8xzwBAgawa2X83e+PrIOopX3P1PNyHqpkCUWl+HVNeTlmkQxuhu63Chzov8Eg3qce85JZzGeHN/GRQ9yjPDLB36BeCDOq9OvgGiXZ7SFjX71GreSt+iO9hHSfGhorBRWWCmssKvvAB7dQ8wh7RqJjSgkays9AIaBpmlYusbllUvU6hVsDYVQ7Qv3MZmaRNd0FRW5zVqNpo6uG+UYmsHuizNYDl3yWnGN3Vmd2Rf/K5otWvotMqJbyi+xUl5TGJH+6GBbMzbuj9Mb7pWNOxwivFqOQl3KbqYqKRlB6jrDHaMqgjs5cJKeUA/ryVn0m+PSUDz3HFf+9A/Jzt1WHDgvTLyAZloKO1CsFVQpoVQvtdGgtD6n/kgfj+/8JAd7DnLvuSWwbSL+KNlymqAnSFdIggl7wj3qPVKVFCuFFSZSE6wWV+mqG+wfPcmjQw+paSfLtrAdhqPusDRyJwdOsq9rn+pPyZuvqfcs1Ap0rxfoj/TzYGgfJybyLOQWmExN8uLEC00QmeMghqKD9L9xAaNQUkjqnXHJlCqcrNLdI+4zt3TQdYPB6CBj8THqVp1EIMEjOx9lZ2InqWoGy2uoa2uTpgVVpnOXrktszr6uvWDL7MjVgmjtQXh0D37Dz2B8GEM37l4CbFkxf4xSXWbZPeEeBiIDfOHol+gMdtHnZLDD0SH2JZpBiKtRYpgOKl7YrBZWqTi1+tYx4YpZUYHeeHJc6UFsVZacaKWs37Ki/ijCAfk97pQQw+spZr/5VTKVDG/Nv8XfTnxfstFWMtjOs3Z7IMV6UU1KuZNMPeEexjrGVDaxvnCLv7zxV6wUV6WUgMMjFvVFf+ogPswVN2Sk7j5kocHZ+Td5ZeYVAp4AAU+A7nAPCU+UM3/5Nod7D7e9PlPN0LmebyOc07QmCdpDIw/x4MiDPLbzMaaTE9StOrlKFs0W3N95lPuH7qf0yUdUTT1VSXFu9QLsUfR6AAAgAElEQVTZUopd4V34vPIadvXso9Qo8cGKZHosNUoUfv5z1EK+JvmYo8GgoWHrGjfWrpHwRMDjJeQN8eiOR7mTliOL4p/9s7bJjL0tHEMNu8FgfFjqWgfibc28uewcG0tXyb/wN3I+250bd5rUF546TaOzQ9WQv3v7u4pxE2SUtFHaYK241qYVMJWeoivYRcghgbtn8CTD8WH6I/3s6dxDzayRLCe5vXmbA2/flobuyhUa596lc3yWmD9Kp08200ULFYVmy4OLEGyUNtrGFd3pr5A3RKVeZjY7y0xmhvjEHLZtkW8UWcwv0h/pZyYzg0f3SNS8tv24CCGoF3Nk9Tr5YkoJILUav7BPUpJ85b2v8CeX/oRLqy10zy0ZnaZpdIW6CHvDzGfn8b/1bpt6m7tKVpU35t7g9ekfMTixQudymi8c+HnneQgOde7HNjR1BV7dq8SGvB4/QSMoGXXtBoZuMJ2ZZjx5i5XCCoVGiZv5aTQ0kmPdzP/9J5sf/Du/A6dPt12L5tyT9xbexTIllfzIzUU5hdOK2YkOEPPHeGvxHfzeILrTVHej+huPHSEW6VfjrnPZOZllVlKyf/G//BLZqJd0Jc2NFYntCXmCaopQZluCnnCPkvitWTUadoP8ow8Q6h1Sz29rduruiddC66rc1bqWvvAFKRL0wANtP9/XtQ+P7pHa1I1mpjGVnZECQLqO6UzyLeYXmXACk9bn2ZoR7+7czXx2Hsu2pGOORln91IOkox5qZo0D3QcY6hjBo3vY2/UTuMH+DtdH1kH0ert4dMejara+PzyAV5Pspq/NvobP8KEZBr+w+0nC3jA3N24q4A3IDbzeyqHvpLf7O/fRF+7jncV38Ope/IaPy0sXZanHtjnWe5SCVZFjoVZBAYL8hh9LE2iWLWvXumzevTr7GolAQmYCmkaylOSV1bepB1t6A04G4dE9xCNdBHU/J7uPsqf/IGPxMVnyqMmIRMSirBXXeHj0YToCHdxJ31EH2aN70HUDYVkU68W2Zp4LKrKFjeZ8f5CGWLcFfbuOce/AKcaTku1UbAk8FVodlC5yK0guEUgw1jHGx3c/poymR/dwM3mzGfELoQydJSw6g530BLvp9nfj1Tz80qFfVJ+nAS9PvcyNjRskgok2oyAcTMVwbJhqvcz4+jgbpQ12JXYRMgJURQOEYDY7i8/wqazobs1NgWAgMkDBa3Nz5QqvTv0QkJGoV/PSHepmLjevOJrytXz7+7Q4iLA3TEegg6n0VBuzrms03Qp9zmksu8SCB9+7Q9xVehOCDl+MPd370ZySmkvfAVCxaxTM5ninez+nstPkqjmqdh3LK3sQo+FBdu9r0t7T1bWtDu/1yIyiUi9Rd0qMezv3ygm3lr+rmTU5WYSkjNAc3RKQWWIt5CfnadyVjqJULzFl5Hju9nMUG03CwvXCGllHRvShkYf4+OjH1dRWq461t6sHv08y0AohlD721jXvvzvFuBmJwM6dEA63/dwVM9I1XWnMQ3M8eOzaPH0f3Gz+3HHZbrloubBMw26oUmKhVsC0TcaTTqY8Okpp5xBC1xCadGyT6Ts0hLlNkvbDWh9ZBzFfmOGNuTeYSk9xcuAkljAVvS84dWpdp2bVGIgOkKqkVKPNbVJ3RXpVVBLzx4j6o9w7dG9zjNPJKHRnRPHnD/wchtCYSd1hIjWBVSygCblhfIYPW5cJ/UxphqncrCT4w2RXYhd+R5fXZVkVusbBHlnTdOVI/R4/hwaOUWtUuLpwno1Gto1AEJqNubcW3lJprFsKsIXNYm6RG6tXKTfK9IR62tJhkIYv4WsaW00Izow9Qk+kl0L17oYPZI3dHQOcTE1yuOew+neulmO5sMyd1B1VI3fBdFvXdEaK1VfNKr3hXnQ0JguTHO87JsVjkPodCEkgKGypOW3ozf5KyBvixsYNVgurchLIbTb6JXLcbrn0mlmTaGJQWJTWDOho71FWi6vUfZLHqcsX59EdErthYxPyhhAg0dhbnkHrfdrRsYOgN8hyfnlb6aVm1dCAoF/W26s0nPeRK+aP4U85ZIe2zQcL7zGZnVaAzInNCSxhcXLgJJ8/+HNt7+32JxoeHU3T8Ebls/UaXj438skmDfaPWUG/azQFDcdBuOVKhFAKgEv5JTZKGwhdY624xpO7P4vX8FKoF7i8ehlb1yhapbt8gtzzirKbZiO7WM0r5PNkapK35t5ko7TB7s7dSsM56AkyEBtiOb8kM5YfU9Zyz5/6Xg4Kv5VRd+vKVOTUla7p+N55r+V6WwYZ1jY5OXCSg90H1e9b6ct1TVel2PncvJIeHY4Ng6ZxrO8YI7ERWUVY/gCfR+Jk/ketj6yDqDviJMlSkpXCCslyEo1m+jeXnQNNY3H2iiKdc8VA3NLOeiWpNu5YxxiD0UGeufEt1aR2VyAto7Ts8jQTqQnKFflvT1VC672GVwJ5Qp2qpm7rGkOxIWxd4haigVjbFJNm2c2mpZNBNKwGVzZv8tk9n6ZaKbBSSSoH0R3o4njf8bYmteoRtNBYxPwxipUcLiW326Rz6QqO9R0ju6W5eHbxLbqCXW0spXu79rUR622WN+kIdKhR2FaQT9wfpzPYSaaaIWcW+UmrUCtQqBXwGT4mU5PkKlksYXEvQ7w7/jIAF1cvEvQEWS2sspRbpGE3moIwoBCthXoBA0m1ARKclStnsDU4rMmBBE3TWCytKoNh2RZRf5Sx+BjH+o4pqcoGNrZhkC9scnHlIqlyiopVYbWwiqbr3N68TV+4j12JXW0GQhHjOaPDpUa7kTzSe4Sh6BAnvKPEney17ty73kA3IIcNvKkmG6luS0Q6mo5X95KpSkM2mZrk3Op5DKt9Iivmj1EP+pj8nV+me8chRmIjNKwGf/7e13h15tWf+DyqTsQf1P2kF2S93rRlhKsJx6k6Dt+je+gIdbJUWOH2+g0Wc4s0rIYMegx9W9YZ88fQ0Nj4+Se2j/oCK4VlTLMuWVuLq6oPc3XtKnWrrsgdM7Ws0i53X69rugrk9nXtY6xjDNuQ5jA13CUFhbwhpR1yt6Vpki15b+de9KJ8bmFvmAfGHmJ/936Ved7cuEnYF1bDCa2Nf7/hV1WJkDdE1BfFo3sUW+tAZIDfOPEbHOqWfauyWWHmJ8nf/h2vj6yDEJZJ1Bdlb9de3tyls3hklIS/A9M2m7V3XSf2zW8rNKab/i7mF+XmMgwVYcxkZlRdV3HMAFQqPDTTIOwNs/CN/yDr364C2RHZ11gprJCr5TgycJygNyidhKGTrqRJRLoxbZN93bL52RfpIxHsJJouqgkZtwcBsFHdRBNgmBamVyd/YCeljjCj8VE1who8IZHObnSXcEYeg54g3eEepbEgEOo1yVIS3dkuW4nHbEPnxTsvsl5YVffjaP8xxWNTqkv6i4HIAKPxUWxhq+xgf9d+8rU8lUYF0zbxGL627GMkNqLUvQDenHuDi6sXqVt1hZA1hcmt//SvGbjYdMzVehmBIFPJUKwXWa02y4Fz2Tm6Q90MRAYIGD4+NnQ/pwdP4/f4EbbNgB5n56QsybgUJ/cP34+hGTTshgoobmzc4PqGFKc53HeEPX0H8VuaZEfduM7V7FXF+Kmh8U/u/Sf82vFfa5e5dZ7bQm6BZCkpBxL8MU4NnKIz2MnZ+bMsF5aZn7tCMieDlz09+xmKDnHcmbgZT45TqzqOpV4nvp5F6NLguqRzw4kxivUidzLTeJwpo0QggWmbdBw+zWf2foYndj/BUGyIXzn2K5gP3M/Gzt7/5uipQBrbw7E9zH3rj1W5xEW/u8vQJE7nxNApNaLrKgyCBBW6OIFEIMGB7gPc038Pe7v2crz/HvZ17WNv517GEjt4ZOwRRmIjfH7fU1imybuL76p96eIdhJBjtPlanqXiCjiTaq6jsYWtDPV0epq14hpHBo6zo2MH88dGKXZGKDfK3Nxoloi2ruWHjrEZkgFewBl3LzfK+L1BZjOzvLMowZU1q0bY23QQ3aFu9R65Wo7J1KQcc7dltWA4NixHq4EX77zI8xPPs17ekL2V/wH0Gq3rI+sgfMJgND4qa8THx8j1xtVUz72D98oywpaH0ZoZDEQG6IsOqCZzvpZXzI4ujTgAtiwzGM60h67p7E/sYUfHDkb339f2/teSN6iaVfqC/Qhdx9AMzuz8OBWzwsW1S6BpBD1BHhp9CJBcQPT2KjETTdOwNHh2/DuIeh3L0Cl/7gk+8X9+m7X8MlNp2dArP/VphmPDcnqrI0HvrFOfNiuE/GHuHTxNT6hHHijnkNetOjY248lxNAFnRs8oaUfTJw+HZTbY3bmbkDfEa3OvK2Uvt+artUTL7uoISF1s11DEQgk1PdMT6mF3525ldDQBY9H2lN9nePHpkuhQ1Jv1a00I9lVCHNR6nCGE5rNMlVOMxkeJ+qMYSDGcXC0nMRm2SdmsMJmaZCg6JPeEE1ke6D6gykOu+JJrQL26l+HOHdzXfVx9jmuM9nXvRyD4t+/+W/78yp+3yaa6e6y4Y4D8UDeJgCRqq1m1bXoW7j0qmWUM3eDGWlM5TY1gXrzI/e8vYTt1a7cslghLo9QRTNDr6ybuj8vMQ/fyowf7qZpV0pU0uqYT8ASwH/8kpc7I9immLctC3gO39OcGCLqm87ixl0/O6nwm3UVvuBe/4efqxnXpVFq4oEBOz/kMP8OxYRp2g9nMLOlKmsnUJPl6gY5AB1868iXKZoVcNcfuzt2EvSFKLbV/T91UmZ5ATtYV60VsBBFvmHggflfCPEtYZKtZSnZVTtnpOiEHS3E34J67PvXLf8CFSJ73l95Xe1QgeG3u9W3PLl1Jc9mRGW7tQbnPtGpW6Qn1sFHacFQHNWyniT+ZmmQpv0RPqIe93fvuigT/sNZH1kHsCe1U6WPcH6cjmFBH4UezkrobXac33KsI/KApaHJ+5TwrpXbeeA0Nn+GT0zGuHrNlcTN5k1w1hy1sdnbspNMXZyQ20raJdE1n3Rn1DPli2LrkuLm4dhkNjbyTlUxnpnlv6X16w73/D3vvHSTXeZ75/r5zOufuyTliBnmQQYAEAQYRYoJISlSidCUra61gW7subd0N2F1712tX+frudZDt3TXltdbSSl6L5NKySUkECYIkGBCIOBgMZgaTc0/H6XTO/ePr83X3zACkZIpylfRWsUj2nD6pz/ne9DzPK2uRuq4G1ggEBia6AQeb9rOhcSt99X081f8UM4kZtZidGD1BZ7iTgsuJo6W94hoy+SyJ5Tg5o3Iucm8xg7G0o87PnFewz7zDht/hZ2vtZs5NnyOVS7GwvKh6HNa9iLgj6EJG46YQrIusw2lzUuOtoSXUyr7mfRzZ9LBydmF3mGPDxyqQJWOLEppolYzcuotuXzea0NjuLbG3hQnam+fQrg5KTSW9kuNhLXy5fIZni83sKk8VTmFXi+p8el5mb0X8eoWGUtnvFnKFeG74OX44+jxvDMtotsnfRF+wD4fuoH++n12Nu0hkEwxFhxS8Up5o0eE01RHu7VNw2vMz51cdy3I448kpri9dZ6FYPtSFXoHR99g9dFavQy8uWlvrtvLmvIT/LmSizC7PspRZ4uLsRalqm5jg76/+fQW6yuqtrXXN5eZ1+NhSu4VENkFbsI2h6JCcMohg70sjHBgxGFm4hsfuwW13M52eY2fjTupcVfgdfiLuCL1VvZi6RpWjmsn4JNWeanRNlkwBBhav8tLoS/zWC7/FeGxcNfBnYlME7QFubbmV3Y276avrw+/0K6eTLUjhxbCnigZfA5PxyRv2IApGgYGofJ5NTZC0dK9uYmOxMVUWS5Vtb2qikgyJzFis96lcT8wwDeVcgi45O/vczDl5nkL+3a7ZFXJpOjnz0zPBfwr7hXUQs8lJpSb5QM8D9DVsw2f3KmcQcoVACPwOf0lWGypgrQ1FrL5VQtCExsPrH6It1FZq1JW9uF2RLup8dVycPs/x68croJfeovqqQHAlNYipywb5SHyM3U27cTk8qgcxl5ojuhyVtWy9tDhoQuO+3gcwMstMLc+h63YKhuQGlENA80aehfQCGSPLaHys4hxi2RjPDx9jPiXZ3FaJwmpSW+Mio8ulBvjWtj3U+epwCrsqSa31GlqZw9LyEi2hVrWPqcQUk4lpRmOjXFq4oq5zZS9HmCaBWfly5IwcW+u24ra5GE2N4ra7VWS1qWYTutDQTKh2SfVYj6vUWC6fJGj1IDSh0R5qlyq4lFjUQggcdvlCn546LT8ri6qtvlS2kKVg0wgKN3ub9qIJjXQhLQENRS0gdR1rNKnbQm14HT7imfiqPoRlivVsNVOtflQZ9wCknlDayKrFK5lN4nJ66Knq4WDnHapEaGKq37X8noBkqR89dFSOsb2JCV1XvSYrmw67wxW1+1whp8hhhiZnG9S6qqjx1jCVmGI5v4yhCcYyE2hCUyqslhkVTeqScxemSXw5KiXV88uMxcZYWl5iR8MOXDYX2UKW9lA7Vb4appYkYihXWFueIm/k1X1tCrZQVySm+sqk7Vfai9dfpCnQTIOvQZ2fxVEqP/9djZXQ4PL1BEqw2zNTZ4hlYoqbgtBo9DdS7akmlo1zbvocpoDaImH33bBfWAcxn55Wi1m2kJXTqoqQNZBjAolGWcosqTTQMiuqWixTC+2r76Pt1X6GnvzL0vhMqHAQDb4GTk+eZiklI1SrUdsV7pJIDSFf9rwwZZPa34ShC8Zj46TyaX7vxO9hadormJuuV5SYhuOjPHA6yYX4NV6feJ1Xx18FwGf3qcUMZMrbEGgqnWexZmvX7GTysgfh1J3qfljcgeZAM46yhx9guhCjr66PsaVRTNPkkQ2PEHKH1XetmmssE1PopLqGdUVggLwW3Wbj2uI1huLXbypjXDNS6iVkXzmB+fjjxPNx+ur61OyLi44lOeMhn8cYG2UxvUjEW6r77mzYqQIBrbjc6ELnwswFzk2exSz7jaPLUc4vyH6JYRpMJiZpC7WpOnm1p5rocpRcIYfdELgHhohlYozGRjkfk41YE5mVWraW1EZrqI14LsF0crois2w7K8XtGv2NNBRHuVooK2tWR1e4C/uKV3k0OcFrk28AMutMmhlyhRzxXLKih2QxdgtmoeK88kaeP3/jz9fMZCqs7DvWIjmTnFm1sKpsVNM4NXmakfkhri9dJ52ThDVrkl3OyDG4MMhSZkk180+Ov1piQZdJlYT/+L8Dkvh4buacIluOxcbIFrL0VPVQ7anGwCSVS1agmASCOq9caNuCbXSEOzCLpUSbzUFLceb8xhX8p3IzTIPx+Dhhd1gBV4KuIOun8vRW93JbcSb7TGoOu24nmU3y/PDznLLLwGrZ76a+eb0i8Dl1p7q2nqoe9GLv6/6e++mt6iWRTZDHYJa3zm7eKfuFdRDkC2i6TO3++vxfA4KOYDuJbAJhmCplnEvNqcjoluZbALm4Bp1BZhMzdIQ6EEIwk5wh88oJppPTzKXmuL3t9uJx8lS5q7BpNo5fPw6AKDqNglGgxlPD4OIgiWyC9/bej1N3YugapqbJBmdx37e0yFm0AWeAD236EDt+93/I/euVNdXzsxdodzdw9nAfBbOgFuneSHdFVKcLnSpfjYK4ClOiRurcNZj5vJKJsJyddT9qvbXk81kWGkvaNYOpMX489GOSyzFcNhcT8QnuXXef6pVYL7oolsFeevQWjuWvqu977B7sRaz/yia13+FXL7IudDkgpmjlYACpMis4fe923thWK9nqhQKzqVnyRp5rS8Nq24X0QmmhME3ag210R7oJu8NkcmmqfTUqyuup6mHs8D74+tdpDjSTyCbUAjifmlczOTrDnei3HgBQbG4oDpVyyuv/17f/a37tll9jc+3m0g9WvNaTYyd5c+Y8Tl2qhO5p2oNDd1A/MKmY25ZcdGuVjFJ31u/A7/BLyHQ2o7SmQDZ9RRlnwePyMxQd4rWpN9RvbpU69jTtYVfjrorzKhgFxuPjlXLXa1nx/CvkQ6Ciz+JzyF6Gz+HjPd3vlRPSchnFjbBpNkxdY6o+QLzKTyjUQE9VD1XuKjkBTxMqS2koap4Bal5GeeYjhFQhyBUkMOTqwlXOL/SDWTxO0dHMdTUo55gtZBEIdjTtoi3YxiObPsCrE6/JbNleGQyVW6aQwUS+E9Y5hFwhNtduJp1L8+L1FwEYTI8TcoWkE8Jk8aDs3Z18eA/Dd2wnlokRdAbJGTk5/Mnu4ez0WXKFHC+MvMBcag6Xw40QgqVMjCub6uFf/sub/y7vkP3COghhlBwESGLXTBHv3lezRUVAiq0MFczgbfXb6Ap1yujHNBldGlWQPyh7aAsFWoItcmh7MTWudVfjc/jYWLOxghF5PT6GYRqEPbWYxfGiD254H3kjz2Cx3t/ob6Q51ILmLvZF9JIsAkAeg9cmXqPglqiIoCvI7W23M5uYYWBezjA+0HoAm2bj4vzlUpnCNGVtM5vhjvMJQq5QRUPPchBnps6AaXJ3730l+Qa7TUIpMyn66vu4unCVc7PnFUrEgglb129ogmShpCkTXY6yWCTy2R2u4uXIkt2Ohh20h9qp9lRLromt1A+ytgMpBaIJjYJNk9GoKZVxJQ+hskndP99f+l1Nk+1126j11spasGHg1F2Mx8fx2r0ksglMmw1cLqo91bQEWkjlUozGRhWKzGv3srtpN3duul+VGMptXYtsXJ8YPcHF2YsVQ4+s3y1n5lkuLMtMoViyWMncthyPzx1kR8MOFlPzykmahbyS5bZpNlmyKSPbravbiCY0/K4gTa4GGv2NikX/6virbK3bWrHIW/f1rZrU1vlb97Mr3KXOwbJNtZsIu8MIBJOpKcBEKxPGtGDdV3rqGPrge8ibeRbTiyxllrgwewGPw0tbsI2jh44S8oSVY1g5Q8I6riUA+fKY5CYU7DpBR4CmQJOc6Oe0ky+DWU8mJhlYGMDnDKBrOrrdgd8VoCXYogQP17KdDTvVdTp0By6bi6XlJV4YeUERSQGyLruM/o087aF2VeYyNcFIbFTJzjh1J7FMjC21W2TJCylieXLsJMdHjmPTbLRHOiT35EYKu++w/cI6iG5XG75izdpK7axF8PzkWYVN9jl8FfVECyVhTUSzlEABVXiveLHzea7MXyG6HEUTGkFXkO5AO7sad6EJraImemb6LIZpUOtpxBAyi5ktqlBOJqcwhURSTSWmStGiJUNdI7MDU8ia76PbPiqRUsFWNtVsYjI2rtBEZ6bOUO2pxm53saEIRbX0oXKFHM3LDvxOPyFXSF1bU1FrP2/kESbMpGcVwsPS7mnxNzEcHWYuNcebM+dUE9vazm2XUdD2pp047W5qvbX4nX5qvbW0BiWcdXfLXrXorK9ez+W5y5wcPyklrP0NZIrSBZYDFwg6vZ3omi4nogmJ3hFAjVPySqrcVZhlJKgK4pxp8v1Lf8trE6/hsXuoc1VzPXad6HJUjXnsrpENel3oFYQ7ywzT4Dvnv8PxiVeUyJsmNDYGJJLtTHxAMfSfGXxGobvkBZTOqz3UgdPm5MLsBaLL0QoEjV2zqwXxzMybnJo8xeDcQGk/hYJaNGo8NTSF28gWyzrNgWb6FwYwTIOlXJzJtNT5KpeV/sNX/5AfD5VmMJRO7y0chCbBHNPJadw2t0IzlTN9hxaHaPI34bF7ODPzJp2RbpzY0IVOZ7hTPltCcFv1bfJ90yVQwAI3JHMpzs2c449e/SMuTJ2T7H9KMuEbqjeoZ916Lqy+IEDIX0O1p5q5YqlnY8sOdtbvqLiMWCbG8bETcjCPphHLxmWAcxMHUeOtIWCTzsppc7K1bmtJIbnsvuXcDjL5DMOLw1S5qyrY4FkjpwKxrkgXE/EJLs9dJm/kGS2isOy6Ha/Dy67GXRRMY1XJ+2dpv7AOwsznVGx0sP0gd3TcSdOSgS2bp85drWrQVZ4qmspq9RZCYjY1q14Cq7YphKCvrq8kww1SQjuXpmBIaYjuSDdDC4OcmjzFmakzKsoOOAMYxX0MLl/HFIJkNsl3Ln2XDdUbMIXAY/MUBb6MkuRBruhg6mQZZnPNJpw2J7UBGYVWe6r5k9f/RM1TAFmaEUIQzca4PFssh5gmPoePZC7JyYnXGImOkMgmSGaTFIwC7iKTWxMaVaNznJo+U1LBjbQRcoVo87dUTGszTINULqVeFquksal2M7W+OhUxzSRnSBfnWgNq0bw0e6mixBHPxKkaklyARDahIt5UQeLea721mJqgr34bmCaziWncdrd0dMXGtxXpKid+7hzBmSVVAmnxNaoG+3RymuX8Mr11cqFfXF6siAyhJEI4lZhiMj1LOp+mM9xJT1UPXt1LR6iDnNPO4a7D6jtr9SD8Dj9t4XYKRoFsIVsBBQZZw7f6JlYPwjlb6lWIgqEyiMnEJE3hVlrD7YBE3mU1+SyE3BEVyCgeDXKxLc8W7Jqdz+z4DAdaD3BTK4oCWs1lkAGXkiZHlmmtiXiGgHZHDc0FD12RLhbSC3iLkNIX517k+tJ1atzVFdPfTAEj0RFmU7Pk8plSk7oIIgk4A7QGW2V2UMixqXYTLpsLUSzJ1oalvlh0OUoym2Q4O0PEsxqdlTPzcoHXNBBCOqiblJiOjxxnfVhmYZrQKtCO5fdyc7ssKSVzSd6YfKPiWcgaOXWt/XP9jN17mwrkXp14letL13HoDhLZJOOxcWLZeGmS5Ltgv7AOYjh2lWVDevtaby2D0Wsksgm2DcRp9TUpB1HrrVXSGiBfpDpvXVFrRyfvsFH4+GOsi6zDY3cTdocrG3TFfsOluUt0hjsZXBjkR4fa+NHhHimhIAS7G3dXzMVdMhOYusw2TCGkhoyAVEE6GhWdfehDJX2Y9nb4yEcI2nz4HX7+5PVv8O3z31bRhktzVEAWZ5OzuJxeVY8O2H0lRItRUMOMhqJDTCenJdkIuSB2jKcqSjbNkQ4Oth1kYE46jHu77yUckA7rzek3FUrD0v351uR2y0sAACAASURBVLn/yfamnYzGRikYBbbXbyfk8HNq8pTkalh8CcxVZZZyE8jG/Gxmlt7qXgYWBuQcBd0mYa6Gid/hZ3BxkPW1JQXVxs99TeoKAZw5g1ZUp40uR3lz4gwmZgUU8b+feVwdb3xDM5mPSM2nznAnnWFZZswZOXSnC7tmp8nfxOW5y7y6+CpjDx5ksqeBpweeLvE5yss2RUff17CNc7PnVcmoPFNpC7axrX6bKulZrsPaT0eoQ6KYysoOL4++xGQxQzg7fRZsNgm9bdiGXoQ3LdWWkFWr5lQgF/Zy1N6a5nDA+9+PYRrq3C3hxXKbT81LxycEL15/kVgmxrWDfQxFtEpeCFKqYy41VyLdeWtU1mDkKxfHeDbOmakzvDDyAtcWr1EwCypga/DJUppudzC0eE2y1e+5k8t3bKmQoPE5fPgcPrY2bJfsZ02jr66P7O4d8PGP3/DSs4Us8XS04jPrN7EydwB/dVPFvT09JfkQYVeY29sPKgdZDhTojnTLrKr1Nva37KfGW8N4fByfK6DKeO+G/VwchBBiWAhxTghxRgjxevGziBDiWSHEQPHf4bfazz/KCgW0Ymr37fPfZiY5Q8AZILY0Q9DmXdX8hVKTeiY5g0N3MBq9TlXfPmzdPbiswSlrHMdq0lplqUWvRjLiU6qNo8kJUrkUt7TsQxc6BU0yJn0OH6YmmE3N0tMuoXKa0LBbDmJDSd8FTYPeXq5MX6TR36jOxXrJesLdFRoumtDoqOpWPYiuYAdO3UnQGZRaT5gqWnbb3IjigtUR7pDDjspKNsfHX+L60nUyRQary+ZiT+8dgIxeLfip5QDzwmCwTC4g6AriFPIe2TV7RdmlfKFcST6yZFFMUzJyU7kUpiZ4Y+oUDs2OMAyGokMUjAKzywtqEWzbe0/F7ysQBF1ByZZfTuB3+Ct6CbGiRpAQgqiRomHbbfRW9VIwJMfFukcOp4eckVNghLnMHJfDBYzijOdP9H2Ch9Y/pNBIxZ0CMLAwwFx6XqG6gs5gcSStzFr9Dr8iOkZ8EpHVGmwl4o7IpnuhUDEaVJgoaWiAWD7F2emzPHf9ecwVs4y31m2l2lOtRORALlbfv/x9+uduLHetzr/8OWS1ZDbI395pc7KrZQ95I08ql+KSM0bG61zlmNzFYVlhd5idDTupr2pVfws6/Ar1s1YPQhNS1mQ5v8y6qnWMLo1yee6yHNhjGmR29pH3ywXZCuSa/E2sr17P9sYd8lnVdc7PXmAu7ITwjZehqcTUqhnW1Z5qDrYdxGP3KL7Ln4auEvj1ryuJl6Gk7A0tLi+yXMiUGtyeCNsa5bz5ifgEJlKhwak72Vy7WQYoiQlVxns37OeZQdxhmuY20zQtkPDXgR+ZprkO+FHx/382ZpqIQr4CwqlpOl67F1s2z+66HWumllaT2sRkY81G/HYvWnHxPzt9toIsoyyfV0JdVhpv9THaQm2yPLEkH5hwMe31ucKYmqBgFPC5Apz40H4Sd95KS7CVnY07CblL6ffKUZix5IKaNQyoSC6eiamoaWPNRpw2JyPxURXLZgsZNfYy4Ayqc+wIdRB0BdVsDMtp9NSsV5LfCMHpqdMkMwl2NOyQ0+pcdjbXbqY12KoW8vIZ2hbJ0DpP62+OFSim3Y27OdB6QC6Wa5CPAAwMBuYHZHPSgkvmM2iGiS6kaNvM8hw5I8dCeoE/ff1PK1RJD7YfpDvSLc+hUKDOX6/IcgCZ4uQul80FQvDGxBv0z/czsjRCtpAl5Arxq3t+lfdueFA1UAHShTSLy4vKMbvtbup99QqFY90LgJkiQinsDtP6qV/HZXPJ367IB7Gm7gGsq+5lT9MefA4fC+kFWZIo5FUG4dAdYJoVjdj9HQdw6A5MIah11FDvq1daQYvpRb6050tKZbfit3qbZiHN6n31FeUlkIAOt82NTbNRXVRwFQgKmJj5fGXTHnAIKVOfyWcYWBjA7vRwuOswv3bLr7Eu3KVmhSio8goHIYQgk8/w5vSbpHIpYpkYAYefjnAHNs1GrbcWYZa4Pf3z/QwuDOJ3FzMqTcPn9LOrZS83Mznet/LcM4UMpyZPMRQdKmXkNp1MJIjP4ZP9zpoaXn5USoc/c+1Z1UuJ5RIEHUG21m2VvUkhyBaynJ0+y99f/XuEEETK9M3eDfunVGJ6H/DN4n9/E3joZ3akQoEad73CkQPEswkmE5M4lnNcmnxzlYNIhrzqRwXJDrZrNi7OXVSCduWDgMqPZdXlrWj4w5s/zL+6/V+xoXoDQW+p7PPk5ScB6A6ux9AE6XyarJEj53bwzMiP5SjJpeuyB2HZilq1MAyWlpfYVr+NA60HcNlcHD10lOuLw4xEZUQzm5zFqTuxO1y0FBerucVxEtkEy/llNtduosHfQI2nRqX7FtHMiuLX12xQcs0WIctn85ApZBhdGuVvRn7A+ZnzjMfH1f2xrt8UArvdKSPlsmjQa/dWQHHbgm2yeXj9uJyNEe5U+lHlcgMRR6To2IqChsgIutFbj12347a5MYUgnomTzqXlglLmbM5OnVVzH1xIff/pxLRa9JrDkj/isXtoCbRUEBytZ+HZwWeZziyoXg1AyB5iT9Me1Wz9y7N/yTde/0Ylt6DsPLSCHOn65NQLpPNpEtkErmTx2aIk1Hh2+k1eHX+1kkhYMJSD8Dv8hJ1BplKyf2PTbEwkp4slHkjl5dxji5swGhvl8TOPVzTPFYrpbWr/WJDRWCamggvLkrkkG2s24tAdPDXwtCo5Grpgc9UG5SAebnqYKncVS5klhqJDShp9XcMm5tPzTMYnGZi5rPp2FtG1xlsj5WyELqHQCApmQfW/vHYvYVeITD5DjaeGhzc8XNFXBBnNX7T6cZpGNBtjerlSCXml7WrcpQQTLbPmfFu9LitgmE5OM7o0yt7mvcSzcTLeItvbyKnSa0ekixdGpcqy9dvMpeaKlQYJ2gg6g6XA7F2wG7fof7ZmAs8IIUzgT03T/DOgzjRNa5LGFLAmXVAI8TngcwB1dXUcO3bsJz64ls1SY6/i+rUhKfA120k4ESYTz0B0mNE/+kOczlbs8RLOfs5c4Nr4KB8wmrgQu8CrsVdJODWmCz6+88x3qJ2fY3R5CJfuqjiWKQRXYpdJF9L4bD6avE08/cLTaGgk8gluufwK0aSMIIYTTkZmRrgSyTKdn2V22s75MYHNJQfOuONpTg1d5PpLL5OPFHskFy7gGR9nuHgf0tMLpApO9OtyMf7B0A/4zuh3+GRSYMfGsWPHuDh2kemJRS4szZPOp6iNxtn1+MssZgsMZ4Y5O32NYFZjOjNN1sjytOMSUY9GW8FgcWGRjJHhT4//ORtG4mSMDBOTkwy7chSqtvK/Jp5iaFguqFujUaLRUo122pyWEhp925mbuEBHys7AtQHsmp28z0e1Uc+ZV84QOXeOwPAwF2MXiTbVseATtIxNIBKC8bTMtqJEsQkbw8PDePAQXYgS9AQZHr2O27TRPD/PzNIirWknrZ5WZu3z/Ivv/gt8Nh9u3U3/FQPnvKyvD0WH8Nq8aD4Nr83FteVhoolFFqMyi6hbbODYsWPUDV7itfQSUc8GJjNJ3GmJQLE77Hzr+LfY7Oyme+IqeqgGd/sW7grcwdD5Ic4snKHb181MZoZ4Ps7Z9Fnm3HLxEbkcbcPDDLp8VOX9DAwPcDVi4/p1QTRRuncj5ggLSwsMm8OczWYJRKOktBRZQ/aYRq5eJeZ24x8bY2Jxgoinjn5tHm80ygb/Bp6/+jr10RjDIyN0Z1JE0xlemnudanuB4eFhhhkmNhxjPiTviWmaGLMGc9k5jl1763csvhTHo3uYyE/gSrlI29Pqb/3xfkL2EH57gDGy7MqEiafjOGc8XO1/HT2aZ3h4mJiIsTC+QGdzL94zbxAtREkkEgyfH+PZiWf5Xu57HJr2sm58hPxCnmQ+STadJU+eGrOGVCHFyMgIZtZkJjHDlJEgndMZ6x+jYWaJ84kzjDzxX9CExu2XpyueTYDha4MEhocZfv55BrJxLl06zoJRQyKRWHOdeXHuRfrGZxmeL2WbS7kloskoRtIglotR5a3CNmfj5Csn6RdznJ8ZojnxcV6ckxyJ5aRGV9JkzJ5myBEj3n+Z1MibAEzN2BgethOKhlgavcr4aIKzr5zk/MJ5ji0du+F5vZP283IQt5mmOS6EqAWeFUJUCP+bpmkWnccqKzqTPwPYtWuXeejQoZ/44GYqxX/72//Mp2tvQwjBJ+/5JNGhSyw8/0P8up86V4CuDRthelp951x8nrb2dtpHTeYn59GERnd1M2YgxWXvMJs/9Rk6Tw6vOchj1O+nUFhmX1RqrSSqEqRyKTbXbqahpZnWTB3//X2tNF0ap93TxFOxk9Q01NPyW79L86n/yv3r7ufpgadxx9JEwlPcduCAgrUyNgZ2O+3F+zDwN104A2GeC40wFB3ia7d9jZMvn2R/T6M8t0OHOHbsGKE6E48zgy/vptfRSEe4g1fGXqE+Uk8yP4vf9FBjq2EwOsSZjx4kMLNE79wYOV+OVC5Fsq4G92IeN24+d8fnudjqRmh2ZjeneaRuC0OLQ8zeY2PdqyVCXENzAy6bC397gPc4DnN25P9jfcN6/E4/sYYqfr93ni/v2UJVOg2pFMPDwywc3s+8L8u2bx6nOlBNcinJTEcttUMztIfaaQ+18+bZN9nSKudtt7Y3cbjlDubO/x6GTUdoKRKuBFta+xhrr+FT2z9F2BXGn/g2jEtnMzw8TMgVor66npn0HPWhOpgrLXD77rgbp81J7B+eZGN7M61bdnEt7OYDTw9R461hYH6A9vZ2tjTswP/DF7jld/6Kf3/hjxke/hse/+TjfIJPcPTYUR7ofECxa5Xl83DiBI0H38NLZ56ivqGe5lY37Qs2ZsZlBtAabKUj1EFToQmnzclgSx59YgK3zU06n8bn8NHe0I7YLuvX189fJVmYoLGlieBsjGkxTU/bVrY4ujFvuZuaE2myzmmyh29hAtjqDBDLxNjbvZe9zaWyym0F+X6sLAGtZccelxlmiBCmy6S9vl39bX5inoFWL3cabdRXJ1hemmd99XqGO10svTlPY8RFe3s7w8PDdLR3sLxnM8vBZQ7N1bCBDbze6ycswkSI0O31UT8eoz3SzmJ6kWMbaml/Y5HXDvUwUAVHDx2l/ehRqWP2gQd5aPuv0BEdZuqpJwjZQhR6JNnRN5UiNCJLtZdvW89Udz2PbDhA+wd+lXYg05LFaXNyqF2+L2utM/MX5+mbtdHqL8GR51JzLM4sUvPQY6x7/hWqPFV88b4vEsvE6Hf3Mw3c3tbJ2MgYH978YVITI/gv/yWn91UTa6pmdzKI68x5Sb5rq4aeLo5sOMLfPzdDyjPDB9/7QT6IBEnc6LzeSfu5lJhM0xwv/nsG+FtgDzAthGgAKP77LSicP70ZLicn9rbK9FnX+ZuLf8OVIkpCYYxXlJhMAfeuu0+SkAIBNKExsjhEbUDWQ3c27rrhlKccBrFsjMnEJKOxUdWrKBgFhM22ShDN0GUt3aoDPz3wNHXeOkzBajXKFT2I9s/+C+o/++trM2DLygWmabKpYSuConZP2TB0irr5U4mpEn9AyLS9t6qXbCGrBtNPddXxePplGnwNat707sbd7G7aDUJQ76tXcFQLnjeyNELGrNTEMYr1cousZpnNLlNxq2QAKASVVZN3alL3fy41h6kJnh36IT67V7GuF5cXCXkiCovvsrlWTUYTCPJGnpn4FJqmqyZ1e6hdwW8Fgvn0PLsad3Gk9wgBZ4CJ+ISSbNFtdgZ7a/nO5NozFMqbwMqK5xHPxplckCVAk0qiWcgVQghBvCgXgUuWcKo91Th0B772HvksC6FmRhuxJVWKNEyDeC7BtcVrnC6KP5bbtvptQOUgG4DfPv7bSqrlJ7EKQbzW4mhaw8SmO2gOt6pn8/zsRXLZ5QoukInJ4vJiBYm1vJ+mm0KVKg3TwNClvPdMrpQNXJi5QLaQ5Ve2/wrXFq9xdeGqmnbXGe6UjH5Rkrs/0HqA+9fdX/EeDkWHVGn0RnZu5hxvjL1W8Znf4Wd7/XZ6azYQdod5pcnkv576r1R7qtXxXhh5AZAlox2NO2kKNOFwuNnfsp/6IoBhfM8Glrb04NAdtAZbeWDd/W+73PdO2rvuIIQQXiGE3/pv4B7gPPAk8IniZp8AnvhZnUPeyJcWWl1nKbNEvpBXC1mtt3Z1k7pIngE4s62O5kAzwjTpLRLNnhz4Pzd8oLpqejCB8dh4xcsZdodxO/28Xpw3rZpvziBBT7iinn2g7QDrIusk1LH8QVnRg7Bv3YajvQSDs14mgVDfc+gO3HY3c8uLFYJvIVcIr8OLiXxRU7mU6i9YDsvCaDtMjaAzyPD2DrJuB9+9+F2iy1Hu6riL50eep8pdxd6WW+Q4xqL6ZPmiN1pk+a5k7Dp0R4WDuLXjdr6w6wtAycFY52QRydKFtGKJW03qZDZRoTl0fu4imUKGP3z1D6UaaNk93Nu0V6FEDNOgp6pHLUpLy0uqnmzdy7nUHD8a/jH98/3MpeYIOAN8dsdn6avfxuCebvoXKkUGQTp2C8VSYcXz6J/vR89Lvsl7uu5RcyEA1Rs4Pyt7F3dtuJf11etp8DeQLWR5MRQr7cvhYKqrjlMP7KQlVKpV91T1qODHK1xsqtmk5GAW0gt8Ze9XVNP6pzGr7yAQaiGkuRk+9SnaQm3YhZx4uLt5r/qtNZudQj6rYJ4Aj2x4hJZAC+uK5ESQyLaQK8TdnXezo26bgp177B4pS2OaCikG8jlJ59KcmjzF5bnLDC0O4XP4CDqDxLNxJS9vAREuzV4iuhytAA8sLS+pHtvNzLZiypFdt5PMJTk++iKvT7zO5e2tKss70FbilAgE3zzzTf7g5P9LrpCjKdTKUmZJkv7Cnby5LsDure/FMA2uzF/h1OQbPzFw4J2wn0cGUQe8KIQ4C7wKPG2a5t8DvwO8RwgxANxd/P+fiZmY1LqKP34R7pjKp1UzOVPIrHIQhoDjoy+qmQt+hx93Do5dk+zTxUz0hlLCObNQweS9b919fHjzh7m97XYa2kr4/MkisqcnsplbOw/ic/hw29xoQuN7F7/HwOLV1RBCc+1jBl1BqtxVeOwejh46ij0UUZC9gDOATbNhszvw2b1qIYouR8nkM9zWJmGcdd46HEU+gFkUErQa3Q9ter+SAC+3fS37GF0a5emBp3ll/CSzyVmF5ig3W3GWsVpYitdl1+3KQbQEWrDbXfTP9eN3+Kn31dPga8AUQmUWltl1u5xfoQkMTeBMZWlDlhAEgozXqVBcK6G0C+kFYpkY1rD72dQcM8kZGnwNcmZGcVuP3UPEXcWJ0RMVbFiL3OZ3+ktzQFZYwSysDRm1HKQJeq6A2+bm3Oz5CseqnishJSQGEtdZWl5Sz6vipGgaOBw0r9tFtibCpYXS8dy6SzbhbS48NjkpzZrncX7mPKcnT1eKTP6Els6nqXJXEXKFKoUkkVDnnV0HQNN4ZuiH6n7qdgcN3nrlgA/XHWZr3Va5IE6fUfsOuiSyZ3vDdhaMpDrPnJHD0DV0TVdsfnmbJMDjzNQZxZXxO/xEs0v4HD7ev+H9eMoa6XPpOU6MnqjgYwScgZsquYLk+7T5mys+W0wvcmX+CotJ2csxdHmto7FRTk+e5uu3SXCmRazNFJ32wa47ubvzboaXRhC6HExVMApqGuDI4vDbcljvtL3rDsI0zWumafYV/9lkmuZvFz+fN03zLtM015mmebdpmgtvta+f1lw2F/fU3SP/R9ep89bRESlxBKYT02uUmErTnAxNMLg4KEcrLud4ZMMjkqB1A1LXyek3QAhFugu7wrQGW3lz+k2uvmcnO3//2wCKtHa1LcS5Lr9kGOfTFVO9Ls5evGkGYdnndn6OL+/9Mpl8hv/84n+m8IXPw2c/C8BjWx7DbXOzbOawCZ0GXwM+h08yUY0cs8k55iMuZlOzLJtZ2oJtkpdhK+pT2XT+MHCZkehIhUt8bOtj/M6Lv0OmkCkqt5ZUPKvcVbhukfX33qpewp4q2kPtarEIuAIcPXRUZhlFMlRXpIvjYyd4bvg54lk5ZnQyMQlCsGwv3es6Zx0BZ4D2UDumEOTcDpJBD9PJaeyanf0t+7m6p1ttXwGl1TT+521BRmOjuGwu+ur7mE7OEM/GOb6xUgwuV8gxv7yAU3eCEOxr3odTd+Jz+Pjm2W9yavIUF2cvEnAG2Fa/jU+2f1Ids8HXcHOJBNNkY8+tLGWWpKxK2e9qTaATQpD/8q/yZrSfycQkM8kZLh3YUCqXCQFOJ7d1HuSf7//n6rfZ8KV/x2vjr5LOp2UPLCuzwHLW+/Hrx1lcMUoWeNuL0rb/+Bds+U//TTp4i7xZlKn4H3fXYt7zHjRNZykXV3yiT2z/JH1Vm+iKdPHoxkcxivOmt9RtoT5Q1IX68pdpDbZyZ8edfOP1b3CiJs35j94NyCDtV3Z9lq11W/n1A7/J53Z+DoDZz3yUiU89qqoEm2s3U+erAyQ7eiG9sObQnfL5J9PJ6Yo5JGvZXGoOUViBIrSeq5z8rdtD7QoNNxob5djwMQ61H+LLe75MPBPHFDKzDnojBJwBZpIzDCxdU3MhQGafQUegkj/zLtk/JZjru28tLdDTw4c2f0ixVKG4gKwsMYlSicbUNQpGgSZ/E07Nzv++9L8Rul7pIMoW8YA3gtvuUcOHvnvxu/zJa3/C2amzxMjgDdfS4GtQdf2TybP8ePoVVXN/b/d75XFZg4S0ogdh2bHhY/yvC/9LptuWomiRSBV2hxGaxmK1j9jyEjkjh0Bg1+T8iItzF7mWGJPNXFeQkaURBJA1srhsLpJhL4t6VkEb97fsp85bRywTo2AUqPfVr+IzuGyuUn3cNPC5AkzGJ1WZKJVLcfTYUblxrvSilmP5rcjRFJC1C1USWMgukDfyLGWWMAXc3Xk3yyHpzCzCUpVfvqS9Vb2ypGEtqjYb6UBJSNFSGAXIuit7SgWzgKitU7Vqp00qr5aX8Tx2D1/Y9QXsmp3vj39ffXc5v7ymjpNlncF2Bvd0M/ql/0uV+CyzIvJ1Vb3okWryNnnupmmSCnpK93nnTnmPNY3h6DC+Ih/hROISNe5q1levl2XUIhzbgixvKY4uXdmDOHroqGT0vg0LNXZAKMRMcoZJe7FpW8wg8j43j5//K7JGjjyG+h0X04v86OozgMxinp1+Vl2XgoxXlXD/BaNAAZNCQF6XEAJhszEWG+NCdECViO/a+QHqWzagCY29zXsJOoPMp+alZMfSCCdGTzARKzlHy8rfraAzqEq+N7KCWSBYxnspt3qHzN7v7b6Xz+z4jHq+Xhl7hY01G6kqBkh99dskn6NMdNPUpAi9dXyvw0t0efGtSYs/A/vFdhCf/jQcOULEHVHppEN3yKbkygyiOMIRoCnUil23Mx4fJ5eTfYdHN33whmgPnztIzswTz8a5NHdJTbrKGTlsmo2CUWAyMYkoLva9/l62129XD9Wx4WNqXysx/DfKIE6OnZTjQW+EZ/+3/5aHH/sPeGweri1eI5lLks6ny7gKMqIyNY2IO4JWkBOxVuLH8w4bQ4tDvLf7vfyfK/8Hm2bjzo475ZQwIecqWOMtLdmRudQcdrtTSo0Ur7FigleZg8gZlYOO5LkJ8g6bwsG7bVJvaTwmewsnrp9QL9xSZomx2Bi7GqWU80e2fES+kNb9KEa5QghyhRwXZi9gCkmCEoZZMegn96//b4YbXGyu3cy+YlRv6VVZv00ql+L05Glem3iNaFnjtJx4t5ZVuSKcnj6LabdBGUCh3Bp89XIQUUCWR7wOWVLrK7Jvcbmgvh7CYZ4dfJZ4Tpb2rkYHyRazuon4hNqzhdG3FtaVAIijx44qifG3axF3pKSjpJf1vwBNs2EKwfdulaXOH1z9AdncMprQaAm2ELLL752aPMV4cXLgSlupchsvpBiLjfHjsePqs9fGX0MXOvetuw+n7mQyMSnLi0KwqWYTmtDwOaQkDUBf3Ta6I90VopwN/oa3LDGdmz7H+NJYxWfirrup9dayq7aPPU170DVdcnHK+ol//NofA7LUfG/PfQosA9JJG7pgS90Wwq4wAWeAiDvCvqZb3lUNJst+sR1EmTl1B7XeWonQEdoqFUen3c1v7P8aACPJcRXV7ayTCJCXxl/hRpY2c2TNvCzJFBd0m2ZTzXJr8bamgu0M7+R969+n0uDl/DL3dt9Ljbd2le7+jRzEyn7IjRpc5Y1cv8NP0BmUE7yQ8t/pQnFWccFAICo0io7/yl0UHDYmE5N89+J3MU2TtlAb04lpdjftptpTQ8QdocpTJa/RKKGKFrOVc6r9Dl9p0laZ3s7+1lu5t/teoAxhJgQFm66UM2O5mGSpF/eVzqfJU5lZnZ+Rdf2jx47KF63MQRxsPyQdQpG52hZql4tDUQDQMmthagu1cUfnnYCcMOi2u9lev71YxoAfXluNYnLoDnqrVvdsLDs5+jIFU2Zf62tK0hXlQceLYy+RzCV577YPEP/NX6M91E5LuJ3T43IwELoOBw5AV5cUjmzYru6XV3fL0ilyih7ITBJkz21jzcYKgTzLflLkzNa6rXRazfjiomcRuzwOD49u+RAUn8Xl/LIKiva37OehJsmN7avvY2fttlX7bgm2sK9lH331pbnfwpDT5rSygG4oOqTma794XTaLPXYPXoePy3OXi/2dJaUd5bbLpn158HN57rIisN3IckaO9HLlNmFXSM4GyVXKmVR7qiuqFACPn3mc33/l/6m4V53hLuoCTZybPkfeyMt9CZ1MbvkXA8X0T9YMQ8HvHLUN0FnqSXD4MJHDD8nI84470Gpq5YKC4PyEbKZdnl+R/t16q4L4NYZb8ZU1MNuCbfRW9/LxrR9nQ80GtXjf0VypnFnnq+Mjmz9Cva+eH1z9AXkzL5nGbyODsKJBt83Nkd4jNyxvHiWLcQAAIABJREFUNPjqpfIlgll7lryRZ3PtFtZV95C+/zA1H/oU93TdQyLiY7GniwszUnvoS3u+xAc2fkDtJ5VL4ba7+fDmD3N2+iwvjb7Ewbs/zYAW5UpiREJSi4vBnqY91AUaKs7TZXfz+V2flzuzMoi+Pta1bWdv817cNjcBv5wJYWoCUyvJsze6Gqn11sregHVbRKkB3r+/l7HYmBoDqwsdNm+GQ4fgyBEOdhySUNLi7zAwf4X51DzLbU08V1/iQ1hR4F+9+Vf89gu/DchGekeog/etf5/iZaxl5aiZtcwoNmob/Y08uP4IAImWevz1pSmAVw9txTANnLqTe3vvxzANhuOjqjRZbgLBmWIN3RSChtvuJXmLnF+QOnQPvP/9qjZ+bPgYH9z0wYooFyT8VSGSfhKzns/iondX511sqN6AAbw5e57lsA92SMTUtVvWw/veV/H1yfgk4wurM4gjvUfU8C3uuAMaGjDDIaa66tDKnm+B4Mr8FX489GMJKjEKJB48TPrIfVyau8T2+u1UH35YZQgnHNM80f+EygRBzrVYFYytsN6qXvT7H4SDB5Ue1WR7Nd8JTxCNV2Ze86l53DY379/wfvXZaKxshkzxXtX6atnWJGeghN1hHlr/EDbNxump0xXB2btlv3QQlpXV8n+8pwbWlQb5sG8f+/Y9Kh/MgweJGWne3Fglx4Tmsuxv2Y/dVomqoaEBbpNN2denT+O0uajx1lDlqeLedfdyZ8edzKXmSGaTKjLV1pDqKJ8LYCEkbnTe5fbpHZ/ma/u+JgXqWLtkAdDgbaDJ34RDd9B/63ryRp4qd4SAK8ilZievuhfY0bCDvMNG6rY71fcuzFyo0J4SCLbUbuGZwWeYS81xee4yo8Yizz+wmcl1DRICXDzX+9bdh9vhpcZTs3ZZznIQDz+sXpx0Pk3YFVLNb7vdxeXbJMS41lVLwBlQPYn2UDtNgWZVU7/94V+ryKiEELBtm3QQ3d1qQdM1nfH1TSyk5ollYtiD4Yrmdvm1WuXGrkgXi8uLHD12lIH5AYajwwoB877GyoXvZhGpQ9hK9X4hkTdnbutiPDmptlnqkBLuf33+r5lPL0jpDE3gEqt1w+7uvFud4+Hu9/KUeZlXej3kjTzzTTWwZQt23X7TReeh9Q/dtG+ypn3846UFv5iF//DaDznYfpACBufnL3HPhgfgyBHuX3c/jz38b6BI8FP3QndQyK2GjD87+CxPDzzN2amzclH+/OepDTWx/yu/x/t6S/daE3Keu/X7b6vfxvo7HiXWKRf81mAr9T076Ap3kQp6mNVkEFD+PA8uDipJjxuZz+FDX9cjndVOObt72e9maGcn2TsPwmOPqW0zhQwnx08ScUfUoCbDNCrAEoD8f11nKDrE0GIxoNF0nLqjQmzz3bJfOgjLCgW1WK1a7NewWHqJoDOIX3MzHB1eRfwqb2obuoanGK1Uuat4sv9Jnux/ku9f/j4L6QWEEDzQ8wAB3bPyMKTzcvC9XbNjiuLkrreRQSznl7m6cJWckeOJ/iduCMGdTkwqpc2AJ0yhdx3PDR/j5bFXSGQTOG1OFeULhCKNPTf8HE9deUrtp8HfQEe4g8GFktJkea3YxKyYz42msZxfXruuml+71qrq4UKwbOboDlcu3hbaa2fDTpZzaWKZGPW++rdmAlslPqFh