Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.5'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajit</td>\n",
       "      <td>portland</td>\n",
       "      <td>sds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joe</td>\n",
       "      <td>boston</td>\n",
       "      <td>ssd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jos</td>\n",
       "      <td>texas</td>\n",
       "      <td>fsd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>john</td>\n",
       "      <td>raleigh</td>\n",
       "      <td>Na</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name      city   id\n",
       "0  ajit  portland  sds\n",
       "1   joe    boston  ssd\n",
       "2   jos     texas  fsd\n",
       "3  john   raleigh   Na"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a new column to a dataframe\n",
    "# method 1\n",
    "data = {'name' : ['ajit','joe','jos','john'],\n",
    "       'city' : ['portland','boston','texas','raleigh']}\n",
    "data = pd.DataFrame(data)\n",
    "data\n",
    "id = ['sds','ssd','fsd','Na']\n",
    "data['id'] = id\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>city</th>\n",
       "      <th>id</th>\n",
       "      <th>id2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajit</td>\n",
       "      <td>portland</td>\n",
       "      <td>sds</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joe</td>\n",
       "      <td>boston</td>\n",
       "      <td>ssd</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jos</td>\n",
       "      <td>texas</td>\n",
       "      <td>fsd</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>john</td>\n",
       "      <td>raleigh</td>\n",
       "      <td>Na</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name      city   id    id2\n",
       "0  ajit  portland  sds   True\n",
       "1   joe    boston  ssd   True\n",
       "2   jos     texas  fsd   True\n",
       "3  john   raleigh   Na  False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert NA to False otherwise True\n",
    "data['id2'] = data['id'] > 'Na'\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bmw</td>\n",
       "      <td>x7</td>\n",
       "      <td>90lac</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ford</td>\n",
       "      <td>mustang</td>\n",
       "      <td>60lac</td>\n",
       "      <td>1959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>benz</td>\n",
       "      <td>gls</td>\n",
       "      <td>95lac</td>\n",
       "      <td>1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tata</td>\n",
       "      <td>sumo</td>\n",
       "      <td>15lac</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  brand    model  price  year\n",
       "0   bmw       x7  90lac  1940\n",
       "1  ford  mustang  60lac  1959\n",
       "2  benz      gls  95lac  1955\n",
       "3  tata     sumo  15lac  1960"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method 2 by using 'insert'\n",
    "df = {'brand': ['bmw','ford','benz','tata'],'model': ['x7','mustang','gls','sumo'], 'year': ['1940','1959','1955','1960']}\n",
    "df = pd.DataFrame(df)\n",
    "df.insert(2,'price',['90lac','60lac','95lac','15lac'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>math</th>\n",
       "      <th>science</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jenny</td>\n",
       "      <td>87</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>georgia</td>\n",
       "      <td>65</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amy</td>\n",
       "      <td>75</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  math  science\n",
       "0    jenny    87       75\n",
       "1  georgia    65       89\n",
       "2      amy    75       84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>math</th>\n",
       "      <th>science</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jenny</td>\n",
       "      <td>87</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>georgia</td>\n",
       "      <td>65</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amy</td>\n",
       "      <td>75</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>katy</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  math  science\n",
       "0    jenny    87       75\n",
       "1  georgia    65       89\n",
       "2      amy    75       84\n",
       "3     katy    88       77"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how to add a rows in dataframe\n",
    "\n",
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# create a dictionary\n",
    "dict = {'name':['jenny','georgia','amy'], 'math':[87,65,75], 'science':[75,89,84]}\n",
    "df = pd.DataFrame(dict)\n",
    "display(df)\n",
    "\n",
    "df2 = {'name':'katy','math': 88,'science': 77}\n",
    "df = df.append(df2,ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          4.1\n",
       "1         54.0\n",
       "2      54545.0\n",
       "3    5454449.0\n",
       "4        510.0\n",
       "5          2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PANDAS\n",
    "data = pd.Series([4.1,54,54545,5454449,510,2])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.100000e+00, 5.400000e+01, 5.454500e+04, 5.454449e+06,\n",
       "       5.100000e+02, 2.000000e+00])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          4.1\n",
       "1         54.0\n",
       "2      54545.0\n",
       "3    5454449.0\n",
       "4        510.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       2\n",
       "b     251\n",
       "c     121\n",
       "d    2121\n",
       "e      22\n",
       "f      99\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series(([2,251,121,2121,22,99]),index=['a','b','c','d','e','f'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd', 'e', 'f'], dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "251"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>state</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>533564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>california</td>\n",
       "      <td>16546546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>texas</td>\n",
       "      <td>4646464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>penn</td>\n",
       "      <td>164967665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>washington</td>\n",
       "      <td>46467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>michigan</td>\n",
       "      <td>1564654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state  population\n",
       "0    new york      533564\n",
       "1  california    16546546\n",
       "2       texas     4646464\n",
       "3        penn   164967665\n",
       "4  washington       46467\n",
       "5    michigan     1564654"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_dict = {'state':['new york','california','texas','penn','washington','michigan'],\n",
    "           'population': [533564,16546546,4646464,164967665,46467,1564654]}\n",
    "population = pd.DataFrame(pop_dict)\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "533564"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "If using all scalar values, you must pass an index",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-e5902290fd93>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m                     \u001b[1;34m'Florida'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m19552860\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                     'Illinois': 12882135}\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mpopulation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpopulation_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    612\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    613\u001b[0m             \u001b[1;31m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 614\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    615\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    616\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m    462\u001b[0m         \u001b[1;31m# TODO: can we get rid of the dt64tz special case above?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 464\u001b[1;33m     return arrays_to_mgr(\n\u001b[0m\u001b[0;32m    465\u001b[0m         \u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtyp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconsolidate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    466\u001b[0m     )\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m    117\u001b[0m         \u001b[1;31m# figure out the index, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m             \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extract_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m             \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    623\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mindexes\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mraw_lengths\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"If using all scalar values, you must pass an index\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhave_series\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: If using all scalar values, you must pass an index"
     ]
    }
   ],
   "source": [
    "population_dict = {'California': 38332521,\n",
    "                    'Texas': 26448193,\n",
    "                    'New York': 19651127,\n",
    "                    'Florida': 19552860,\n",
    "                    'Illinois': 12882135}\n",
    "population = pd.Series(population_dict)\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26448193"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['Texas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'California': 423967,\n",
       " 'Texas': 695662,\n",
       " 'New York': 141297,\n",
       " 'Florida': 170312,\n",
       " 'Illinois': 149995}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,\n",
    " 'Florida': 170312, 'Illinois': 149995}\n",
    "area_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "new york       257848\n",
       "florida         42872\n",
       "texas         5485682\n",
       "california     548455\n",
       "penn            54445\n",
       "illinois        54856\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.Series(dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Texas         695662\n",
       "New York      141297\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area = pd.Series(area_dict)\n",
    "area\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>149995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297\n",
       "Florida       19552860  170312\n",
       "Illinois      12882135  149995"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = pd.DataFrame({'population': population,\n",
    " 'area': area})\n",
    "states\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['new york', 'florida', 'texas', 'california', 'penn', 'illinois'], dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['population', 'area'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Texas         695662\n",
       "New York      141297\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population\n",
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "Florida       19552860\n",
       "Illinois      12882135"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(population, columns=['population'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "5    e\n",
       "6    f\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc = pd.Series(['a','b','c','d','e','f'],index=[1,2,3,4,5,6])\n",
    "cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "5    e\n",
       "6    f\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.iloc[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "5    e\n",
       "6    f\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.loc[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'c', 'd', 'e', 'f'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.iloc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.loc['California':'New York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = pd.Series({'alaska':564654,'newyork':546756,'texas':45458,'colorado':466144},name='area')\n",
    "pop = pd.Series({'newyork':8168,'texas':67117,'alska':5445},name='population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alaska           NaN\n",
       "alska            NaN\n",
       "colorado         NaN\n",
       "newyork     0.014939\n",
       "texas       1.476462\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop / area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['alaska', 'alska', 'colorado', 'newyork', 'texas'], dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area.index | pop.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639491</td>\n",
       "      <td>1.356810</td>\n",
       "      <td>1.412665</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>-0.148999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.622137</td>\n",
       "      <td>-0.691322</td>\n",
       "      <td>1.897189</td>\n",
       "      <td>-0.892162</td>\n",
       "      <td>0.412205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.757635</td>\n",
       "      <td>-0.382691</td>\n",
       "      <td>-0.848843</td>\n",
       "      <td>0.019704</td>\n",
       "      <td>-2.051270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.718424</td>\n",
       "      <td>2.457078</td>\n",
       "      <td>0.969265</td>\n",
       "      <td>-0.674132</td>\n",
       "      <td>1.252756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.734149</td>\n",
       "      <td>-0.523455</td>\n",
       "      <td>0.759118</td>\n",
       "      <td>-0.473208</td>\n",
       "      <td>0.066690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.438495</td>\n",
       "      <td>-0.048929</td>\n",
       "      <td>0.231834</td>\n",
       "      <td>0.857857</td>\n",
       "      <td>-1.563392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.928900</td>\n",
       "      <td>1.109356</td>\n",
       "      <td>-0.016588</td>\n",
       "      <td>0.623447</td>\n",
       "      <td>0.451441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-0.035206</td>\n",
       "      <td>-0.985602</td>\n",
       "      <td>0.973406</td>\n",
       "      <td>-2.765537</td>\n",
       "      <td>-0.749843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.736065</td>\n",
       "      <td>-0.929431</td>\n",
       "      <td>1.450618</td>\n",
       "      <td>1.000114</td>\n",
       "      <td>0.849193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.447540</td>\n",
       "      <td>0.064863</td>\n",
       "      <td>-0.651460</td>\n",
       "      <td>0.148203</td>\n",
       "      <td>0.036642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4\n",
       "0   0.639491  1.356810  1.412665  0.011490 -0.148999\n",
       "1   0.622137 -0.691322  1.897189 -0.892162  0.412205\n",
       "2  -0.757635 -0.382691 -0.848843  0.019704 -2.051270\n",
       "3  -0.718424  2.457078  0.969265 -0.674132  1.252756\n",
       "4  -0.734149 -0.523455  0.759118 -0.473208  0.066690\n",
       "..       ...       ...       ...       ...       ...\n",
       "95  1.438495 -0.048929  0.231834  0.857857 -1.563392\n",
       "96  0.928900  1.109356 -0.016588  0.623447  0.451441\n",
       "97 -0.035206 -0.985602  0.973406 -2.765537 -0.749843\n",
       "98  0.736065 -0.929431  1.450618  1.000114  0.849193\n",
       "99  0.447540  0.064863 -0.651460  0.148203  0.036642\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(100,5), index=np.arange(100))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.458039</td>\n",
       "      <td>0.502119</td>\n",
       "      <td>0.480964</td>\n",
       "      <td>0.465876</td>\n",
       "      <td>0.499861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.295157</td>\n",
       "      <td>0.280831</td>\n",
       "      <td>0.276975</td>\n",
       "      <td>0.271715</td>\n",
       "      <td>0.284073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.018364</td>\n",
       "      <td>0.007558</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>0.000692</td>\n",
       "      <td>0.023333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.203838</td>\n",
       "      <td>0.276062</td>\n",
       "      <td>0.221325</td>\n",
       "      <td>0.262782</td>\n",
       "      <td>0.241082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.406737</td>\n",
       "      <td>0.504104</td>\n",
       "      <td>0.486064</td>\n",
       "      <td>0.453788</td>\n",
       "      <td>0.515951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.727048</td>\n",
       "      <td>0.711599</td>\n",
       "      <td>0.722012</td>\n",
       "      <td>0.699993</td>\n",
       "      <td>0.734462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.995485</td>\n",
       "      <td>0.999393</td>\n",
       "      <td>0.998540</td>\n",
       "      <td>0.997529</td>\n",
       "      <td>0.985889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0           1           2           3           4\n",
       "count  100.000000  100.000000  100.000000  100.000000  100.000000\n",
       "mean     0.458039    0.502119    0.480964    0.465876    0.499861\n",
       "std      0.295157    0.280831    0.276975    0.271715    0.284073\n",
       "min      0.018364    0.007558    0.001038    0.000692    0.023333\n",
       "25%      0.203838    0.276062    0.221325    0.262782    0.241082\n",
       "50%      0.406737    0.504104    0.486064    0.453788    0.515951\n",
       "75%      0.727048    0.711599    0.722012    0.699993    0.734462\n",
       "max      0.995485    0.999393    0.998540    0.997529    0.985889"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.755658</td>\n",
       "      <td>0.940157</td>\n",
       "      <td>0.581932</td>\n",
       "      <td>0.295695</td>\n",
       "      <td>0.587560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.043853</td>\n",
       "      <td>0.666047</td>\n",
       "      <td>0.750871</td>\n",
       "      <td>0.347735</td>\n",
       "      <td>0.043959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.579371</td>\n",
       "      <td>0.106776</td>\n",
       "      <td>0.953161</td>\n",
       "      <td>0.923096</td>\n",
       "      <td>0.191682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.379358</td>\n",
       "      <td>0.330796</td>\n",
       "      <td>0.141746</td>\n",
       "      <td>0.815529</td>\n",
       "      <td>0.356223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.340004</td>\n",
       "      <td>0.781160</td>\n",
       "      <td>0.916011</td>\n",
       "      <td>0.149224</td>\n",
       "      <td>0.124317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.755658  0.940157  0.581932  0.295695  0.587560\n",
       "1  0.043853  0.666047  0.750871  0.347735  0.043959\n",
       "2  0.579371  0.106776  0.953161  0.923096  0.191682\n",
       "3  0.379358  0.330796  0.141746  0.815529  0.356223\n",
       "4  0.340004  0.781160  0.916011  0.149224  0.124317"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change index data\n",