6BrClC90uSR0+f5XNuAts0peD61/iDs77mQmUyIrtgZbb0ikBFmasBYOBYZYcQyr3FBehzeBfHaZldYeasdT7DEIXcdj97CzQS5ihbLG/4HWA6pJ/Y5YV1epsVxEMw1Hh/mL03+B0DQ03abmpeiaXoGkKj/3evdqUuHg4mDFfBHLDNOoILnd33M/3ZFuHLqD9lA7zYFmbJpNZZh/N/B3QOn3Wss+u+OzCm11I9vXso++umK5q7MTPvnJ0h+DwYog0wrQ/vzUnys+hM/ho85qhJerR+s6n93xWcW614RGNp9RJcJ3037pICwzDKnqKjQ6q9fddNNaby0+u4zG0pkEE/EJ7uu5v3Kjsoe4o3odrUXBtyvzV9Q0r1QupSK0p688vWqcJsiFPpaJKQjeqjrkDTKIH137EU/0l7iGN8og5pNzDEeHSeVSLGaX0Dq7wShQwKAt2EZrsFWdYyKfkHDVFVFnb1UvD/Y8yDde/wY2zcZD6x9iffV6NKHRU9WDQ7fLfkr5uWoa8Wy8BOEtv67cCmeLfEksRJPFdTBWOD1rdrgQgkixNDKVmOJb577Fkd4jrK9ez9FDR9e8D1DUH9I1nLqDak/1qr9bi3JrsJWQc3W93nKkBbPAk/1P8vL8y+pv15eu3xTmWu+tK420LUPLlQcAR3qPyN8pvYhNsylHtTmyftX+/uLMX6ixrn939QdyGFKwBYfuQCt77d129yom/ztiX/0q3C6JeLlCTi7GQpAuZFST9vEzj/P0wNOrvvri9Re5OnN51ecWkbE88FhIL/Bnb/wZ3zr3LfXZYnqRoDPIusg6eqp6mIhPcHb6rFKrtd4lSy3XWuTLA4mmQFNpgNUNrNpTXVKu1TRob1f7KCf/QeV7azmor+37Gp/e+Rn5YfnoYF2vOL4udPx2nyKpvpv2Swdhmc+HQ3fIhXSNWRDllivkMAyDZC6Jr1O+nCqDsIa2BIPqRU+aGfIOG9jtRPXSImENbLf+ey6+OkLIG3mWlpcUg9rn8FUupm1tFVBAyzrDnRUP/I0aXLlCVo5NNU00zYbH4UUYBgYm15euV0was9jW5aMwQbKALUkGm2bDrtnZ1biL9lA73ZFuDu35kITslTuIIuR2Te5IW5tk4ZaZYRrEOxrJuuwSBqprZFach1VTTuVSq9LxWCZGIpvgm2e+ufp4ZfcmEfFRu3kvkZ4+As4An97+6dJmxftwT9c9fGXfV+WH7e3YamUUaL3Q33qztFiVW7lS7Uq7MH2+1KMo49uUI2uevvI0Qgi+uPuLhFwhQq4QXfUbmFi8vmp/AsHWooRGxFPFeHyc71/+PtlCtgKtZJHx3nELh1WJyQoC7LqDu7vvUfex3le/ZuO+t7qXxu7tq+VQhKAz3Fk5O7u4r/JrOjl+kryRpyvSxUujL3Fy/KSUfS86W4uYZ323PdTO53Z+bpVM+U9jLcEWjh46uopnUe+rpyXQApQkYv5h8B/40bUfyQ2sa3W5wFMZKOqaTt+6Az8XJvXPS6zvn55VV3Pq4+/hQ08PU9NcHFHx8MPwt3+7atOv3vJV5me+wzl+zGu7myAb59lrP2QzQfj610sLzqAstzy8+QPQ2wtH4PQrf4DPyOPUnbyn8z0VTUAjvzpyfqDnAQD+y8n/wl0dd9ESeGHFBg+seTn7W/Zza+utKiu6kWU39OCYlaWbgllA1200eOrA38BX9n5FyXFH3BFETkipa6+scadyKV4afQmQU7Kq3FV8bOvH+N7F71Hvq6ct2MaZqTNUbbqLugPvhW9/u3Tg7m4Off0blZ9Z9uijqz469PgxTk2e4rWtHppfv4IphJTMoIRVt5zCtcVreFeULkaXRpmIT1SQDleaeOghNoaiTAlB//ISsanTcnC99XchOHroKH838HeMLAzxRYAjR6iORDgKN9033MAZFm02MQ1ErAOBphFw+XBu2wnOHvjIRzh7/D/y2sRrKgsS/+7fUz/yArH2GcivXkxH46NEgM/t/gKX06OcnT7L5bnLFU7n8txlphPT3NFxx03P/R9jH978Yb59/tugCWw2B6LYHPnCri+sGq0KcuFe2LEBPlrZw/nU9k9VoP6gFPisnAnxw2s/xMQknZNEU13TlQaSlR1aKKax2BjbGyr7ID+tpXIpfvfE7/LlPV+mqmx2QyqXoi3UhtvuVn3EV8ZeIZjTuat8B/X18NGPVuzTNE2ejL3KLV/9ODevbbzz9ssMoswWc8VSzltkEIBK/ePZOOsi60pwyPJIfQXWHqAj1MGDPQ/yiW2fYHvD9gpykl5Yu08AMpX+4dCPZM32bcDdrBfHptn4Nwf/zQ23a3rgoxWEIKHrbK7awOa6Lco5WMfXhKZkM7oj3Wyu3cytLbeq44TdYSbiE1xbvMbzI88zsDDARHyi1OhcWQ67ybzftWxHww6qPFWYmsDvXl3isSyVSyEoDZTZXLuZwcXBt1zA0aRsw7HhY0SXoxxqP7TmZkOLQ0ynSv2F0aVRfv/l31fRuIUG2hzYXPG9peUblwg8mosqd3FBKZYZYpkYL3U74SMfAVizRHV72+08cOSfw2/8RsXnEXeE+SIRTmga52bOqRp2eSSazqVv2J96p2w+Pc9jWx8jbxZ4evAHpV4La2e2g4uDUjFghQ0tDvFE/xMV8zR8Dh/7W/ZXSJwIBElL2BCJDGvwNajeiwWT9dq9IARvTL7xzlwoJa7OymctkU3w0uhL3NlxZyU73elclTGsNOserVICfhfslw6izOqDTbJMUY4ouIFNxyW6pL5IXsreTPmxzEHsatxFdDnK2amz/O6J31XQus21m3FwY8dkkZqSZRpAb8fSuXSJoXwDC7vCOHSHmph2avx1Xhx7uQJ147a5sQmbcmjfeP0bfP/y95XDqPHUsL1+OyfHT6rvKHSWFd2tdBBrOdOb2OjSqFSYBaL5eOVgGpsNtm4l4o7QFmxjcTlKKpeizlvHwbaDb7lveaKaKlXMpeYqCIrlVvHyC6EG21gO4lD7IR7d+Ci7IrvUZiFX6KbEq2pPlVJVtTIIYFU5rxzKezOr99VXKPFOxidx2VwVizNIFJbin/yM7Lmh5zg2fAxj00aMSJjD3Ydvuv2Ohh1SDXiFvTDyAiPREVUiAhkE3NN1D/taSsO8+ur7pI6Y7sDr8LK9fjtNgSZFjLMGPlm9iPvW3fePvkbLVg5LKjfDNHhj4g2ljADIIOk3f/Nt7bschvtu2S8dRJmNJycl1O1tOAin5qDOWyfn0s730+RvWr3RCjw4wInRE7wx+QY/HpIif1Z0nSvkVjW2yi2ZS5YYtj8BYeatosNLc5cYi42RKWQkJlsIYuko8+n5ioUwnU+TKqTwO/xqHvVMcobR2Chf3ftV7uy4kyf6n0AXOrsad9EcaFaOQUXG/0hT4nTFmd0N5azuQAAeeYTuSDc13hrM4rlPJ6f5o9f+iFuab6HeV8/nd37+xgfQNAWVfct6tCgtvkp8rjjRrMHXUKHrA1RMCVvLttX1ldQ+ixnESnnwT2//dIUi6M3smcFnKiCULcEWbmm+hce2PlYRtbcEWt62nMZPawvpBc5MnYG9e4k5TJ7qf+qm2w8uDCp13nLLG3kWlxcrsu7ocpSjx47yxOUSIKMl0EJnuBOfw8ctzbcwk5yhf65fNakt+Ko1otYSLXwnzDq3lRmElc1MxCeUZMYXd32RT2775Nve91tpQ/0s7Jc9iDL7le2fItKYLEX8GzeWhMdW2NJylFhymk/0/Q7PjzxPj+gAxis3WqPEdHH2IrrQVbqryFzz/Sze8wlqjHugf7Xmyse2fozvvPoX8gH8CRyE2+bmY1s/dsO/T8YnGbmnh+3rPsZDzT4YTyEMkwJGBQb+Y1s/xujZUcK//i9JTZ2A7CT/bPc/w6k7uTB7gbZgG3d13EVToAnTNKn31RN0Bjncdbjk+B56CMqm9P0k1yE3l9u7t+3m9uZbZSO0cwH+4A/UPbaiwfFQBwOc5uw9spxQ76vHbXPfXPBM02gLtbGtflvl3OgV9qU9X5ILwLH/IM/LapRqukIwLaQXOEBpMf/Y1o+tPQ/CsvLsyuuFRx7h4ToPf/bGn6mPW4ItFT2RtzITOX8cIRhaHGI8Nl7BQgbesdr7zcwqu9g0m0IV3cyO9B6pgOJa1h5qZzIxWTH321p4yx36Pwz+AyFXiI5wB1cXrnJ66jRdkS7Vo7Iy48IXP8/U8DP/uItbwz6/8/OrAAkWgGEhvcB4fJx7192rmPdvx25vu331LJh3wX6ZQZRZW6gNR2+ZXLOuVzKqyyxQTFc7wh0MR4d5fujYjXe8QrZD13SqPdU82PNgRTSXdumSYLeGdUe6+Y39X1NIiLdrQoi3jBDj1X60vm10VUnSmF23407nKx7I7kg3QgiqOzfjidRR7amm1ltL0BXkmcFneOrKU+xt3ktzoJkzU2fIGTnqfHWcHD9ZUgn1+Sqv7ycsMVnWb8zQ1NlHU7hVEt5gVT+jUFyUFhslCGA4OlzRU1nThODCzAV8Dp/SLVrLzs2cqxik01vdy1f2fkVlSvPp+VULnHX/bmhGZdmK7m48dg/b63/6Bdym26VT0jT2NO2paJq+m7anaQ/tIQkBPdh28KbNepASIGvBjA93H+booaO0hUrscuueljPYBYIXRl5gIb3A6NKoVF0u46BYWfWku8CoeOdRXA3+hlXX6LK5uKvjrjUd39ux/7+9cw+Oq77u+OesJOtpyZIly5Zsy2/LL2xsY/kRjO3SgGkd3AAemI4DNJQWGtLUDa9pmiEzNBk6HdJ2moEAJU3aBELTlGYglNLgGMdxwQ/wQ9jgB34/Zdmy5IdkSb/+ce9d313d3dWudvdeWeczs6O797H3u3t/uud3z+/8ztlxcocvqTb0CSJFhuSX0eIYiSFjqe3qIOYThMvFNLVqKuPKxzG0cGgPN0aiMLbXGl/j9o5WhqSxoSwctTDsk3U0Lxy1EAoLIc/bn7qyfmWPdW7tu5p28eGJDxlRMoJzl8/FniCW5PeYPmw6B88dpPF0Y8+NUQaidvBIQhUT+DVWz3Vv816aLjZd9fN7EQpxpfsyvzn0G26ouSE8AB/NO/veobWjlQX2d2htb2XHyR0sGr2IgtyC+Gm9Y+ERzXOh40I4Y26yFOUVccV0WqHYIlbthESD9Bli1vBZTO+eTkdXBy9ufZG6srrEB3nQ2t7Kj7b9iNUzV4fDSJ1QbvdcDhErDculK5fCN1UnYKGurC58Xb1SfmeKgtwCbqy7kdL8Uvad3Zf4gChWz1ydMHlgJtAniBQ5cf5YOEyuNL+UinyPnDUeLqZZw2dRWVTJp2c+tbKO2pTllyX0ex9uPeKZs78vDLGTi7k1Hzl/hH1n98e8oWw4tIHvbvxuxDp3j6mzu5Nu0x32uUenkU6VbtPtbRygh4HICYUozS+lKK+Ixxc9TltHW+KZqK5B6hNtJyy/eQwdbk5eOMnaA2tp62jjcudlGmobkvdrexiI5kvNCd0xsbhr6l1Xy9baGW73n92f0mf1lTf3vMnpi1YodV4oj/uvvz+lz2nraOP0xdMRJUpLBpXw1JKnIhINOlFCjov0prqbwpF69ZX14euXG8qNcFdlg/audsaXe1QWTEBFYUXcmfiZQg1EipTkFYcbYmd3J4UjRlkuFC9cBmJ3024aTzWy8cjG8IQZsGK8E/VoakpqrJ5SGp8gDrYcjOzR2LUE9rV8FvOJZuepnRGzOm+beFtEWoLawbWU5peGj3fH3UcQI01ILHY3WbNrp1VNi1h/YbTL1eSiNL+U+sp6ntv8HLWDaxPm9ycUCoeotna0xowYix74L8svozivODyedMuEW5KPjPGYET+8ZHjKs5x/uM01IVCEmcNnhufUZJtDLYcsFwnWk4xzHZPF8eO7Oxztne089eunIqrhzRo+K5zUb17tPCuVvV1z/VjrsfCM+/zc/KwnwNt/dn/Gw4rTiRqIFDl/uSWc/fWuaXcxZXwDfP3rkTt5PEFsPb41fEN2DwKXFZQl9M3+0ZwvJ/ajJ0njqUa2n9x+dYVYZTedEqNeRPegR5eNjqi3PH/kfK4fbs3xmDR0UmzfabTfPQGOwYkOkzy9bBlMizQajvE5c/EMzZeaqa+sj/BdexIKMaFiAtXF1cwfOT+cpjqaL075IqumrQq/ry6p5tFFj4Z7eF6TvxLicUxVcRVfbfhq8p/lIjdk1Zc4deFURJ6sbLK4bjGL6xaH24FTYztZnBBfd0/aPQDusO3ENubVzqOisIKOrg42H9sc9v3vOLWDA+cOANaTxiMNj6SkJVV2N+1m09FNWT1nX1ADkSKdXbFDFsN4jEHA1bQAsVJD9/pz04DXzTtRDye61/X85ufD+WXAqvdQVVxFeUF57KcHSPoJwtH67MZnE++8dCmsXBm+gYhI4tTVoRBNF5vCtbaPnj/quduEigmetadFJPXJTDFyaqXKsrHLmFI1JdzrLhlU0qea033VUjekjtxQLssnLI8bzh2PwjwrXbZ7LojTJtztrPlSM9tPbqfbdIc7PxGD1KkY8DSxatoqVkxe4dv5k0UNRIpUFw1LHN/v/NO7cspUF1czoWICKyatSD5CJQNRDBMrJkZGRnV1MbN6JvOGz415zO31t/fo2ToT5gC2n9zOhkMbOH3xdOwxA/tcyVAzuCZmr74HlZUwaxblheWEJMT+s/vD9SBiEgpxvv08Gw5t4ELHhbihrmGirsnN425OLdokzTetxXWL6TbdVpp1rPoGftQ0dtNtunlr71sp+/1DEmJG9YzIVBuOG9PldgpJyKr26OrEOQZkWPEwX9JmO0ytmprY1Rkg1ECkyIV8ibgpeuJxA1w6dikzqmdwvv182BeaNGk0FFOqpkTMQnXSnufGuXc79R7cuN1jTRebON52vMcs4B4k6WIaXjI8MuKqF9w64VYevuFhmi81Jy6fmZ9v1eU2XVzqvNS7wfXepGXpDbHGr1Kktb2VT5o+CbsD3973dsxB92zh9Nyj52L0hYLcAh5b9FhE+3OnmRGE66qvC69bOGqhrwaiv6Fhriky/o4/pvaWnknlIvAwEPWVVvbXF7a8wIxhM+KHXUaTgSeIHj3yri42HdsEOTksiXHMm5++SUt7ixUOizVT2t3bzgvl0dHVwcSKiaxZsCbGp5B0r3lv896k9gfLtVIyqISH5j4Uf8c1a6C0lFrTzYNzHmT9wfWJz7dmTcyJlEnx6KNXswCnCcdN6NwYx5eP9ywp6gfpjBwSkXAIq8ONo29k7WdryQnlMLpsdETNkaT+3xR9gkiVnLxBFJcPi79TAhdKytEMmZwwY2vOj9N3iB6kfqThEZaNvVptbsmYJcytmYuIxI/MStLv7viev7H4G0kdB1bEStz8/qWWzpCEqBlcw/iK8YlvZKVpiqMvLu4xmbKvuIs8geWDjy4pmm0cY3W87XiCPftGS3sLkysnExIr1NmPFBXXCmogMkkCAxHd80lINmZS2prjuVeiDcS313+bdQfWhd+XFZR55vnv+UHJGQhncPPp955O6rhUKM4r9m3mcTpwZus7RvFgy0G2Ht/qqyan9G2m5x6cu3wunNhu/aH1vg3OXwuoiymTxDEQc2vmekbC9IoMP0EMKx4W1x2xYvKKiNnCHV0dEb3C7Se3IwgThybIXp/kGEQ2JzUdbT2a9HhHkCjILWB61TS6dn9MDlbocbya2NlARDCYlKOYen0eJDy+1x4vy7KSEDUQmWT8eKu4ugcpTVrKxhPE9OlMnfd70NAQcxdnHMVhxaQV1Loyq+45s4f2rnbumHpH/HNNnGj9PuvWxd/PJj83nzUL1mRlsLWqqMqq4tdP6TbdrDu4jvGmghwso+1neKdDQ21Dr1OWp0rN4JpwuHFZQVni8GYlJupiyiRFRVY8frrJpKEoLYV774X6nnWOYzGnZk5qoXsp/D6l+aUsrluc/LmSZP/Z/ZETCPspzhiEMSZ5l2YGWD5xecaTzuXl5HHzuJsBy1hEROkpSaEGoj/hQzbHVJg9YjZTKqf4LaNP+JEYLZ1Ez8ofWz6WSUMn+aTGP4wx/dpV6DdqIPojATcUE4dOTN49E7DvVFta2++NHFw1FFuObWHjkY0+q8k+H5/+mMZTcSZrKnFRA9GfCNhNNBaHWg5FJCJMSGEhjEquzkWm6ezujEhI2B9ZNW1V2EA0jGxg+rDpCY649hicP9j3GeT9GR2k7o8E3FBsPLyR9q52lo7t5fjC449nVlAKXFd9Xb8epAYiMv+euXgmYTLIa5F01pseiKiB6E8E3DAowWVP855wOnJF6S1qIJS0M6pslO8x9woRqUzmj5yf8fBS5dpDDYSSdmaPmK0TlIKAK5Hg4ZbDWS+Oo/R/1ED0N+67L30ZRDNEyjPElfQybRoMsWbEr6xfOSDHIJS+EbgWIyK3isgnIrJXRJ7wW0/gGDPGbwVKfyEnB0Zb2XrLC8vTXo1QufYJlIEQkRzge8ByYCpwj4hod1RRFMUHAmUggHnAXmPMfmNMB/AqcLvPmhRFUQYkEoQEXg4icidwqzHmAfv9aqDBGPMV1z4PAg8CVFdXz3n11VdTOldbWxslaa7ilS6Cqk11JYfqSg7VlRx90bV06dItxpjYdYUdjDGBeQF3Ai+53q8G/inW/nPmzDGpsnbt2pSPzTRB1aa6kkN1JYfqSo6+6AI2m17ck4PmYjoKuHMujLTXKYqiKFkmaAZiEzBRRMaKyCDgbuAXPmtSFEUZkARqHoQxplNEvgK8DeQALxtjNBWjoiiKDwTKQAAYY34J/NJvHYqiKAOdQEUxJYuInAYOpnh4JdCURjnpJKjaVFdyqK7kUF3J0RdddcaYqkQ79WsD0RdEZLPpTZiXDwRVm+pKDtWVHKorObKhK2iD1IqiKEpAUAOhKIqieDKQDcQLfguIQ1C1qa7kUF3JobqSI+O6BuwYhKIoihKfgfwEoSiKosRBDYSiKIriyYA0EH4WJRKRl0XklIjsdK2rEJF3RGSP/bfcXi8i8o+2zu0iMjuDukaJyFoR+VhEGkXkz4OgTUQKROQDEdlm6/qWvX6siLxvn/+ndmoWRCTffr/X3j4mE7pc+nJE5EMReSMoukTkgIjsEJGPRGSzvS4IbWyIiPxMRHaLyC4RWeC3LhGZbP9Ozuu8iHzNb132uf7CbvM7ReQV+38hu+2rNxn9rqUXVgqPfcA4YBCwDZiaxfMvBmYDO13r/hZ4wl5+AnjGXr4NeAsQYD7wfgZ1jQBm28uDgU+xijb5qs3+/BJ7OQ943z7fa8Dd9vqBDfaAAAAFNElEQVTngYfs5YeB5+3lu4GfZvh6rgF+Arxhv/ddF3AAqIxaF4Q29kPgAXt5EDAkCLpc+nKAE0Cd37qAWuAzoNDVru7LdvvK6A8exBewAHjb9f5J4MksaxhDpIH4BBhhL48APrGXvw/c47VfFjT+F/C7QdIGFAFbgQasGaS50dcUK4/XAns5195PMqRnJPArYBnwhn3TCIKuA/Q0EL5eR6DMvuFJkHRFafk8sCEIurAMxGGgwm4vbwC3ZLt9DUQXk/PDOxyx1/lJtTHmuL18Aqi2l33Raj+eXo/VW/ddm+3G+Qg4BbyD9QR4zhjT6XHusC57ewswNBO6gL8HHgO67fdDA6LLAP8jIlvEKrAF/l/HscBp4Ae2S+4lESkOgC43dwOv2Mu+6jLGHAX+DjgEHMdqL1vIcvsaiAYi0BirC+Bb7LGIlAD/AXzNGHPevc0vbcaYLmPMLKwe+zygPtsaohGR3wdOGWO2+K3Fg88ZY2Zj1Xb/MxFZ7N7o03XMxXKtPmeMuR64gOW68VsXALYv/wvAv0dv80OXPeZxO5ZhrQGKgVuzqQEGpoEIYlGikyIyAsD+e8pen1WtIpKHZRx+bIz5eZC0ARhjzgFrsR6th4iIk43Yfe6wLnt7GXAmA3IWAV8QkQNYtdOXAf8QAF1O7xNjzCngP7GMqt/X8QhwxBjzvv3+Z1gGw29dDsuBrcaYk/Z7v3XdDHxmjDltjLkC/ByrzWW1fQ1EAxHEokS/AO61l+/F8v87679kR07MB1pcj71pRUQE+GdglzHm2aBoE5EqERliLxdijYvswjIUd8bQ5ei9E3jX7gGmFWPMk8aYkcaYMVht6F1jzB/6rUtEikVksLOM5Vffic/X0RhzAjgsIpPtVb8DfOy3Lhf3cNW95JzfT12HgPkiUmT/bzq/V3bbVyYHfYL6wopE+BTLl/1XWT73K1g+xStYvaovY/kKfwXsAf4XqLD3FeB7ts4dwNwM6voc1mP0duAj+3Wb39qA64APbV07gW/a68cBHwB7sdwC+fb6Avv9Xnv7uCxc0yVcjWLyVZd9/m32q9Fp335fR/tcs4DN9rV8HSgPiK5irN52mWtdEHR9C9htt/t/BfKz3b401YaiKIriyUB0MSmKoii9QA2EoiiK4okaCEVRFMUTNRCKoiiKJ2ogFEVRFE/UQCiKD4jIb/3WoCiJ0DBXRVEUxRN9glCUOIjIDXbe/wJ7lnKjiEz32O91Ozleo5MgT0Tq7HoClSISEpH1IvJ5e1ub/XeEiLxn1yLYKSI3ZvcbKkps9AlCURIgIk9jzVQtxMon9B2PfSqMMc12OpBNwE3GmDMi8gBWmuYPgAnGmD+x928zxpSIyF8CBcaYvxGRHKDIGNOare+mKPFQA6EoCbBzdm0CLgMLjTFdHvs8BfyB/XYMcIsx5v/sbW8DE4BZzs3fZSAWAy8D/wa8boz5KMNfR1F6jbqYFCUxQ4ESrEp7BdEbRWQJVvbNBcaYmVi5owrsbUVYWTexPyMCY8x7WFUGjwL/IiJfyoB+RUkJNRCKkpjvA38N/Bh4xmN7GXDWGHNRROqxSlE6PGMf903gxegDRaQOOGmMeRF4CSsFtqIEgtzEuyjKwMXu0V8xxvzEHiP4rYgsM8a869rtv4E/FZFdWCUoHdfSTcANwCJjTJeI3CEi9xtjfuA6dgnwqIhcAdoAfYJQAoOOQSiKoiieqItJURRF8UQNhKIoiuKJGghFURTFEzUQiqIoiidqIBRFURRP1EAoiqIonqiBUBRFUTz5fzXyvWlb2v1kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "data.Speed.plot(kind = 'line', color = 'g',label = 'Speed',linewidth=1,alpha = 0.5,grid = True,linestyle = '--')\n",
    "data.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": 9,
   "metadata": {
    "_cell_guid": "0a60d9c4-bf3b-4c13-9cbf-c6e759824d36",
    "_uuid": "6fbb6171d8a0d7e228fd380d1b86c02cc0682873"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Attack Defense Scatter Plot')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BLu9zoNRxkuRiFtPnRWSVZ4Ia5m0bD2C775gd3jYlF9TVUTkMHgyMG8fvG27Iv5FEIQQepSpjIdxjOsjlfQ6UOu4D2VYQvwYwDcBRAHYBuC3ZC4jI1SKyTESW7d27N93yKQCwbRtHDv6Anq4ubs8nCiHwKFUZC+Ee00Eu73Og1HEfyKqCMMbsMcZ0G2N6APwWNCMBQC2ACb5DD/O2RbvGb4wx84wx80aNGpVZgQcqEyfSrNTUBLS387ukhNsjyaXdthACj1KVsRDuMR3k8j4HSh33gawqCBEZ6/v5UQB2htOjAC4UkXIRmQJgBoCl2ZRN8VFTA1x1FbB5M7BmDb+vuorb/axaBXzlKzRHfeUrwOrV2ZWzEAKPUpWxEO4xHeTyPgdKHfeBjAXKicj9AE4CMBLAHgDf9n4fBcAA2ALgM8aYXd7x3wRwBYAuAIuMMU8mKkMD5TJESwsb/KIi56Tu6eF6EPafxh5TWclPczM//mOyKW++B9OlKmMh3GM6yOV9DpQ6Rh4EyhljLoqy+c44x98M4OZMyaMkgbXJTvBZ/bZv53b7jxPNbtvQEH5MtiiEwKNUZSyEe0wHubzPgVLHSaC5mJTeBLHJFpLdNpGfJNH+QokHGQjos8gqmmpD6Y21yS5ezFGBnRfu710FOSYfSDS/PdH+QokHGQjos8g6mqxPiU0Qm2w+220T+UkS7a+rA046iXEgdn9rK/Dss70d9kpm0WeRVoL6INTEpMQmFALGj4/f8Ac5Jlckmt+eaH+hxIMMBPRZ5ARVEEr/JZGfJNF+Gw/i3x8rHkTJLPosckJSCkJEBmdKECUH9PfkZInmtyfaX1NDO3drK7BzJ7+/972+mTT6e11nmnQ+CyUwgXwQIvJuAP8DoNIYM1FE3gXGMHwu0wLGQ30QKTCQkpMl8pMk2l9XR1PGxIl9a5D6Wtf57N/JFak+CwVAcB9EUAXxLwDnAXjUGHO0t+0NY8w7UpY0BVRB9JF8CnLr7/S1rgeSAleyTtqd1MaY7RGbupOWSskPNDlZ+khkOupLXWt2USVPCBoHsd0zMxkRKQXwRQDrMieWklH8zlnbq83nILdcT7WNZdYI0svvS11HUyp79wLr1gGzZvX9HnNVT8nIoGa1vCKogvgsgNvBNRpqATwNYGGmhFIyTH8Jcgt6TCo89BDwrW8BHR1AWRlw880MzvL38m3Dv3hxb9NRX+o6Uqls3gy89hrwi18wDqAv95iNekoUxJZq0KKSdTRQbiCTz721ILb7TPtS6uqA444DDh4EiouB7m5gyBDg1VfZiN14Y+98VTfdxLiQaPeTTF2vXk2lcugQlcP8+cCUKX27x2zUU6IgtlSDFpW0klYfhIjcLSLVvt/DROSuVARU8oBCDnILekwqrF8P1NcDFRXuU1/P7cnmokq2rufMYeN4zTXAvHlUDkDf7jHT9RQkiC3VoEUlJwR1Ur/TGPOfJ2WMaQBwdGZEUvoVfU2uls2EgbEczVVVgAhHDgC/RbjdH0Px1ls0A114YXw/SbIJA0Mh+hwGD07tHjOdWDFIEFuqQYtKTgiqIIp860dDRIZDE/0piXjoIZoeLr6Y3w8/HPzcIIu4pGOhl3iLHk2fDixYwFX1Dh7k94IF3A6wl3/CCTQBvf468LnPRb/HRAsrxdufjnvM9II4QYLYUg1aVHJC0DiISwB8A8CfAQgYE3GzMebezIoXH/VB5DHpSq6WyVlMQezeq1cDt97qrn/ttc5xmoztvbzc+THa25O3vafDX6SzmBSPtC4YZIy5R0SWAzjZ2/QxY8zaVARU+jnR7NJNTdyejIIIsohLXxd6sXbvsjLKVlHh7N72enPmAD/5SfSGz97joEFs9AcN6n2PjY3Anj3Ali1uJtTkya6MaLb3urreU1rTsZhNphfEqalJ/GwTyaCL9uQVyZiJ3gTQYM8RkYnGGE2lqETHb5e2PeN8S65WXc0GfdkyLq/a0wMcfni43Tve1MuJE6kg3nwzfJaT/x5LS2l+amqi/8IY4MABbrcyRE5pXbYM+OUvqXB0qqeSQ4LOYroGXFd6CYDHADzufSv9mVQSzKUruVouk9zZWIfycmDoUH77I5pDIfojenqoQHp6+NvfAz5wAGhr4zlNTfxua+N2a0654goqiE2bwqe0+iOo01EPmzcDTzzB777WR64TDqa6OqCSFEFHEF8EMNMYU59JYZQ8Ih1BS+eeC5x4Yt+Tq2U6cKqxkTOSPvQhNtoVFTQH+c0/e/bQadrdzVHChAnh+ydMAN71Ljb4Q4cC+/eHm6haW7mvo4MjiK4u/l65kn4Ge2+XX87r//KX4VNaGxqAV16hwz+Verj1Vgb5dXVxtHT99cDXvx78/HwIYtNAu6wTdBbTdgAHMimIkmGS6VmlMxdQTQ3n8fdl5JDpfETWvNPRQUXR0RE+tbK0lDEPABt/gL8jzUPGAOPG8TtyamZrq1MORUX87ugA7rsv/N5+9zsq0UGDwqd6AsD994cf+/OfAxs2BK+LzZupHET4u6MD+M53gCefDHZ+PuSGSiRDPsjYDwmqIDYBeFZEvi4iX7afTAqmpJFE0ywjyYegpWzIkGhqZWcnMG0aG9SGBm6bOZPbg5wPsMdeXs5PaSm/y8qi31tnZ+/rXXSROwZgg/fSS8ANNwR7lgAd3l1dvL4IZTCGo5UgDWghvA/5IGM/JKiJaZv3KfM+SqEQNGeQn2QSzGVqWqKVoaHBOYAzIYONWI52/o4dwMaNbEwBKofhw8NlmDOH6TVimdFmzuS2xkZexwbajRwZXr8ARxtTp4bLA9C81NxMxfLyy2zgp06l4kr0LAHOhioq4vHl5Ww4i4pYtt8cFotknkWmSPROZjMBZTam4ubJdN+g01y/C3BFOWNMa2ZFUtJKtJ5VQ0P8hiFogrlM2nxDIeC009hT9ieAy4QM0aZWtrTQ7DNtGnMvdXUBu3cDv/pV+LGJyq+pAT72MSba6+lhw3zFFcCll7r6bWrisbfcEv0a9lk0NnI67cknU1mUlSV+lgB9GtdfT7NSaytlOO643souXv0EeRaZJNE7ma0ElNnwc+SRLyVooNzxAO6ErihXeKSSBC1eLybTydUSBZhlWobaWjpx165l+UVFdC7PnQvccUfwIDcbTFdW5u6jo4PBdKEQZ3jdcgswbFjiQLmgx8biySdpVqqspHJIZmW7RM8iW+Qy0C4bCQWzlLQw3QsG/RzABwHUA4Ax5nUA7+27eErWSCWFQbwEc5m2+drrDxtGk8ywYdlN1lddTZOQDYATYcR0UVFydm8bTFddzRiJ6mqXyC4U4jXtubGuYRu9ceOARYv6no7ijDOAP/8Z+NnP2OAE7ZUGeRbZIlHSw0wmoMyGnyPPfCmBA+WMMdvFzoIguqJcoRDPzt5XMm3zDXL9TMoQCgGf/zynn+7bxymws2dTWSRj904UMJjoGtHMDak8y75EKueDDyIfyIafI88W8wo8zdW/opyIXAtdUa6wSHfPKujIpK+BTckk62toYDRzQ0PfZIjFsccCv/89FeyUKWzYk00wZwMGm5s5amhuDg8YjHcN/wSD0aM5ovn5z3leNtO0Wx/EkiXAY4/x+7TTepvAshGglmo5fc0uDGQnoWCeJS3UFeWUvpNoZJJqYFOQkU8iH1qqDr+KCpqB/A2S384dRMYZM5gF1h5z+OHh+2Ndw5obWlros+ju5gJCr7wCnHpq8HtIlZYWpxTsCGLJEuD00ylrtpyqQcqJ54MIsupdIjIxGs9FGQGJ66QWkR8ZY74mIucbY/6cRbkCoU7qPCboCmIHD3IRnhEjaKNP50ppqTr8op2/dSvt8ECwxjDVSQJf+AJ7vH7H/Lx5nBWVrYajtjb26nnV1dlZCS5IPcZTIOnKLtxPSJeT+kyh4yGJmHxFQbDApqefZpTw3/7G76efTu9Kaak6/CKzvQIMTCspCR6tm4oMoRAD5drbOXLo6ADe/W533WwRbzGfbDlVE5WTKJI6yKp3Si8SmZieAjO4VopIE7gWhLHfxpiqDMs3sMmTYJk+kcjZtnGjSxpnM6lu3szt/jWd49VBIuepTaNRV0dTUeT+RPUbme21vZ05m0aM4H4bU7JzJ3um8WSMVQ9Who4OjqQig+2OP57mqZ4eYMwYbjOmt9Myk+9KtBiDyy9neaWl7v7KyngP9r7TSaJ6TBTvUwjZhfOQuArCGHMdgOtE5BFjzDlZkkkB8ipYpk8kClxascId29Pj/l6xAnivN4M6UR3EC+Cy5+7cyWsOGcKGIXJ/MvVbVMRPSwsbw+ZmKpBbbuH+WDLGqgcrw1tvAf/+t1vK1G8b37iRowebUmPOHJp7kgnWSwd+u/iOHQwitOWddhrwwAPhMm7alF4ZEr1PiRSInSxwww18ZvZdGYDmpWQIFCgHACIyCcAMY8zfRWQQgBJjzMGMSpeAfuuDyFKwTFaI1bN9/nngfe/j33YEAQDPPUcFEaQOYgVw3XSTy1S6YoXLonrCCSznppvYyCaqX2t7Hz3aZXtdt47nlHkZZ5qagEmTEj+nyHqwshcVMTahqIj3MGYMy7KBdLYObO+8qyvc/5DtdyVaeQ0NlKuigqOrjo7MyRBvpLR6NRVIPEUZZNW7AUBaV5QTkU8DuBrAcADTABwG4A4Ap6QipBKDvqTHyFdizbufNo2fjRudcrDbgGB1YI+xpheAztN//IMJ7UpKmB5jwgSXrru9nQ1EkPqNzPba3ExlcdNNPL+1laOHIM8psh6s7MXFvP9QiIqhvNxNiR07NlzOsWN5f9HqIFvvSrTyduzg3zZNedAUIH0hXhxHkNk/QVa9U/5D0GmuCwEsAPAvADDGbBCRuLUsIncBOAtAnTHmHd624QAeBDAZwBYAFxhjGjxH+O0AzgTQCuAyY8yKaNcdEKQrWCZVu3Qme1vV1ZyqOW8eG/ExY7gtMggtXnBWtGMA4Kmn2NBWVNBev3Yt//7734EjjuA5XV3Arl3s8ba0uFTffmKZNfzrTQcNIHv+eeCf/wTe/36OkKzs1mzV2sqRzqFDzjYeCqU3WHDNGmD5cqYLmT07+f11dTQddXWFl2ffrXwI7koUCJgPa3vngwwBCZqL6V/GmGNF5N/GmKNFpATACmPMO+Oc814AzQDu8SmIHwPYb4y5RUSuBzDMm0Z7JoBrQAVxLIDbjTHHJpKr35qYgGDD5XikapcOOmc8lRf14Yd7+w/8ZSTaH+2YRYuAf/2LvdhXX+X6DbbRFaGcxx7LxtC++yJc9Gf06OTn1geR8ayzgMcfD//917+6Z7x8OZ+XdfjecAPwZS+bfpD3IMgxX/4yj7EJA6+5hosIBd3vfx+6urhy3oQJrjwgtfc1G6TDV5PqNfJBBgQ3MQVVED8G0AjgErAh/xyAtcaYbyY4bzKAx3wKYj2Ak4wxu0RkLIBnjTEzReS/vb/vjzwu3vX7tYIA+t74pmqXDjpnPJUXNVECuGSS9fmPsVMxhw1jz/7ee7mvuprJ9rq7mSo7FKLc7e2U/eyzk7edB5HR72vxj3Ksr6WujteoqKCiKilhIx15n4neg3jHrFkDHHMMyy8r4312d9M/M3t24v3R3ofmZuDBB8OXWM3nWXfp8NVkIq4m2zJ4pDtZ3/UA9gJYDeAzAJ4A8K3A0jhG+xr93QBGe3+PB1ets+zwtvVCRK4WkWUismzv3r19EKGA6Gt6jFTnpgeZM57qCl6JEsAlk6zPfwzA2IHmZi4XCnBfURFHDMa4e7N+iZIS2v/7GicRT8Z//pPfxcXh33a7jbMYP57J+Gpqel8jyHsQ75jly6l0rGO9rIy/ly8Ptj/a+2DX4Y5MfZLNFCDJkI54jXTF1eRShiQJpCCMMT0A/gLgc8aY84wxvzVBpz/FvqYBYyqSPe83xph5xph5o0aNSkWE/ku8wKYg+OeM2/Mj54yn+qImkjHIPcQ65vjj2aO6+WY2WB0dXCu6vd2Zley8fTuDqqIi+XoKIuP7389vO3Kw33Z7qs8qCHPnugWDAH4XFXF7kP1B3od8Jx31nOo18kGGJImrIIR8R0T2AVgPYL2I7BWRG/tY3h7PtATv22bMqgXgi+PHYd42pS+kmvDLzhlvaqJTsqmp95xxv4O4qck5caMFcMVLxleV9D0rAAAgAElEQVRXR1NGXV3vRHiXX85RwKZNfUvWN3Ik7fnd3TTfVFZyJpB1sk6aRKf1pEnAG2/0liFoPe/cyZXedu7sff5730ufA+CUw1lnuViPdCU9jMfs2fQp2Pvu6uJv64i2+7u7ef3u7vD9kQkHGxu54FFf5MtWUr9IkvmfSCWBZLpkyOQ1kiDRLKYvAXgPgPnGmM0AICJTAfxaRL5kjPlZkuU9CuBSALd434/4tn9eRB4AndQHEvkflASkmvAr0QAxXpCaJZGPYv16YOlS9ljLyhgwZvevWsVgLNvDv+KK6P6NaHL6y125kv/oRUVs+GbMAEaNYpzEyJHAM88A3/9+dBmC8Le/AU884epg3rze5998M7+3b6c57gc/CN+fatLDIFxyCcu3M8YiG/hbb6VCjjWLacYM59zfsIHy3H03n/mMGeHyXX45cNhhve8l18GfQf4n0pFAMlUZsnGNgCRK1vdvAKcZY/ZFbB8F4GljzNFxzr0fwEkARgLYA+DboJnqTwAmAtgKTnPd701z/RWA08FprpcbYxJ6n/u9kzpXBHFSB3Uyx3KmxSvDHyAWzxEXK2gLcP6AO+6gbIMGUdF0dXEJ0P/5H56fSgK3zZvp3C0r4/VtvqQVK1xMQH9wbMYL6mttZSqQmhpee/Nm4LXXgPnzWSe2gS2E4M9CkDFNpMtJXRqpHADAGLMXQGmU4/3HXGSMGWuMKTXGHGaMudMYU2+MOcUYM8MYc6oxZr93rDHGLDTGTDPGzAmiHJQMYp2Sgwa5FdUindRBncyxfBTxHOFB/RvRjmtp4aeykgv9AC7uAeCI48QT+Q+fagK3deuc8gH43d3N7fFkLDTHpj2/q4tK1irb8nIqxMZGXrOjgyOMoiLGl/gnLuTZSmlRKQQZs0wiE1NHH/cp+UBf1++dOJGNwbp1zsRTVRXulEwUyBYvgMsGmNl1nisqOIsoWoBYZAI4f/BeNBn8QVsjR7pRg50+WlbGpTftfQZJ4BYrYHDWLJbb2uqmhxYXc3tkPdXVOTmi+WpilWHPr61lOYMH992xmUiGaO9DSwsVbWurexfs82tv531XV7Puenq4raKCH39EddCAPr8MQG95Mh28mUerueUDiRTEu7wsrpEIgIoMyKOki1QW6wmFaD6o9c0TOPzw3g7ieD6IWFHIGze6cocNo3NYhA3rDTe4f/qFC5nSwp8A7o47gDvvDC8vmgyHH85ybcPd2upm6IwY4coIksAtXsDglCnAlVcCv/xleICZNS/Zepgxg+fZa0Qm24tXho0M/tWvwstI1rE5YwZ9LXZq7w03JPYPGENZVq/mLDBrvuvpAYYPp1K/+ebw+u7upv/CJjO0DWyiZHuRMtj06lVV7tj161Nf8CdRPSWScYAROFlfPqI+iBikupDOhg0MHBsyxDVKBw8y+nfGjPAy4gWJ2eP8PUJ/8rnHH+c/+4IFzmzhl/ELX2BDMGIEr3H//Wx8QyGOPNraOKW1urq3DACXx7zoIsovwgavp4cpN+w0U8D1SkeMcD3ieH6Sxx/ncaWlbLC6u13vvrg4vA7q6oDjjmOD5x+NvfoqFVEif4/1c5SWulFKZ2e4nyMRtgybfqStjfVky4iXgG/lSpb7xhu81pAhdPK3trJ+rTPbPufaWuCuu2J3TGKNWv3vU08PJw8UFwMf+hDvee9eRsj7ZczUgj/5HPCXJtKarE8pMBIlcAua4M3fKMUqIzJRXuQ1/LlxamtdubbBLCtzfgz/+dbuO3Ysv7u62BB3dbkEf62tXOHN31Daa4wf73qhxcXhCmLjxnAFUVPD2T0/+lF4w9be3ttHUVfHdB6DBvGabW1M0xGrDtavp4msqsopsfp6bq+pie4HaWri9poa5+cYOpT7S0rYm1+3LriCsGXYhrS8nFNybRmxEvAdOhSeabe4mHVYVcXjDx3q/ZzHj6fSiNXAxsqV1NjIKc3bt7NOd++mibCtjeVt2MD3MFY9pZNE+ZwGEEEjqZV0kum54KkGoY0b52aeHDjA7zlzuD1oGdHu1X9ORQUbnlhBapHXLylhY7VzJxuqoiJu27mTyq2jg8n37LkAV1+zjbL9FBe7Vdn8MkaLCh8xIjxA7MABNmQbNrCBX70aeP11Z3qJVgdVVVQk/kA5EbdsaaIgNOvnsI3xoUO9/RyJSFRGtGcZCnG7HUFa2W1G3HiBcn2JqC4tZZ0C7DCIcNRQVER5qqud2SraPSgZQRVEtlm1ikPpG2/kt7WxR6OviiRIEFq8YJtQiPLNmwfMnMnvSLu5vcbevVzsZu/e8Gu0tNCU84UvuHvdtMmVW1vLxnjiRG5vaHCrlLW09A6U6+kBvvhF9oRbW9mDPfVU9lZXrgR+/3suWWqD+wDuu/hiN/Lo7ubvyDn+b79N5SLC33b2SlkZ8M1vUqYtW6gghgxhYzV0KBt5Y1herEC56dNpQmtt5cjBTgudPp37I4PQmpvD/SBTptBfYKPBOzr4O3L0EO9dSVRGtPdh0SLg2mvpXzh4kD6HkhKOPtraoi+2k0rHp7OT7xrA8mtqWNc7dvD3tdfS39HaynpubdUFf7KAmpiyib+nau2oixdHn2edalBRvCA0IHGwzZw5XJgmni12/XrahSPLWLUKuP12rslQXs4eeyjk7vWyy/i3CHvjU6a4f/jhw3m/xx7La/sD5WbPZkPd3k6Zysu59sOaNZTHZmu15QC87owZbsGfgwdZ5rhxlOmhh4BvfYsK4sUXgVNOodIqLWVj+Ze/uPudNInl+2luph0cYO86MlAuFOLvZ591vfF3vjO8PmfMoNKwdX344eFlfPnLwEc/SrPSrFm9lUOQdyVRGbHeh9//ng0yQAURbVnUoDLEo7qa2XQnTgxPvHj99e5ZzZnDKcq64E/WUCd1NrErlE3wZRXZvp2zdSLXYc5GNtZUiOfA/dGP2LNeupS2+o4O4AMf4Gjg+ut5H+XlVCAAG22beHH0aDYy+/YxGtfGLNj7f+opN2upu9utay3ibOXvfz9wzz2U5+yzqXTslMutWznyGDkSuPBC4HOf4z0A3NfZCZxzDhu4//5vrkdt6/zgQX6GDHEJ/t5+2ykr67PwO5ATOZmzEUiX6QCwdF0/1RT3SmDUSZ2PBJ1nneoqYYkcn+kgVhnr1lH20aNd5lLrmLUL8nR2slHu7uZ5mza56Nx9+6hciorY6L72GpVLZyd7skuWcGprcTEblE2bnGnI9jzXreu9+E93Nx2fRUV0fJeXc3pqR4erkyOOoBK/6ioqbJuiw2Y5LSnhMcOGUcb9+zmiaGujb8AqKb8DOZGTOdVnnczKe30tIx0yBCGLKSSUYKiCyCZB51mnGrBjnZJNTa5n2xeHXrzpfrHKsM7T+nrgqKPYwNue9Wc+w31dXbyuCOvBGNe4WpOSXRuhtZWJ+Gzj4585NWMGTUw2jbdl1iweZ53tK1ey53/oEEclQ4aw0bfZXP33UFFBW7idyWIDw9rb2dCPHw/88IdOYZ1wAu+nuNhFGk+aFC5LcTGfob0362RuaeH9AYmf9ebN0U1MsYLgbHCdXaUvXlBj0GmdiWQIsrJeIjI9g2gATGFNJ6ogsk2QXlKqATs1NewFf+974YFRyYweEtmUY5Vx8CAbXOt8nzED+MQnOKK46y6amV5/nQ16ezuPt9NPhw+nI1iEi+wsW8Ze/9atNAktXRquOAcP5pz8ujon15gxLNPWrTUDWQ4dcrNhhg6lI/zHP45eT4sWAVdfzQR2xlB5XHlleD1OmMBps5Zp08IbxilTqEQee8xtO+ss3redVtvUxFFFeTllXrQo/FnfeisdtH4Z7Ypz0YLgrrySpkz/szvtNDrcrb/o5pt5rv85A4wbOf743u9aIhkSJW7MB3KdLLAAUR9EPpPqinJFReFpJjJh1/aX0d7OfcOGOaf1G2/QBr9yJXD00W4qo13NrbiYTts33uDvadPYEA8axCypNTVULocOsbf9058Cf/gDlUhXFxtekfC1Hv7yF6bTeO01BqnZSG3bwz/lFPbyzzuPSfvsiCKynurqmJa7pITKqKuLCicyiM1Ou+3pcSuxRfog/Md0dQFnnskRTlkZneavv856GDqUCsI2XIkSAlpfUEUFFcyhQ1SoF11EGW3Q2/79VJR2tHbEEeyA3Hgjz9u/n8+osxN4z3uSkyFo0GQuGUCJ+IKQ7hXllFzQl/nkgLMJ19SwEaqu5jY7GyXo+WVl7N2WlcVeza2mhr37igoXM2HNQVu2sKdWWcnGcfVqF+xke6JlZfQJnH02G6Ff/AK47z7gk5/kdceN43E2Ed9vf+tWh5s/n+UMG0Y5Ro3i9fbu5bEvvcTj7HrU1idSX0/ZPvtZ+jQef5z3E7mam/WzDBnCxq+qKjyZX329Uy7WVzFkiMsdBTgfhB3x2ISAO3awnCefZNTwvn1sfIcNC1+ZL1FCQCtjVRVltPttckLrG1q1irKNHMnv1auprPfsAV54gXLU1bn6SkaGICvr5RpNxNcn1MTUH/H7MFpaOEe/vR245ZbwnmE06uro+K2ro4nH9noPPzz6am7r1rHxsb33WbNc4ra2NjbMNquqVQo2CZ/NENrZyX3V1W5K47x5zqE7dCjvo6mJownbM37tNRdIZUcR7e0cVbz8Ms1UdqRRVOTMKEceydlQIiyru5sxG0OHhtvOm5vZ2Dc2uhQcJSVsIJct47dVfv5Ibb+vxx/oNmgQTUsdHZy99sYbbLCt8lq6FDj9dNdwhULhPozSUu7zB8pFJhy0o7KSEncPJSXhPXs728s+P7/yrKtzwXCRMsRKSlgISe6SkVH9FP9BRxD9EevDaGhg7xQATj65d+80koceorni8suB555zEcKxypg4keacjRvZIx8zho1RQwMb1ro6Opgff5zmpro6mnZaW/k9Zgxl+dvf3Owk+w+5ezft+9u2UQHV19NEZe3/lZVscOfN4759+9j4lpW5VNPPPccZUN3dTjlMn+7KKClxI5ODB+nwtjK0tAAPPMBptiJs4Hftonnq/PMZcHf++TSJbdjA+9ywgaYjv48iMtCtuZk2/tmzWcbWrWyMGhpYh/fcw3u2DdeUKcCHP8z9e/bw+8MfdiYsGwRnA8ja2lheT48LevvSl4DJk2lieustfk+cyJFWezufy/79buR1//29ZbjySo4Q9+zh95VXOhkSBV7mA0FlTCaQdQCgPoj+zIYNbCymTnVTNaPFXQDhcQ2lpTwXoJ1++HA2DP7z6upoq96/P9yscdppwHe/ywb7Ax9wM3y6u9kgL1lCeQ4c4IimsjJ6oj1rL+7spMwlJTSNDBvmeoA7d1L5WN+HVQKzZtEHsWULr11cTPm3buX9nXQSzVgivFebhuNjH3PJ9hobXcyKTTmybx97/VVVlKGxkT3w6dNpYuvupiKIFm+yeTNNOTbh4VNP0czU2cl7MYayhELRk/nZHj7AciLLiEyDHZkk8bLLqMSsD2L6dNbn66/z2m+8weuWlfWWwdrv4yUlBAqj5x1PxgHkp1AfhOL8DzbZXrxhtT+uoaSEDUV3N3uiHR29z9u2jY1NeTkbnPJynt/eznIbGrh91CieN2oUf+/fz0bHBqdFs1vv3MnvvXs5Anr7bfbsjj02fP3pE06gfDYFRHExf9fXA48+yhQgK1bw+iecwAC59nYqi2OOoQwtLbyPU06h8rMy+E0SpaUun1JPj7Nj2/IqKlwKjlgLDtXUcORQVUX5jKHvxcZ8lJaylz9rFu/HXsM+lxEj3CdaGTU1HE3502dY/1VjI8s9+2wq7bPPdov9vPvdLoajrCy6DNZ+P348lZu/nvz01WeWTeLJqH6KXqiC6M8kM/T327KLi50yOHgw+nkTJzq7vE2LALi1Cqzd2sZHWNt4NLs14BriHTs4snjlFZp4tm9ng97TQ0eqdb4CNHHY0Yc/FmLfPiqXd76To5uXX3Z+kQULaJL5y19ogjrtNJqKjjwy+voFW7cC//u/PNYmF7Qyd3e7WVz2HqLFm1izxS230I9SV+eS751+OhXM4MH0Rxw6FH6NRIn2gmDruqODiqKjw8UbhEJUGoMH8+9oMgRNzFjoDJT7TAJVEIVMouRodXVsmL/2NZqHbrst3EFtHdF1db1t2QDw7W/TVHPNNe48W2Yo5BbBKSpiw/OrX9EcAjjbe2srG/jW1vAkc34/yZtv0o8xejTn21dWupFJWxs/3d3MuVRRQRNVSQl9Jl/7mnOgdnezgZs0iT3dbds402nfPk6NXbKEjV9bG8uvqQH+3/+jEnriCcrgV4RTp/K+jjmGKb3HjKHPwZq3OjpYNzZoLloCOZt/6+BB1ltZGU12t97Ka61c6ZzLGzf2TqRnn4tNQtjUFD1Jnf9ZRr4b0ZI3LlrEjzWjzJ9POWprgyXz66uPIdVMxpnMhFwIvpQso7OYCpVEQT/xVimLt98mQ3vsMfoS/IFRp54aXqZdPMim3q6IWGRwxw42xnbuf+Q0W9vjX7GCZqS//pXHHnUUe/62N9fezoZx+HDK8/TT/D50iOaCMWPYALe00GRSW0uZOzpoY9+1y/kiJk6ko/6yy1wGWJvPadkyKoS77+bvxkY6Ke0sLREqigcfZB1Ye/9Xvxo7gVxjIxWTzTALUPFcfz3Nau97H+/llVco/zHH9E6kl8hPGPksr7qKzmj/uxEteeO55zp/y/btfLbJJvNLhlQD1bIR6KbpPsJQJ3UhksiZlihZX9BVzPyBUe3tNEUMG9Z7hbeqqt7XWLPGBYjZqZFdXZyxtGAB7+MrX2HD/sAD/O1fnGbqVBehbOMeiorY6x4+nNsPHKAymDqV9/3mmzz/7LPpf7DprYuKeB92Ou2ZZ1Km9vbwbK0AlcCzzzJALto92EC4yZNdI9LSEltBPP88lQDgzGEAG/UnnuCo6emnef1Dh1g3In1/lk1NfH7+QLl4q7GFQlTct9wSPgEg3c7ZbCQlVAKjyfr6M4mSoyVK1hdvfyjE2TZdXS7B3KBB3P/yy3Q2FxfTjNPdTVNQtDKWL2djbYOrios5mrjxRk7xPO88mp7+/W+nFGyeImvrB9hwFhfTCdzUxEZh0CBuO+II9nytg3rSJM6+amigw3bqVDZ89to2jsAGqdkV5/wYw4borrt4bTvysLOxADasq1fzWtu2UZGVlLiR2OmnO+WxYgXPscrPfm/cSHnq653SKC6m8t2zp+/Psry8d6BcrNXY/vEP+lYaG2nqOvlk1wCnM5kfkJ2khEraUQWRKTI55S9R0E9k8FSkY9Pub2wMn4La3MwFfqzvoqGB59ue85AhrsF/8002dtbZGpkQcO5c7rfbbTDbEUfwmvfc4yJ0/dgR7Te+wVw/xcVuKmpZGWcCDR/uRjHWEW4V1dix9LdMn877uPVWNxPLXru62iUQjMaIETRDXXONUz7+NBn//CfroaqKCqCoiPd16BBw3XXAI4+4hHk2WM8mILTfJ59M/87Pf+5GaEceyd6+nTXmf1axEi9G7rcrzvkD5exqbAcOuDWpi4o41dbOMCstZcfg5JPdrKp0OmdTTehXCMF4/RB1UmeCTAfbJHKmRTqcI52nNTU0s9go6HXrOJtn8WLa4TdsYCPW1OSW9Jw5k6aMjg42Qt3dwMc/zlk+a9bQrHHVVa6M2bO53kJHh5sVUl3NEUNLC7fv28fefCQzZ9LWf9ZZbNT27uX02HPOYZ2KuJlNN9zABm/HDsp74408NxRyifKamnhMe7tTMNOm8drRmDOHvdXqahektm8fv484gj3vJ5+kic3OzurqotKor6dcdunSv/+dIxnAKajp06kM7KJM55/Pen7ySSrO9et5H6tXu6SImzdHr+fI/Vu3MojNHyh37bXApz/NuJA1a/j98Y+zXl56iQsltbZy++OP9w5aTAc2od+SJfRvJVuGOpBzgvog0k02baWJRimRwVP+7Sed5FJDd3byWiLsPW/bxsazp4cxDTYO4IMfZDn19WxwbfxBtISAth4OHmQD1NTE64wfz+PHjGHjGcmCBTznHe9gI2LzPAEuQMzO7bf3/cwznEFVVkazmHVeRibK6+hgQzh+POUZNYrOcf8oJhRiwFxpKVea+9CHnGmpo4PO5iFDeJxVctXVbPAPHGDdXXWVW/SoqYnHVlSwAZ48mdezQYf+Z7Fnj5uue/75rM+bbqKyiJV4MVZixptuckoO6H1MczOd1jYi/c03WQ8XXOBGKpnwQaSa0K8QgvEKAPVB5Ips2koT5c6vqYme4tvarf37mprYMNmV2kT4qapyimL/frd+wwUX0MHrv8b27e4+bT0MH84GdfBgNtidnTzfv6qen40b2Shbk1FLC5VJcTF72du20b/gzyr74IMcLdi6/vGP2fD4F+vp6eHIxxjeS2MjZzcVFVEp2rxRLS10LM+fT8eurafmZh5vg+Xa2pzzuryczvKSEp732ms81/pRZsxwJqr6eo5CbMNtn8XQoSzf5muyz2HbNtZZ5CqEkfUcud8GtgGULfKY/ftZZ3v3umdqAxirqsLLSAdWTruWR+R9BCXT60UoYaiCSDeFYCuN5qOoqGBDtmVLeO6i2lo2bNOm0XRx551s7P7yF+c0jnaf1dXcv3QpG9aWFjY+kyax8bO+i0je8Q42XNZv0t7OXr/NMhoZIOZXyHv2sHFuamJv9eKLXaK80lKXEry+3mWILfKsrHaWUyhEU1ZpKU0+dkGh3bvZgFv7fHExRzc2H5RdJ/u445j7yK6IV1LC3vmQIW4k4m8k7bOwfoFDh9y04aIitz52vHpO9L5FO8Y2tIcfzuf7zDOUr6IiM+9sIfxfKL1QH0S6iWYrvfxyNmTxgnsyEQC0eTOnUtp5/hbro7DTQJubuRjMD35AE8/RR7Nxq6hggzpiBM/73e/YyI8fzymRAHuf8WzCra3OuWsdtCecwN6sv6EE2LMfO9Y5x488kr3p9etp2vHb3i1WEb30Env+3d0u9cUjjzBGob2dPWa7FOqYMW5UMHu2W9+gs5PrWduRQUkJHeXWTAi4FOOtrWzUp0+n3NOns04ee8wlDBw2jArE1uG4cVSQ27e7mBD7LFpa3Mjkne/k3wsXcr8/oLChIbye7fu2dy/9O3v39n4OkUGJDQ0uUM4q4Fmz6CvZtKl3GZbIYLxI4r3D6fIhZCPQLpPBeAWGjiAygT/YZscONqzxgnuCBgAlY3+NtwIYwNHCsceGB0ZZp+m6dVyYp6qKDciIEUzmtnq16wnPn8/9119PpeGXqaWFs3uefz68MenoYIK/8nIeM3kye+aWoiI2kEuWcMSyfTuVkZ3qaoPJ/Pd+ww0czdh0GxUVbISXLqUSOu88Bs+tXOlMSLt3UwGdeCJHK6tWsdyODiao27zZpTj/xCfoTL32Wpdq/OijOVq57jquCvfSS66ehw1zqcUHDXKjltpat3a2jeOwWD9gWRnl+vjHWa69z0R+wvXradLyB8FFvj/RrhH5nv7mN/Ez/cYLvAzyDqcahJaNQDtddS4MdVJnkmRWZkvk1E7mxQ26AlisMltaGGn81lvOsXnwIJXDkCFO7nnzqFAi5fz2t9mY79nTW7ZTT+X3mjU0PQHhAWRXXeX8FWvXskG2QWr799PfMHIkG5gtW5wT2q6ZAFAuY/j7mGNcig5rPhszhrOZ9u3jlFW7GtuePaxfm6zu8MMZbR0KUTkuXhxe/6NHh6/m1t5ORXPhhcDPfubuyfor/NM7X389fEW4WIFwqQZFBnnHUi0jGxMzshFoN4CC8TSbaz4QJDtkkGNsPp/KSjd9Mt66DkFXAItXpk1dbZfRFOGIw05zbW9ntG5kuudvf5sjh4MHo8s2fTpHBkW+V89eH+A+u2RpJHbRIzt9+JFHKF9ZGc+3c//tqGXyZMpkI6mtk7ahgQFiu3a5uAEbSGcVw4c+xBGSrRPb+/XntOrspEls+3Yq0+3b+fvKK6mMH3iAacXHjKFCsyOt6mq36ly0QDh/ttZEzyrR+UGukWoZ2ciCmmoZ6fpfHGCoiSkWqU6na2lhLwtI3oFoj7HTVO1U1MiZUW+/HZ4TyGIzqba0sMGzUcCRmVRra11+fxvYVVvLxm3tWpcB9JhjGBldXMx0G/X1vObxx4dPpd21ywWOVVa6+/dzzDGMI6ipYVkWO5KdMIF1MHQoG+G33nIJAUV4XmkpzUSvvMJtdoUzmy/ppJN4j83NPHfjRiqskhL2+tvaOJIpLaXdfv9+t5yoCO/Fn+Lc/y7YmUEtLRyB1NZymx0d7NrlpvPaEYJNPmiD1NrbYwe6RQbCpRoUGeQaqZaRjdXaUnVy99WZP8Ad6aogopFOW2dTEz9VVe5a0RyIixez0bfHPPmks/kWFbHh9OfK2baNtmq7HoLfJmxXIbvvPtdofvKT4ZlUQyHGDtj0DxdeyPIOHHBJ3Wz08VNPMYFeVxfNMKWldHD6ZezqcutAdHa6+7WzoQDmNzrnHJpfioporvKPNEaPdum0Fy1ijqJnnnF1MHMmG+BnnnEjm6OPpuPUxg/MmOFmPy1aRAXzwgvs3YvQATxihFOoM2fSfGSXRZ0+3TUMCxdSuUS+C8a4xHZNTayToiJ+7MjCYp3QN9zgFHZk0OJVV3Gb318UmUk18v2IDIq84QbKEnn9INdItYxE50f7v0j2/ypoGamcn2oZ/RD1QUSSCVtnQwOduXa95Vjn+ZO/Rdp89+/n7B/b6L74Ip2d8RK4RVuFzNrTTz+dppmyMvZqDx6kWeXtt9mYWmew7dXOnQv89rduDYi6Oo4m7H3aQKuRIzmTxk5NtVlfZ86kgrvtNiqcr3+dPgGA9zFsGI9/8EE20rYOrH3/0CH6Jay5yE5f7eriYj8tLWz4KypcXQMuOKunh8fYtCJ2FtbvfucW77HOZSuDPT/yWQI8X4Q5m0Q4A6izk3UVbUW5WEGLsQLdkl2tLdb1kzkm1TKysVpbOkb2ic4fAMF4GijXVzKVVOVPPpoAABuqSURBVMwuyBILfwDQunXRE7R9+tNuKuKrryZO4Ob/J965kw7ZZ5/l+da0ZNN0NzbScWp7/nY5zupq9q63bOFo4atfpYnpu99lz/ngQTdzp6fHRTDv3ctrfOADwGGHUQYbGHX66WxIv/pVt/CQldHGItg6qKrivvJyKobmZhdEN34804KsXct72LePIy27Wt26dVQidjrtiBGU4aKLOCtn506WMW2ai1mwjupQyAWY+evZpgaZMMFFY+/bx0Zl0CAqQv8IwhIraDFWoFvk+9bXoEhLOmbnJCojnozpCiBNNVAuyPkajPcfcqIgRGQLgIMAugF0GWPmichwAA8CmAxgC4ALjDENWRcuXbbOujrXI4x2frxeirX5WkcmwN8zZzobfEkJ/8GsjyFaArc9e9zUx6IixkTU1NAX8eyzvL5dEQ5go97U5Bq41lbKOXo0jxsyhLESZWXcVlTEe9i/n8qgooJmrMMOc/6FsjJe05axYgWnpR48yM++fS6SOdo97NvHaxvj1oIePJg+isZGZ36qqKDSWb+eSuOBByj/smW8Rnc3yxkyhL6Td76TU19ff50K1V/GxInhPiT/s7TPygYXlpRwBHT00S7qPBmbdTbs3v5JDraMxYvDe++Znt6ZrvsMMlJS0kYuRxAnG2P2+X5fD+AfxphbROR67/fXsi5VOmydM2YA3/9+uE05mSmrdn3hP/wh3IcQmWzvl790PoRrrgnfP2oUZxNZjj2WZdmI4yFD2LDbGT+jRtH/0NHhIobtLKnNm9kQLl3Kc4qKnAmrvd2VUVHhltO060//4x8sq6SEs4quu875TSoqaOO3K635be+x6uDDH+Zx27a53ufGjRwBlJUx8O3OO11A3AsvUDFazjrL+RUOHeLnwAGXWmT6dN6vjV1Zu5YxICLuWdrEhjYtd2MjFzsaMQL4yU+S7xFn2u6dqPceRIGkSjruM1EshpJ2cuKD8EYQ8/wKQkTWAzjJGLNLRMYCeNYYMzPedTIaB9FXO6S1/0fOjU9mcRYbx2DXGLBOYBvHYMuwAWR2Sqq1fb/2GlM+AOFrNn/kIzRlPPccfQaAmwLb1kbzVX09e86292ynnxrDHEL2WBGaW2zDWlLCfffdx0WBKisZQGZTbdu8RMXFlN8Yt0bE0KGuzmw9RCbas07pF1/kyOOHP2QdPv64k2HsWMo9dy5HW7W1wP/8D8u1KcNtPUydymdz992Ub/BgljV4MAPrxo/v7WPo6nLPEmBq9DVreE5XF6+zYEHv2JBMvm9Brx3P/l9by4Y30sxlEwqmW5ZU/q/ixXsogcn3OAgD4GkRWS4iV3vbRhtjvMgp7AYwOtqJInK1iCwTkWV79+7NnIShEP85kv1ntfb/qioqCDt98p//5D/pDTew4bS982hzrW0cg3USh0LhcQy2DGv2qa4On5f+0kvOQWxzBwEc2ezfz49Ne9Hezk93N//h7D8ewHPt9FGAPW/rxN62zV2jvJxlGONSaFvFZH0IVonYlBu2vI4OmqBWrHA9clsHVrbWVqdor7uOayisX8/rDB8eft2ZM92UzO3b3X1Yn4qNarb13tHB/Xaqb0MDRxWVle5eSkp4P/ZZ2gR6dt+gQW6UZBvAZOnr+xb02vHSXPjNP0Bmp3em+n8VL95DSTu5UhAnGGOOAXAGgIUi8l7/TsNhTdShjTHmN8aYecaYeaNGjcqCqEninzMO8Nva/ysr2RMtL+fqbHathMh/RhvHYBPa2WAuG8cQrQy//f4972GDaBfEsfP7zzuPvca5c9lQdnby09XF3zbthR01WPOVpbPTLeBjj7H7Ozs5Anj3u11v3UY3Wx+INeHYqb9WbmvLX7/eKTO7Yp2ddWRHSTU1rg5XrGDDPno0G50TTuD35z/vygWcicrWw/jxrt7LylxddHe7/Ek2FgMIX1DJ1rPtAdsRkh0lhUL5OW8+WqCfJV15kjJJondeyQg5URDGmFrvuw7A/wFYAGCPZ1qC9x0jI1ieE22xni9/2dn/y8rYiLa00BRUV9f7n7Gmhj4Fm2Suo4MjDxvHEC3Znn9e+vz5tNcb42IZPvlJbq+pAa64gnLYpT2Li9kYlpW5UYttXK1PYtQo18gDbtTgn5753e/SB7BwIWW3M32qqij71Kl0II8fz+vZwDERNtD+GUDV1Yy96O52AW/DhnHEYOvQritx6BCntba3s+xjj2Uj+Otfc8aSDaYzhvEen/scRwpNTZyWO3Ysyxs5kiaiL33JTS2eO9flN9q/39VzKMQ4i8MPd6Oyww/ntmgNazoTwPX1WvF67/EUSD6QaBEsJSNk3UktIiEARcaYg97fHwBwE4BHAVwK4Bbv+5Fsy5Y2zj2XieDsbItQiL1dO4Njxw43s8amabD/kH4H9hln8DpnnOGUg2XGDDZm/mR7fu6+mz3pl17iiMJmIQXoSxg61I0wrJlo8GA2toMHh5tJysvZiNolPq1Jx5qgrKnJMnUqzWlPPsmYi02bqAyuuYbX37qV17KmrT17OIOqrMyNIKqreX3b+wfCczaFQryvj3wE+POfqQD82KmKf/wj8NnP0sQ3aRKT2t1/P4/51Kc4Dfd733OK8MILWa+zZ1PGW26hnbunh0rllVecY3TOHOZqsplZY8W5pHOGUCZnG+X79M7I/ytVDhkn605qEZkKjhoAKqg/GmNuFpERAP4EYCKAreA01/3xrpX3yfr82GRvBw4wUGzsWJdy2jrbQqFwZ2JDA3umt90WPbiqr0FHmzdzWc7OTje9s6SE29audcn5iopovrHO2QkTqMwANwIZMsQl0+vsZIP8yCPsnT/2GBv18nI27NbOf+AAy7MmLf/I5IILGERXWcmpqDZNiFUMZ5/NMktLmUb9d7+LXw+2QbVTXufPd+tN2KC3WKvirVnjHOX2Hru7qexnzw723NOZAG4AJZNTMkveOqmNMZuMMe/yPrONMTd72+uNMacYY2YYY05NpBwKDjuE/9SnOB1yxAj2oAcNcs62yMVvXniBI4AvfjF8XetUk4rV17OxFnG9/4oKNoQTJriMrSJszA8dooJobHQpry+91PlJGhtdxthf/tLlHmpvd3EYFRVskIcPp0Jqa2MZNtrbLkXZ1kZF+u9/O9NWKOTWfW5spNnottsYbxGvHvzTN0eO5DXWrKFMlZXOVLN0Kc19S5eGO8qXL3fJAAGnFJcvD/7c05kATpPJKVlGI6kzRbTpfKEQ5/b39HCaqe0ZDxniTFF2Js3zz7Ox6u5mMNdNN7nU00GD8WxQ0YgRLkVGKOSUk10QyNroa2t5zNixNJt0dzszlF3feNQo9lqfesolybN+COvststwirjZSnakMnq0myVllzkF+G1zTjU301xjndwtLW6UM2kS58Mff3zi4Ct/g9rRQZna2tzaD+XljBAvKXF+EL+jfO5ct5a136w2d27w9yCdgXCpXivRFNNCSDFRCDL2I1RBZIJ4duJQiKkdVqxwsQHTpjn778KFXOhnyxY3K6a+nj3cnTvpe4gXjGf/gZ55hlHPLS1siI8+mn6KhQtdDiebNgKgs3f6dPpG7LKXdoaOxU5Zrayk6WvUKGd/ByjH6tXctmABG+CmJhdMV1nJhnbcOCquIUO4H+AxCxa4rLJz5tDE5A/2GzOGTlabimL8+PjBV5EN6uzZrMf6eo7cPvUpmp3efNPN2PI7ymfPpk/ivvucM/2TnwxuXrLPO12BcKlcK5HvohAWyikEGfsZmqwv3QQNSho2zM3waWgID0p69VU6TwcPZi+/o4O97ueeo5/AH4xXUsJ9XV3AHXcA997LBvDZZ9mg2jQXPT3A+efz++KLgZNPZlnRAul27aLjF3A9f4AKZuxY3k9jI4+zpiJrqpo6lY1vSwsbcptl1cZlXHIJj/30p4Hbb6cS2buXjmxj6HhetIiN9THHuOmp/mhqf0CdrfNYvcrIhX4uv5ymqcgFh+wIwe9jsM+yrY2mtqFDe5edzHuRrp5vstdKdcGgfKAQZCwgNFlfrkiU1sD2agH2pKOZCayPwq57bAwVik1CZ4OGioo4Cujp4bHXX+/8Ba2tbHhtINfBg+ENIODs/1ZJzJvHa2/dyu3+WIeeHt6DNfVceimjmW0Us23A7XoMK1dSkVVVOae0laukhCOQsjKm35g8mYpv0ybew4wZwD33uBFUdzdHGNZXc/vtwRuFeMtcbt3qYjrsdN+iIm63s5g6OymfJVoivSCkc4ZQstdK9E6mK5FeJikEGfshqiDSTSI7cRAzwbhxbKxt5DTAIDmbwnriRNeQVVRwW1ERZyDNmsVrbdvGXteQIc5kVFLC7/e8x0UH256zCNdqmDyZo4+zz3aNvh0d3HMPFZddHOjWW1m29WHY5TSLithLX73aKTI7Y+rQIZp0OjroWwE4q6ijg3Vk79Fv/y8rc6vF/ehHyZtGYjWos2Y5k5Z/JlbkwkqFvoBMqgsG5QOFIGM/RJccTTdWATQ0sCFsaOitAObMoUnp//0/fkdr0G68ETjySPoLjjySv+01amoYfNfe7mz8xx/vzDFlZWzErV29vZ1l9PRQlvnzGZPQ2Ukl0tnJNagnT2YZJ53k1kOw5qXp0xmpPG8ey58+nT6DkhJnsx80iP6U9nbgG9+gzDZCubsbeMc7WCfz59PWP38+Z2m98AL9Fv56mj2bMnZ1UcauLuY+OvJIFyRmZynZeIyiouhLscYKLJsyhb4bu7BTZ2d4QGIhRBgHIdF9FMJ9FoKM/RAdQWSCRH6dIL3ev/2NK6pZJ/Rxx4Uf45+mCrBxtGmz29p43pgxPKeqiuskH3+8+4eyUdT289JLtPFaJ/bQoeEpM6qrw4fzoRAD0N56y6XknjuXU3JtOXPmAB/9KEdCkyZx9PCLX7gGeNcujoLsMp3z5oXf4yWXcP/u3byX44+njLbezjuP5a9e7ZThnDnhciaq6y9/2ck4a1bvgMR4JqpCItF9FMJ9FoKM/Qx1UqebdDgEbSbTSNNHZDZXgP8s1ryzaBGjhNvbOXI58UQ22pFl2ACwoiLnwAacA/jKKzlaiBcg1tLCHv3Spc4E1NbGRt6fzdTvUAXcvXd20qleVMTZUDZmwt5jZD01NABLljDV9rBhvJ+dO5njyi4mZGMxYl1DHZuKAiCPA+X6PYmCmYIEO61bx23+ILTOzt7ZXGtqaNKZMYON5qpVwAc/yEZ03Dj2zG1QmL8MGwBmneU2gvjAAVeO9VHYSOaSEue8tvdh11geMoQNtF1DwpazahUb6Btv5PemTc5MsHatc77bZUT9GWsj68kvi603uyyqDbCzGVftQksaWKYoKaEmpnRjnWkNDW52ULIOwUmTnFPXziAyhtuB6CvO2ZQWw4ax0Rw82KXI7ugIL8M6gK3vwDqSrVnJJqiLXI/COm/tfYRCvHZ9vVv32c7Usv4Bu36E9Q/cdhs/dl1sm0IjMmNtZD35s7oC3G7TnQ8eHL72hs3wqY5NRUkJHUGkm1CIPfglS5iLyJpFknEIVlfz097OhrO93W0D3Gpra9fSXLR2LR2+VVUuzfXs2WxM9+3rXYZ1AFv/g10MqKKCx82eTTNTUxPPb2rib799PhSiw3j7dkYfr1rFUYDNZtrYSFPPvfcy8vnee5k+w/oHjjuOI4uOjugZayPrqb2dSfXa2129XXstgwrb2qik2trCM3yqY1NRUkJ9EOnG2r1tbiE7hz/S7h1vbV27Ipxdda27m6OJV1+lIoi14pxNlGcdsldcweC7WA69NWtobrJTaO1x9h5smgy72pr/HmIF6z3/PO8naKK7zZtjO4htffqdktGCxBKtU6zpGRQlDA2UyxXW7j1mjNsWGVyVaGaNtcPbuf/WtLJuHRWEXXHOLmhTXs5euDHJzfKYPTt62gh7D5FLUPrvwb9yHkAlsnMnt9fU0F9RXNzbd2CD0CxTpkRXDJbIGIZoMQ01NfFTP+d7GmtFyVPUxJRuEi3faG3z5eVs4MvLe8/dnzvXOYltltWSEpckLt6Kc/EWhUnXPQDOD2LjMJqawlf4mjXLzS6qruZ3eXm4H0NRlLxGFUS6SWT3bmxkltaXXmJCvZdeCk8xDYT7CA4d4vc117ietw3wimW/z/Q9AOyxX3UVTURr1vD7qqtcTz5REJqiKHmP+iAyRSy7t7XdDx7sZtbYBYMizSTWRzB3bnRTUCL7fbruobTUpfv2xzd85SvhS476F9vJloyKoiSN+iByTSy7t117eft2xh0UF4enmPYTy0dgSWS/T5VQCNi4Mbq/JIifIhsyKoqSMVRBZJvqai6aM3Fi+CynfJyb71+RzY52bCyDxhgoSr9HfRB9JVYCuERY+357O0cQ7e25m5uf6B7iRSIPpBiDvj5rRYlGAb1POoLoC6mubJUPSceC3EOiUUI+3Eem0VXMlHRSYO+TjiCSxW92mTCB39FSTCciHdNR+0rQewgySsjlfWSadD1rRQEK8n3SEUSy9IeVrZK5h4EwSohFf3jWSv5QgO+TjiCSJUgQGZAeO2NdHbBsGb/7QiwZgt6DpT+PEuKRbD0pSjwK8H1SBZEsQcwukWmuV69OvpyHHmK8xMUX8/vhh5M7P54MA8nBnApaT0o6KcD3SQPl+kqsQLh0LFKTTDBdLNmCyKBJ7IKh9aSkkzx4nzRQLtPECoRLh53RJsLzX6OpySXCS0RQGTSJXTC0npR0UkDvk5qY0k067Iw2EZ7/Gv5EeNmQQVGUAY8qiFikGgiXip2xpoYL37S2MoV2a2v4QjjZkEFRlAGP+iCikY5glnTYGRMthJMNGRRF6XeoD6KvxMs/lEwjmw47Y6KFcLIhg6IoAxY1MUUSL/+QoijKAGJgKoh4AWjq4FUURQEwEBVEogA0dfAqiqIAGGhO6mQC0NTBqyhKPyWok3pgjSCiBaB1dXF7JAM1/5CiKIpH3ikIETldRNaLyNsicn1aL55qAJqiKMoAIq8UhIgUA1gM4AwARwK4SESOTFsBqQagKYqiDCDyLQ5iAYC3jTGbAEBEHgBwDoC1aSvh3HOBE09MLQBNURRlAJBvCmI8gO2+3zsAHOs/QESuBnA1AEzsq2ko1QA0RVGUAUBemZiCYIz5jTFmnjFm3qhRo3ItjqIoSr8l3xRELYAJvt+HedsURVGULJNvCuI1ADNEZIqIlAG4EMCjOZZJURRlQJJXPghjTJeIfB7A3wAUA7jLGLMmx2IpiqIMSPJKQQCAMeYJAE/kWg5FUZSBTkGn2hCRvQC2xtg9EsC+LIrTF1TG9FEIcqqM6UFlTJ1JxpiEs3wKWkHEQ0SWBck1kktUxvRRCHKqjOlBZcwe+eakVhRFUfIEVRCKoihKVPqzgvhNrgUIgMqYPgpBTpUxPaiMWaLf+iAURVGU1OjPIwhFURQlBVRBKIqiKFHplwoio4sO9RERmSAiz4jIWhFZIyJf9LZ/R0RqRWSl9zkzx3JuEZHVnizLvG3DRWSJiGzwvoflUL6ZvrpaKSJNIrIo1/UoIneJSJ2IvOHbFrXehPzCez9XicgxOZTxJyLypifH/4lItbd9sogc8tXnHdmQMY6cMZ+viHzdq8v1IvLBHMr4oE++LSKy0tues7pMGWNMv/qAKTo2ApgKoAzA6wCOzAO5xgI4xvt7CIC3wEWRvgPg2lzL55NzC4CREdt+DOB67+/rAfwo13L6nvVuAJNyXY8A3gvgGABvJKo3AGcCeBKAADgOwL9yKOMHAJR4f//IJ+Nk/3F5UJdRn6/3P/Q6gHIAU7z//eJcyBix/zYAN+a6LlP99McRxH8WHTLGdACwiw7lFGPMLmPMCu/vgwDWgetfFALnALjb+/tuAB/JoSx+TgGw0RgTK5o+axhjngewP2JzrHo7B8A9hrwKoFpExuZCRmPM08aYLu/nq2AG5ZwSoy5jcQ6AB4wx7caYzQDeBtuAjBJPRhERABcAuD/TcmSa/qggoi06lFcNsYhMBnA0gH95mz7vDfHvyqX5xsMAeFpElnuLMwHAaGPMLu/v3QBG50a0XlyI8H/CfKpHIHa95es7egU4srFMEZF/i8hzInJiroTyEe355mNdnghgjzFmg29bvtVlIPqjgshrRKQSwMMAFhljmgD8GsA0AEcB2AUOTXPJCcaYY8B1wReKyHv9Ow3HzDmfG+2lg/8wgD97m/KtHsPIl3qLhYh8E0AXgD94m3YBmGiMORrAlwH8UUSqciUf8vz5RnARwjsu+VaXgemPCiJvFx0SkVJQOfzBGPO/AGCM2WOM6TbG9AD4LbIwPI6HMabW+64D8H+ePHusCcT7rsudhP/hDAArjDF7gPyrR49Y9ZZX76iIXAbgLACf8BQZPJNNvff3ctC2f3iuZIzzfPOtLksAfAzAg3ZbvtVlMvRHBZGXiw55dsk7AawzxvzUt91ve/4ogDciz80WIhISkSH2b9CB+QZYf5d6h10K4JHcSBhGWC8tn+rRR6x6exTAJd5spuMAHPCZorKKiJwO4KsAPmyMafVtHyUixd7fUwHMALApFzJ6MsR6vo8CuFBEykVkCijn0mzL5+NUAG8aY3bYDflWl0mRay95Jj7gLJG3QE39zVzL48l0AmhiWAVgpfc5E8C9AFZ72x8FMDaHMk4FZ4S8DmCNrTsAIwD8A8AGAH8HMDzHdRkCUA9gqG9bTusRVFa7AHSCdvArY9UbOHtpsfd+rgYwL4cyvg3a8O07eYd37LneO7ASwAoAZ+e4LmM+XwDf9OpyPYAzciWjt/33AD4bcWzO6jLVj6baUBRFUaLSH01MiqIoShpQBaEoiqJERRWEoiiKEhVVEIqiKEpUVEEoiqIoUVEFoSgJEJGPiIgRkSO835NF5GLf/qNSyR7rZf4cmQ5ZFSWdqIJQlMRcBOBF7xtgds6LffuPAmNaFKVfoQpCUeLg5c46AQzWutDbfAuAE73c/l8DcBOAj3u/Py4iC0TkFS8528siMtO7VrGI3Coib3hJ566JKGuQiDwpIp/O4i0qSkxKci2AouQ55wB4yhjzlojUi8hccG2Ha40xZwGAiOwBo6E/7/2uAnCiMaZLRE4F8AMwmvZqcPRxlLdvuK+cSjA1/T3GmHuydXOKEg9VEIoSn4sA3O79/YD3+7EE5wwFcLeIzADTq5R6208FU1l0AYAxxr+ewCMAfmyM+QMUJU9QBaEoMfB6+O8HMEdEDLiCnQHweIJTvwfgGWPMR721P54NUNxLAE4XkT8azX+j5Anqg1CU2JwH4F5jzCRjzGRjzAQAmwH0gMvGWg5G/B4Kl3L6Mt/2JQA+46WERoSJ6UYADWASP0XJC1RBKEpsLgLXxPDzMOis7haR10XkSwCeAf5/e3dswjAQBFF0JlQrbs5lGFSDejJSCa7EgVEg2MSBsvc6uOiz3HGbx3lJnd8u6lfbd65T+pbkk+Rou+f6EipJnkmWtusNZ4G/+c0VgJEJAoCRQAAwEggARgIBwEggABgJBAAjgQBg9AXmy8THhOkd3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot \n",
    "# x = attack, y = defense\n",
    "data.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": 10,
   "metadata": {
    "_cell_guid": "412c1288-0384-4f86-b137-3a02f47bf538",
    "_uuid": "d69973d1928e9b4d24966525d83da9bddf9d9d9f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram\n",
    "# bins = number of bar in figure\n",
    "data.Speed.plot(kind = 'hist',bins = 50,figsize = (12,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "40aa131c-2144-4f21-a8b6-64272dcbd5ba",
    "_uuid": "1c7e526be074992e2ac541be7ebcca444771bd4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e4bed68>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# clf() = cleans it up again you can start a fresh\n",
    "data.Speed.plot(kind = 'hist',bins = 50)\n",
    "# We cannot see plot due to clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "90ac9db1-b3ab-4f5c-bd59-40d4d8ab24cb",
    "_uuid": "58490ca9aa6a4d4890ad263b7a8da438a074475e",
    "collapsed": true
   },
   "source": [
    "<a id=\"3\"></a> <br>\n",
    "### DICTIONARY\n",
    "Why we need dictionary?\n",
    "* It has 'key' and 'value'\n",
    "* Faster than lists\n",
    "<br>\n",
    "What is key and value. Example:\n",
    "* dictionary = {'spain' : 'madrid'}\n",
    "* Key is spain.\n",
    "* Values is madrid.\n",
    "<br>\n",
    "<br>**It's that easy.**\n",
    "<br>Lets practice some other properties like keys(), values(), update, add, check, remove key, remove all entries and remove dicrionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "a2294c36-2b0e-4e84-bb4c-3ed58a8d7c6b",
    "_uuid": "c06906f515397db73027b5ec33054669e15a46b9"
   },
   "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": 13,
   "metadata": {
    "_cell_guid": "54407176-0923-415f-926e-fb8d34afd615",
    "_uuid": "eba173f97bd538a285bfccf73a40b213ddc98656"
   },
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "24716949-62ce-4049-8e3c-953b2dfde104",
    "_uuid": "ebe6049e945e9d3f1e2933de0957004f76810655"
   },
   "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": {
    "_cell_guid": "f61ebed3-1c91-4420-9c97-73736923db7a",
    "_uuid": "f2d5359eafe4d8e6755d308a89cca6e613f18171"
   },
   "source": [
    "<a id=\"4\"></a> <br>\n",
    "### PANDAS\n",
    "What we need to know about pandas?\n",
    "* CSV: comma - separated values\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "b491c0e2-409f-4f09-b431-f8451bf2b1e3",
    "_uuid": "db353b7a004a3502c2bf00c1bf21c7b58b167e87"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('/Users/garvitkhurana/Desktop/Projects/Data_Science/pokemon.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "f6e53d0b-784f-4e95-8f92-f83091558044",
    "_uuid": "e2c3fe30bf5208c25f297b1a570d4e59066bdd17"
   },
   "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))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5dcb3a55-7d7b-4313-ba7f-f1bf7e6de29b",
    "_uuid": "8ebf125ce8fb94a3d150dc9e34ddd8679fd11b88"
   },
   "source": [
    "<a id=\"5\"></a> <br>\n",
    "Before continue with pandas,   we need to learn **logic, control flow** and **filtering.**\n",
    "<br>Comparison operator:  ==, <, >, <=\n",
    "<br>Boolean operators: and, or ,not\n",
    "<br> Filtering pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "18834766-d00b-42c7-acc2-574c93d2b257",
    "_uuid": "828d1147d691b169038625b7898c9921f9b10158"
   },
   "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": 18,
   "metadata": {
    "_cell_guid": "69545093-7fb3-480f-81ce-da43e454f6a1",
    "_uuid": "938e72fa22c91518109b392bca37d8bdf818ba74"
   },
   "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>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>225</td>\n",
       "      <td>Mega Steelix</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Ground</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>231</td>\n",
       "      <td>Shuckle</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Rock</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>334</td>\n",
       "      <td>Mega Aggron</td>\n",
       "      <td>Steel</td>\n",
       "      <td>NaN</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  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "224  225  Mega Steelix  Steel  Ground  75     125      230       55       95   \n",
       "230  231       Shuckle    Bug    Rock  20      10      230       10      230   \n",
       "333  334   Mega Aggron  Steel     NaN  70     140      230       60       80   \n",
       "\n",
       "     Speed  Generation  Legendary  \n",
       "224     30           2      False  \n",
       "230      5           2      False  \n",
       "333     50           3      False  "
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {
    "_cell_guid": "4a2f40ce-369d-47ba-8770-69cf9206dfe3",
    "_uuid": "2cee83f86893df74a7fba5815ee60e40714d37e7"
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
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       "      <th>224</th>\n",
       "      <td>225</td>\n",
       "      <td>Mega Steelix</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Ground</td>\n",
       "      <td>75</td>\n",
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       "      <td>334</td>\n",
       "      <td>Mega Aggron</td>\n",
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       "      <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",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #          Name Type 1  Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "224  225  Mega Steelix  Steel  Ground  75     125      230       55       95   \n",
       "333  334   Mega Aggron  Steel     NaN  70     140      230       60       80   \n",
       "\n",
       "     Speed  Generation  Legendary  \n",
       "224     30           2      False  \n",
       "333     50           3      False  "
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {
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   },
   "outputs": [
    {
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       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</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",
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      "text/plain": [
       "       #          Name Type 1  Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "224  225  Mega Steelix  Steel  Ground  75     125      230       55       95   \n",
       "333  334   Mega Aggron  Steel     NaN  70     140      230       60       80   \n",
       "\n",
       "     Speed  Generation  Legendary  \n",
       "224     30           2      False  \n",
       "333     50           3      False  "
      ]
     },
     "execution_count": 20,
     "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": {
    "_cell_guid": "bb8e4ba1-d92a-4219-8f08-84e2e044a980",
    "_uuid": "7d9ff0ae3ea190bb3da753591ca769fbace9c852"
   },
   "source": [
    "<a id=\"6\"></a> <br>\n",
    "### WHILE and FOR LOOPS\n",
    "We will learn most basic while and for loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "a0ad9c00-7264-4d72-a53d-6f6d9ade6077",
    "_uuid": "35212537f956657159838e77317e08c8b328b68f"
   },
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "db251333-13e3-4dd2-9352-cd60fb1dde93",
    "_uuid": "15f6f6fde5a78143f0550c7042e964cfb33f4026"
   },
   "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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "df56f9cd-0921-4a5b-90a8-1278a034aa8d",
    "_uuid": "404a758881c5ba93f9232e02663e8f26d7a45066"
   },
   "source": [
    "In this part, you learn:\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": {
    "_cell_guid": "b79aeead-29d5-4a51-a19c-c27a5e317eda",
    "_uuid": "3e304ee7d585dbe9abb7748fc7afc30d142dd185",
    "collapsed": true
   },
   "source": [
    "<a id=\"7\"></a> <br>\n",
    "# 2. PYTHON DATA SCIENCE TOOLBOX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bd7fe5fc-5a05-46ac-8e65-3fe067e2348e",
    "_uuid": "702f9ce8e973eace72ca4d4a7e4a8330d6728518",
    "collapsed": true
   },
   "source": [
    "<a id=\"8\"></a> <br>\n",
    "### USER DEFINED FUNCTION\n",
    "What we need to know about functions:\n",
    "* docstrings: documentation for functions. Example:\n",
    "<br>for f():\n",
    "    <br>\"\"\"This is docstring for documentation of function f\"\"\"\n",
    "* tuble: sequence of immutable python objects. \n",
    "<br>cant modify values\n",
    "<br>tuble uses paranthesis like tuble = (1,2,3)\n",
    "<br>unpack tuble into several variables like a,b,c = tuble\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "ba997f70-6e04-41ed-81e5-aa6b1db858be",
    "_uuid": "272bfdc274e6a06374a461db55d5c832387e45b4"
   },
   "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": {
    "_cell_guid": "0389d2b7-f453-4d37-ba31-f34e9a29bfab",
    "_uuid": "51a4502ebd55b73cbd8389ded11f83f99fcd7ea7"
   },
   "source": [
    "<a id=\"9\"></a> <br>\n",
    "### SCOPE\n",
    "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\n",
    "<br><br>Lets make some basic examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "e66efe45-d23d-42c6-ad2e-7d1c6c81d00b",
    "_uuid": "7b641a05d5f6713914b2c8b050c54e18c5b1c814"
   },
   "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": 25,
   "metadata": {
    "_cell_guid": "e2896a91-8bf6-4a03-b043-f6149b67b7b0",
    "_uuid": "5d3aa6ee7871085b2dbc6ee228dec83b69bf26f3"
   },
   "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": 26,
   "metadata": {
    "_cell_guid": "3c02f726-9949-4376-9c70-ef656a1d53dd",
    "_uuid": "29c42e58da5082f818302e6b6eed90a6cd68269c"
   },
   "outputs": [
    {
     "data": {
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       "['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",
       " '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",
       " '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",
       " '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": 26,
     "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": {
    "_cell_guid": "1112921c-cdbe-46c5-a47b-eedbbb5b24f9",
    "_uuid": "afc667bf0eef59ae6ea646ae52196d2f724ea28d"
   },
   "source": [
    "<a id=\"10\"></a> <br>\n",
    "### NESTED FUNCTION\n",
    "* 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": 27,
   "metadata": {
    "_cell_guid": "78decf80-3efd-446d-b8d8-1a6bbd1023ab",
    "_uuid": "4932231358d9afae2b978f133ed9facedeeeb0e3"
   },
   "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": {
    "_cell_guid": "8f18e20a-edde-43fb-8e78-8cb01f90c5df",
    "_uuid": "3274dc25dd887a8a5ff84e38749782fb89bfe5fb"
   },
   "source": [
    "<a id=\"11\"></a> <br>\n",
    "### DEFAULT and FLEXIBLE ARGUMENTS\n",
    "* Default argument example:\n",
    "<br> def f(a, b=1):\n",
    "        \"\"\" b = 1 is default argument\"\"\"\n",
    "* Flexible argument example:\n",
    "<br> def f(*args):\n",
    "       \"\"\" *args can be one or more\"\"\"\n",