    "df[0][0] = 'ajit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajit</td>\n",
       "      <td>0.264495</td>\n",
       "      <td>0.583698</td>\n",
       "      <td>0.553182</td>\n",
       "      <td>0.077867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.345639</td>\n",
       "      <td>0.378516</td>\n",
       "      <td>0.571080</td>\n",
       "      <td>0.432195</td>\n",
       "      <td>0.973587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.129958</td>\n",
       "      <td>0.233022</td>\n",
       "      <td>0.652113</td>\n",
       "      <td>0.518325</td>\n",
       "      <td>0.211556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0739044</td>\n",
       "      <td>0.350907</td>\n",
       "      <td>0.543523</td>\n",
       "      <td>0.875726</td>\n",
       "      <td>0.073480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.935363</td>\n",
       "      <td>0.821073</td>\n",
       "      <td>0.539012</td>\n",
       "      <td>0.437463</td>\n",
       "      <td>0.755946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.253359</td>\n",
       "      <td>0.900567</td>\n",
       "      <td>0.704431</td>\n",
       "      <td>0.918358</td>\n",
       "      <td>0.060113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.193356</td>\n",
       "      <td>0.129879</td>\n",
       "      <td>0.110534</td>\n",
       "      <td>0.244284</td>\n",
       "      <td>0.768090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.0533768</td>\n",
       "      <td>0.007321</td>\n",
       "      <td>0.774008</td>\n",
       "      <td>0.723468</td>\n",
       "      <td>0.230406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.752264</td>\n",
       "      <td>0.672113</td>\n",
       "      <td>0.586109</td>\n",
       "      <td>0.184636</td>\n",
       "      <td>0.959688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.318872</td>\n",
       "      <td>0.800362</td>\n",
       "      <td>0.585959</td>\n",
       "      <td>0.724223</td>\n",
       "      <td>0.305850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4\n",
       "0        ajit  0.264495  0.583698  0.553182  0.077867\n",
       "1    0.345639  0.378516  0.571080  0.432195  0.973587\n",
       "2    0.129958  0.233022  0.652113  0.518325  0.211556\n",
       "3   0.0739044  0.350907  0.543523  0.875726  0.073480\n",
       "4    0.935363  0.821073  0.539012  0.437463  0.755946\n",
       "..        ...       ...       ...       ...       ...\n",
       "95   0.253359  0.900567  0.704431  0.918358  0.060113\n",
       "96   0.193356  0.129879  0.110534  0.244284  0.768090\n",
       "97  0.0533768  0.007321  0.774008  0.723468  0.230406\n",
       "98   0.752264  0.672113  0.586109  0.184636  0.959688\n",
       "99   0.318872  0.800362  0.585959  0.724223  0.305850\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     object\n",
       "1    float64\n",
       "2    float64\n",
       "3    float64\n",
       "4    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "            68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "            85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
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    "df.columns"
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   "execution_count": 20,
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   "outputs": [
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       "           0         1         2         3         4\n",
       "0       ajit  0.940157  0.581932  0.295695  0.587560\n",
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       "4   0.340004  0.781160  0.916011  0.149224  0.124317"
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   "execution_count": 23,
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       "      <td>0.724417</td>\n",
       "      <td>0.139897</td>\n",
       "      <td>0.893653</td>\n",
       "      <td>0.208934</td>\n",
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       "      <td>0.013064</td>\n",
       "      <td>0.836233</td>\n",
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       "      <td>0.113558</td>\n",
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       "      <td>0.008758</td>\n",
       "      <td>0.667037</td>\n",
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       "           4         3         2         1          0\n",
       "0   0.587560  0.295695  0.581932  0.940157       ajit\n",
       "1   0.043959  0.347735  0.750871  0.666047  0.0438533\n",
       "2   0.191682  0.923096  0.953161  0.106776   0.579371\n",
       "3   0.356223  0.815529  0.141746  0.330796   0.379358\n",
       "4   0.124317  0.149224  0.916011  0.781160   0.340004\n",
       "..       ...       ...       ...       ...        ...\n",
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       "97  0.204989  0.447824  0.477404  0.113558   0.969063\n",
       "98  0.876969  0.153831  0.412492  0.649096   0.356622\n",
       "99  0.442077  0.733042  0.717123  0.008758   0.667037\n",
       "\n",
       "[100 rows x 5 columns]"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.sort_index(axis=1,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          ajit\n",
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       "        ...    \n",
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       "96     0.836233\n",
       "97     0.969063\n",
       "98     0.356622\n",
       "99     0.667037\n",
       "Name: 0, Length: 100, dtype: object"
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     "execution_count": 24,
     "metadata": {},
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    "df[0]"
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "          0         1         2         3         4\n",
       "0  0.850709  0.381402  0.221519  0.778557  0.083864\n",
       "1  0.530034  0.429443  0.177951  0.496243  0.421999\n",
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       "3  0.939445  0.471881  0.645208  0.615835  0.262793\n",
       "4  0.455754  0.548974  0.765319  0.059929  0.687439"
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df.head(5)"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-8d9c5ee3e05a>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[0][0]= 'ajit'\n"
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       "      <td>0.184636</td>\n",
       "      <td>0.959688</td>\n",
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       "      <th>99</th>\n",
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       "      <td>0.585959</td>\n",
       "      <td>0.724223</td>\n",
       "      <td>0.305850</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4\n",
       "0        ajit  0.264495  0.583698  0.553182  0.077867\n",
       "1    0.345639  0.378516  0.571080  0.432195  0.973587\n",
       "2    0.129958  0.233022  0.652113  0.518325  0.211556\n",
       "3   0.0739044  0.350907  0.543523  0.875726  0.073480\n",
       "4    0.935363  0.821073  0.539012  0.437463  0.755946\n",
       "..        ...       ...       ...       ...       ...\n",
       "95   0.253359  0.900567  0.704431  0.918358  0.060113\n",
       "96   0.193356  0.129879  0.110534  0.244284  0.768090\n",
       "97  0.0533768  0.007321  0.774008  0.723468  0.230406\n",
       "98   0.752264  0.672113  0.586109  0.184636  0.959688\n",
       "99   0.318872  0.800362  0.585959  0.724223  0.305850\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0][0]= 'ajit'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "            0         1         2         3         4\n",
       "0       0.454  0.264495  0.583698  0.553182  0.077867\n",
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       "99   0.318872  0.800362  0.585959  0.724223  0.305850\n",
       "\n",
       "[100 rows x 5 columns]"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.iloc[0,0]= 0.454\n",
    "df"
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  {
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   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "            0         1         2         3         4\n",
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       "\n",
       "[100 rows x 5 columns]"
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     "execution_count": 8,
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    "df.iloc[0,4] = 0.2222\n",
    "df"
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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    "df.loc[0,0] = 0.001\n",
    "df"
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   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>0.585959</td>\n",
       "      <td>0.724223</td>\n",
       "      <td>0.305850</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D         E\n",
       "0       0.001  0.264495  0.583698  0.553182  0.222200\n",
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       "4    0.935363  0.821073  0.539012  0.437463  0.755946\n",
       "..        ...       ...       ...       ...       ...\n",
       "95   0.253359  0.900567  0.704431  0.918358  0.060113\n",
       "96   0.193356  0.129879  0.110534  0.244284  0.768090\n",
       "97  0.0533768  0.007321  0.774008  0.723468  0.230406\n",
       "98   0.752264  0.672113  0.586109  0.184636  0.959688\n",
       "99   0.318872  0.800362  0.585959  0.724223  0.305850\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.columns = list(\"ABCDE\")\n",
    "df"
   ]
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  {
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   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<p>100 rows × 6 columns</p>\n",
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       "            A         B         C         D         E      F\n",
       "0       0.001  0.264495  0.583698  0.553182  0.222200  0.554\n",
       "1    0.345639  0.378516  0.571080  0.432195  0.973587    NaN\n",
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       "3   0.0739044  0.350907  0.543523  0.875726  0.073480    NaN\n",
       "4    0.935363  0.821073  0.539012  0.437463  0.755946    NaN\n",
       "..        ...       ...       ...       ...       ...    ...\n",
       "95   0.253359  0.900567  0.704431  0.918358  0.060113    NaN\n",
       "96   0.193356  0.129879  0.110534  0.244284  0.768090    NaN\n",
       "97  0.0533768  0.007321  0.774008  0.723468  0.230406    NaN\n",
       "98   0.752264  0.672113  0.586109  0.184636  0.959688    NaN\n",
       "99   0.318872  0.800362  0.585959  0.724223  0.305850    NaN\n",
       "\n",
       "[100 rows x 6 columns]"
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    "df.loc[0,'F'] = 0.554\n",
    "df"
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   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "<p>100 rows × 7 columns</p>\n",
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       "            A         B         C         D         E      F        G\n",
       "0       0.001  0.264495  0.583698  0.553182  0.222200  0.554  0.15455\n",
       "1    0.345639  0.378516  0.571080  0.432195  0.973587    NaN  0.15455\n",
       "2    0.129958  0.233022  0.652113  0.518325  0.211556    NaN  0.15455\n",
       "3   0.0739044  0.350907  0.543523  0.875726  0.073480    NaN  0.15455\n",
       "4    0.935363  0.821073  0.539012  0.437463  0.755946    NaN  0.15455\n",
       "..        ...       ...       ...       ...       ...    ...      ...\n",
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       "96   0.193356  0.129879  0.110534  0.244284  0.768090    NaN  0.15455\n",
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       "\n",
       "[100 rows x 7 columns]"
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     "execution_count": 12,
     "metadata": {},
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    "df[\"G\"] = 0.15455\n",
    "df"
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  {
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   "outputs": [
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       "      <td>0.15455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.935363</td>\n",
       "      <td>0.821073</td>\n",
       "      <td>0.539012</td>\n",
       "      <td>0.437463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.253359</td>\n",
       "      <td>0.900567</td>\n",
       "      <td>0.704431</td>\n",
       "      <td>0.918358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.193356</td>\n",
       "      <td>0.129879</td>\n",
       "      <td>0.110534</td>\n",
       "      <td>0.244284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.0533768</td>\n",
       "      <td>0.007321</td>\n",
       "      <td>0.774008</td>\n",
       "      <td>0.723468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.752264</td>\n",
       "      <td>0.672113</td>\n",
       "      <td>0.586109</td>\n",
       "      <td>0.184636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.318872</td>\n",
       "      <td>0.800362</td>\n",
       "      <td>0.585959</td>\n",
       "      <td>0.724223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D      F        G\n",
       "0       0.001  0.264495  0.583698  0.553182  0.554  0.15455\n",
       "1    0.345639  0.378516  0.571080  0.432195    NaN  0.15455\n",
       "2    0.129958  0.233022  0.652113  0.518325    NaN  0.15455\n",
       "3   0.0739044  0.350907  0.543523  0.875726    NaN  0.15455\n",
       "4    0.935363  0.821073  0.539012  0.437463    NaN  0.15455\n",
       "..        ...       ...       ...       ...    ...      ...\n",
       "95   0.253359  0.900567  0.704431  0.918358    NaN  0.15455\n",
       "96   0.193356  0.129879  0.110534  0.244284    NaN  0.15455\n",
       "97  0.0533768  0.007321  0.774008  0.723468    NaN  0.15455\n",
       "98   0.752264  0.672113  0.586109  0.184636    NaN  0.15455\n",
       "99   0.318872  0.800362  0.585959  0.724223    NaN  0.15455\n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['E'], axis=1,inplace=True)\n",
    "df"
   ]
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   "execution_count": 15,
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   "outputs": [
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       "      <td>0.437463</td>\n",
       "      <td>NaN</td>\n",
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       "           A         B         C         D      F        G\n",
       "0      0.001  0.264495  0.583698  0.553182  0.554  0.15455\n",
       "1   0.345639  0.378516  0.571080  0.432195    NaN  0.15455\n",
       "2   0.129958  0.233022  0.652113  0.518325    NaN  0.15455\n",
       "3  0.0739044  0.350907  0.543523  0.875726    NaN  0.15455\n",
       "4   0.935363  0.821073  0.539012  0.437463    NaN  0.15455"
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     "execution_count": 15,
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   "execution_count": 16,
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   "outputs": [],
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    "df.drop('D', axis=1, inplace=True)\n"
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       "      <td>0.539012</td>\n",
       "      <td>NaN</td>\n",
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       "</div>"
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       "           A         B         C      F        G\n",
       "0      0.001  0.264495  0.583698  0.554  0.15455\n",
       "1   0.345639  0.378516  0.571080    NaN  0.15455\n",
       "2   0.129958  0.233022  0.652113    NaN  0.15455\n",
       "3  0.0739044  0.350907  0.543523    NaN  0.15455\n",
       "4   0.935363  0.821073  0.539012    NaN  0.15455"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   "execution_count": 18,
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   "outputs": [
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       "      <th>95</th>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.193356</td>\n",
       "      <td>0.129879</td>\n",
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       "    <tr>\n",
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       "      <td>0.752264</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
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       "      <td>0.800362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B\n",
       "0       0.001  0.264495\n",
       "1    0.345639  0.378516\n",
       "2    0.129958  0.233022\n",
       "3   0.0739044  0.350907\n",
       "4    0.935363  0.821073\n",
       "..        ...       ...\n",
       "95   0.253359  0.900567\n",
       "96   0.193356  0.129879\n",
       "97  0.0533768  0.007321\n",
       "98   0.752264  0.672113\n",
       "99   0.318872  0.800362\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[ :,['A','B']]"
   ]
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  {
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   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "           A         B         C      F        G\n",
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       "3  0.0739044  0.350907  0.543523    NaN  0.15455\n",
       "4   0.935363  0.821073  0.539012    NaN  0.15455"
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     "execution_count": 19,
     "metadata": {},
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   "execution_count": 20,
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   "execution_count": 21,
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      ]
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     "execution_count": 21,
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   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "           A         B         C      F        G\n",
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       "            A         B         C   F        G\n",
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       "5    0.506027  0.743824  0.174946 NaN  0.15455\n",
       "..        ...       ...       ...  ..      ...\n",
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       "\n",
       "[99 rows x 5 columns]"
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     "execution_count": 25,
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   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['F','G'], axis=1, inplace=True)"
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       "           A         B         C\n",
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       "       A         B         C\n",
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       "..   ...       ...       ...\n",
       "95  None  0.900567  0.704431\n",
       "96  None  0.129879  0.110534\n",
       "97  None  0.007321  0.774008\n",
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       "\n",
       "[100 rows x 3 columns]"
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     "execution_count": 29,
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   "source": [
    "df['A'] = None\n",
    "df"
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       A      B      C\n",
       "0   True  False  False\n",