    "<br>def f(** kwargs)\n",
    "       \"\"\" **kwargs is a dictionary\"\"\"\n",
    "       \n",
    "<br><br> lets write some code to practice  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "a236e2bb-35ac-472d-9cf5-1df78681595e",
    "_uuid": "bcd3af982d700b5a7bde40e22ab0a1adff14b917"
   },
   "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": 29,
   "metadata": {
    "_cell_guid": "6ed24035-8486-4d37-809c-845692deaa91",
    "_uuid": "6529482583c46658dfe3906243600ce875cb1a53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "capital   madrid\n",
      "country   spain\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",
    "# 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": {
    "_cell_guid": "837451b7-4b8d-4238-ada5-1dcd7a70ca1a",
    "_uuid": "cd1208fab566fa19e6113d33769bd4bdb1c288df"
   },
   "source": [
    "<a id=\"12\"></a> <br>\n",
    "### LAMBDA FUNCTION\n",
    "Faster way of writing function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "0592c111-5e5c-44de-a215-4d15aca23c89",
    "_uuid": "40f4345032941d91590476be075e153b09f9faaa"
   },
   "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": {
    "_cell_guid": "ccacae57-520a-4764-aa7d-bef44f751e12",
    "_uuid": "0e6e2a684622e564faecbe213e38cc4a3dd2db2f"
   },
   "source": [
    "<a id=\"13\"></a> <br>\n",
    "### ANONYMOUS FUNCTİON\n",
    "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "3b472c0f-5eb3-497f-a5b2-13024ba8530a",
    "_uuid": "044c5e0970d9dbf0696412ddac0898783123919c"
   },
   "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))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a0c2879e-9889-411f-bcd3-ca4cd74999b6",
    "_uuid": "cbaeed46ece066c8ed53d156bb3e2867a7971934"
   },
   "source": [
    "<a id=\"14\"></a> <br>\n",
    "### ITERATORS\n",
    "* iterable is an object that can return an iterator\n",
    "* iterable: an object with an associated iter() method\n",
    "<br> example: list, strings and dictionaries\n",
    "* iterator: produces next value with next() method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "6a4a77a8-241b-4f85-811c-99461ffe0307",
    "_uuid": "01eb4bd8e109b0c2f6ba4b70c552557b9e7cbe01"
   },
   "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": "markdown",
   "metadata": {
    "_cell_guid": "a906d18d-daff-4ad9-bb10-5cb12a185aca",
    "_uuid": "36c4a3c649964a5d5d1f2be66a95bf373f8507ec"
   },
   "source": [
    "zip(): zip lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "946f74d8-556d-418d-b8d6-93fae0d4163e",
    "_uuid": "33e7bc5d6bdf809c5d6d12168cb5fbea339583c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<zip object at 0x1a1e828cc8>\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": 34,
   "metadata": {
    "_cell_guid": "adf6eb96-1549-4650-a6c5-2cc3d6da69be",
    "_uuid": "982958f5f9b5c3625a4677fc649ea417e55a257a"
   },
   "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": {
    "_cell_guid": "9b15b18c-9480-4ccf-943d-decf9456a941",
    "_uuid": "3d9ea77004586462944b3bba2bbaac4979325098"
   },
   "source": [
    "<a id=\"15\"></a> <br>\n",
    "### LIST COMPREHENSİON\n",
    "**One of the most important topic of this kernel**\n",
    "<br>We use list comprehension for data analysis often. \n",
    "<br> list comprehension: collapse for loops for building lists into a single line\n",
    "<br>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 list comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "312b0109-e55c-4362-a3be-24d3ccf52818",
    "_uuid": "47e21eea59b09d727abc3fd5a19597a268ecdcc5"
   },
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d360598f-78c1-4e9e-adae-225ff743932b",
    "_uuid": "b8d7403c4147c0dc70d04835c744d1fd27b74ff8"
   },
   "source": [
    "[i + 1 for i in num1 ]: list of comprehension\n",
    "<br> i +1: list comprehension syntax\n",
    "<br> for i in num1: for loop syntax\n",
    "<br> i: iterator\n",
    "<br> num1: iterable object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "7e41f0cc-c036-4168-b0ff-de5be82f5d80",
    "_uuid": "d737e60cdade7c2a9db743dae912b0dac74bb364"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25, 15, 10]\n"
     ]
    }
   ],
   "source": [
    "# Conditionals on iterable\n",
    "num1 = [5,10,15]\n",
    "num2 = [i**2 if i < 7 else i+5 if i == 10 else i-5 for i in num1]\n",
    "print(num2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "8b2341a6-fcff-48ce-8063-d3c65e80cd7f",
    "_uuid": "18020e20af302091032b5df79a9ea71fd7150f04"
   },
   "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": 37,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6fc1c6e4-db02-4ff0-ac75-7a06d1f7ef83",
    "_uuid": "8a8d730155a304e3815c352cdc9cad80e7420f97"
   },
   "source": [
    "Up to now, you learn \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": {
    "_cell_guid": "a26825df-9079-4f97-93e3-63c8b98ab34f",
    "_uuid": "b4b1aa76a6f01fba71ee4651644833f31f2f28e3"
   },
   "source": [
    "<a id=\"16\"></a> <br>\n",
    "# 3.CLEANING DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a6541a7c-6b90-42be-b29e-279820a635ef",
    "_uuid": "2f2fa15880c903eab5493adf3e89ac7648c2ab01"
   },
   "source": [
    "<a id=\"17\"></a> <br>\n",
    "### DIAGNOSE DATA for CLEANING\n",
    "We need to diagnose and clean data before exploring.\n",
    "<br>Unclean data:\n",
    "* Column name inconsistency like upper-lower case letter or space between words\n",
    "* missing data\n",
    "* different language\n",
    "\n",
    "<br> We will use head, tail, columns, shape and info methods to diagnose data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "21c88b75-45c8-4e5f-b9c8-c74079ffc3b1",
    "_uuid": "81ac8f450a9d5b2d53c96c2be7c1fbeda1e590ff"
   },
   "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>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>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>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>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>4</td>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "0  1      Bulbasaur  Grass  Poison  45      49       49       65       65   \n",
       "1  2        Ivysaur  Grass  Poison  60      62       63       80       80   \n",
       "2  3       Venusaur  Grass  Poison  80      82       83      100      100   \n",
       "3  4  Mega Venusaur  Grass  Poison  80     100      123      122      120   \n",
       "4  5     Charmander   Fire     NaN  39      52       43       60       50   \n",
       "\n",
       "   Speed  Generation  Legendary  \n",
       "0     45           1      False  \n",
       "1     60           1      False  \n",
       "2     80           1      False  \n",
       "3     80           1      False  \n",
       "4     65           1      False  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('/Users/garvitkhurana/Desktop/Projects/Data_Science/pokemon.csv')\n",
    "data.head()  # head shows first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "dfa5f2f3-8e4b-4056-8eda-2b0e994c1a84",
    "_uuid": "4e6d6ab90c930a6efb3ec2f5f89cb964cc3891b8"
   },
   "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>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>796</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</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>797</td>\n",
       "      <td>Mega Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</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>798</td>\n",
       "      <td>Hoopa Confined</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</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>799</td>\n",
       "      <td>Hoopa Unbound</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Dark</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>800</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</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  HP  Attack  Defense  Sp. Atk  \\\n",
       "795  796         Diancie     Rock  Fairy  50     100      150      100   \n",
       "796  797    Mega Diancie     Rock  Fairy  50     160      110      160   \n",
       "797  798  Hoopa Confined  Psychic  Ghost  80     110       60      150   \n",
       "798  799   Hoopa Unbound  Psychic   Dark  80     160       60      170   \n",
       "799  800       Volcanion     Fire  Water  80     110      120      130   \n",
       "\n",
       "     Sp. Def  Speed  Generation  Legendary  \n",
       "795      150     50           6       True  \n",
       "796      110    110           6       True  \n",
       "797      130     70           6       True  \n",
       "798      130     80           6       True  \n",
       "799       90     70           6       True  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tail shows last 5 rows\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "023d0da4-d194-465d-a486-aeb8d3669723",
    "_uuid": "29cf7a1b2960de57f30f5ee9145868c37eef3bbf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['#', 'Name', 'Type 1', 'Type 2', 'HP', 'Attack', 'Defense', 'Sp. Atk',\n",
       "       'Sp. Def', 'Speed', 'Generation', 'Legendary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns gives column names of features\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "c2d689ca-965a-401f-936b-45a18b439235",
    "_uuid": "df33df50b7f37060c5c023ae3d9b2850224e56b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 12)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape gives number of rows and columns in a tuble\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "c049689c-f008-4719-87e7-fe76759a187d",
    "_uuid": "10e28d2e3a0e60e75978e0fec6d7b71713842284"
   },
   "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",
      "#             800 non-null int64\n",
      "Name          799 non-null object\n",
      "Type 1        800 non-null object\n",
      "Type 2        414 non-null object\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": [
    "# info gives data type like dataframe, number of sample or row, number of feature or column, feature types and memory usage\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1f715c87-3b8a-494e-8bfd-ec6dbe20d649",
    "_uuid": "668f97561ff0da7328c34c7076c67ec58d83d2be"
   },
   "source": [
    "<a id=\"18\"></a> <br>\n",
    "### EXPLORATORY DATA ANALYSIS\n",
    "value_counts(): Frequency counts\n",
    "<br>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",
    "<br>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",
    "<br> What is quantile?\n",
    "\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",
    "\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": 43,
   "metadata": {
    "_cell_guid": "b7491a4e-0181-496c-9d9d-0218924e8573",
    "_uuid": "ab137e0391b821d32c0a55479838d4d6fb842610"
   },
   "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",
      "Dragon       32\n",
      "Ground       32\n",
      "Dark         31\n",
      "Poison       28\n",
      "Fighting     27\n",
      "Steel        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": 44,
   "metadata": {
    "_cell_guid": "204c5fd8-c6e4-465c-a58b-a0e60c5c0cd0",
    "_uuid": "db7c2f3555e4b8e213ee188bf3b3a8196b376a27"
   },
   "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>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>800.0000</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>400.5000</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>231.0844</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>1.0000</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>200.7500</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>400.5000</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>600.2500</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>800.0000</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": [
       "              #          HP      Attack     Defense     Sp. Atk     Sp. Def  \\\n",
       "count  800.0000  800.000000  800.000000  800.000000  800.000000  800.000000   \n",
       "mean   400.5000   69.258750   79.001250   73.842500   72.820000   71.902500   \n",
       "std    231.0844   25.534669   32.457366   31.183501   32.722294   27.828916   \n",
       "min      1.0000    1.000000    5.000000    5.000000   10.000000   20.000000   \n",
       "25%    200.7500   50.000000   55.000000   50.000000   49.750000   50.000000   \n",
       "50%    400.5000   65.000000   75.000000   70.000000   65.000000   70.000000   \n",
       "75%    600.2500   80.000000  100.000000   90.000000   95.000000   90.000000   \n",
       "max    800.0000  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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example max HP is 255 or min defense is 5\n",
    "data.describe() #ignore null entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fb634d71-1e4b-4bf5-ba7e-aacab2d5e335",
    "_uuid": "e389f1cf2540022db4c9114d177af0d4d920aa6f"
   },
   "source": [
    "<a id=\"19\"></a> <br>\n",
    "### VISUAL EXPLORATORY DATA ANALYSIS\n",
    "* Box plots: visualize basic statistics like outliers, min/max or quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "0381cfb1-755a-4cf3-83fc-654ea648263d",
    "_uuid": "2725f1196c0742a3ba5c172cdd28cd8e214884fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e9a8cc0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4N9o7lf3Yi9rzYy4akH9Pwca7iBhweN3ZawacZrXnM4UG1N7ezvHHH8+WLVvysunTp9PZ2VnHqMxsIvCZQgNavXo1W7Zsoakp+/c2NTWxZcsWVq9eXd/AzGzc85lCA1q7di177LEH119/fX710Xve8x7Wrl1b79DMbJzzmUKDuvrqq2lra2Pq1Km0tbVx9dVX1zskM5sAnBQa1Jo1awZ9bWbWHzcfNaAZM2awatUqLrvsMnbu3ElTUxM7d+5kxowZ9Q7NzMY5nyk0oCOPPBIg/23b8t9yuZnZQJwUGtBtt93GySefzOzZs2lqamL27NmcfPLJ3HbbbfUOzczGOTcfNaCtW7eyatUqdt999/wxFy+99BJXXXVVvUMzs3HOZwoNaNq0aaxcuXKXspUrVzJt2rQ6RWRmE4XPFBrQaaedxtlnnw3A4YcfzkUXXcTZZ5/N0qVL6xyZmY13TgoN6JJLLgHg3HPPZevWrUybNo2lS5fm5WZmA3HzUYO65JJLePnll+nu7ubll192QjCzYXFSMDOznJuPGoCfTW9m1eIzhQbgZ9ObWbUMmRQkXS7pSUnrC2WfkfSopHvSsLAw7RxJD0n6iaQFtQrczMyqbzhnCquB4/opvzgi5qbhRgBJhwMnAbPTMpdKmlKtYM3MrLaGTAoRcTvwzDDXdwLwjxGxNSL+G3gIOKKC+MzMbAxV0tF8lqQPAeuAv4iIZ4GDgDsL82xKZb9E0hJgCUBzczOlUqmCUGww3rY20XifrZ/RJoUVwOeBSH8vBD46khVExCpgFUBLS0u0traOMhQb1E034G1rE4r32boa1dVHEfFEROyIiJ3A13iliehR4JDCrAenMjMzmwBGlRQkHVB4+T6gfGXS9cBJkqZJej1wGPD9ykI0M7OxMmTzkaQuoBXYT9Im4DygVdJcsuajDcDHACLiR5KuAR4AtgNnRsSO2oRuZmbVNmRSiIhF/RR3DjJ/B9BRSVBmZlYfvqPZzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmuUp+o9nMbFBv+exant+ybcTLzVx+w7Dn3Wv6btx73vwR12H9c1Iws5p5fss2Nlxw/IiWKZVKI/qN5pEkEBuam4/MzCw3ZFKQdLmkJyWtL5T9P0k/lnSfpOsk7Z3KZ0raIumeNKysZfBmZlZdwzlTWA0c16fsFmBORPwm8F/AOYVpD0fE3DQsrU6YZmY2FoZMChFxO/BMn7K1EbE9vbwTOLgGsZmZ2RirRp/CR4F/K7x+vaQfSrpN0juqsH4zMxsjFV19JKkd2A5clYo2A4dGxNOS3gr8i6TZEfHzfpZdAiwBaG5uplQqVRKKDcLb1upppPtfb2/viJfxPl49o04Kkj4MvAs4OiICICK2AlvT+N2SHgbeCKzru3xErAJWAbS0tMRILkGzEbjphhFd3mdWVaPY/0Z6Sar38eoaVfORpOOATwLviYiXCuX7S5qSxt8AHAb8tBqBmplZ7Q15piCpC2gF9pO0CTiP7GqjacAtkgDuTFcaHQV8TtI2YCewNCKe6XfFZmY27gyZFCJiUT/FnQPMey1wbaVBmZlZffiOZjMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMchX9noKZ2WD2nLWcN1+xfOQLXjGSOgCOH3kd1i8nBTOrmRd6LmDDBSM7YI/09xRmLr9hhFHZYNx8ZGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlhtWUpB0uaQnJa0vlO0r6RZJD6a/+6RySfqKpIck3Sfpt2sVvJmZVddwzxRWA8f1KVsO3BoRhwG3ptcA7wQOS8MSYEXlYZqZ2VgYVlKIiNuBZ/oUn8Art5hcAby3UH5lZO4E9pZ0QDWCNTOz2qrk5rXmiNicxh8HmtP4QcAjhfk2pbLNhTIkLSE7k6C5uZlSqVRBKDYYb1urp1HdXHbT8JeZsZv38Wqqyh3NERGSYoTLrAJWAbS0tMRI7mC0EbjphhHdHWpWTRtaR77MzOU3jPguaKueSq4+eqLcLJT+PpnKHwUOKcx3cCozM7NxrpKkcD1waho/FfhWofxD6SqktwPPF5qZzMxsHBtW85GkLqAV2E/SJuA84ALgGkmLgY3AiWn2G4GFwEPAS8BHqhyzmZnVyLCSQkQsGmDS0f3MG8CZlQRl/XvLZ9fy/JZtI15upB19e03fjXvPmz/iesxs4vOjsyeQ57dsq/ljiMGPIjabzPyYCzMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmuVH/yI6kNwHfKBS9Afg0sDdwGvBUKj83Im4cdYRmZjZmRp0UIuInwFwASVOAR4HryH6T+eKI+HJVIjQzszFTreajo4GHI2JjldZnZmZ1UK2kcBLQVXh9lqT7JF0uaZ8q1WFmZjU26uajMkmvAt4DnJOKVgCfByL9vRD4aD/LLQGWADQ3N1MqlSoNZVIY6Xbq7e0d1bb1/8Pqyftf/VScFIB3Aj+IiCcAyn8BJH0NWNPfQhGxClgF0NLSEq2trVUIpcHddAMj3U6lUmnEy4ymHrOq8f5XV9VoPlpEoelI0gGFae8D1lehDjMzGwMVnSlImgEcC3ysUPwlSXPJmo829JlmFdhz1nLefMXykS94xUjrATh+5PWY2YRXUVKIiBeB1/QpO6WiiGxAL/RcwIYLRnawHk3z0czlN4xofjNrHNXoUzAzGxFJg0//Yv/lEVGDaKzIj7kwszEXEQMO3d3dA06z2nNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc6/pzDBjOoHcG4a2TJ7Td9t5HWYWUNwUphARvqra5AlkdEsZ2aTU8XNR5I2SLpf0j2S1qWyfSXdIunB9HefykM1s0bW1dXFnDlzOProo5kzZw5dXV31DmlSqtaZQltE/Kzwejlwa0RcIGl5en12leoyswbT1dVFe3s7nZ2d7NixgylTprB48WIAFi1aVOfoJpdadTSfAFyRxq8A3lujesysAXR0dNDZ2UlbWxtTp06lra2Nzs5OOjo66h3apFONM4UA1koK4O8iYhXQHBGb0/THgea+C0laAiwBaG5uplQqVSEU64+3rY13PT097Nixg1KpRG9vL6VSiR07dtDT0+P9d4xVIynMi4hHJf0qcIukHxcnRkSkhEGf8lXAKoCWlpZobW2tQij2S266AW9bG+9mzZrFlClTaG1tpVQq0draSnd3N7NmzfL+O8Yqbj6KiEfT3yeB64AjgCckHQCQ/j5ZaT1m1rja29tZvHgx3d3dbN++ne7ubhYvXkx7e3u9Q5t0KjpTkDQDaIqIF9L4fOBzwPXAqcAF6e+3Kg3UzBpXuTN52bJl9PT0MGvWLDo6OtzJXAeVNh81A9dJKq/r6oi4SdJdwDWSFgMbgRMrrMfMGtyiRYtYtGhR3nxk9VFRUoiInwJv6af8aeDoStZtZmZjz88+MjOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7PcqJOCpEMkdUt6QNKPJH08lX9G0qOS7knDwuqFa2ZmtVTJz3FuB/4iIn4gaU/gbkm3pGkXR8SXKw/PzMzG0qiTQkRsBjan8Rck9QAHVSswMzMbe1XpU5A0E/gt4Hup6CxJ90m6XNI+1ajDzMxqr5LmIwAk7QFcC3wiIn4uaQXweSDS3wuBj/az3BJgCUBzczOlUqnSUGwA3rY2kfT29nqfrSNFxOgXlnYD1gA3R8RF/UyfCayJiDmDraelpSXWrVs36jhsYDOX38CGC46vdxhmw1YqlWhtba13GA1H0t0R0TLUfJVcfSSgE+gpJgRJBxRmex+wfrR1mJnZ2Kqk+ehI4BTgfkn3pLJzgUWS5pI1H20APlZRhGZmNmYqufroDkD9TLpx9OGYmVk9+Y5mMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZma5Sn55zcaJ7JdRB5n+xf7LK/l9bjNrTD5TaAARMeDQ3d094DQzs75qlhQkHSfpJ5IekrS8VvWYmVn11CQpSJoC/C3wTuBwYJGkw2tRl5mZVU+tzhSOAB6KiJ9GxC+AfwROqFFdZmZWJbXqaD4IeKTwehPwtuIMkpYASwCam5splUo1CmVy6+3t9ba1CcX7bH3V7eqjiFgFrAJoaWmJ1tbWeoXS0EqlEt62NpF4n62vWjUfPQocUnh9cCozM7NxrFZJ4S7gMEmvl/Qq4CTg+hrVZWZmVVKT5qOI2C7pLOBmYApweUT8qBZ1mZlZ9Wg83MQk6SlgY73jaFD7AT+rdxBmI+B9tjZeFxH7DzXTuEgKVjuS1kVES73jMBsu77P15cdcmJlZzknBzMxyTgqNb1W9AzAbIe+zdeQ+BTMzy/lMwczMcv6RnQlG0g7g/kLReyNiwwDzzgTWRMSc2kdm1j9JrwFuTS9fC+wAnkqvj0gPzbRxwklh4tkSEXPrHYTZcEXE08BcAEmfAXoj4svFeZT9fKAiYufYR2hFbj5qAJJmSvpPST9Iw+/1M89sSd+XdI+k+yQdlsr/uFD+d+m3MMxqTtKvS3pA0lXAj4BDJD1XmH6SpMvSeLOkb0pal/bXt9cr7kbnpDDxTE8H8HskXZfKngSOjYjfBj4AfKWf5ZYCf5POMlqATZJmpfmPTOU7gJNr/xbMcr8BXBwRhzP4QzO/Anwp3dR2InDZWAQ3Gbn5aOLpr/loN+CrksoH9jf2s9x3gXZJBwPfjIgHJR0NvBW4Kzt7ZzpZgjEbKw9HxLphzHcM8Ka0nwLsI2l6RGypXWiTk5NCY/gz4AngLWRnfy/3nSEirpb0PeB44EZJHwMEXBER54xlsGYFLxbGd5Ltk2WvLowLd0qPCTcfNYa9gM2pk+4UsifT7kLSG4CfRsRXgG8Bv0l2RcgfSfrVNM++kl43dmGbvSLtv89KOkxSE/C+wuR/B84sv0hnxVYDTgqN4VLgVEn3krXRvtjPPCcC6yXdA8wBroyIB4BPAWsl3QfcAhwwRjGb9edsskfuf4fsZ3zLzgSOTBdJPACcVo/gJgPf0WxmZjmfKZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFKzhSOqtdwwwfuIwGwknBbNxQBl/Hq3uvBPapCBpf0nXSrorDUcWym+R9CNJl0naKGm/NK3fJ8hK6pXUIeleSXdKak7lr5f0XUn3S/pCoe49JN2anmB7v6QTUvlMST+RdCWwHvgrSf+/sNxpki4eu61k5qRgk8ffkD2N83eAP+SVp2yeB/xHRMwG/hk4FGCIJ8jOAO6MiLcAt/PK3bV/A6yIiDcDmwt1vwy8Lz3Ftg24UK882e0w4NJU/4XAuyXtlqZ9BLi8WhvAbDj8QDybLI4BDi88ZfNXJO0BzCM9YycibpL0bJo+2BNkfwGsSeN3A8em8SPJEg7APwBfTOMC/lrSUWQPfTsIaE7TNkbEnan+Xkn/AbxLUg+wW0QUf2XPrOacFGyyaALeHhG7PEG2kCT6GuwJstvilefD7GDXz1F/z405GdgfeGtEbJO0gVeeANr3OVWXAecCPwb+fqDgzGrFzUc2WawFlpVfFJ6y+W2yhwUiaT6wTyofzRNkvw2clMaLP1a0F/BkSghtwIDriYjvAYcAHwS6hvG+zKrKScEa0e6SNhWGPwf+FGgpPGVzaZr3s8B8SeuB9wOPAy+M8gmyHwfOlHQ/WRNR2VWp7vuBD5GdBQzmGuDbEfHsEPOZVZ2fkmqTmqRpwI6I2C7pd8k6iuv6rH5Ja8g6xW+tZxwqj/fgAAAASUlEQVQ2OblPwSa7Q4Fr0j0Cv6COz+mXtDfwfeBeJwSrF58pmJlZzn0KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPL/S+OL3jDune3EwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": {
    "_cell_guid": "1f9bc5b9-a732-4844-b355-6729786baa0d",
    "_uuid": "727bbcc5add844f068310378b8e710d18be543a3"
   },
   "source": [
    "<a id=\"20\"></a> <br>\n",
    "### TIDY DATA\n",
    "We tidy data with melt().\n",
    "Describing melt is confusing. Therefore lets make example to understand it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_cell_guid": "3d98ae45-a64c-450c-aa2e-b7b7a1f99cde",
    "_uuid": "eaa9bbb12d2aa9f7f691e16555627f7e9726c82a"
   },
   "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>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>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>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>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>4</td>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "0  1      Bulbasaur  Grass  Poison  45      49       49       65       65   \n",
       "1  2        Ivysaur  Grass  Poison  60      62       63       80       80   \n",
       "2  3       Venusaur  Grass  Poison  80      82       83      100      100   \n",
       "3  4  Mega Venusaur  Grass  Poison  80     100      123      122      120   \n",
       "4  5     Charmander   Fire     NaN  39      52       43       60       50   \n",
       "\n",
       "   Speed  Generation  Legendary  \n",
       "0     45           1      False  \n",
       "1     60           1      False  \n",
       "2     80           1      False  \n",
       "3     80           1      False  \n",
       "4     65           1      False  "
      ]
     },
     "execution_count": 46,
     "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": 47,
   "metadata": {
    "_cell_guid": "5e1d0367-0cf3-4ac2-9a5b-cda7a86ffed4",
    "_uuid": "b2aa33597ea79113c7c986691436b3c4f8c6ec36"
   },
   "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>Mega 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>Mega 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  Mega 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  Mega Venusaur  Defense    123\n",
       "9     Charmander  Defense     43"
      ]
     },
     "execution_count": 47,
     "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": {
    "_cell_guid": "9c3cb307-d969-4ba6-bf8e-9cbbaed738f4",
    "_uuid": "d2bd6f566fadb6cae31d03952cf8718a9176af74"
   },
   "source": [
    "<a id=\"21\"></a> <br>\n",
    "### PIVOTING DATA\n",
    "Reverse of melting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "f228fae4-22a1-4516-87fc-744b0e353ae0",
    "_uuid": "7ffe547a4db19744d7c795ce71f8fccf784345cc"
   },
   "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>Mega Venusaur</th>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venusaur</th>\n",
       "      <td>82</td>\n",
       "      <td>83</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",
       "Mega Venusaur     100      123\n",
       "Venusaur           82       83"
      ]
     },
     "execution_count": 48,
     "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4df2f724-eb10-4745-8d1c-47632f98c730",
    "_uuid": "eada6d94f24abfdea48bd31750f528a779200aa7"
   },
   "source": [
    "<a id=\"22\"></a> <br>\n",
    "### CONCATENATING DATA\n",
    "We can concatenate two dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "5c0a68a8-21b1-45d7-ac11-697f95665123",
    "_uuid": "7fa6017baeec686bf4ca68fd097b4067d37af1e6"
   },
   "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>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>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>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>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>4</td>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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>796</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</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>797</td>\n",
       "      <td>Mega Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</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>798</td>\n",
       "      <td>Hoopa Confined</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</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>799</td>\n",
       "      <td>Hoopa Unbound</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Dark</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>800</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</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  HP  Attack  Defense  Sp. Atk  \\\n",
       "0    1       Bulbasaur    Grass  Poison  45      49       49       65   \n",
       "1    2         Ivysaur    Grass  Poison  60      62       63       80   \n",
       "2    3        Venusaur    Grass  Poison  80      82       83      100   \n",
       "3    4   Mega Venusaur    Grass  Poison  80     100      123      122   \n",
       "4    5      Charmander     Fire     NaN  39      52       43       60   \n",
       "5  796         Diancie     Rock   Fairy  50     100      150      100   \n",
       "6  797    Mega Diancie     Rock   Fairy  50     160      110      160   \n",