       "1   True  False  False\n",
       "2   True  False  False\n",
       "3   True  False  False\n",
       "4   True  False  False\n",
       "..   ...    ...    ...\n",
       "95  True  False  False\n",
       "96  True  False  False\n",
       "97  True  False  False\n",
       "98  True  False  False\n",
       "99  True  False  False\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data =data.to_csv(\"  .csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.704431</td>\n",
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       "      <th>96</th>\n",
       "      <td>None</td>\n",
       "      <td>0.129879</td>\n",
       "      <td>0.110534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>None</td>\n",
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       "      <td>0.774008</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>None</td>\n",
       "      <td>0.672113</td>\n",
       "      <td>0.586109</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
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       "      <td>0.585959</td>\n",
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       "<p>100 rows × 3 columns</p>\n",
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       "       A         B         C\n",
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       "1   None  0.378516  0.571080\n",
       "2   None  0.233022  0.652113\n",
       "3   None  0.350907  0.543523\n",
       "4   None  0.821073  0.539012\n",
       "..   ...       ...       ...\n",
       "95  None  0.900567  0.704431\n",
       "96  None  0.129879  0.110534\n",
       "97  None  0.007321  0.774008\n",
       "98  None  0.672113  0.586109\n",
       "99  None  0.800362  0.585959\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.031359</td>\n",
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       "      <td>0.854132</td>\n",
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       "      <td>0.436348</td>\n",
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       "      <td>0.263428</td>\n",
       "      <td>0.593579</td>\n",
       "      <td>0.754256</td>\n",
       "      <td>0.972502</td>\n",
       "      <td>0.352900</td>\n",
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       "      <td>0.154642</td>\n",
       "      <td>0.248114</td>\n",
       "      <td>0.588541</td>\n",
       "      <td>0.583403</td>\n",
       "      <td>0.531377</td>\n",
       "      <td>0.204264</td>\n",
       "      <td>0.270503</td>\n",
       "      <td>0.028875</td>\n",
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       "      <td>0.227086</td>\n",
       "      <td>0.654603</td>\n",
       "      <td>0.639181</td>\n",
       "      <td>0.964739</td>\n",
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       "      <td>0.702871</td>\n",
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       "      <td>0.285947</td>\n",
       "      <td>0.562606</td>\n",
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       "      <th>399</th>\n",
       "      <td>0.123187</td>\n",
       "      <td>0.131309</td>\n",
       "      <td>0.813683</td>\n",
       "      <td>0.358208</td>\n",
       "      <td>0.298040</td>\n",
       "      <td>0.183996</td>\n",
       "      <td>0.519077</td>\n",
       "      <td>0.101586</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 8 columns</p>\n",
       "</div>"
      ],
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       "            0         1         2         3         4         5         6  \\\n",
       "0    0.300700  0.820771  0.384300  0.033313  0.313539  0.494925  0.459984   \n",
       "1    0.597431  0.226058  0.268408  0.609945  0.232938  0.199879  0.542333   \n",
       "2    0.471933  0.546062  0.813562  0.031359  0.116797  0.203120  0.999372   \n",
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       "..        ...       ...       ...       ...       ...       ...       ...   \n",
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       "399  0.123187  0.131309  0.813683  0.358208  0.298040  0.183996  0.519077   \n",
       "\n",
       "            7  \n",
       "0    0.801524  \n",
       "1    0.430060  \n",
       "2    0.308958  \n",
       "3    0.401501  \n",
       "4    0.136014  \n",
       "..        ...  \n",
       "395  0.352900  \n",
       "396  0.028875  \n",
       "397  0.171121  \n",
       "398  0.562606  \n",
       "399  0.101586  \n",
       "\n",
       "[400 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(400,8), index=np.arange(400))\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.972502</td>\n",
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       "    <tr>\n",
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       "<p>400 rows × 9 columns</p>\n",
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       "            A         B         C         D         E         F         G  \\\n",
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       "4    0.115082  0.948553  0.588130  0.436348  0.301620  0.814618  0.286438   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "395  0.588012  0.205726  0.703404  0.263428  0.593579  0.754256  0.972502   \n",
       "396  0.154642  0.248114  0.588541  0.583403  0.531377  0.204264  0.270503   \n",
       "397  0.024716  0.948908  0.227086  0.654603  0.639181  0.964739  0.878768   \n",
       "398  0.924342  0.064278  0.219374  0.702871  0.264677  0.975126  0.285947   \n",
       "399  0.123187  0.131309  0.813683  0.358208  0.298040  0.183996  0.519077   \n",
       "\n",
       "            H         I  \n",
       "0    0.801524  ABCDEFGH  \n",
       "1    0.430060  ABCDEFGH  \n",
       "2    0.308958  ABCDEFGH  \n",
       "3    0.401501  ABCDEFGH  \n",
       "4    0.136014  ABCDEFGH  \n",
       "..        ...       ...  \n",
       "395  0.352900  ABCDEFGH  \n",
       "396  0.028875  ABCDEFGH  \n",
       "397  0.171121  ABCDEFGH  \n",
       "398  0.562606  ABCDEFGH  \n",
       "399  0.101586  ABCDEFGH  \n",
       "\n",
       "[400 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = list('ABCDEFGHI')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.436348</td>\n",
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       "      <td>0.183996</td>\n",
       "      <td>0.519077</td>\n",
       "      <td>0.101586</td>\n",
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       "<p>400 rows × 8 columns</p>\n",
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       "            A         B         C         D         E         F         G  \\\n",
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       "\n",
       "            H  \n",
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       "4    0.136014  \n",
       "..        ...  \n",
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       "397  0.171121  \n",
       "398  0.562606  \n",
       "399  0.101586  \n",
       "\n",
       "[400 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = df.drop(\"I\", axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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    "df.iloc[:5, :5]"
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   "execution_count": 21,
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   "outputs": [
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       "          A         B         C\n",
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      ]
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    "df.loc[:3, :'C']"
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[0] = 0.1111"
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  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "          A         B         C         D         E         F         G  \\\n",
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       "\n",
       "          H       0  \n",
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       "1  0.430060  0.1111  \n",
       "2  0.308958  0.1111  \n",
       "3  0.401501  0.1111  "
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     "execution_count": 23,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>0.597431</td>\n",
       "      <td>0.226058</td>\n",
       "      <td>0.268408</td>\n",
       "      <td>0.609945</td>\n",
       "      <td>0.232938</td>\n",
       "      <td>0.199879</td>\n",
       "      <td>0.542333</td>\n",
       "      <td>0.430060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.471933</td>\n",
       "      <td>0.546062</td>\n",
       "      <td>0.813562</td>\n",
       "      <td>0.031359</td>\n",
       "      <td>0.116797</td>\n",
       "      <td>0.203120</td>\n",
       "      <td>0.999372</td>\n",
       "      <td>0.308958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.424552</td>\n",
       "      <td>0.093150</td>\n",
       "      <td>0.071726</td>\n",
       "      <td>0.854132</td>\n",
       "      <td>0.500740</td>\n",
       "      <td>0.413171</td>\n",
       "      <td>0.428323</td>\n",
       "      <td>0.401501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.115082</td>\n",
       "      <td>0.948553</td>\n",
       "      <td>0.588130</td>\n",
       "      <td>0.436348</td>\n",
       "      <td>0.301620</td>\n",
       "      <td>0.814618</td>\n",
       "      <td>0.286438</td>\n",
       "      <td>0.136014</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>0.588012</td>\n",
       "      <td>0.205726</td>\n",
       "      <td>0.703404</td>\n",
       "      <td>0.263428</td>\n",
       "      <td>0.593579</td>\n",
       "      <td>0.754256</td>\n",
       "      <td>0.972502</td>\n",
       "      <td>0.352900</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.154642</td>\n",
       "      <td>0.248114</td>\n",
       "      <td>0.588541</td>\n",
       "      <td>0.583403</td>\n",
       "      <td>0.531377</td>\n",
       "      <td>0.204264</td>\n",
       "      <td>0.270503</td>\n",
       "      <td>0.028875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0.024716</td>\n",
       "      <td>0.948908</td>\n",
       "      <td>0.227086</td>\n",
       "      <td>0.654603</td>\n",
       "      <td>0.639181</td>\n",
       "      <td>0.964739</td>\n",
       "      <td>0.878768</td>\n",
       "      <td>0.171121</td>\n",
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       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0.924342</td>\n",
       "      <td>0.064278</td>\n",
       "      <td>0.219374</td>\n",
       "      <td>0.702871</td>\n",
       "      <td>0.264677</td>\n",
       "      <td>0.975126</td>\n",
       "      <td>0.285947</td>\n",
       "      <td>0.562606</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>0.123187</td>\n",
       "      <td>0.131309</td>\n",
       "      <td>0.813683</td>\n",
       "      <td>0.358208</td>\n",
       "      <td>0.298040</td>\n",
       "      <td>0.183996</td>\n",
       "      <td>0.519077</td>\n",
       "      <td>0.101586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>400 rows × 8 columns</p>\n",
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       "2    0.471933  0.546062  0.813562  0.031359  0.116797  0.203120  0.999372   \n",
       "3    0.424552  0.093150  0.071726  0.854132  0.500740  0.413171  0.428323   \n",
       "4    0.115082  0.948553  0.588130  0.436348  0.301620  0.814618  0.286438   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "395  0.588012  0.205726  0.703404  0.263428  0.593579  0.754256  0.972502   \n",
       "396  0.154642  0.248114  0.588541  0.583403  0.531377  0.204264  0.270503   \n",
       "397  0.024716  0.948908  0.227086  0.654603  0.639181  0.964739  0.878768   \n",
       "398  0.924342  0.064278  0.219374  0.702871  0.264677  0.975126  0.285947   \n",
       "399  0.123187  0.131309  0.813683  0.358208  0.298040  0.183996  0.519077   \n",
       "\n",
       "            H  \n",
       "0    0.801524  \n",
       "1    0.430060  \n",
       "2    0.308958  \n",
       "3    0.401501  \n",
       "4    0.136014  \n",
       "..        ...  \n",
       "395  0.352900  \n",
       "396  0.028875  \n",
       "397  0.171121  \n",
       "398  0.562606  \n",
       "399  0.101586  \n",
       "\n",
       "[400 rows x 8 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(0,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[0][0] = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      <td>0.593579</td>\n",
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       "      <td>0.583403</td>\n",
       "      <td>0.531377</td>\n",
       "      <td>0.204264</td>\n",
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       "            A         B         C         D         E         F         G  \\\n",
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     },
     "execution_count": 28,
     "metadata": {},
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   ],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[0,0] = \"abc\""
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  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "      <td>0.031359</td>\n",
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       "      <td>0.999372</td>\n",
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       "      <td>0.1111</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.424552</td>\n",
       "      <td>0.093150</td>\n",
       "      <td>0.071726</td>\n",
       "      <td>0.854132</td>\n",
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       "      <td>0.413171</td>\n",
       "      <td>0.428323</td>\n",
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       "      <td>0.1111</td>\n",
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       "          A         B         C         D         E         F         G  \\\n",
       "0       abc  0.820771  0.384300  0.033313  0.313539  0.494925  0.459984   \n",
       "1  0.597431  0.226058  0.268408  0.609945  0.232938  0.199879  0.542333   \n",
       "2  0.471933  0.546062  0.813562  0.031359  0.116797  0.203120  0.999372   \n",
       "3  0.424552  0.093150  0.071726  0.854132  0.500740  0.413171  0.428323   \n",
       "\n",
       "          H       0  \n",
       "0  0.801524  0.2000  \n",
       "1  0.430060  0.1111  \n",
       "2  0.308958  0.1111  \n",
       "3  0.401501  0.1111  "
      ]
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     "execution_count": 30,
     "metadata": {},
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    "df.head(4)"
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   "execution_count": 19,
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       "      name addres  index\n",
       "0     ajit   pune      1\n",
       "1  nishita  surat      2\n",
       "2      mat    usa      3\n",
       "3     dean     sa      4\n",
       "4  monique    usa      5"
      ]
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     "execution_count": 19,
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    "df.rename(columns={\"A\":'ajit'}).head(5)"
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   "execution_count": 43,
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       "       ajit         B         C         D         E         F         G  \\\n",
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       "\n",
       "          H  \n",
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    "df.rename(columns={\"A\":'ajit'}).head(2)"
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   "execution_count": 44,
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   "outputs": [
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       "      <td>0.263428</td>\n",
       "      <td>0.593579</td>\n",
       "      <td>0.754256</td>\n",
       "      <td>0.972502</td>\n",
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       "      <td>0.588541</td>\n",
       "      <td>0.583403</td>\n",
       "      <td>0.531377</td>\n",
       "      <td>0.204264</td>\n",
       "      <td>0.270503</td>\n",
       "      <td>0.028875</td>\n",
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       "      <td>0.227086</td>\n",
       "      <td>0.654603</td>\n",
       "      <td>0.639181</td>\n",
       "      <td>0.964739</td>\n",
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       "      <td>0.975126</td>\n",
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       "      <td>0.183996</td>\n",
       "      <td>0.519077</td>\n",
       "      <td>0.101586</td>\n",
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       "<p>400 rows × 8 columns</p>\n",
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       "             A         B         C         D         E         F         G  \\\n",
       "0          abc  0.820771  0.384300  0.033313  0.313539  0.494925  0.459984   \n",
       "1     0.597431  0.226058  0.268408  0.609945  0.232938  0.199879  0.542333   \n",
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       "..         ...       ...       ...       ...       ...       ...       ...   \n",
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       "397  0.0247158  0.948908  0.227086  0.654603  0.639181  0.964739  0.878768   \n",
       "398   0.924342  0.064278  0.219374  0.702871  0.264677  0.975126  0.285947   \n",
       "399   0.123187  0.131309  0.813683  0.358208  0.298040  0.183996  0.519077   \n",
       "\n",
       "            H  \n",
       "0    0.801524  \n",
       "1    0.430060  \n",
       "2    0.308958  \n",
       "3    0.401501  \n",
       "4    0.136014  \n",
       "..        ...  \n",
       "395  0.352900  \n",
       "396  0.028875  \n",
       "397  0.171121  \n",
       "398  0.562606  \n",
       "399  0.101586  \n",
       "\n",
       "[400 rows x 8 columns]"
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     },
     "execution_count": 44,
     "metadata": {},
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    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"B\":\"gg\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          A        gg         C         D         E         F         G  \\\n",
       "0       abc  0.820771  0.384300  0.033313  0.313539  0.494925  0.459984   \n",
       "1  0.597431  0.226058  0.268408  0.609945  0.232938  0.199879  0.542333   \n",
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       "4  0.115082  0.948553  0.588130  0.436348  0.301620  0.814618  0.286438   \n",
       "\n",
       "          H  \n",
       "0  0.801524  \n",
       "1  0.430060  \n",
       "2  0.308958  \n",
       "3  0.401501  \n",
       "4  0.136014  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
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  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82077101, 0.22605759, 0.54606213, 0.09314994, 0.94855271,\n",
       "       0.17392883, 0.7510306 , 0.09629522, 0.91673971, 0.1744398 ,\n",
       "       0.68161038, 0.80536893, 0.98217328, 0.84429093, 0.87046463,\n",
       "       0.39530953, 0.69786851, 0.89738931, 0.61544682, 0.25726751,\n",
       "       0.09215662, 0.64625972, 0.7434044 , 0.65080814, 0.29885217,\n",