       "7  798  Hoopa Confined  Psychic   Ghost  80     110       60      150   \n",
       "8  799   Hoopa Unbound  Psychic    Dark  80     160       60      170   \n",
       "9  800       Volcanion     Fire   Water  80     110      120      130   \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  \n",
       "5      150     50           6       True  \n",
       "6      110    110           6       True  \n",
       "7      130     70           6       True  \n",
       "8      130     80           6       True  \n",
       "9       90     70           6       True  "
      ]
     },
     "execution_count": 49,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "84657f55-6217-4d54-acf9-4a774283a2a7",
    "_uuid": "d6cd784edaa511994dbd07375883cfeb46e9200a"
   },
   "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": 50,
     "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": {
    "_cell_guid": "c6506494-9a34-4900-88d0-a183e6ec676e",
    "_uuid": "758a297da4257a0e8b13b834cb38e0cfd31a42c4"
   },
   "source": [
    "<a id=\"23\"></a> <br>\n",
    "### DATA TYPES\n",
    "There are 5 basic data types: object(string),booleab,  integer, float and categorical.\n",
    "<br> We can make conversion data types like from str to categorical or from int to float\n",
    "<br> 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": 51,
   "metadata": {
    "_cell_guid": "2d14a63b-baaa-4043-9354-c4eb2005eaec",
    "_uuid": "189a136dda80d249075a4babfce8f2bf427e1e14"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#              int64\n",
       "Name          object\n",
       "Type 1        object\n",
       "Type 2        object\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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "e2295cd3-c374-4454-996f-e8c47d4e65ae",
    "_uuid": "677183c4cb3b0068c0969ea0221622f912e0d726"
   },
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_cell_guid": "64432d04-989a-4157-ad33-18148ed8808f",
    "_uuid": "6f7930b9c10fc4dbb6114cceaedb430cba5859c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#                int64\n",
       "Name            object\n",
       "Type 1        category\n",
       "Type 2          object\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": 53,
     "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": {
    "_cell_guid": "5f13c750-479a-4fee-8cf4-2fbe776add00",
    "_uuid": "7818b9bf6a9eaf925df9a6b6edd27a0937a4a3fb"
   },
   "source": [
    "<a id=\"24\"></a> <br>\n",
    "### MISSING DATA and TESTING WITH ASSERT\n",
    "If we encounter with missing data, what we can do:\n",
    "* leave as is\n",
    "* drop them with dropna()\n",
    "* fill missing value with fillna()\n",
    "* fill missing values with test statistics like mean\n",
    "<br>Assert statement: check that you can turn on or turn off when you are done with your testing of the program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_cell_guid": "0dbc25f3-0baa-4bc7-aca2-30bbc22a29f9",
    "_uuid": "6540211728242f373650e1ea7154b1a614fe47d7"
   },
   "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",
      "#             800 non-null int64\n",
      "Name          799 non-null object\n",
      "Type 1        800 non-null category\n",
      "Type 2        414 non-null object\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(7), object(2)\n",
      "memory usage: 64.9+ 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_cell_guid": "9417ff2a-52f2-4265-a480-6e3b28008627",
    "_uuid": "e0770123fd627fca063e3c7c44df938c1bcc7bc9"
   },
   "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",
       "Ice          14\n",
       "Water        14\n",
       "Ghost        14\n",
       "Rock         14\n",
       "Fire         12\n",
       "Electric      6\n",
       "Normal        4\n",
       "Bug           3\n",
       "Name: Type 2, dtype: int64"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "metadata": {
    "_cell_guid": "a8f0fe39-3574-4184-bedd-c0ab2eec66aa",
    "_uuid": "1f67742c1571ec5013292f1822b3263861139f9e"
   },
   "outputs": [],
   "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 data\n",
    "# So does it work ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_cell_guid": "031f0c5a-f91e-4eac-855e-1e6843c3a0b7",
    "_uuid": "571ede30ef8feb08769489f69a5baaf3a49c8a36"
   },
   "outputs": [],
   "source": [
    "#  Lets check with assert statement\n",
    "# Assert statement:\n",
    "assert 1==1 # return nothing because it is true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_cell_guid": "d6d94f0e-1b9d-4a85-a54c-ba4c3d3b8568",
    "_uuid": "573479243c7a6769489806cf0f04aa5445391176"
   },
   "outputs": [],
   "source": [
    "# In order to run all code, we need to make this line comment\n",
    "# assert 1==2 # return error because it is false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_cell_guid": "e363b3ee-0cd5-42ca-b0dc-19a8fd40fbf0",
    "_uuid": "26da00b51411fc990ab515c09d6f8bcb05901d7e"
   },
   "outputs": [],
   "source": [
    "assert  data['Type 2'].notnull().all() # returns nothing because we drop nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_cell_guid": "2a596b4f-d4c5-44fb-8938-9f10005de782",
    "_uuid": "5d588a3656a9b80172eef1d19da393beea1cf2a2"
   },
   "outputs": [],
   "source": [
    "data[\"Type 2\"].fillna('empty',inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_cell_guid": "7523e65c-6e1c-42c4-a442-e9b47285d428",
    "_uuid": "2e6eeec442123a81ab793d4f88e53e543175693f"
   },
   "outputs": [],
   "source": [
    "assert  data['Type 2'].notnull().all() # returns nothing because we do not have nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_cell_guid": "c1fb6b51-6fae-43f6-bf48-9836be3c85df",
    "_uuid": "e1f1444f3743051ee2fe3178642e250792dbb342"
   },
   "outputs": [],
   "source": [
    "# # 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2126428f-30af-4b3b-ab19-c46ed54dbff9",
    "_uuid": "ee444113164dac9edc39c91c470408c18186736b"
   },
   "source": [
    "In this part, you learn:\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": {
    "_cell_guid": "c29ea56e-4edd-4418-a5d3-53a966c31ed2",
    "_uuid": "c1638843bfb15c24f134a8cc1b91e39e2a1bbc0d",
    "collapsed": true
   },
   "source": [
    "<a id=\"25\"></a> <br>\n",
    "# 4. PANDAS FOUNDATION "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fb8c8c59-4e57-4db3-ae34-999d8b1153cc",
    "_uuid": "71300084e18e296d9c77555072b6e81bdec13b98"
   },
   "source": [
    "<a id=\"26\"></a> <br>\n",
    "### REVİEW of PANDAS\n",
    "As you notice, I do not give all idea in a same time. Although, we learn some basics of pandas, we will go deeper in pandas.\n",
    "* single column = series\n",
    "* NaN = not a number\n",
    "* dataframe.values = numpy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6ea9fc17-9b65-4cd0-9de5-877d7b4a5ae2",
    "_uuid": "547d06e3659e4cdf38b896395d60b2fc47d7d0ec"
   },
   "source": [
    "<a id=\"27\"></a> <br>\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": 63,
   "metadata": {
    "_cell_guid": "3e5a9c3a-7f19-4060-a2f7-75c20b28d9fa",
    "_uuid": "da50963dc2c58cd48ca7e65dfecaf9188efaef12"
   },
   "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": 63,
     "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": 64,
   "metadata": {
    "_cell_guid": "cd4b98da-05ca-4f80-9074-3baf3b092e16",
    "_uuid": "a5a1f7bc33cc427f79fc7fe837f8bf2465906629"
   },
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add new columns\n",
    "df[\"capital\"] = [\"madrid\",\"paris\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_cell_guid": "08dc80f8-2f7a-43a1-9d4e-d8088c636f18",
    "_uuid": "17a4a27eeeadfb24ec3ab20737838c9f9d61960b"
   },
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Broadcasting\n",
    "df[\"income\"] = 0 #Broadcasting entire column\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "24ea1c9f-32bb-462a-b8ad-06660ba24612",
    "_uuid": "be15b4430231fd124af5f823afdd029ed52ce710"
   },
   "source": [
    "<a id=\"28\"></a> <br>\n",
    "### VISUAL EXPLORATORY DATA ANALYSIS\n",
    "* 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": 66,
   "metadata": {
    "_cell_guid": "64baf078-8000-4a2e-a1f4-a53f762eedac",
    "_uuid": "77596cbc5da0ac65152d8975b0398b7c3797bd69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e8c5a90>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 67,
   "metadata": {
    "_cell_guid": "f013c335-ff75-4e5b-a1f2-33677a7c1bed",
    "_uuid": "2a48984be00e2aa32327edf045d9baa0c45a72bf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplots\n",
    "data1.plot(subplots = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_cell_guid": "a5059f53-a8b8-462f-9963-8ee268122e41",
    "_uuid": "754e3b0fcea1180cbc84d0b53e04beaec0b1e829"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DZhOWzmNv7TKd2+0vbEXDuCMQjZsnjfTFYnjw1RbTtj96ZiNWb//MdV807ehIKAeddTs68f9e3uZq/2LIDeUkQzHIOBBxqyB2A3gZauxDpeGPKQGcplkmUwxBS/mQwWlq5cG+GII+swkp5CMc7Iu52h8ADkfNCkTHl2TaCigKDvbFLMe75qwpCPrM/RCOxvHdx9a7upYAsPpDecDfA69+5OoBWgr3QzHIOBBxO4vp54A63VUIcSi3IjFeYnyz0mfK3LBsI2bVjbSdLZJO0FKupiXmS4YFDWMxq26kdP/Ne7rQFzO/0ZNCJhlSzXICgDPqRsobJutIoau3D7PqRmLt4rkJeQDgwddaLLsfiqhKyulaAsCcKSNx/yrrMQI+84wtO4ohiK2YAu3yMRW3WKb7uq0oN5OItkKb2kpE04nov3IqGeMJmbxZuQ1aSndkkg7VFSFc0lhrWnZJY21OZJClwejoCeP2F7Zatl0w/SjTdsub9+D8B9bg589txfkPrLG0X1dTiWNryk3Ljq0pxy8vbkj0r18B4gK4+on3MOvOVVjbsj8hT3LEdjJu3pIbJ1XjtElVluUCwtUD1O21yCXFEmiXy3s+n224xW2g3Dqo6b1XGAoGbRZCHJ9j+VLCgXLOZJMELdVbTK6TqxU6Wd+G1s9x2W/fSpiTdIJ+BW/e6D7ILVUwXVV5EFs++QLfWdKEcNQ5UG7LJ13atiLltnb8v5e34YFXP0LAp0DAfVbYYkqkV8hAu3z0Q7762utkfRBCtCYtikk3ZIqKbN6sUiWYy7XNt9DJ+mqryhCJWf0HAR+lZfdOFUxXXRHC8LKAxceQfAz9oTf1qOG4e+H0jN+Sv//lY7HuJ/Pw5JUzsHbxXNczfIrJvu+U9DCXCSjz0Q/F1NdAGtNcieh0AIKIAlDjIt7PnViMl6Sys2dKrm2++UzWJ6O6IoRbLpiKnz672bQ8Fhdp2b2dgumcjiGbumn0UaR7LTOJVC4GH0QxkI9+KLa+djuC+C6AqwGMBbAHQIP2nSkRvH6zcjsyyTSwKZ1kfSG/ap8P+TOTwY5vzJiAX/zT8Qj6FJQFFMvx3cjoFDCY6hiyqZt6Xqh8pml344PIV4Batu1kml0YyI+fo9iSFqYcQRDRnUKIxQDOEkJ8I08yMSWC08gk28AmNyMfof8rKPEtHRmcqAj5QSRAUKBnmTHaud3IePKEEXjy7d0gKBCIo3HCCFf9KMvTFI7G8Yd1u/Fv86a4Pods6egJY2lTm2nZ0qY2XDtPTW+erwA1N+2k8kG4qXrnRC5G44Vowy0pndREtAlqvqX1QoiT8iaVS9hJXbzko4JYrtuQ7R/wERQCgj5fxuVD05kkcPodq0wObAAI+Qlv3Dgvbw+ODa2f21bPq60qy4tT1U0/plIgXmUXHih45aT+G4BOANOI6Asi6jb+74mkzIDETWCTLErZy0pp2Tr8ZPtHYgLhqHAdrZuNDNUVIVxzVp1ledDny6vTMpVdPF9OVad2nCKp3VS9Y6ykVBBCiB8JIY4A8IIQYpgQotL4f55kHLSUcuIxJ2fbptZO6X7Jy1P1gZtUGeFozHa9U//Ksr0mE1AUbPmkK2MZdRmadnRIbeOXnTYeyeEPMqdlLu8VmV38pvPq0dbZi/Kgz3J+fbGY505Vp350UiClkF24GHEbSX0hEU0AMEUI8QoRlQHwCyHS9/Qwrij1xGP6Q+WGpHPQzQGv2aR/eO3D/bj89EkAnPuguiKES06uNdmVdeepvm+yATV5vVP/qtld7c2wh6MxfGdJk63JKVU/6DJEonEYA7aNtvE1Lfsh0C+DX4HFaZmPe8VoF9+8pwu3v7A10d4ljbX4w7rd0C1hcQGsbdnvqQxO95OTAtEnCyx50+yDGIzmpXRwGyj3HQBXAhghhJhMRFMAPCSEmJdrAVMxUH0QxRSYlC12TsPH39iBn62wRir/+4J6XH76pKwC5Z6/5gyc95+vmwLKktef/8Aax/6V2d6HBBTE4wIhvw99sThi8TiMLgK765TcDzLZjeiBdMnbhPwK3rgxP8GCMmTthfwEgByD/bxq3855u6J5j0WBJCspN1XvBgOeVpSDOqX1VADrAEArO3pkFvIxKXBT6axUsJt3f8K4KiS/m5O2HHDXB3bbPLp2h1Q56OubWz931b+yt1IA+Mv3ZuNgXwxdvRFc/cS7JgVid52S+0Emu5Hm1s8xpabSsk3Q564PcnWvyCvgKerFM5ArGVLFcbiZ/eOm6h3Tj9s4iLAQIpHYnoj8SDXuVrd5lIg+JaLNhmUjiOhlIvpQ+79KW05EdD8RtRDRRiIquhlT+cSrYJlCzhl3oraqDKGA+fYLBZS0gtBk2/TFYlj2rn3umkN9UQwJqG//RsKxuKlgD2A/J72uphLTxx2BqUcNc32dHn9jBy5+6A08/sYOW9mNNIw7wvNgwZVb92HxMxuwcus+aZtO61vau/He7k5L38VEHLGkNOWFCu5yivfxwleT7TGKQQa3uB1B/J2IfgKgjIi+DOBfATznsM//AHgAwBLDshsBrBRC3EFEN2rfFwM4F8AU7e80AL/W/h+UONlb3ZCtXdrtnPFMc9/owVdGm7Ax+MppfWKbJB/EvC8dide3d1imhurEBPDDp5stIwwSAuc/sCatWIxUPhAj02/9G7oOq87yd3Z24u6XtmHDreckrnE4Eje9bc2uq0685TrdB27vlbPvew3b2w8CAJ5qasOxNeV48bozXa9Pvh98CmFooN/vAiCr+zUfeOGryfYYxSBDOrj1QSgAvg3gbKiDyRcB/E447ExEEwE8ryf1I6JtAM4UQuwlojEAXhNCHEtEv9E+/zF5u1THH6g+CJ1MH77Z2qXdzhnP5kb1IobBrT08HbKNk0je38nX0tLeja/c/7oprbjsPJ3ug1TbrNy6D99est6yzyOLTsa8+tGO6+3uhwcuPREzJ1enJWehyEfcTSnIoONpsj4hRBzAswD+VQixUAjxWyflYEON4aG/D0CN9nks1LKmOm3aMgtEdCURNRFR02effZaBCKVDpukxsp2b7mbOeLYVvLyIYZBtE/T5cM1ZdRgSUBDyubWg2reRzTkAwPKN8nccffnBvhhCfrNpK/kYbu6DVNu8tLVduo++3Gm93f1wOBKzjGbymQIkHbyI18hFXE2+ZUiXlL8gzTdwKxHtB7ANwDYi+oyIbs62YU3BpK1khBAPCyEahRCNo0aNylaMAUm2Pgw3c8azvVG9KABjt81lp43H2sVzcc8l01zJkqqNbM4BAC6cNka6r748H8nZzq6vSbncaf1AiCHwop+zPUYxyJAuTq9Y1wGYBeAUIcQIIcQIqL6BWUR0XQbttWumJWj/f6ot3wNgnGG7Wm0ZkwHZJvxySjAHuL9RnZLxBX1AyKcg6DPP76+uCOGm8+oR9BHKgz6HZH2qPTzkJ9M240aU48Lp8gc0oJYP9StqbEHIryDoo7T6qf8c1H2DPquMl58+CcOHmEcIw4f4ErEeXiU9TMW8+tHSokXz6ke7Wi+7H06fbM4n5Va+QgV/pvObyCaBpFcy5PIY6eDkpP5fAL4shEhENQkhPiaiywG8BOC+NNtbAeCbAO7Q/l9uWH4NET0JVQF1OfkfmNRknfDLYWznxkHr5KNo2nkAaj0eVdE07TqQWL+8eQ9uf2Ergn4FfTGBWy6ol/o3VDFJ9YwJsrTb02eNhB4a9OGur07DuBFD8cS6XVja1IZo3CqDG5Y2tRpm9Qg83dRq2f+2i07AdU82Q6iS4vaLzM7+bJMeuuFfz5qCHz29AUIARMDVZ5mT/b143ZlYuXUfXtrajrPraxLKQefkCSPw1Dtt6IuqDvU3PjqA+fetxqKZ43HyhBEm+W46rx7Hjx1uOZdCB3+6+U14kUAyWxnycQy3OCXrs60a51RRjoj+COBMACMBtAO4BaofYymA8QB2AbhECHGA1HDVBwCcA+AQgP8thHD0Pg90J3WhcOOkztbJ7FRpzY0jLlMndUAB3vrJfHQe7MsqgVvTjg4s/M1bluXPXDUDjZOqbWUsNcemU1Bf0AckFd5DRciHaLy/al0pBH+Wgoxe4ZWTui/DdRBCXCqEGCOECAghaoUQjwghOoQQ84QQU4QQ84UQB7RthRDiaiHEZCHECW6UA5M73Dips3Uyp2rDrX9Dtp2PFPiUpKitJOYdV4PqilDWCdxW26QLMS4fCI5N2f5GSPIY6QnHTBMXiq1SmoxSkDHfOCmI6Vr21uS/bgDpJVJn8k6mxXrcOCWzcTJ39IQxJJCUgc7QhjwATt3XGLwn204WtJXMFbMnuT5PwD5gcM6UkdL9jcvd+mrs2sinY1N2P3T0hNF64BAOp0haKGyiwYH+B2wmMsjkyXXwZjFVcysGUvoghBDyXzFT9GRTrKeqPAiF1KRrOgqpy3WcfBB2AVxrWvYn2k1uIzlA7IdPb0BEiw+IxeO49sn3sKalI7H9opnjLTJ87ZRxaJwwAjcs2wgfEQ4m2z4ATBpVAcBdArdUAYONk6pxbE05tmkBZoDq3NXNS3o/NE6oMsl9yoQqk8kiVRvVFSGMH1GWCGIDgAkj0g9KPGVCFV5PIYPsfhAArjdcAxmLZo5P2d/6A9ZNQJ9RhsPRGIQQKAv4E9s27TyQdcGfVHgRoDrQcBUoV6ywD0JOtv6B1ds/xaJH37Ecd8m3TsGcY4501YZRFv0NEkBKW3ayjKffsdI2p5JOyK9Ik8QBwIOrPsSjb+yy7POzrxyHK+ZMTnzXE7hNrB6KgN+XeKDZ+UmeuWoGAn4fyoM+x6R/Tv4cp/Vu/BxOOLWRqS9HD6QD+q/z5k+6cPvzW21fTOyC6Zz8HMnXOfkcvKSYA/68wutkfUwJ4ZTAzTnBm50Nv3+52yRxxuRqGyRJ8owkyxj0+RCOpq7HEEvKC6QfY/q4I9BxUO4m27Sny/S9rqYSW/Z+gcsffdv0YOuzeTh+/bfrEPQriMbilp5K7oM1LXI/xZqW/airqUzpB6mrqUzp53CrIJzasEvAF3d4eew8FEl81q/z9HFH4Jypo20fsHbJ9jK18+vn4CWpEgIONtIPNWWyJtdzwbMNQpt61DD4k+4Mv6Iud9uGjvFcnRLUOckoI3mAofsqAGDB9KOk+yQvt4sKn1g9VN5mXOBQXwx9MYFwLHWSupE2Dxp9uZMfxI2fwwmnNux8OU62BbvjZhJRXR702Y4eMpGB8QZWEHlmefMezLpzFS7/3TrMunMVVjTbxwNmqkj6A7hIGgDmFGxTXRHCvZc0mALQ7r2kwSZITUn8GY/R0RPG/Ss/xOl39J/r2pb9iXbLQz74FXX+i96GXqWsoycsDZRrqB1uOdchAdWXoROLx7FWe2ufVz8aY4YFTduPGRa0zPF/86MOS9xHQFEQ8PtwSWOtY3/7SK1VLQu0mzm52jKryqcQZk5W3/6dghIbJ1Vjdp15pDC7rtoyekh1rzi1Ibsf7l44HXcvnIaATz6alBXbyebF52BfDCFJW0O1a3/3wmmOwZuM97CJKY8Y31T14fwNyzZiVt1Iz4Oj1CC0/mwmyQFgTsE2boJxmnYeMNmF9TaWN+/BDc9sSPgP9G1uWLYRaxfPxU3n1ePnz22BQkAU6ht5XAjcvHwzhgTU+fPzjjsSKz/41BQod87U0Tj9jlWmNpMzoUbj/X0KAPsPRmBk/8EIVm//DFOPGobqipDFQawTicexeU8X/vyec0B/TAAxbSSRHChXXRHC5FFDTU7mulFDTf158oQRePLtVhARhBBonGCOUn7sihlo2tGB1R/ux5wpIy3Kwc29oraxGwQFAnFLG3bXe1bdSGz5pAsAYWhAwc6OQ9JiO9ner7VVZSCFYCytF/ITHrr8JEw9ajiqK0JY0DAWi2ZM5II/eYSd1HlEVqGsMuTH41echumGoXK+srFmQyoH7uWPvi01F1SG/HjwGyfiysfWp21O0M//Vyu3m2YdyQgowDP/Zxa6evukzvYhfrXAzQ/mH4P/+OsHlvUhv4KbL6jHbc9tzSgrrNGB7ORkzkcgXa4DwLw6vpuKcIw3sJO6CHFrt8+2SpiTU9IL7NpY/eF+W0e0eu6U0lFtR0BRsOWTLixtanPcNhJXbdpdvXLzyGHtoX/Xi1YG7m01AAAgAElEQVTlAAA/Pvc4HH/UcGnAXdCnvuUHfD4cjsSkZ2F0IDs5mbO91tlU3vOq4ptXx89nCgnGHeyDyCNuE23lIxurG1LZlO2ONWfKSPTFrLEHIb+Cm86vBwBLRTI3hKMx6MrFCT+pNm2Zs91I0CYd+Bl1I1FbVSYNuCMC/nrtHPzxyhm41yZb7HSDrySVk7mjJ4yu3j5Lf8muddOODtz70jY07egwLbe7V8qDvpSTA4xtuPUdpCtDJgFmuU4ZXqhkgaUKjyDyjJu3pGwDdupqKjG7rtoUGGUMQnODk03Zro09XYdNwW8+Ar5+ynhMqB6K259XC+dkYrbpiwk8v/ETVzOb/H4l0bflQV+imlsycQAzJlXhrR2dpnPQ++nuhdPw/aeaTedz6sQRjv04snJI4nPjpGoMH2KWYfgQH/Z0HU5Mq40LtZ9CAR9iWv4iU0bY372VCLS7f1ULZtdV47ErZgCQB+KNH1GG8x9YY7p2dkGNxuvcF4vjmrPqcNlp4y33mpMMbirrFZpCJwssRXgEUQDcvCUtaBiLtYvn4vErTsPaxXPTupE7esJ4Z1enadk7uzpdvzW5KQYka+PtnQdwwzMbTZG3MQE829yG//jrB6pz3sH34FMIPgKGBq235tKmNlw15+jEzKagzQybq2YfjeqKEJ59t1WqHPxQHaBXzT4aTbvNpjJjP9WPGYbkQcTrLR2JNA+RqFzxGJc37eiwyNB1OIYfPb0h0b+RmEBMACJuLZHStKPD9PDXZdDf4lvauy3rt7cfNF27Hz2zEU+sM/tt/rBuN1rau03XORyN456Xt+P0O1aaZtc5ydDRE8bS9WbT39KmtqJ6S8+2wNVghRVEEZPpcFuWdEwBYcsnX2S8v5tqbgpIarc/2OduxDA04MN//8spePun83HFGUdLt3nwtY8Q9CuIxEViplIy+744jI6eMJ7ftE+6Pg4gEhP41aoWRJPiGNwmFASAnR2HpOuNy+18ELIgtN5oHOGoMD24nBICukksqBAhOVtGNK4G6wmJGS1dGUohyV0pyFiMsIIYgMhswociMXxnSVPKuAtAfSN9b/cBHAybI5h7I1HHam690TjCEflbtRviEInpp3ZBbtGYQE84hr5of7xDMs9t2ItZd67CmOFyxRoHLCMDHZNt3iYSu2poAM80taJqaEC63uifsfNBpLKyGR9cToFybvxKMRuzXMivWAL9MpGhFJLcpSMj+yn6YQUxADFWWjMSjqYeVt/87CbMv281bn3ufcvsHLVkh7mN6ZLANUUhhPyE8pA8z6MuU8hHljQVRrv1lr1fIHkwkuxwDvl9KA9aRyyHImqq6afX70FlKL1bXJehoyeM+17Zblk/edRQfHvJelz/zEZ8e8l6S7W4Y2vKTT4KWaCbEwf7+pVx46RqjJYE++mzpGRBcLPrqk0TIW5ZMFUarLf7gHwEBAA9YbMMsopzugz5rnKWCW5lTCeQdTDATuoByoKGsThiaADfffxdHDJk2LSbftjS3i0NGNMZ4veZ9mtp78a6HZ2W7QI+BQ9dfhI+6ezFjX/ebFl/6wVTccrEEfik6zC+s6TJ5LBe2tSGa+cdAwBYvGyj6S0/oBAURQ2q0wlHY1owoBxC/5RWt+gyyKZuDvEr+Ogz80M12b+wrf0gWtq7TUpCD3R7en0bVjTvQa9DAsK4ADoP9iUSBu77wjyS2ftFn6mN2y48wRJAlpwk8ZblSddCCDyyZoetDAL9MnT0hLHrgNkUs+tAf8Q7UBpTVJ1kTCeQdbDAI4gBzNSjhlts3XbDaidbdvJ+dttHYnFMPWo4Puk6LF2/t+sw6moqMbwsYJlmqiuvLZ98ASVpfBGJC8z70pGm9B9nHTsqpcy9kThkFq+hQZ9ldJIsg8wkEXWoM6Ej65tJoypw3rQxrqM/9GO4LWpUV1OJhY3jTOkzdP9VW2cvygLmd8GQ3we/zTTf5Dbc2u9zPUXVC1LJyH4KK6wgBjDpDP3tbNlDA4p0P7vtf3T2saiuCGVst968pwvfWdKEQ5In+4ub25GoPw3Cl8YMs2zjRNAHPHT5SXjnp/PVtN2+ZEVkrl9gXB93qSCS+0Y3W1z9xHuJKa1uj+FFTEumhZVSJfMrNh+DFwyW80wHVhAljJMzraW9G33ROB7/1qnS6bLG6lwyW/bcY0figulH4cFLT0zsp7dZVR60bH9J41hc+Q9qnQWnJHPGZH9Dgz74AEwdU4lblm+2jZOICdWPcqgvhnA0jl///WOcNqnKfYcBECB8fiiC6ooQJo2qwPfmToEC9YfgA0yKcFbdSNNIIw5YRh7JtvnkBHLJ0ysjMQG/T8H/+YejXSXCc0q0p5Ncac14b/QnbwRCPgVBHxLJ+PSXh2QfhVMyv0x9DNk6gHPpQC4FX0q+YR9EieIU9JOqSlmq9bot+w9v78Kqbeosoaea2jC7rhoLG8eZ2mycYH44J5cRbe82m5k+TfoutH8PadNg1+8212lwIhx1jqtIJhJTp3B2H47i9he2Wvb/v3/ZmujHts5eyzRYAvDApQ04HIkn7P16wSFZArm2zl5LG+FoHDMnV+PRtVYfQNCnWBLpOeXdTr6Ws+uq8c6uTtO9oSZvBKAZuZp2HcBtF56QsMlv3tOFnz+3xTZhoBc+hmwD1fIR6FYKvpR8wiOIEsQp6EfmcF7y5u7E22Wq9XU1lZhYPRTvJj2sX2/pwPVLN5jaTA6eMraxcus+UwZTQHXg/nb1R4l6w4uXbXSsGOfEhrb0lAqgPuRvfW6zVLns/aIPj7+hPrj3dx+2xA/EBFAW8OGs447Ewb6Y45vsplarIx8A1n64H0GfdaZXXyz7a/l6S4clUC7VMbp6I7jt+a1qfYtoHH0xIZ3tlo2PIdtAtXwGupWCLyVf8AiiBHFKjuaUrC/V+qryIJ5eL0+IF3Fhg9fbeGlru3T9HX/9APe8vB1Xn1knDdLygpA2H/bEccNNaTR0wtG4bRwEAPxsxVYMGxpEy6c90vVLm9pw9R/fQ0BR0BOOml7wF80cn5gFVVtVhtdsgsze39dtmzbEi2vphkfX7sCf3tsDRVJa1MtkfkD2Cf1ynXCQkcMKIkfksq6tkzPNybFpt77jYB9Ov2NVVrLpxz67vgZPSTKvxgQQi8Txn6u2oy9FTN0lJ43F0netc9D9ZK0il8w9F0/HzMnV2PFZjzTVtmOpNKjTGx+89ETpulUftCMShzQj7ZI3d+PJt1sR8vsQicdx8UlyE8g/n3gUFp5cix8Z6mbohGNqsj0g82vphmXv7rH193jtnM3WAcwO5MLAJqYckOtgGydnmpNjs66m0uJcnTxqKH750jaEo3HpQ0M2LXT8iCGm78ZEd7Jqbkb8ijyQTufUo6txZKU5UnnMsCDu/VqD6byTHeGXNNbi/OlHqUnstER5Rsr8Vl+JjICiYGTlEMs5BH1qOvFU9MVEwgyydL382s8+5kgsaBiLN26ch4Za82ysaCyO8x9YgxXNexJJEU37GvpZtv7YmvKk6nDTcEbSNg21w4EUtWC8TranJ/TLtA12IBcGHkF4TL6CbZycabLgKZ2W9m5sS/IPJAeAJZNskgn5FbR/Ybb/6onu9OCqzl5zug4jsmmsRm5evgmHIuZG937Rh/oxw7B28VzTeT/8949w14sfIOhTsGLDJzijbiQWNIyVJspTRXJOB9IXiyESjWFvUpBaqlGPHUMDPtP5Vob8CdNI58E+NLeZc2TFBRI29voxw2wTL+r9nLx+14FePH/NGTjYF0u8Yd+wbKNpm617u1Keix4w6NU9a5fQL5022IGcf3gE4TH5DLZxcqYlB0/pZGK3DiiEoF9JZFH91qyJFier8Txl/WAk+c0+mWTloNPc+rnpvDt6wrj3le2IxoFDWrbY65/egJb2btskc26IC+BPLsqNJpM80gpH4xZlaMxrlepaBBQFza2fp7yf7O63g30xU6Bc8jZ+xWdbDyO5DS/w6nfBDuT8wgrCY0rBVmpnt04VwBUXAj/88jGIxAWCfgWPrt2J3oh5hGA8z9qqMhy2SYcNWFNU6PgdgsiSZZc9ePpiAl/5zzW20dJuiMQElkl8IDL81B9fcPaXahy3N47GUvkQInF1Km2q+8nN/WYXKEcp+qfYfBBMYWAF4TEyW+lN59WjrbM35ZS8XAQA2VUAs/NR3Pe1BoT8JH1IEwH3vrwdfdE4esJqoBqRmpjPziacqt657oRNRkkx6pAVPaqtKkNvn9WU1ReN46HVzoF0qXRI0KfgKyekfuArmtM8HIujLwa8/L589paRuEAi9brsWgR8lOjPuprKROJFPcWIsZ+NAYf6X/J1SA5KDPkVS6BcwKfV4ZC0oZMcjJdMqnvYKx9CPgLtOJtrP+yDyAFGW+nmPV24/YWtKYN73AYApTMzKlUFMAA4ecIIPPVOGwjqpJ7GCSMScr/6wae4ZcUWHDQYqQnWSnBD/D48+I0TMbwsaJKpoyeM5zZ8kjKldSQWQ8gHhJMGEqRNMQr5FUt7b+04YEoQBwCX/vZN21lNIi4wq24U1u/6XJpHKeRXMH5EGT789KBkb9UUdPuFJ+Bbp0/C13+3zhQ05/cRbl8wFbeu2GJKma3AjYcDME2lShLt7C/V4LaLjk+cp7paSzEirCqtaecBU1817TpguX/0oER1f/Vb8n162/NbbdtwCrx0cw9n60PIR6AdV50zwyOIHFFdEUJtVVkiWjdVZTY3AUDpzIxyUwFMDVKL47A2a0lvs7oihLOOO1KrAd2PbOZOn5aYz2gTXt68B6f+4hXc+txWqWwBqM+gvphVOQBIPGyjkhiBSEzgzY86Em93smC85GM9sKrFNsleXAhb5QD0pzhvnFSNey+ebnr7vffi6Thl4ghLPYWIAE4eb02DnsxQbSaVLNDthc370KnVojBeKz3FSDqBdOZjCByKxEwFgYz3aaZtpBPElqkPIR+Bdlx1zgqPIHKIm+AeN9ukOzMqVQWwxknVrtpUH46pAwauOavO1H5HTxg/eKrZEn1sZPzIcny03/6hrBP0KeiVKInrnnoPQwJ+ROJxnFibOgYg6KOUfohYKkFhTnEue/vd0Po5Aj4ylVgN+Ag3X3A8ItEYVn+4H7F4HA++9rHl2Ds7DqFxUrVjIFy2QZGA8z2WbRv5CGLLR6AdB+NZYQVhQ7aBbh09YXT1RtAXS9+BqG+j5/gZEvBJb9w3P+rA4UjMMo11zpSRuH9Vi0Ump0yq5UEfNrR+jr9t3mt66MkI+QmXnTbelIfog33dKZUDAEyrHeZKQehZT5OPF4kDEa3a3bodB1Iegyi1inPK4qRfB+O9MF1zKnf0hNF64KClnyIxgfKgD3XjjkDjpGq0tHdLFYTbbK3ZBkW6OUa2baRbrS2T31U+Au3YkW6FFYQEL22dsXhcdTpqkbV2DsQbktr71SvbTcP65BfhnnAU1/zxvcR3o024cVI1xgwLmubwG6uQVVeEMH5Emck8c0SZH+c/sAYK1OmiySikFgMK+tzLmEyZn3D1WVPw5+a9tttUhvyJ4y9tajWZypLHNHEAQQVILnltPEbTrgNY8qZZxlBASZnkz7j/mpb9lntBQC1opEimAfkIJt+N7oQ2ypActDi7rhqvG87T6Iy3uz+SgyLtju/mGNm24bS/Tja/K7dtZLN/tm0MRCjVTJNip7GxUTQ1NXl6zI6eMGbducr0ABkSULB28VxXN4ps/5Cf8NtFjZh61HDbYxjfrDoP9mH+fast2wR9QMjvRzgalQY5vXLdnER2Udn+r1w3B1XlQby0ZR9+LKn2lgoC8PRVMxDw+1BbVWafxiIFej/+auV208PGyAOXNmDm5JG2feCEsa8BSK6FAkCkTBKoyyDfnwBJ7iIj+nUwYpfx1e395vTmnSqjrNttsm0j1f7Z/q7cyujF/rlMk1MsENF6IUSj03Y8gkgiF7bOoM+H4WXBlPtXV4QS61/94FPpNj/5ypdw4vgReG/3Adz63PuW9U4J3B5duxN/eq/NdeEbIwLAZY+8jV8unIadHQdx/dMb0j6G3o/XzjsG8Tjw+DqrkjgciaO6ImTbB24EHV6mpsd49YNP4U9yQgR9Cq6cczQefK0FQlhnZhll2KAFqRmvpY+UlEOlIQHFNILQqauplD5U3d5vxvtDht3xdbyYnePURioZvbLvO/WDF/tn28ZAoiAKgoh2AuiGOiMwKoRoJKIRAJ4CMBHATgCXCCHkuZJzSL6SiqV6S7Gz+Z5RNwp1NZVaDIFVQTjZtZ9Z35qyhrMTfdE4fri0GYqiOPootISqpqmufbEY/rZ5Lx5duxM+myitbJPQhWMC6z7uwNde2Q4fkeVhHYnHcdlp43Hu8aPx4pa9uPulD6UyqD6kPvTFzPvHRFw6DdRIOjbrfNi93UxyyPX0Tq/O081IifGOQk5zPUsI0WAY5twIYKUQYgqAldr3vJNtQE91RchSSOeUCVWm/Z2mrNbVVGJ0UpK4McOCKZPtHVtTblpflhTtFlTUOsTZEonL37p1yoM++BV1FhQlKYG+qMCv//6xNP0EYE1CJ+uD5KCyZPwK4ZcvbcPhSFz6Jh/yEda07Mf5D6zBr/9udR4DqvNbLxGarAhPnTgiEWA2JGD9+aSb5C4fSeic0lzkY3qnF+d587ObMP++1bj+mY2Yf99q3Lx8k2fyMXKKycR0IYAztc+/B/AagMWFECSbgJ6W9m5pDEJLezeqyoPY8kkXbtBSPNu9zTXt6MC+pCRxe7/oQ9OOjsTMmORke9vaDyYK/jz7bit6k2zsfXEgLok49pqvNdbiibdbpUrEaexiTEJn1wf/MGUUvjR6GG5avlkaiBeNCwwNKLYjpa7DMVz/9IaUI6Bbn9uCSExI03m/3tKBWy6YiuevOQPn3v+6Zf1T77SmneQu10nonN7e8zW9M9vflSwWY9GMiTySyCGFUhACwEtEJAD8RgjxMIAaIYQ+vWUfAGmOAyK6EsCVADB+fOq3yWzI1A7pZP9XiCwO0uQfo1Mcg9O89Oc37ZOuP25MJba392RlZnKi42AEQZ81CtoNIi4S/WDXB1c+th4hv882SjvoI9vAOB0nF4yPCJEU6qy59XNMqalEwKcgkmSC8lFmD9Zc2r2dZufkc3qn178rY7wH4z2FMjGdIYQ4CcC5AK4mojnGlUKdWiX9hQohHhZCNAohGkeNGpUHUdMjlf3/cESNVE0m+ceoxyskoy93mpd+/gmjpeuvOGMS/vK92QikysqXJQumj7GtlOZEWIshAIDptfJo5JhInSpcUQi3XDBVNQH55be3UxK/mMPMvoZxR6C2qgwxiaaJieKcN7+gYSzWLp6Lx684DWsXzzX5F0qh1oKbeA/GewqiIIQQe7T/PwXwZwCnAmgnojEAoP2f4TSWwiJLvnbe8aOl9v+AQgj6YPkxThpVoRZ0MTC7rjoRx+BUEOiik8ZZCt2MGRbERSepqb+vOGNS5ieYgtl11ZhXPzrxsNFt9D5SfQNOesk4A2hk5RDH7QEkUlYHFEokmfvGjAlYu3gunrpqJo6sMBcdOrIigO/NnYKQX0F5yHpN/Apw64KpiYelL0mb6P1cXRHC3QunmZStXwHuXjhd+mD1MgFcpsdKleYilQIpBpzueSY35N3ERETlABQhRLf2+WwAtwFYAeCbAO7Q/l+eb9m8IrlYT1V5ELPutJby1Gs8G5OrGWeTBBRg3nE1uGL2pIRy0Dl5wgg8+XYriAhCCDROGGFa/+ZPvoxn323F85v24fwTRuOik8Yl1s2cXG3roPWCWXUj8fD/asSSN3filfc/VaOhhcD840bh79v329a2Dkf7S23WVpU5+ixCfrUuxSNrdsDvU0xv9Lop4+2fnY3H39iB5Rv3YuKIoXhu0148vPpjAALfnVOH1s6DWNrUP0ngstPG4xunTcA5U0ejrbMXP1j6nqmY0rqP+/1Luk19yyddAAhTjxomffh6OUMol7ONin16Z6oiWExuyHugHBEdDXXUAKgK6g9CiF8QUTWApQDGA9gFdZprylwKuQiUyxUrmvfghmUbbSOV9SC25GCioI/wl+/Nzii4yo6mHR1pB7m55dq5dfjN6x9rEcXpm5qCPsIvL56Oo4YPkcoYUJDIxXTTefWJZIg6yf2gP1BlU15lQXPG/Vdu3YdvL1lvkeGRRSdjXr3cjJeMVwFiXh+LGdy4DZTLu4lJCPGxEGK69jdVCPELbXmHEGKeEGKKEGK+k3IoNfQh/BWzj5aub2793L74zf2vm6bCZluda2eHvLxoOq6Jo0fK7ewPvNaiTTGVKwenNvpiaqbRF7fK6yrEBXDlnKOxdvFcHD92uOvpm7Iprz6FLEGDuqMcAF6ykcFuuQwvKwzms1ohwwCc7jtnyOzE1RUhLJh+lHR73fEpc/D2xQR++PSGxLHczjrRC7w07egwyTKxeqhUhnQmN328X/5Qcoq0SFXFTCegKDh6ZLl0XUwAD76mJiJ0O33TjlhcWNKYGx3lZ9fLiwXZLZfh5QyhbI/l5LsohUI5pSDjQKKY4iAGDKnsxFXlQUvSOdKW67NJfiiZpx+JCWz5pAtzjjkyEYxnjLfQg/H0CO0n3tqJpev7Rx0+AgJatbEJ1fKHb9BHWU+BdbIqCaG+uctmAOlE4nGcPXU0bntuiyWeA+h/a54+7oi0p28CQHnIh1hc4Ooz63DPy9st6/XRxrz60dKkh27NS4C3CeCyOZaT76IUCuWUgowDDVYQHuOU1qCtsxcVIT+6w/1BaxUhv6nuwMFwVJpM74tedR+7YLzf/P0j3PvydhDBkq00JoCYFiH7s3OPk8qeSY6mdIkJIOQD/v3C43HLiq2WdOj6TKTm3Z1S5QCY35pTBV/JHqg3nVeP48cOR21VGZp3yzO57O8+DEC9lp295uDCzt6opaqdE14GwmVyLKd7Mt16I4WgFGQciLCC8BinqFQ3ZoKxNiaDYWXq5bILGrrzbx84BoEFFAWv2QShNU6sQnNbFyLRuKO56ZQJVXhnl32qLFktBx2/T8HersMI+smUlXZo0IeHLj8Jc445EoufkScDVMg6LTgVqR6oG9q6pPtsaOvCvPrRnkYYezlDKN1jZVswqBgoBRkHIuyD8BgnBeAmKGnqUcMtwWwBHyVSWNsFB7kZAETicdtAuu/MVp2/35tbJ13/4388Br9cOA2vXDcH3zx9Qsp2UgWjHQzH8LvXP0ZPUs3RuBCJc7Sz8997sdU04lSK1W7+v1NA4kApIJNtwaBioBRkHIiwgvAYXQGE/AqGBn0I+a0KYEHDWDx/zRm45YJ6PH/NGRY7anVFCPdcPB0BheBX1CCwey7uD8Cqq6nEece7t4MD6hu9rowuOmmcJdnf+KoyNIxX/RiXz5woPcbCU8ZjYaMabDdz8kipEgj6CEMCCu65pAGz68yxG0adZ5zqG/ARgj5zP82rHy1NSDj7mCMTTkq3SebsHJuNk6otMhoDEkshwtgNTudRCudZCjIORNjElAOE/q+Q13V242xb2tRqCCgTeLqp1bSNLApYRtCnBtJ9b94xuOy08YkfVLJUuzt7MevOVQkntkLmEYlCMA3nqytCuHzGeEvhn3+bOyXRzoKGsWja0YHVH+7H9Nrh6DwUwS0rtlhTcKuRdJZz/NezpuCHTzVDEEACOG1SNWbduSrRb1efWYdokg8jGoub5HTq68eumJGQcc6UkZaAxFwn0ssXTudRCudZCjIONHgE4TH6W204KnAoEkM4KkxvtW7eept2dEid0E071GUt7d2mGUo6J4+35i/qi6lTOfWpoQCwcus+U7lRHV2WHZ91W8xVcdHvvNXP46l32izHeOBVc32FSaMqMO9LNWgYX4WzjjsyZZ4j4znq/RQVQCwORAWw5K3dpn77z1UtlqR90TgQicZMx3AaYTROqsYPzj7Wohx0UqWoKCWczqMUzrMUZBxIsILwGKdgJjfBTqmyuQL2TuqNe76wlcvYRqpAr4Ci4M2P5TGKRqduW2evJU8R0J/NFLD6B9a27E+YCewSBurn6BTDANjHVOiBgBxYxjDZwQrCY7xwCNplMtWX2zmp9cR1MoxtpAr0isTjtuuNTl2nbKZ2b++z6kZi7eK5uG3B1JRt2MUwuEHvH3ZsMkx2sILwmOqKEC5prDUtM1YZc+NsG1k5RHpsfbms2tqRFQGp+aY86LO0IXMAA/1O7Hn1o3GMxEFsNMFUV4RwysSq5EMkspm2dfYikmT/iUT7/QOXnjYhbQfxopnjTd/vXjgtZYZPdmwyTHawk9pjOnrCWNpkts0vbWozVRlb0DAW9WOG2Wal3PFZt/TYOz7rxvRxR0irrX3aE8H359XhodUf9weFnV+P448aLnXovXjdmVi5dR9e2tqOmUePwKRRlYntOnrC2H3AbIbZdaDXFCAmC9YDgPoxwwCo/orkOIhYkh8jEwfxtfOOMX1f0DA2ZYZPdmwyTOawgvAYNwE9TjNr7HwAb358ABedNM7WRxEXwNrFc10/DOfVj5amjXBzDk4VvpyC0HQaJ1XbOocBa1CYLEisrqYyZernYk9jzTDFCpuYPMbJ7u1mZo1TkrhUAV5ezPJwY7t3qvDlFITGMEzxwwrCY5zs3m2dvRApUkwD9kFi+pu3U4BXrs8BUN/aZTLob/K5lpFhmNzDJqYckMruXR70IZxknDemmNYx+gjOrq+xmIKc7PdenkN50IeDfTGTD6KjJ2zJxfTOrk7TNrmWkWGY3MIKIkfY2b0P9sUwJKBYqoLJCtrY+Qh0nOz32VJdEcKalv1Sf4nb5Gm5lpFhmNzBJqY8YzcHvxjn5qfyl3CMAcMMfFhBZEimla2KaW6+0zmkikQupvPINVzFjPGSUrqf2MSUAdlWtiqGufluzsFplFAM55FruIoZ4yWldj/xCCJN3CaAc6KQScfcnoObUcJATp7m1bVmGKA07yceQaTJQKhslc45DIZRgvi6sAMAAAjtSURBVB0D4VozxUMp3k+sINLErXO2oyec9UO1pb3bNoWEG+xkSNfBPFgjkdkRz3hJKd5PbGJKEzdmFzdlMJ24+dlNmH/falz/zEbMv281bl6+Ka39U8kwmBzM2cD9xHhJKd5PJFIUcCl2GhsbRVNTU0Hatns77+gJY9adqyxxDmsXz3V9I7S0d2P+fasty1+5bo6rkYRbGbwY5QwGuJ8YLymG+4mI1gshGp22YxNThtiZXbywMzolwnPCrQyD1XSULtxPjJeU0v3EJiaP8cLO6JQILx8yMAzDsIKwoZCBcHU1lSkL4eRDBoZhGPZBSPAimKWYZzExDDO4YR9EhhiDWXQbvl5LOZ2HrBd2RqdCOPmQgWGYwQubmJJIlX+IYRhmMDEoFURLezeeaWpFS7u19jM7eBmGYVQGnYnp5mc3YclbuxPfF80cj9suPCHxXXfw3pDkg2BTDcMwg41BpSBa2rtNygEAlry5G4tmTDTZ+gdz/iGGYRidQaUg0glAYwcvwzCDnaLzQRDROUS0jYhaiOhGL4+dbQAawzDMYKKoFAQR+QA8COBcAPUALiWieq+On20AGsMwzGCi2ExMpwJoEUJ8DABE9CSACwFs9aqB2y48AYtmTMwqAI1hGGYwUGwKYiyAVsP3NgCnGTcgoisBXAkA48ebRwNuyTYAjWEYZjBQVCYmNwghHhZCNAohGkeNGlVocRiGYQYsxaYg9gAYZ/heqy1jGIZh8kyxKYh3AEwhoklEFATwdQArCiwTwzDMoKSofBBCiCgRXQPgRQA+AI8KIbYUWCyGYZhBSVEpCAAQQvwFwF8KLQfDMMxgp6TrQRDRZwB22aweCWB/HsXJBJbRO0pBTpbRG1jG7JkghHCc5VPSCiIVRNTkpiBGIWEZvaMU5GQZvYFlzB/F5qRmGIZhigRWEAzDMIyUgawgHi60AC5gGb2jFORkGb2BZcwTA9YHwTAMw2THQB5BMAzDMFkwIBVELmtKZAoRjSOiV4loKxFtIaJrteW3EtEeImrW/r5SYDl3EtEmTZYmbdkIInqZiD7U/q8qoHzHGvqqmYi+IKLvF7ofiehRIvqUiDYblkn7jVTu1+7PjUR0UgFlvJuIPtDk+DMRHaEtn0hEvYb+fCgfMqaQ0/b6EtGPtb7cRkT/WEAZnzLIt5OImrXlBevLrBFCDKg/qBHYHwE4GkAQwAYA9UUg1xgAJ2mfKwFsh1rz4lYA1xdaPoOcOwGMTFp2F4Abtc83Ariz0HIarvU+ABMK3Y8A5gA4CcBmp34D8BUAfwVAAGYAWFdAGc8G4Nc+32mQcaJxuyLoS+n11X5DGwCEAEzSfvu+QsiYtP4eADcXui+z/RuII4hETQkhRB8AvaZEQRFC7BVCvKt97gbwPtT05qXAhQB+r33+PYCLCiiLkXkAPhJC2AVL5g0hxGoAB5IW2/XbhQCWCJW3ABxBRGMKIaMQ4iUhRFT7+hbUBJkFxaYv7bgQwJNCiLAQYgeAFqjPgJySSkYiIgCXAPhjruXINQNRQchqShTVg5iIJgI4EcA6bdE12hD/0UKabzQEgJeIaL1WewMAaoQQe7XP+wDUFEY0C1+H+UdYTP0I2Pdbsd6j34I6stGZRETvEdHfiWh2oYQyILu+xdiXswG0CyE+NCwrtr50xUBUEEUNEVUAWAbg+0KILwD8GsBkAA0A9kIdmhaSM4QQJ0Et+3o1Ec0xrhTqmLngU9+0bL8LADytLSq2fjRRLP1mBxH9FEAUwBPaor0AxgshTgTwAwB/IKJhhZIPRX59k7gU5heXYutL1wxEBVG0NSWIKABVOTwhhPgTAAgh2oUQMSFEHMBvkYfhcSqEEHu0/z8F8GdNnnbdBKL9/2nhJExwLoB3hRDtQPH1o4ZdvxXVPUpE/wLgfADf0BQZNJNNh/Z5PVTb/jGFkjHF9S22vvQD+GcAT+nLiq0v02EgKoiirCmh2SUfAfC+EOJew3Kj7fmfAGxO3jdfEFE5EVXqn6E6MDdD7b9vapt9E8DywkhowvSWVkz9aMCu31YAWKTNZpoBoMtgisorRHQOgBsALBBCHDIsH0VEPu3z0QCmAPi4EDJqMthd3xUAvk5EISKaBFXOt/Mtn4H5AD4QQrTpC4qtL9Oi0F7yXPxBnSWyHaqm/mmh5dFkOgOqiWEjgGbt7ysAHgOwSVu+AsCYAsp4NNQZIRsAbNH7DkA1gJUAPgTwCoARBe7LcgAdAIYblhW0H6Eqq70AIlDt4N+26zeos5ce1O7PTQAaCyhjC1Qbvn5PPqRt+1XtHmgG8C6ACwrcl7bXF8BPtb7cBuDcQsmoLf8fAN9N2rZgfZntH0dSMwzDMFIGoomJYRiG8QBWEAzDMIwUVhAMwzCMFFYQDMMwjBRWEAzDMIwUVhAM4wARXUREgoiO075PJKLLDOsbsskeq2X+HOmFrAzjJawgGMaZSwGs0f4H1OyclxnWN0CNaWGYAQUrCIZJgZY76wyowVpf1xbfAWC2ltt/MYDbAHxN+/41IjqViN7UkrO9QUTHasfyEdEviWizlnTu35LaKiOivxLRd/J4igxji7/QAjBMkXMhgL8JIbYTUQcRnQy1tsP1QojzAYCI2qFGQ1+jfR8GYLYQIkpE8wH8B9Ro2iuhjj4atHUjDO1UQE1Nv0QIsSRfJ8cwqWAFwTCpuRTAr7TPT2rfn3fYZziA3xPRFKjpVQLa8vlQU1lEAUAIYawnsBzAXUKIJ8AwRQIrCIaxQXvDnwvgBCISUCvYCQAvOOx6O4BXhRD/pNX+eM1Fc2sBnENEfxCc/4YpEtgHwTD2LATwmBBighBiohBiHIAdAOJQy8bqdCd9H47+lNP/Ylj+MoCrtJTQSDIx3QygE2oSP4YpClhBMIw9l0KtiWFkGVRndYyINhDRdQBeBVCvO6mh1qL+v0T0Hsyj9N8B2A1gIxFtgHkmFABcC6CMiO7KwbkwTNpwNleGYRhGCo8gGIZhGCmsIBiGYRgprCAYhmEYKawgGIZhGCmsIBiGYRgprCAYhmEYKawgGIZhGCmsIBiGYRgp/x9CX6TWC4Or4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot  \n",
    "data1.plot(kind = \"scatter\",x=\"Attack\",y = \"Defense\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "bf5d8c4c-3d16-416f-833f-fc123ce511fe",
    "_uuid": "3291543af72e3cd679f1ef45913b7487ee82c4e2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1eb53908>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 70,
   "metadata": {
    "_cell_guid": "45222cee-73fa-4982-a0b5-fc309de88b1e",
    "_uuid": "26c60e7d446566b07e5247cc9c923643f568ee2c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XK7/TTBOJpEuBbwF1wHci4qtd1tcDDwKTgT3AJyJiR7ruDuBGoAO4JSIez+eYpVIr/wCsuhztHxj+g+Qd/j+bncxubUmqA+4DPgyMA/5C0rgum90I/C4i3g98E7gn3XcccDUwHrgU+GdJdXke08zMSijLGsk5wLaI2A4g6WFgOrApZ5vpwLx0eilwrySlyx+OiDeB30ralh6PPI5ZVv4L0Poa/6VvWSaSU4CXcuZbgA/2tE1EtEvaCwxNlz/TZd9T0ukjHRMASTcDN6ez+yVtOYYyAAwDXj3GfauVy9w3dFtm3VOcg1facci4vMWUdZmPwnvz2ahmG9sjYhGwqNDjSFoVEY1FCKlquMx9Q18rc18rL5SuzFl2/90JnJozPypd1u02kvoDQ0ga3XvaN59jmplZCWWZSFYCp0saLek4ksbz5i7bNAPXp9MfA34REZEuv1pSvaTRwOnAc3ke08zMSiizW1tpm8cs4HGSrrqLI2KjpC8BqyKiGfgu8FDamP4aSWIg3e5HJI3o7cDMiOgA6O6YWZUhVfDtsSrkMvcNfa3Mfa28UKIyK6kAmJmZHRsPkWJmZgVxIjEzs4I4kfRA0qWStkjaJmluuePJiqQdktZLWitpVbrsRElPStqa/vyDcsdZCEmLJe2WtCFnWbdlVGJ+et3XSZpUvsiPXQ9lnidpZ3qt10q6LGfdHWmZt0hqKk/UhZF0qqSnJG2StFHS36TLa/Za91Lm0l7riPCny4ekIf+/gTHAccBvgHHljiujsu4AhnVZ9g/A3HR6LnBPueMssIwXAJOADUcqI3AZ8HNAwIeAZ8sdfxHLPA/42262HZf+G68HRqf/9uvKXYZjKPNIYFI6PRh4IS1bzV7rXspc0mvtGkn3Dg3vEhFvAZ1DsfQV04Hvp9PfBz5axlgKFhG/IukVmKunMk4HHozEM8B7JI0sTaTF00OZe3JoSKKI+C2QOyRR1YiIXRGxJp1+HdhMMiJGzV7rXsrck0yutRNJ97ob3qW3i1PNAnhC0up0WBmAkyJiVzr9v8BJ5QktUz2Vsdav/az0Ns7inFuWNVdmSQ3AHwPP0keudZcyQwmvtROJnRcRk0hGVJ4p6YLclZHUh2u6j3hfKGNqIfA+4GxgF/D18oaTDUknAD8Bbo2IfbnravVad1Pmkl5rJ5Lu9ZmhWCJiZ/pzN/AzkmruK51V/PTn7vJFmJmeyliz1z4iXomIjoh4G/g279zSqJkySxpA8oX6g4j4abq4pq91d2Uu9bV2IulenxiKRdK7JA3unAamAhv4/aFrrgeWlSfCTPVUxmbgurRHz4eAvTm3Rapal/v/V5Jca+h5SKKqIkkko2Vsjohv5Kyq2WvdU5lLfq3L3eugUj8kPTpeIOnVcGe548mojGNIenD8BtjYWU6Sofz/A9gKrABOLHesBZbz30iq920k94Rv7KmMJD147kuv+3qgsdzxF7HMD6VlWpd+oYzM2f7OtMxbgA+XO/5jLPN5JLet1gFr089ltXyteylzSa+1h0gxM7OC+NaWmZkVxInEzMwK4kRiZmYFqdlX7eYaNmxYNDQ0lDsMM7Oqsnr16lcjYviRtquoRCJpMXA5sDsizuxmvYBvkfRK+D/gU5EOD9CbhoYGVq1aVexwzcxqmqT/yWe7Sru19T3g0l7Wf5ik3/PpwM0kT2+amVkZVVQiiSMPNFf1g6yZmdWaikokeaipQdbMzGpBRbWRFFM6ku3NAKeddtph69va2mhpaeHgwYOlDq0qDRw4kFGjRjFgwIByh2I1qmHuo+UOoebs+Oq0kpyn2hJJ3gOORcQiYBFAY2PjYY/vt7S0MHjwYBoaGkja8K0nEcGePXtoaWlh9OjR5Q7HisRf3FYs1ZZImknG2H8Y+CAFDLJ28OBBJ5E8SWLo0KG0traWOxTrgZOClVNFJRJJ/wZcBAyT1AL8PTAAICLuB5aTdP3dRtL99y8LPF8hu/cp/l2VlhODVZOKSiQR8RdHWB/AzBKFk7m6ujomTJhAW1sb/fv357rrrmP27Nn069d7H4g5c+awfPlyLrvsMr72ta+VKFozs+5VVCIpp2L/BZhPI9fxxx/P2rVrAdi9ezczZsxg3759fPGLX+x1v0WLFvHaa69RV1dXlFitfFzzsFpQbd1/a9aIESNYtGgR9957LxFBR0cHc+bMYcqUKUycOJEHHngAgCuuuIL9+/czefJklixZQmtrK1dddRVTpkxhypQp/PrXvwZg3rx53HDDDVx00UWMGTOG+fPnA3DgwAGmTZvGWWedxZlnnsmSJUsAWL16NRdeeCGTJ0+mqamJXbuq6v0+Fa9h7qPdfsxqgWskFWTMmDF0dHSwe/duli1bxpAhQ1i5ciVvvvkm5557LlOnTqW5uZkTTjjhUE1mxowZzJ49m/POO48XX3yRpqYmNm/eDMDzzz/PU089xeuvv87YsWP57Gc/y2OPPcbJJ5/Mo48mX2J79+6lra2Nz33ucyxbtozhw4ezZMkS7rzzThYvXly234WZVQ8nkgr1xBNPsG7dOpYuXQokX/hbt249rPvtihUr2LRp06H5ffv2sX//fgCmTZtGfX099fX1jBgxgldeeYUJEybw+c9/nttvv53LL7+c888/nw0bNrBhwwYuueQSADo6Ohg50gMGHAvXMqwvciKpINu3b6euro4RI0YQESxYsICmpqZe93n77bd55plnGDhw4GHr6uvrD03X1dXR3t7OGWecwZo1a1i+fDl33XUXF198MVdeeSXjx4/n6aefLnqZzKz2uY2kQrS2tvKZz3yGWbNmIYmmpiYWLlxIW1sbAC+88AIHDhw4bL+pU6eyYMGCQ/Odt7x68vLLLzNo0CCuueYa5syZw5o1axg7diytra2HEklbWxsbN24sYulqj9s8zN7hGkkZvfHGG5x99tmHuv9ee+213HbbbQDcdNNN7Nixg0mTJhERDB8+nEceeeSwY8yfP5+ZM2cyceJE2tvbueCCC7j//vt7POf69euZM2cO/fr1Y8CAASxcuJDjjjuOpUuXcsstt7B3717a29u59dZbGT9+fGZlrxZODmZHpuTRjNrW2NgYXd9HsnnzZj7wgQ+UKaLq1Bd/Z04kVs0KHWtL0uqIaDzSdr61ZWZmBfGtLTNc8zArhGskZmZWkD6dSPpC+1Cx+HdlZj3ps4lk4MCB7Nmzx1+Qeeh8H0l3z6qYmfXZNpJRo0bR0tLid2zkqfMNidXObSFmxddnE8mAAQP8tj8zsyLI7NaWpAlZHdvMzCpHljWSf5ZUD3wP+EFE7M3wXGaH+PaVWWllViOJiPOBTwKnAqsl/VDSJVmdz8zMyiPTXlsRsRW4C7gduBCYL+l5SX+W5XnNzKx0smwjmSjpm8Bm4E+Bj0TEB9Lpb2Z1XjMzK60s20gWAN8BvhARb3QujIiXJd2V4XnNzKyEskwk04A3IqIDQFI/YGBE/F9EPJThea2PcKO6WWXIso1kBXB8zvygdJmZmdWQLBPJwIjY3zmTTg/K8HxmZlYGWSaSA5Imdc5Imgy80cv2ZmZWhbJsI7kV+LGklwEBfwh8IsPzWY1yW4hZZcsskUTESkl/BIxNF22JiLaszmdmZuWR9aCNU4CG9DyTJBERD2Z8TjMzK6HMEomkh4D3AWuBjnRxAE4kZmY1JMsaSSMwLvzmKDOzmpZlr60NJA3sZmZWw7KskQwDNkl6Dnizc2FEXJHhOa2KuXeWWXXKMpHMy/DYZmZWIbLs/vtLSe8FTo+IFZIGAXVZnc/MzMojy2HkPw0sBR5IF50CPJLV+czMrDyybGyfCZwL7INDL7kakeH5zMysDLJsI3kzIt6SBICk/iTPkVgf50Z1s9qSZY3kl5K+AByfvqv9x8C/Z3g+MzMrgywTyVygFVgP/BWwnOT97T2SdKmkLZK2SZrbzfpPSWqVtDb93JRJ5GZmlrcse229DXw7/RyRpDrgPuASoAVYKak5IjZ12XRJRMwqarBmZnbMshxr67d00yYSEWN62OUcYFtEbE/3fxiYDnRNJGZmVkGyHmur00Dg48CJvWx/CvBSznwL8MFutrtK0gXAC8DsiHipm23MzKxEMmsjiYg9OZ+dEfFPwLQCD/vvQENETASeBL7f04aSbpa0StKq1tbWAk9rZmY9yfLW1qSc2X4kNZTezrcTODVnflS67JCI2JMz+x3gH3o6WEQsAhYBNDY2utuxmVlGsry19fWc6XZgB/DnvWy/Ejhd0miSBHI1MCN3A0kjI2JXOnsFsLlo0VrR+XkRs74hy15bf3KU27dLmgU8TjIm1+KI2CjpS8CqiGgGbpF0BUlieg34VJHDNjOzo5Tlra3belsfEd/oZtlykudNcpfdnTN9B3BHsWI0M7PCZd1rawrQnM5/BHgO2JrhOc3MrMSyTCSjgEkR8TqApHnAoxFxTYbnNDOzEstyiJSTgLdy5t9Kl5mZWQ3JskbyIPCcpJ+l8x+ll+c+rHq5d5ZZ35Zlr60vS/o5cH666C8j4r+yOp+ZmZVHlre2AAYB+yLiW0BL+oyImZnVkCxftfv3wO280113APCvWZ3PzMzKI8sayZUkT58fAIiIl4HBGZ7PzMzKIMtE8lZEBOlQ8pLeleG5zMysTLJMJD+S9ADwHkmfBlaQ50uuzMysemTZa+sf03e17wPGAndHxJNZnc+y526+ZtadTBJJ+trcFenAjU4eZmY1LJNbWxHRAbwtaUgWxzczs8qR5ZPt+4H1kp4k7bkFEBG3ZHhOMzMrsSwTyU/Tj5mZ1bCiJxJJp0XEixHhcbWqlBvVzexoZNFG8kjnhKSfZHB8MzOrIFkkEuVMj8ng+GZmVkGySCTRw7SZmdWgLBrbz5K0j6Rmcnw6TTofEfHuDM5pZmZlUvREEhF1xT6mmZlVrqzfR2JmZjXOicTMzAqS5QOJVuH8vIiZFYNrJGZmVhAnEjMzK4gTiZmZFcSJxMzMCuLG9j7AjepmliXXSMzMrCBOJGZmVhAnEjMzK4gTiZmZFcSJxMzMCuJeWzXEvbPMrBxcIzEzs4I4kZiZWUGcSMzMrCAV10Yi6VLgW0Ad8J2I+GqX9fXAg8BkYA/wiYjYUeo4y8XtIGZWaSqqRiKpDrgP+DAwDvgLSeO6bHYj8LuIeD/wTeCe0kZpZma5Kq1Gcg6wLSK2A0h6GJgObMrZZjowL51eCtwrSRERpQy0WFzDMLNqV2mJ5BTgpZz5FuCDPW0TEe2S9gJDgVdLEuERODGYWV9TaYmkaCTdDNyczu6XtOUYDzWMCklSJeQy9w19rcx9rbzonoLL/N58Nqq0RLITODVnflS6rLttWiT1B4aQNLr/nohYBCwqNCBJqyKisdDjVBOXuW/oa2Xua+WF0pW5ohrbgZXA6ZJGSzoOuBpo7rJNM3B9Ov0x4BfV2j5iZlYLKqpGkrZ5zAIeJ+n+uzgiNkr6ErAqIpqB7wIPSdoGvEaSbMzMrEwqKpEARMRyYHmXZXfnTB8EPl7CkAq+PVaFXOa+oa+Vua+VF0pUZvmukJmZFaLS2kjMzKzKOJH0QNKlkrZI2iZpbrnjyYqkHZLWS1oraVW67ERJT0ramv78g3LHWQhJiyXtlrQhZ1m3ZVRifnrd10maVL7Ij10PZZ4naWd6rddKuixn3R1pmbdIaipP1IWRdKqkpyRtkrRR0t+ky2v2WvdS5tJe64jwp8uHpKH/v4ExwHHAb4Bx5Y4ro7LuAIZ1WfYPwNx0ei5wT7njLLCMFwCTgA1HKiNwGfBzQMCHgGfLHX8RyzwP+Ntuth2X/huvB0an//bryl2GYyjzSGBSOj0YeCEtW81e617KXNJr7RpJ9w4N1RIRbwGdQ7X0FdOB76fT3wc+WsZYChYRvyLp4ZerpzJOBx6MxDPAeySNLE2kxdNDmXsyHXg4It6MiN8C20j+D1SViNgVEWvS6deBzSQjYdTste6lzD3J5Fo7kXSvu6Faers41SyAJyStTkcDADgpInal0/8LnFSe0DLVUxlr/drPSm/jLM65ZVlzZZbUAPwx8Cx95Fp3KTOU8Fo7kdh5ETGJZMTlmZIuyF0ZSX24prv29YUyphYC7wPOBnYBXy9vONmQdALwE+DWiNiXu65Wr3U3ZS7ptXYi6V4+Q7WsaytHAAABSklEQVTUhIjYmf7cDfyMpJr7SmcVP/25u3wRZqanMtbstY+IVyKiIyLeBr7NO7c0aqbMkgaQfKH+ICJ+mi6u6WvdXZlLfa2dSLqXz1AtVU/SuyQN7pwGpgIb+P1haK4HlpUnwkz1VMZm4Lq0R8+HgL05t0WqWpf7/1eSXGtIyny1pHpJo4HTgedKHV+hJIlk5IvNEfGNnFU1e617KnPJr3W5ex1U6oekR8cLJL0a7ix3PBmVcQxJD47fABs7y0kyLP9/AFuBFcCJ5Y61wHL+G0n1vo3knvCNPZWRpAfPfel1Xw80ljv+Ipb5obRM69IvlJE529+ZlnkL8OFyx3+MZT6P5LbVOmBt+rmslq91L2Uu6bX2k+1mZlYQ39oyM7OCOJGYmVlBnEjMzKwgTiRmZlYQJxIzMyuIE4mZmRXEicTMzAriRGJmZgX5/xaYaBt3QrPTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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('graph.png')\n",
    "plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e0896c4f-8086-4577-9a0d-f06015c7877e",
    "_uuid": "1c3330acf16ddc6c35c90e5ab21dd9e459ada94f"
   },
   "source": [
    "<a id=\"29\"></a> <br>\n",
    "### STATISTICAL EXPLORATORY DATA ANALYSIS\n",
    "I already explained it at previous parts. However lets look at one more time.\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_cell_guid": "c673b7bb-a0ad-42c3-8614-696ec119028f",
    "_uuid": "1d2883a439385295867a92c8070b3576dd356f9b"
   },
   "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>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>800.0000</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>400.5000</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>231.0844</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>1.0000</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>200.7500</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>400.5000</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>600.2500</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>800.0000</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": [
       "              #          HP      Attack     Defense     Sp. Atk     Sp. Def  \\\n",
       "count  800.0000  800.000000  800.000000  800.000000  800.000000  800.000000   \n",
       "mean   400.5000   69.258750   79.001250   73.842500   72.820000   71.902500   \n",
       "std    231.0844   25.534669   32.457366   31.183501   32.722294   27.828916   \n",
       "min      1.0000    1.000000    5.000000    5.000000   10.000000   20.000000   \n",
       "25%    200.7500   50.000000   55.000000   50.000000   49.750000   50.000000   \n",
       "50%    400.5000   65.000000   75.000000   70.000000   65.000000   70.000000   \n",
       "75%    600.2500   80.000000  100.000000   90.000000   95.000000   90.000000   \n",
       "max    800.0000  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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "82016726-fd80-48ca-a39e-70a6a8787e1d",
    "_uuid": "99765451e9fdeaa8d95504dfe9fc26924ff28805"
   },
   "source": [
    "<a id=\"30\"></a> <br>\n",
    "### INDEXING PANDAS TIME SERIES\n",
    "* datetime = object\n",
    "* parse_dates(boolean): Transform date to ISO 8601 (yyyy-mm-dd hh:mm:ss ) format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_cell_guid": "a74e4bea-bfee-4da5-8853-75f5a570365e",
    "_uuid": "58316a1739b2e4bea42900c8a466009cfe2eece5"
   },
   "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": 73,
   "metadata": {
    "_cell_guid": "8212b965-a682-49eb-879d-1b32a62bf3e6",
    "_uuid": "41be27a210b2c3ba545b3aed6cb558bc6afbdb53"
   },
   "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",
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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>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",
       "    </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>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>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>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>4</td>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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  HP  Attack  Defense  Sp. Atk  \\\n",
       "date                                                                        \n",
       "1992-01-10  1      Bulbasaur  Grass  Poison  45      49       49       65   \n",
       "1992-02-10  2        Ivysaur  Grass  Poison  60      62       63       80   \n",
       "1992-03-10  3       Venusaur  Grass  Poison  80      82       83      100   \n",
       "1993-03-15  4  Mega Venusaur  Grass  Poison  80     100      123      122   \n",
       "1993-03-16  5     Charmander   Fire     NaN  39      52       43       60   \n",
       "\n",
       "            Sp. Def  Speed  Generation  Legendary  \n",
       "date                                               \n",
       "1992-01-10       65   45.0           1      False  \n",
       "1992-02-10       80   60.0           1      False  \n",
       "1992-03-10      100   80.0           1      False  \n",
       "1993-03-15      120   80.0           1      False  \n",
       "1993-03-16       50   65.0           1      False  "
      ]
     },
     "execution_count": 73,
     "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": 74,
   "metadata": {
    "_cell_guid": "782ce3c6-b55f-45b8-a6c4-bb657d5adfef",
    "_uuid": "0461271e106794b972ae3a9a20678a3ba64828e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#                      5\n",
      "Name          Charmander\n",
      "Type 1              Fire\n",
      "Type 2               NaN\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  HP  Attack  Defense  Sp. Atk  \\\n",
      "date                                                                        \n",
      "1992-03-10  3       Venusaur  Grass  Poison  80      82       83      100   \n",
      "1993-03-15  4  Mega Venusaur  Grass  Poison  80     100      123      122   \n",
      "1993-03-16  5     Charmander   Fire     NaN  39      52       43       60   \n",
      "\n",
      "            Sp. Def  Speed  Generation  Legendary  \n",
      "date                                               \n",
      "1992-03-10      100   80.0           1      False  \n",
      "1993-03-15      120   80.0           1      False  \n",
      "1993-03-16       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": {
    "_cell_guid": "6aafbc8e-78fd-4caf-b6c0-4d4e60017c23",
    "_uuid": "3be93ff15ffbdb0e1010fe51f2e494cffd931a5d"
   },
   "source": [
    "<a id=\"32\"></a> <br>\n",
    "# 5. MANIPULATING DATA FRAMES WITH PANDAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a3631e66-7b74-463b-9813-b5f6eabc8ca4",
    "_uuid": "c61c026ac0b65b02ec30a06eb145dfb848dd1ddb"
   },
   "source": [
    "<a id=\"33\"></a> <br>\n",
    "### INDEXING DATA FRAMES\n",
    "* Indexing using square brackets\n",
    "* Using column attribute and row label\n",
    "* Using loc accessor\n",
    "* Selecting only some columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_cell_guid": "31c51f75-9bfb-46b9-93a7-bc67285cdcd0",
    "_uuid": "8fa63c559933356ffb778da05712529b91849e53"
   },
   "outputs": [
    {
     "data": {
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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>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
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       "      <th>Legendary</th>\n",
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       "    <tr>\n",
       "      <th>#</th>\n",
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       "      <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>45</td>\n",
       "      <td>49</td>\n",
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       "      <th>2</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>4</th>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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  HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  \\\n",
       "#                                                                               \n",
       "1      Bulbasaur  Grass  Poison  45      49       49       65       65     45   \n",
       "2        Ivysaur  Grass  Poison  60      62       63       80       80     60   \n",
       "3       Venusaur  Grass  Poison  80      82       83      100      100     80   \n",
       "4  Mega Venusaur  Grass  Poison  80     100      123      122      120     80   \n",
       "5     Charmander   Fire     NaN  39      52       43       60       50     65   \n",
       "\n",
       "   Generation  Legendary  \n",
       "#                         \n",
       "1           1      False  \n",
       "2           1      False  \n",
       "3           1      False  \n",
       "4           1      False  \n",
       "5           1      False  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data\n",
    "data = pd.read_csv('/Users/garvitkhurana/Desktop/Projects/Data_Science/pokemon.csv')\n",
    "data= data.set_index(\"#\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_cell_guid": "b2dc6f07-d41a-41ce-bc84-d11928a2faaa",
    "_uuid": "2e28d35ad9d91424254e109e6cddbbd39e7d1cdb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing using square brackets\n",
    "data[\"HP\"][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_cell_guid": "fc1668e0-fe68-4d92-b80b-4b9424293095",
    "_uuid": "bfd54632669590a571a2eb72b6df59f7faf7f171"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using column attribute and row label\n",
    "data.HP[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_cell_guid": "ea69256d-1666-420a-8644-3757eabe6601",
    "_uuid": "a753163d6f74b1211fb7d83ee4848b9533a545f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HP    45\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using loc accessor\n",
    "data.loc[1,[\"HP\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_cell_guid": "4b7926fc-3883-4c33-8012-2bd428affb98",
    "_uuid": "62711d7f4d03285b8914ce8d7906cf4b5acd74da"
   },
   "outputs": [
    {
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>130</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>78</td>\n",
       "      <td>104</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>59</td>\n",
       "      <td>63</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>65</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>63</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>55</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</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>771</th>\n",
       "      <td>95</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>67</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>85</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>44</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>54</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>59</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>55</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>55</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>95</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>85</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>126</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>126</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</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",
       "4     80     100\n",
       "5     39      52\n",
       "6     58      64\n",
       "7     78      84\n",
       "8     78     130\n",
       "9     78     104\n",
       "10    44      48\n",
       "11    59      63\n",
       "12    79      83\n",
       "13    79     103\n",
       "14    45      30\n",
       "15    50      20\n",
       "16    60      45\n",
       "17    40      35\n",
       "18    45      25\n",
       "19    65      90\n",
       "20    65     150\n",
       "21    40      45\n",
       "22    63      60\n",
       "23    83      80\n",
       "24    83      80\n",
       "25    30      56\n",
       "26    55      81\n",
       "27    40      60\n",
       "28    65      90\n",
       "29    35      60\n",
       "30    60      85\n",
       "..   ...     ...\n",
       "771   95      65\n",
       "772   78      92\n",
       "773   67      58\n",
       "774   50      50\n",
       "775   45      50\n",
       "776   68      75\n",
       "777   90     100\n",
       "778   57      80\n",
       "779   43      70\n",
       "780   85     110\n",
       "781   49      66\n",
       "782   44      66\n",
       "783   54      66\n",
       "784   59      66\n",
       "785   65      90\n",
       "786   55      85\n",
       "787   75      95\n",
       "788   85     100\n",
       "789   55      69\n",
       "790   95     117\n",
       "791   40      30\n",
       "792   85      70\n",
       "793  126     131\n",
       "794  126     131\n",
       "795  108     100\n",
       "796   50     100\n",
       "797   50     160\n",
       "798   80     110\n",
       "799   80     160\n",
       "800   80     110\n",
       "\n",
       "[800 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting only some columns\n",
    "data[[\"HP\",\"Attack\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c61351a6-78cf-4a1c-b5c1-2dedb44cdc32",
    "_uuid": "6cd90e23121d999c48b6602f7066d49697bfa359"
   },
   "source": [
    "<a id=\"34\"></a> <br>\n",
    "### SLICING DATA FRAME\n",
    "* 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": 80,
   "metadata": {
    "_cell_guid": "7f801b29-7033-434a-8508-646b0993f4e9",
    "_uuid": "ca6ddfc20aaba3283022a89566109775d2e0bf5f"
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_cell_guid": "e37a60e3-cf0c-42c5-8fcd-c5f8998139c1",
    "_uuid": "291eb0edec4c438141852b5e07056ce464ec78dd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>HP</th>\n",
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       "      <th>Defense</th>\n",
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       "      <td>58</td>\n",
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       "      <td>48</td>\n",
       "      <td>65</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",
       "4   80     100      123\n",
       "5   39      52       43\n",
       "6   58      64       58\n",
       "7   78      84       78\n",
       "8   78     130      111\n",
       "9   78     104       78\n",
       "10  44      48       65"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing and indexing series\n",
    "data.loc[1:10,\"HP\":\"Defense\"]   # 10 and \"Defense\" are inclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_cell_guid": "60f8475b-8e2f-4743-abfb-c9efcb8ad785",
    "_uuid": "9fd49cda8b5371ded105cb9bd4406177218f128a"
   },
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
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       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
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       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HP  Attack  Defense\n",
       "#                      \n",
       "10  44      48       65\n",
       "9   78     104       78\n",
       "8   78     130      111\n",
       "7   78      84       78\n",
       "6   58      64       58\n",
       "5   39      52       43\n",
       "4   80     100      123\n",
       "3   80      82       83\n",
       "2   60      62       63\n",
       "1   45      49       49"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reverse slicing \n",
    "data.loc[10:1:-1,\"HP\":\"Defense\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_cell_guid": "af097a7c-fde3-46dd-8563-3c801a32ade0",
    "_uuid": "bb5d774902f7c7670e8d2265b89c6134c6caf51d"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "    Speed  Generation  Legendary\n",
       "#                               \n",
       "1      45           1      False\n",
       "2      60           1      False\n",
       "3      80           1      False\n",
       "4      80           1      False\n",
       "5      65           1      False\n",
       "6      80           1      False\n",
       "7     100           1      False\n",
       "8     100           1      False\n",
       "9     100           1      False\n",
       "10     43           1      False"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From something to end\n",
    "data.loc[1:10,\"Speed\":] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9246372d-106c-4c0f-b03f-f012d97a0015",
    "_uuid": "be4988429faeac30c260a679bb352ef9048be935"
   },
   "source": [
    "<a id=\"35\"></a> <br>\n",
    "### FILTERING DATA FRAMES\n",
    "Creating boolean series\n",
    "Combining filters\n",
    "Filtering column based others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_cell_guid": "fc541a2a-bc4c-419d-b522-3b02fe872dc9",
    "_uuid": "f891aaba3cd45694784582936cd1136948ea4dff"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>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",
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       "      <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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Chansey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</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>262</th>\n",
       "      <td>Blissey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</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   HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  \\\n",
       "#                                                                            \n",
       "122  Chansey  Normal    NaN  250       5        5       35      105     50   \n",
       "262  Blissey  Normal    NaN  255      10       10       75      135     55   \n",
       "\n",
       "     Generation  Legendary  \n",
       "#                           \n",
       "122           1      False  \n",
       "262           2      False  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating boolean series\n",
    "boolean = data.HP > 200\n",
    "data[boolean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_cell_guid": "29136dd4-a7ad-4629-8a27-f6e4013c50be",
    "_uuid": "170187e4d2a7a7286946094d3fec29bf7995569a"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Chansey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</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>262</th>\n",
       "      <td>Blissey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</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>352</th>\n",
       "      <td>Wailord</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</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>656</th>\n",
       "      <td>Alomomola</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</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   HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  \\\n",
       "#                                                                              \n",
       "122    Chansey  Normal    NaN  250       5        5       35      105     50   \n",
       "262    Blissey  Normal    NaN  255      10       10       75      135     55   \n",
       "352    Wailord   Water    NaN  170      90       45       90       45     60   \n",
       "656  Alomomola   Water    NaN  165      75       80       40       45     65   \n",
       "\n",
       "     Generation  Legendary  \n",
       "#                           \n",
       "122           1      False  \n",
       "262           2      False  \n",
       "352           3      False  \n",
       "656           5      False  "
      ]
     },
     "execution_count": 85,
     "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": 86,
   "metadata": {
    "_cell_guid": "05b15cd5-0ce7-4f63-8b57-51517d1ce6ee",
    "_uuid": "c896763dc3353f688bc98bb7be0426e6313688c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#\n",
       "231     20\n",
       "360     45\n",
       "487     50\n",
       "496    135\n",
       "659     44\n",
       "Name: HP, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering column based others\n",
    "data.HP[data.Speed<15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "90a26ae7-8d20-449c-afdb-12ac2eb266ee",
    "_uuid": "dc5b6dc4ea66aa03108284983a2139450faf10f7"
   },
   "source": [
    "<a id=\"36\"></a> <br>\n",
    "### TRANSFORMING DATA\n",
    "* Plain python functions\n",
    "* Lambda function: to apply arbitrary python function to every element\n",
    "* Defining column using other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_cell_guid": "32ecd8cf-0dce-4632-a7a3-8fcf22d91055",
    "_uuid": "65b27006706fbf23e2da1e111ee6be9370f5bca6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#\n",
       "1      22.5\n",
       "2      30.0\n",
       "3      40.0\n",
       "4      40.0\n",
       "5      19.5\n",
       "6      29.0\n",
       "7      39.0\n",
       "8      39.0\n",
       "9      39.0\n",
       "10     22.0\n",
       "11     29.5\n",
       "12     39.5\n",
       "13     39.5\n",
       "14     22.5\n",
       "15     25.0\n",
       "16     30.0\n",
       "17     20.0\n",
       "18     22.5\n",
       "19     32.5\n",
       "20     32.5\n",
       "21     20.0\n",
       "22     31.5\n",
       "23     41.5\n",
       "24     41.5\n",
       "25     15.0\n",
       "26     27.5\n",
       "27     20.0\n",
       "28     32.5\n",
       "29     17.5\n",
       "30     30.0\n",
       "       ... \n",
       "771    47.5\n",
       "772    39.0\n",
       "773    33.5\n",
       "774    25.0\n",
       "775    22.5\n",
       "776    34.0\n",
       "777    45.0\n",
       "778    28.5\n",
       "779    21.5\n",
       "780    42.5\n",
       "781    24.5\n",
       "782    22.0\n",
       "783    27.0\n",
       "784    29.5\n",
       "785    32.5\n",
       "786    27.5\n",
       "787    37.5\n",
       "788    42.5\n",
       "789    27.5\n",
       "790    47.5\n",
       "791    20.0\n",
       "792    42.5\n",
       "793    63.0\n",
       "794    63.0\n",
       "795    54.0\n",
       "796    25.0\n",
       "797    25.0\n",
       "798    40.0\n",
       "799    40.0\n",
       "800    40.0\n",
       "Name: HP, Length: 800, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plain python functions\n",
    "def div(n):\n",
    "    return n/2\n",
    "data.HP.apply(div)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_cell_guid": "23a6157f-cf67-4d30-9b0e-32d8f1d56c7b",
    "_uuid": "ddd9ccf2f154da142c7b025fbc40942d8174ebd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#\n",
       "1      22.5\n",
       "2      30.0\n",
       "3      40.0\n",
       "4      40.0\n",
       "5      19.5\n",
       "6      29.0\n",
       "7      39.0\n",
       "8      39.0\n",
       "9      39.0\n",
       "10     22.0\n",
       "11     29.5\n",
       "12     39.5\n",
       "13     39.5\n",
       "14     22.5\n",
       "15     25.0\n",
       "16     30.0\n",
       "17     20.0\n",
       "18     22.5\n",
       "19     32.5\n",
       "20     32.5\n",
       "21     20.0\n",
       "22     31.5\n",
       "23     41.5\n",
       "24     41.5\n",
       "25     15.0\n",
       "26     27.5\n",
       "27     20.0\n",
       "28     32.5\n",
       "29     17.5\n",
       "30     30.0\n",
       "       ... \n",
       "771    47.5\n",
       "772    39.0\n",
       "773    33.5\n",
       "774    25.0\n",
       "775    22.5\n",
       "776    34.0\n",
       "777    45.0\n",
       "778    28.5\n",
       "779    21.5\n",
       "780    42.5\n",
       "781    24.5\n",
       "782    22.0\n",
       "783    27.0\n",
       "784    29.5\n",
       "785    32.5\n",
       "786    27.5\n",
       "787    37.5\n",
       "788    42.5\n",
       "789    27.5\n",
       "790    47.5\n",
       "791    20.0\n",
       "792    42.5\n",
       "793    63.0\n",
       "794    63.0\n",
       "795    54.0\n",
       "796    25.0\n",
       "797    25.0\n",
       "798    40.0\n",
       "799    40.0\n",
       "800    40.0\n",
       "Name: HP, Length: 800, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or we can use lambda function\n",
    "data.HP.apply(lambda n :n/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "_cell_guid": "81d9e124-4716-4f81-8657-24396d766243",
    "_uuid": "b9f14e5f28ee3436511130607c6ab1896d06b9c0"
   },
   "outputs": [
    {
     "data": {
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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>HP</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",
       "    </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>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>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>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>4</th>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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  HP  Attack  Defense  Sp. Atk  Sp. Def  Speed  \\\n",
       "#                                                                               \n",
       "1      Bulbasaur  Grass  Poison  45      49       49       65       65     45   \n",
       "2        Ivysaur  Grass  Poison  60      62       63       80       80     60   \n",
       "3       Venusaur  Grass  Poison  80      82       83      100      100     80   \n",
       "4  Mega Venusaur  Grass  Poison  80     100      123      122      120     80   \n",
       "5     Charmander   Fire     NaN  39      52       43       60       50     65   \n",
       "\n",
       "   Generation  Legendary  total_power  \n",
       "#                                      \n",
       "1           1      False           98  \n",
       "2           1      False          125  \n",
       "3           1      False          165  \n",
       "4           1      False          223  \n",
       "5           1      False           95  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining column using other columns\n",
    "data[\"total_power\"] = data.Attack + data.Defense\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "02c688d9-f7af-4af8-985b-4a9c63247f41",
    "_uuid": "2c15881291bee6bb7fa5cea4fd0ce2c8bbadf0e3"
   },
   "source": [
    "<a id=\"37\"></a> <br>\n",
    "### INDEX OBJECTS AND LABELED DATA\n",
    "index: sequence of label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_cell_guid": "53638ea6-e21e-45cc-a488-bd2ed95a65b0",
    "_uuid": "8be52591e5de0ee14041f70a888403dd8aaab9df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>Type 1</th>\n",
       "      <th>Type 2</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",
       "      <th>total_power</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index_name</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>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>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>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>4</th>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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  HP  Attack  Defense  Sp. Atk  \\\n",
       "index_name                                                               \n",
       "1               Bulbasaur  Grass  Poison  45      49       49       65   \n",
       "2                 Ivysaur  Grass  Poison  60      62       63       80   \n",
       "3                Venusaur  Grass  Poison  80      82       83      100   \n",
       "4           Mega Venusaur  Grass  Poison  80     100      123      122   \n",
       "5              Charmander   Fire     NaN  39      52       43       60   \n",
       "\n",
       "            Sp. Def  Speed  Generation  Legendary  total_power  \n",
       "index_name                                                      \n",
       "1                65     45           1      False           98  \n",
       "2                80     60           1      False          125  \n",
       "3               100     80           1      False          165  \n",
       "4               120     80           1      False          223  \n",
       "5                50     65           1      False           95  "
      ]
     },
     "execution_count": 90,
     "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": 91,
   "metadata": {
    "_cell_guid": "023876a0-324c-4373-bc38-ae7528a091b0",
    "_uuid": "db5f40300314cf1791184b9c3d38b0985d669445"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>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",
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       "      <th>Legendary</th>\n",
       "      <th>total_power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
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       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
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       "      <td>98</td>\n",
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       "      <th>101</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>102</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>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  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "100      Bulbasaur  Grass  Poison  45      49       49       65       65   \n",
       "101        Ivysaur  Grass  Poison  60      62       63       80       80   \n",
       "102       Venusaur  Grass  Poison  80      82       83      100      100   \n",
       "103  Mega Venusaur  Grass  Poison  80     100      123      122      120   \n",
       "104     Charmander   Fire     NaN  39      52       43       60       50   \n",
       "\n",
       "     Speed  Generation  Legendary  total_power  \n",
       "100     45           1      False           98  \n",
       "101     60           1      False          125  \n",
       "102     80           1      False          165  \n",
       "103     80           1      False          223  \n",
       "104     65           1      False           95  "
      ]
     },
     "execution_count": 91,
     "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": "code",
   "execution_count": 92,
   "metadata": {
    "_cell_guid": "34cae9f2-89b9-4dfb-b74c-75704e1bc97b",
    "_uuid": "0730b9476a508ced28ca1e22cef43cff54a6373c"
   },
   "outputs": [],
   "source": [
    "# We can make one of the column as index. I actually did it at the beginning of manipulating data frames with pandas section\n",
    "# It was like this\n",
    "# data= data.set_index(\"#\")\n",
    "# also you can use \n",
    "# data.index = data[\"#\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "eb53b529-dd31-4d1c-82ab-77b740bd4994",
    "_uuid": "9df54698ddb8f61532eab4a690c3d3178832d106"
   },
   "source": [
    "<a id=\"38\"></a> <br>\n",
    "### HIERARCHICAL INDEXING\n",
    "* Setting indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_cell_guid": "37b4db17-5174-48ca-bec3-1aa332e31a6a",
    "_uuid": "0fa10aab7c4b2c6555340767740314d066db67a6"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Name</th>\n",
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       "    <tr>\n",
       "      <th>index_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
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       "      <td>49</td>\n",
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       "      <td>65</td>\n",
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       "      <td>False</td>\n",
       "      <td>98</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
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       "      <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>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>4</th>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</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>5</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</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  HP  Attack  Defense  Sp. Atk  \\\n",
       "index_name                                                               \n",
       "1               Bulbasaur  Grass  Poison  45      49       49       65   \n",
       "2                 Ivysaur  Grass  Poison  60      62       63       80   \n",
       "3                Venusaur  Grass  Poison  80      82       83      100   \n",
       "4           Mega Venusaur  Grass  Poison  80     100      123      122   \n",
       "5              Charmander   Fire     NaN  39      52       43       60   \n",
       "\n",
       "            Sp. Def  Speed  Generation  Legendary  total_power  \n",
       "index_name                                                      \n",
       "1                65     45           1      False           98  \n",
       "2                80     60           1      False          125  \n",
       "3               100     80           1      False          165  \n",
       "4               120     80           1      False          223  \n",
       "5                50     65           1      False           95  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets read data frame one more time to start from beginning\n",
    "# data = pd.read_csv('../input/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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "_cell_guid": "2673b298-881b-4c5b-95fd-f2cd21c63042",
    "_uuid": "6def00d98c1f603f7099fb702711c2c98d362449"
   },
   "outputs": [
    {
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       "      <th>Name</th>\n",
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       "    <tr>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Grass</th>\n",
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       "      <td>45</td>\n",
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       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>Ivysaur</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>Poison</th>\n",
       "      <td>Venusaur</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>Poison</th>\n",
       "      <td>Mega Venusaur</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 rowspan=\"5\" valign=\"top\">Fire</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Charmander</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",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Charmeleon</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",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Charizard</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",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dragon</th>\n",
       "      <td>Mega Charizard X</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",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Mega Charizard Y</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",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Water</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Squirtle</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",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Wartortle</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",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Blastoise</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",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Mega Blastoise</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",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Bug</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Caterpie</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",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Metapod</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",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Butterfree</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",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>Weedle</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",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>Kakuna</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",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>Beedrill</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",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>Mega Beedrill</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",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Normal</th>\n",
       "      <th>Flying</th>\n",
       "      <td>Pidgey</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",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Pidgeotto</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",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Pidgeot</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",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Mega Pidgeot</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",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Rattata</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",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Raticate</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",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Spearow</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",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Fearow</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",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Poison</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Ekans</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",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Arbok</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",
       "      <td>154</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Psychic</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Alakazam</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",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Mega Alakazam</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",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Fighting</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Machop</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",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Machoke</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",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Machamp</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",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Grass</th>\n",