       "       0.21473205, 0.45751032, 0.23691867, 0.01548019, 0.20611491,\n",
       "       0.60669931, 0.83339332, 0.04926936, 0.08895171, 0.69972968,\n",
       "       0.87247963, 0.31083709, 0.41988872, 0.64726866, 0.40286282,\n",
       "       0.17707605, 0.34872182, 0.45421914, 0.48383817, 0.97335146,\n",
       "       0.24094714, 0.53350214, 0.28719795, 0.37373608, 0.47973011,\n",
       "       0.22031218, 0.99939061, 0.85078403, 0.7222482 , 0.98838084,\n",
       "       0.87332089, 0.11878582, 0.26898291, 0.18121616, 0.09947912,\n",
       "       0.18936068, 0.96314698, 0.58903294, 0.00149518, 0.09455757,\n",
       "       0.28569901, 0.47822093, 0.9521195 , 0.5432512 , 0.60499881,\n",
       "       0.42967435, 0.64563812, 0.84342062, 0.17227198, 0.41343119,\n",
       "       0.00807927, 0.39230502, 0.77256083, 0.25748911, 0.58587411,\n",
       "       0.4731681 , 0.37220395, 0.91455838, 0.20604598, 0.02318158,\n",
       "       0.93976582, 0.76186089, 0.33258428, 0.29155106, 0.60020099,\n",
       "       0.86948195, 0.13814822, 0.57150154, 0.66366562, 0.40765181,\n",
       "       0.82364943, 0.44115258, 0.31634248, 0.44384751, 0.7815799 ,\n",
       "       0.02642915, 0.69874894, 0.12537851, 0.11244331, 0.72832953,\n",
       "       0.0205377 , 0.82600593, 0.77107789, 0.91785517, 0.69779089,\n",
       "       0.86519958, 0.92964901, 0.38701247, 0.64914937, 0.16279807,\n",
       "       0.7861479 , 0.79913549, 0.74384291, 0.56479607, 0.42582659,\n",
       "       0.28464544, 0.36726297, 0.8051362 , 0.96894041, 0.24306689,\n",
       "       0.71728958, 0.70430743, 0.35484159, 0.29917058, 0.39196291,\n",
       "       0.09516581, 0.05726335, 0.43307786, 0.47745647, 0.39268092,\n",
       "       0.72920774, 0.35846131, 0.5836879 , 0.54216736, 0.65554176,\n",
       "       0.87170156, 0.92785439, 0.83023407, 0.35990591, 0.7136745 ,\n",
       "       0.14919912, 0.66186858, 0.40565119, 0.5101747 , 0.91585685,\n",
       "       0.03303791, 0.55106467, 0.52708618, 0.77072909, 0.6999292 ,\n",
       "       0.74106434, 0.50629814, 0.01777509, 0.67896517, 0.13371347,\n",
       "       0.81879649, 0.66215832, 0.60806149, 0.18530387, 0.47634383,\n",
       "       0.87057395, 0.70207844, 0.09494648, 0.38116163, 0.12849083,\n",
       "       0.8652006 , 0.20937703, 0.84460633, 0.33565622, 0.40302984,\n",
       "       0.51644246, 0.7218807 , 0.5511457 , 0.45569398, 0.71220258,\n",
       "       0.88673049, 0.77205416, 0.93117759, 0.53321925, 0.64805384,\n",
       "       0.69481193, 0.67900314, 0.5300258 , 0.49995683, 0.1791417 ,\n",
       "       0.29750621, 0.68405627, 0.21763669, 0.07846235, 0.98506021,\n",
       "       0.24443564, 0.44520421, 0.66650133, 0.81186996, 0.78033482,\n",
       "       0.47737997, 0.84945469, 0.43601219, 0.62697617, 0.56325354,\n",
       "       0.65162921, 0.02911333, 0.91153273, 0.39356585, 0.12586553,\n",
       "       0.85634075, 0.18225669, 0.77567073, 0.75161682, 0.04371966,\n",
       "       0.52960406, 0.087106  , 0.99920115, 0.57236288, 0.89432721,\n",
       "       0.2895004 , 0.25516134, 0.67644192, 0.89200746, 0.36372123,\n",
       "       0.61943161, 0.51715795, 0.97712777, 0.36239846, 0.03179872,\n",
       "       0.95366817, 0.3661372 , 0.43557366, 0.56809781, 0.64555621,\n",
       "       0.71728278, 0.23658202, 0.98047674, 0.76124293, 0.70998464,\n",
       "       0.73381776, 0.5362838 , 0.25095427, 0.85239033, 0.19871181,\n",
       "       0.62680982, 0.68055709, 0.35391367, 0.43603382, 0.45537725,\n",
       "       0.3786327 , 0.05220117, 0.634054  , 0.47251389, 0.27242195,\n",
       "       0.74978577, 0.00995891, 0.51204235, 0.91436618, 0.4344717 ,\n",
       "       0.03706607, 0.87698256, 0.80812434, 0.26120805, 0.14257663,\n",
       "       0.88832586, 0.08605153, 0.73622862, 0.19579316, 0.72223452,\n",
       "       0.98133118, 0.06858224, 0.61389414, 0.15039949, 0.40823518,\n",
       "       0.0107125 , 0.92399983, 0.42808245, 0.17033063, 0.26964056,\n",
       "       0.01579633, 0.46636106, 0.36728044, 0.55245231, 0.84264443,\n",
       "       0.10309892, 0.10091498, 0.93873084, 0.3623555 , 0.4963313 ,\n",
       "       0.86220942, 0.29718866, 0.44042256, 0.57887308, 0.36025393,\n",
       "       0.38200157, 0.30429027, 0.11676187, 0.23648239, 0.10109406,\n",
       "       0.41187442, 0.83383312, 0.67873208, 0.68513384, 0.75908675,\n",
       "       0.81805326, 0.36411722, 0.15806904, 0.46494466, 0.00709023,\n",
       "       0.06478721, 0.56252733, 0.09583644, 0.25148527, 0.07104255,\n",
       "       0.88297945, 0.06231366, 0.39561656, 0.35035175, 0.49170566,\n",
       "       0.82985358, 0.80579244, 0.80387441, 0.14092647, 0.21736106,\n",
       "       0.87970386, 0.9607493 , 0.65064797, 0.76076153, 0.24945864,\n",
       "       0.6713433 , 0.43504952, 0.25055221, 0.50401968, 0.48139291,\n",
       "       0.63026544, 0.29595846, 0.41401854, 0.18316376, 0.52620272,\n",
       "       0.00330382, 0.06748554, 0.87822525, 0.4201907 , 0.14595272,\n",
       "       0.94066353, 0.87824511, 0.70395298, 0.8397586 , 0.68952203,\n",
       "       0.92449001, 0.17827049, 0.41060064, 0.29386436, 0.5819129 ,\n",
       "       0.83679459, 0.35484201, 0.70298811, 0.50277382, 0.16755275,\n",
       "       0.16428013, 0.18554716, 0.45402832, 0.26315482, 0.39871673,\n",
       "       0.59599632, 0.31222954, 0.76461399, 0.04370674, 0.65766257,\n",
       "       0.33297187, 0.33370252, 0.30297128, 0.64344245, 0.21168187,\n",
       "       0.31559829, 0.5966535 , 0.07224573, 0.32508959, 0.83495608,\n",
       "       0.84170333, 0.50336436, 0.02072007, 0.50716991, 0.24129424,\n",
       "       0.75722841, 0.89149111, 0.38482071, 0.34720141, 0.18847074,\n",
       "       0.27364556, 0.46877044, 0.58600334, 0.81208265, 0.08173816,\n",
       "       0.20572556, 0.24811423, 0.94890782, 0.06427774, 0.1313091 ])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"gg\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.392305    1\n",
       "0.360254    1\n",
       "0.862209    1\n",
       "0.551146    1\n",
       "0.973351    1\n",
       "           ..\n",
       "0.799135    1\n",
       "0.504020    1\n",
       "0.704307    1\n",
       "0.698749    1\n",
       "0.960749    1\n",
       "Name: gg, Length: 400, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"gg\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'alexa': 1, 'joe': 5, 'rob': 7}, {'kate': 4, 'soham': 8}, {'nishu': 'sas'}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = [{'alexa':1, 'joe':5,'rob':7},{'kate':4,'soham':8,},{'nishu':'sas'}]\n",
    "df                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>alexa</th>\n",
       "      <th>joe</th>\n",
       "      <th>rob</th>\n",
       "      <th>kate</th>\n",
       "      <th>soham</th>\n",
       "      <th>nishu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alexa  joe  rob  kate  soham nishu\n",
       "0    1.0  5.0  7.0   NaN    NaN   NaN\n",
       "1    NaN  NaN  NaN   4.0    8.0   NaN\n",
       "2    NaN  NaN  NaN   NaN    NaN   sas"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>alexa</th>\n",
       "      <th>joe</th>\n",
       "      <th>rob</th>\n",
       "      <th>kate</th>\n",
       "      <th>soham</th>\n",
       "      <th>nishu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alexa  joe  rob  kate  soham  nishu\n",
       "0    1.0  5.0  7.0   NaN    NaN  False\n",
       "1    NaN  NaN  NaN   4.0    8.0  False\n",
       "2    NaN  NaN  NaN   NaN    NaN   True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['nishu'] = df['nishu'] > 'NaN'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   #                   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  3  VenusaurMega Venusaur  Grass  Poison  80     100      123      122   \n",
      "4  4             Charmander   Fire     NaN  39      52       43       60   \n",
      "\n",
      "   Sp. Def  Speed  Generation  Legendary  \n",
      "0       65     45           1      False  \n",
      "1       80     60           1      False  \n",
      "2      100     80           1      False  \n",
      "3      120     80           1      False  \n",
      "4       50     65           1      False  \n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/user/Desktop/Data_Science/Dataset/pokemon_data.csv\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Grass\n",
      "1 Grass\n",
      "2 Grass\n",
      "3 Grass\n",
      "4 Fire\n",
      "5 Fire\n",
      "6 Fire\n",
      "7 Fire\n",
      "8 Fire\n",
      "9 Water\n",
      "10 Water\n",
      "11 Water\n",
      "12 Water\n",
      "13 Bug\n",
      "14 Bug\n",
      "15 Bug\n",
      "16 Bug\n",
      "17 Bug\n",
      "18 Bug\n",
      "19 Bug\n",
      "20 Normal\n",
      "21 Normal\n",
      "22 Normal\n",
      "23 Normal\n",
      "24 Normal\n",
      "25 Normal\n",
      "26 Normal\n",
      "27 Normal\n",
      "28 Poison\n",
      "29 Poison\n",
      "30 Electric\n",
      "31 Electric\n",
      "32 Ground\n",
      "33 Ground\n",
      "34 Poison\n",
      "35 Poison\n",
      "36 Poison\n",
      "37 Poison\n",
      "38 Poison\n",
      "39 Poison\n",
      "40 Fairy\n",
      "41 Fairy\n",
      "42 Fire\n",
      "43 Fire\n",
      "44 Normal\n",
      "45 Normal\n",
      "46 Poison\n",
      "47 Poison\n",
      "48 Grass\n",
      "49 Grass\n",
      "50 Grass\n",
      "51 Bug\n",
      "52 Bug\n",
      "53 Bug\n",
      "54 Bug\n",
      "55 Ground\n",
      "56 Ground\n",
      "57 Normal\n",
      "58 Normal\n",
      "59 Water\n",
      "60 Water\n",
      "61 Fighting\n",