       "      <th>Poison</th>\n",
       "      <td>Bellsprout</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",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>Weepinbell</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",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>Victreebel</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",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Water</th>\n",
       "      <th>Poison</th>\n",
       "      <td>Tentacool</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",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison</th>\n",
       "      <td>Tentacruel</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",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Rock</th>\n",
       "      <th>Ground</th>\n",
       "      <td>Geodude</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",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ground</th>\n",
       "      <td>Graveler</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",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ground</th>\n",
       "      <td>Golem</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",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Fire</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Ponyta</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",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Rapidash</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",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Water</th>\n",
       "      <th>Psychic</th>\n",
       "      <td>Slowpoke</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",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychic</th>\n",
       "      <td>Slowbro</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",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychic</th>\n",
       "      <td>Mega Slowbro</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",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Electric</th>\n",
       "      <th>Steel</th>\n",
       "      <td>Magnemite</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",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel</th>\n",
       "      <td>Magneton</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",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Normal</th>\n",
       "      <th>Flying</th>\n",
       "      <td>Farfetch'd</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",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Doduo</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",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flying</th>\n",
       "      <td>Dodrio</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",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Water</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Seel</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",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice</th>\n",
       "      <td>Dewgong</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",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Poison</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Grimer</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",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Muk</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",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Water</th>\n",
       "      <th>NaN</th>\n",
       "      <td>Shellder</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",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice</th>\n",
       "      <td>Cloyster</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",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ghost</th>\n",
       "      <th>Poison</th>\n",
       "      <td>Gastly</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",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Name   HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "Type 1   Type 2                                                              \n",
       "Grass    Poison          Bulbasaur   45      49       49       65       65   \n",
       "         Poison            Ivysaur   60      62       63       80       80   \n",
       "         Poison           Venusaur   80      82       83      100      100   \n",
       "         Poison      Mega Venusaur   80     100      123      122      120   \n",
       "Fire     NaN            Charmander   39      52       43       60       50   \n",
       "         NaN            Charmeleon   58      64       58       80       65   \n",
       "         Flying          Charizard   78      84       78      109       85   \n",
       "         Dragon   Mega Charizard X   78     130      111      130       85   \n",
       "         Flying   Mega Charizard Y   78     104       78      159      115   \n",
       "Water    NaN              Squirtle   44      48       65       50       64   \n",
       "         NaN             Wartortle   59      63       80       65       80   \n",
       "         NaN             Blastoise   79      83      100       85      105   \n",
       "         NaN        Mega Blastoise   79     103      120      135      115   \n",
       "Bug      NaN              Caterpie   45      30       35       20       20   \n",
       "         NaN               Metapod   50      20       55       25       25   \n",
       "         Flying         Butterfree   60      45       50       90       80   \n",
       "         Poison             Weedle   40      35       30       20       20   \n",
       "         Poison             Kakuna   45      25       50       25       25   \n",
       "         Poison           Beedrill   65      90       40       45       80   \n",
       "         Poison      Mega Beedrill   65     150       40       15       80   \n",
       "Normal   Flying             Pidgey   40      45       40       35       35   \n",
       "         Flying          Pidgeotto   63      60       55       50       50   \n",
       "         Flying            Pidgeot   83      80       75       70       70   \n",
       "         Flying       Mega Pidgeot   83      80       80      135       80   \n",
       "         NaN               Rattata   30      56       35       25       35   \n",
       "         NaN              Raticate   55      81       60       50       70   \n",
       "         Flying            Spearow   40      60       30       31       31   \n",
       "         Flying             Fearow   65      90       65       61       61   \n",
       "Poison   NaN                 Ekans   35      60       44       40       54   \n",
       "         NaN                 Arbok   60      85       69       65       79   \n",
       "...                            ...  ...     ...      ...      ...      ...   \n",
       "Psychic  NaN              Alakazam   55      50       45      135       95   \n",
       "         NaN         Mega Alakazam   55      50       65      175       95   \n",
       "Fighting NaN                Machop   70      80       50       35       35   \n",
       "         NaN               Machoke   80     100       70       50       60   \n",
       "         NaN               Machamp   90     130       80       65       85   \n",
       "Grass    Poison         Bellsprout   50      75       35       70       30   \n",
       "         Poison         Weepinbell   65      90       50       85       45   \n",
       "         Poison         Victreebel   80     105       65      100       70   \n",
       "Water    Poison          Tentacool   40      40       35       50      100   \n",
       "         Poison         Tentacruel   80      70       65       80      120   \n",
       "Rock     Ground            Geodude   40      80      100       30       30   \n",
       "         Ground           Graveler   55      95      115       45       45   \n",
       "         Ground              Golem   80     120      130       55       65   \n",
       "Fire     NaN                Ponyta   50      85       55       65       65   \n",
       "         NaN              Rapidash   65     100       70       80       80   \n",
       "Water    Psychic          Slowpoke   90      65       65       40       40   \n",
       "         Psychic           Slowbro   95      75      110      100       80   \n",
       "         Psychic      Mega Slowbro   95      75      180      130       80   \n",
       "Electric Steel           Magnemite   25      35       70       95       55   \n",
       "         Steel            Magneton   50      60       95      120       70   \n",
       "Normal   Flying         Farfetch'd   52      65       55       58       62   \n",
       "         Flying              Doduo   35      85       45       35       35   \n",
       "         Flying             Dodrio   60     110       70       60       60   \n",
       "Water    NaN                  Seel   65      45       55       45       70   \n",
       "         Ice               Dewgong   90      70       80       70       95   \n",
       "Poison   NaN                Grimer   80      80       50       40       50   \n",
       "         NaN                   Muk  105     105       75       65      100   \n",
       "Water    NaN              Shellder   30      65      100       45       25   \n",
       "         Ice              Cloyster   50      95      180       85       45   \n",
       "Ghost    Poison             Gastly   30      35       30      100       35   \n",
       "\n",
       "                  Speed  Generation  Legendary  total_power  \n",
       "Type 1   Type 2                                              \n",
       "Grass    Poison      45           1      False           98  \n",
       "         Poison      60           1      False          125  \n",
       "         Poison      80           1      False          165  \n",
       "         Poison      80           1      False          223  \n",
       "Fire     NaN         65           1      False           95  \n",
       "         NaN         80           1      False          122  \n",
       "         Flying     100           1      False          162  \n",
       "         Dragon     100           1      False          241  \n",
       "         Flying     100           1      False          182  \n",
       "Water    NaN         43           1      False          113  \n",
       "         NaN         58           1      False          143  \n",
       "         NaN         78           1      False          183  \n",
       "         NaN         78           1      False          223  \n",
       "Bug      NaN         45           1      False           65  \n",
       "         NaN         30           1      False           75  \n",
       "         Flying      70           1      False           95  \n",
       "         Poison      50           1      False           65  \n",
       "         Poison      35           1      False           75  \n",
       "         Poison      75           1      False          130  \n",
       "         Poison     145           1      False          190  \n",
       "Normal   Flying      56           1      False           85  \n",
       "         Flying      71           1      False          115  \n",
       "         Flying     101           1      False          155  \n",
       "         Flying     121           1      False          160  \n",
       "         NaN         72           1      False           91  \n",
       "         NaN         97           1      False          141  \n",
       "         Flying      70           1      False           90  \n",
       "         Flying     100           1      False          155  \n",
       "Poison   NaN         55           1      False          104  \n",
       "         NaN         80           1      False          154  \n",
       "...                 ...         ...        ...          ...  \n",
       "Psychic  NaN        120           1      False           95  \n",
       "         NaN        150           1      False          115  \n",
       "Fighting NaN         35           1      False          130  \n",
       "         NaN         45           1      False          170  \n",
       "         NaN         55           1      False          210  \n",
       "Grass    Poison      40           1      False          110  \n",
       "         Poison      55           1      False          140  \n",
       "         Poison      70           1      False          170  \n",
       "Water    Poison      70           1      False           75  \n",
       "         Poison     100           1      False          135  \n",
       "Rock     Ground      20           1      False          180  \n",
       "         Ground      35           1      False          210  \n",
       "         Ground      45           1      False          250  \n",
       "Fire     NaN         90           1      False          140  \n",
       "         NaN        105           1      False          170  \n",
       "Water    Psychic     15           1      False          130  \n",
       "         Psychic     30           1      False          185  \n",
       "         Psychic     30           1      False          255  \n",
       "Electric Steel       45           1      False          105  \n",
       "         Steel       70           1      False          155  \n",
       "Normal   Flying      60           1      False          120  \n",
       "         Flying      75           1      False          130  \n",
       "         Flying     100           1      False          180  \n",
       "Water    NaN         45           1      False          100  \n",
       "         Ice         70           1      False          150  \n",
       "Poison   NaN         25           1      False          130  \n",
       "         NaN         50           1      False          180  \n",
       "Water    NaN         40           1      False          165  \n",
       "         Ice         70           1      False          275  \n",
       "Ghost    Poison      80           1      False           65  \n",
       "\n",
       "[100 rows x 10 columns]"
      ]
     },
     "execution_count": 94,
     "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": {
    "_cell_guid": "a2df33a8-7398-48de-b29a-dcf9aaba390f",
    "_uuid": "bd07bab70f1cfb1207e511385a1df9a677ce7323"
   },
   "source": [
    "<a id=\"39\"></a> <br>\n",
    "### PIVOTING DATA FRAMES\n",
    "* pivoting: reshape tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_cell_guid": "ac142efd-5d5e-45e3-be59-549ad232b1a7",
    "_uuid": "796bf147649c93831621909e3187b5cc75276583"
   },
   "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>gender</th>\n",
       "      <th>response</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender  response treatment\n",
       "0   15      F        10         A\n",
       "1    4      M        45         A\n",
       "2   72      F         5         B\n",
       "3   65      M         9         B"
      ]
     },
     "execution_count": 95,
     "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": 96,
   "metadata": {
    "_cell_guid": "424deb53-e02f-46a9-9811-57710ba44f58",
    "_uuid": "6ec6f1f2090fb190b6a9adbdcac2369199a2c43c"
   },
   "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": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivoting\n",
    "df.pivot(index=\"treatment\",columns = \"gender\",values=\"response\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c72a36e8-a753-4eee-bb74-164706b0fa9d",
    "_uuid": "8f3a258a8ff9afabd8a19b5554950fae2161bf90"
   },
   "source": [
    "<a id=\"40\"></a> <br>\n",
    "### STACKING and UNSTACKING DATAFRAME\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": 97,
   "metadata": {
    "_cell_guid": "fb5178cf-3dd7-4cef-b4ec-cb3f60989c48",
    "_uuid": "8f4846fdd77c9b284337cef1610a3bbd207e9f8a"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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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>age</th>\n",
       "      <th>response</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>15</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>F</th>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>65</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  age  response\n",
       "treatment gender               \n",
       "A         F        15        10\n",
       "          M         4        45\n",
       "B         F        72         5\n",
       "          M        65         9"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.set_index([\"treatment\",\"gender\"])\n",
    "df1\n",
    "# lets unstack it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "_cell_guid": "f538cb10-d07b-4337-a524-0ffa7e620ed9",
    "_uuid": "5d9144f411a83c3ddc142c24f121a30e383865fa"
   },
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">response</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>15</td>\n",
       "      <td>72</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age     response   \n",
       "treatment   A   B        A  B\n",
       "gender                       \n",
       "F          15  72       10  5\n",
       "M           4  65       45  9"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# level determines indexes\n",
    "df1.unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "_cell_guid": "704bd07f-eb5a-4e4d-804b-bf1950318cd8",
    "_uuid": "e6b41dad24fa906a8ce92d84335bb51de99b36c3"
   },
   "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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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">response</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>15</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>72</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age     response    \n",
       "gender      F   M        F   M\n",
       "treatment                     \n",
       "A          15   4       10  45\n",
       "B          72  65        5   9"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.unstack(level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "_cell_guid": "96b9aa1a-92e6-4ab3-8916-00b6a387257f",
    "_uuid": "30f2c2ca84b34b4ac70123c16b091cbeacae111e"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>response</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>15</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>A</th>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <th>B</th>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>B</th>\n",
       "      <td>65</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  age  response\n",
       "gender treatment               \n",
       "F      A           15        10\n",
       "M      A            4        45\n",
       "F      B           72         5\n",
       "M      B           65         9"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change inner and outer level index position\n",
    "df2 = df1.swaplevel(0,1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b619b27d-4dc5-4656-b897-c0d2ee1592b9",
    "_uuid": "3c28c7b44b556cdce5f6afd3e77de703d92347eb"
   },
   "source": [
    "<a id=\"41\"></a> <br>\n",
    "### MELTING DATA FRAMES\n",
    "* Reverse of pivoting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "_uuid": "cccc136311bd11ec46bcf362684e775552664f6e"
   },
   "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>gender</th>\n",
       "      <th>response</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender  response treatment\n",
       "0   15      F        10         A\n",
       "1    4      M        45         A\n",
       "2   72      F         5         B\n",
       "3   65      M         9         B"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "_cell_guid": "148e90ee-df31-4bef-85f3-e331229afa05",
    "_uuid": "ac5f6346acaec0a87d8390fe95537123e5da87f5"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "melt() got multiple values for argument 'id_vars'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-118-325654623f0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"treatment\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"gender\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"response\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmelted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpivot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mid_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"treatment\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpivot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmelted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: melt() got multiple values for argument 'id_vars'"
     ]
    }
   ],
   "source": [
    "pivot=df.pivot(index=\"treatment\",columns = \"gender\",values=\"response\")\n",
    "\n",
    "melted=pivot.melt(df,id_vars=\"treatment\")\n",
    "print(pivot)\n",
    "print(melted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "80601e16-1e35-486a-bfa7-dde7a3ffda8c",
    "_uuid": "769a4cb71d88171a32a53b7b8425702b44e434ff"
   },
   "source": [
    "<a id=\"42\"></a> <br>\n",
    "### CATEGORICALS AND GROUPBY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "_cell_guid": "0e93f845-b991-4001-8eba-354785e27bfe",
    "_uuid": "9758f90e21d48ad1d3b744f44420cf0da3705bde"
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>response</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender  response treatment\n",
       "0   15      F        10         A\n",
       "1    4      M        45         A\n",
       "2   72      F         5         B\n",
       "3   65      M         9         B"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will use df\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "_cell_guid": "6e6643f0-620f-43d7-a169-32ba9aa46519",
    "_uuid": "c53e53366eb493a0e84ffd7dc545e765116a1b81"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>9.5</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>68.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  response\n",
       "treatment                \n",
       "A           9.5      27.5\n",
       "B          68.5       7.0"
      ]
     },
     "execution_count": 104,
     "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": 105,
   "metadata": {
    "_cell_guid": "af5cf6f7-45fe-4e2c-82b9-51151ea2d946",
    "_uuid": "d1c5820867e194188c5b0e45b9193e6f709278a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "treatment\n",
       "A    15\n",
       "B    72\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can only choose one of the feature\n",
    "df.groupby(\"treatment\").age.max() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "_cell_guid": "3270807c-4a50-409e-8be7-5e72120c5676",
    "_uuid": "d3e7bdaf7ac35c68077e2aab1cf36ba79935f566"
   },
   "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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or we can choose multiple features\n",
    "df.groupby(\"treatment\")[[\"age\",\"response\"]].min() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "_cell_guid": "659bb026-fa52-4d48-9d55-a65053f90854",
    "_uuid": "8634d83d6a2bf666df2c8cc7f4e22103d32acbc9"
   },
   "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",
      "age          4 non-null int64\n",
      "gender       4 non-null object\n",
      "response     4 non-null int64\n",
      "treatment    4 non-null object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 208.0+ bytes\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      "age          4 non-null int64\n",
      "gender       4 non-null category\n",
      "response     4 non-null int64\n",
      "treatment    4 non-null category\n",
      "dtypes: category(2), int64(2)\n",
      "memory usage: 344.0 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": "code",
   "execution_count": 108,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     response\n",
       "age          \n",
       "4          45\n",
       "15         10\n",
       "65          9\n",
       "72          5"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"age\").mean() "
   ]
  }
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