      "62 Fighting\n",
      "63 Fire\n",
      "64 Fire\n",
      "65 Water\n",
      "66 Water\n",
      "67 Water\n",
      "68 Psychic\n",
      "69 Psychic\n",
      "70 Psychic\n",
      "71 Psychic\n",
      "72 Fighting\n",
      "73 Fighting\n",
      "74 Fighting\n",
      "75 Grass\n",
      "76 Grass\n",
      "77 Grass\n",
      "78 Water\n",
      "79 Water\n",
      "80 Rock\n",
      "81 Rock\n",
      "82 Rock\n",
      "83 Fire\n",
      "84 Fire\n",
      "85 Water\n",
      "86 Water\n",
      "87 Water\n",
      "88 Electric\n",
      "89 Electric\n",
      "90 Normal\n",
      "91 Normal\n",
      "92 Normal\n",
      "93 Water\n",
      "94 Water\n",
      "95 Poison\n",
      "96 Poison\n",
      "97 Water\n",
      "98 Water\n",
      "99 Ghost\n",
      "100 Ghost\n",
      "101 Ghost\n",
      "102 Ghost\n",
      "103 Rock\n",
      "104 Psychic\n",
      "105 Psychic\n",
      "106 Water\n",
      "107 Water\n",
      "108 Electric\n",
      "109 Electric\n",
      "110 Grass\n",
      "111 Grass\n",
      "112 Ground\n",
      "113 Ground\n",
      "114 Fighting\n",
      "115 Fighting\n",
      "116 Normal\n",
      "117 Poison\n",
      "118 Poison\n",
      "119 Ground\n",
      "120 Ground\n",
      "121 Normal\n",
      "122 Grass\n",
      "123 Normal\n",
      "124 Normal\n",
      "125 Water\n",
      "126 Water\n",
      "127 Water\n",
      "128 Water\n",
      "129 Water\n",
      "130 Water\n",
      "131 Psychic\n",
      "132 Bug\n",
      "133 Ice\n",
      "134 Electric\n",
      "135 Fire\n",
      "136 Bug\n",
      "137 Bug\n",
      "138 Normal\n",
      "139 Water\n",
      "140 Water\n",
      "141 Water\n",
      "142 Water\n",
      "143 Normal\n",
      "144 Normal\n",
      "145 Water\n",
      "146 Electric\n",
      "147 Fire\n",
      "148 Normal\n",
      "149 Rock\n",
      "150 Rock\n",
      "151 Rock\n",
      "152 Rock\n",
      "153 Rock\n",
      "154 Rock\n",
      "155 Normal\n",
      "156 Ice\n",
      "157 Electric\n",
      "158 Fire\n",
      "159 Dragon\n",
      "160 Dragon\n",
      "161 Dragon\n",
      "162 Psychic\n",
      "163 Psychic\n",
      "164 Psychic\n",
      "165 Psychic\n",
      "166 Grass\n",
      "167 Grass\n",
      "168 Grass\n",
      "169 Fire\n",
      "170 Fire\n",
      "171 Fire\n",
      "172 Water\n",
      "173 Water\n",
      "174 Water\n",
      "175 Normal\n",
      "176 Normal\n",
      "177 Normal\n",
      "178 Normal\n",
      "179 Bug\n",
      "180 Bug\n",
      "181 Bug\n",
      "182 Bug\n",
      "183 Poison\n",
      "184 Water\n",
      "185 Water\n",
      "186 Electric\n",
      "187 Fairy\n",
      "188 Normal\n",
      "189 Fairy\n",
      "190 Fairy\n",
      "191 Psychic\n",
      "192 Psychic\n",
      "193 Electric\n",
      "194 Electric\n",
      "195 Electric\n",
      "196 Electric\n",
      "197 Grass\n",
      "198 Water\n",
      "199 Water\n",
      "200 Rock\n",
      "201 Water\n",
      "202 Grass\n",
      "203 Grass\n",
      "204 Grass\n",
      "205 Normal\n",
      "206 Grass\n",
      "207 Grass\n",
      "208 Bug\n",
      "209 Water\n",
      "210 Water\n",
      "211 Psychic\n",
      "212 Dark\n",
      "213 Dark\n",
      "214 Water\n",
      "215 Ghost\n",
      "216 Psychic\n",
      "217 Psychic\n",
      "218 Normal\n",
      "219 Bug\n",
      "220 Bug\n",
      "221 Normal\n",
      "222 Ground\n",
      "223 Steel\n",
      "224 Steel\n",
      "225 Fairy\n",
      "226 Fairy\n",
      "227 Water\n",
      "228 Bug\n",
      "229 Bug\n",
      "230 Bug\n",
      "231 Bug\n",
      "232 Bug\n",
      "233 Dark\n",
      "234 Normal\n",
      "235 Normal\n",
      "236 Fire\n",
      "237 Fire\n",
      "238 Ice\n",
      "239 Ice\n",
      "240 Water\n",
      "241 Water\n",
      "242 Water\n",
      "243 Ice\n",
      "244 Water\n",
      "245 Steel\n",
      "246 Dark\n",
      "247 Dark\n",
      "248 Dark\n",
      "249 Water\n",
      "250 Ground\n",
      "251 Ground\n",
      "252 Normal\n",
      "253 Normal\n",
      "254 Normal\n",
      "255 Fighting\n",
      "256 Fighting\n",
      "257 Ice\n",
      "258 Electric\n",
      "259 Fire\n",
      "260 Normal\n",
      "261 Normal\n",
      "262 Electric\n",
      "263 Fire\n",
      "264 Water\n",
      "265 Rock\n",
      "266 Rock\n",
      "267 Rock\n",
      "268 Rock\n",
      "269 Psychic\n",
      "270 Fire\n",
      "271 Psychic\n",
      "272 Grass\n",
      "273 Grass\n",
      "274 Grass\n",
      "275 Grass\n",
      "276 Fire\n",
      "277 Fire\n",
      "278 Fire\n",
      "279 Fire\n",
      "280 Water\n",
      "281 Water\n",
      "282 Water\n",
      "283 Water\n",
      "284 Dark\n",
      "285 Dark\n",
      "286 Normal\n",
      "287 Normal\n",
      "288 Bug\n",
      "289 Bug\n",
      "290 Bug\n",
      "291 Bug\n",
      "292 Bug\n",
      "293 Water\n",
      "294 Water\n",
      "295 Water\n",
      "296 Grass\n",
      "297 Grass\n",
      "298 Grass\n",
      "299 Normal\n",
      "300 Normal\n",
      "301 Water\n",
      "302 Water\n",
      "303 Psychic\n",
      "304 Psychic\n",
      "305 Psychic\n",
      "306 Psychic\n",
      "307 Bug\n",
      "308 Bug\n",
      "309 Grass\n",
      "310 Grass\n",
      "311 Normal\n",
      "312 Normal\n",
      "313 Normal\n",
      "314 Bug\n",
      "315 Bug\n",
      "316 Bug\n",
      "317 Normal\n",
      "318 Normal\n",
      "319 Normal\n",
      "320 Fighting\n",
      "321 Fighting\n",
      "322 Normal\n",
      "323 Rock\n",
      "324 Normal\n",
      "325 Normal\n",
      "326 Dark\n",
      "327 Dark\n",
      "328 Steel\n",
      "329 Steel\n",
      "330 Steel\n",
      "331 Steel\n",
      "332 Steel\n",
      "333 Steel\n",
      "334 Fighting\n",
      "335 Fighting\n",
      "336 Fighting\n",
      "337 Electric\n",
      "338 Electric\n",
      "339 Electric\n",
      "340 Electric\n",
      "341 Electric\n",
      "342 Bug\n",
      "343 Bug\n",
      "344 Grass\n",
      "345 Poison\n",
      "346 Poison\n",
      "347 Water\n",
      "348 Water\n",
      "349 Water\n",
      "350 Water\n",
      "351 Water\n",
      "352 Fire\n",
      "353 Fire\n",
      "354 Fire\n",
      "355 Fire\n",
      "356 Psychic\n",
      "357 Psychic\n",
      "358 Normal\n",
      "359 Ground\n",
      "360 Ground\n",
      "361 Ground\n",
      "362 Grass\n",
      "363 Grass\n",
      "364 Normal\n",
      "365 Dragon\n",
      "366 Dragon\n",
      "367 Normal\n",
      "368 Poison\n",
      "369 Rock\n",
      "370 Rock\n",
      "371 Water\n",
      "372 Water\n",
      "373 Water\n",
      "374 Water\n",
      "375 Ground\n",
      "376 Ground\n",
      "377 Rock\n",
      "378 Rock\n",
      "379 Rock\n",
      "380 Rock\n",
      "381 Water\n",
      "382 Water\n",
      "383 Normal\n",
      "384 Normal\n",
      "385 Ghost\n",
      "386 Ghost\n",
      "387 Ghost\n",
      "388 Ghost\n",
      "389 Ghost\n",
      "390 Grass\n",
      "391 Psychic\n",
      "392 Dark\n",
      "393 Dark\n",
      "394 Psychic\n",
      "395 Ice\n",
      "396 Ice\n",
      "397 Ice\n",
      "398 Ice\n",
      "399 Ice\n",
      "400 Ice\n",
      "401 Water\n",
      "402 Water\n",
      "403 Water\n",
      "404 Water\n",
      "405 Water\n",
      "406 Dragon\n",
      "407 Dragon\n",
      "408 Dragon\n",
      "409 Dragon\n",
      "410 Steel\n",
      "411 Steel\n",
      "412 Steel\n",
      "413 Steel\n",
      "414 Rock\n",
      "415 Ice\n",
      "416 Steel\n",
      "417 Dragon\n",
      "418 Dragon\n",
      "419 Dragon\n",
      "420 Dragon\n",
      "421 Water\n",
      "422 Water\n",
      "423 Ground\n",
      "424 Ground\n",
      "425 Dragon\n",
      "426 Dragon\n",
      "427 Steel\n",
      "428 Psychic\n",
      "429 Psychic\n",
      "430 Psychic\n",
      "431 Psychic\n",
      "432 Grass\n",
      "433 Grass\n",
      "434 Grass\n",
      "435 Fire\n",
      "436 Fire\n",
      "437 Fire\n",
      "438 Water\n",
      "439 Water\n",
      "440 Water\n",
      "441 Normal\n",
      "442 Normal\n",
      "443 Normal\n",
      "444 Normal\n",
      "445 Normal\n",
      "446 Bug\n",
      "447 Bug\n",
      "448 Electric\n",
      "449 Electric\n",
      "450 Electric\n",
      "451 Grass\n",
      "452 Grass\n",
      "453 Rock\n",
      "454 Rock\n",
      "455 Rock\n",
      "456 Rock\n",
      "457 Bug\n",
      "458 Bug\n",
      "459 Bug\n",
      "460 Bug\n",
      "461 Bug\n",
      "462 Bug\n",
      "463 Bug\n",
      "464 Electric\n",
      "465 Water\n",
      "466 Water\n",
      "467 Grass\n",
      "468 Grass\n",
      "469 Water\n",
      "470 Water\n",
      "471 Normal\n",
      "472 Ghost\n",
      "473 Ghost\n",
      "474 Normal\n",
      "475 Normal\n",
      "476 Normal\n",
      "477 Ghost\n",
      "478 Dark\n",
      "479 Normal\n",
      "480 Normal\n",
      "481 Psychic\n",
      "482 Poison\n",
      "483 Poison\n",
      "484 Steel\n",
      "485 Steel\n",
      "486 Rock\n",
      "487 Psychic\n",
      "488 Normal\n",
      "489 Normal\n",
      "490 Ghost\n",
      "491 Dragon\n",
      "492 Dragon\n",
      "493 Dragon\n",
      "494 Dragon\n",
      "495 Normal\n",
      "496 Fighting\n",
      "497 Fighting\n",
      "498 Fighting\n",
      "499 Ground\n",
      "500 Ground\n",
      "501 Poison\n",
      "502 Poison\n",
      "503 Poison\n",
      "504 Poison\n",
      "505 Grass\n",
      "506 Water\n",
      "507 Water\n",
      "508 Water\n",
      "509 Grass\n",
      "510 Grass\n",
      "511 Grass\n",
      "512 Dark\n",
      "513 Electric\n",
      "514 Normal\n",
      "515 Ground\n",
      "516 Grass\n",
      "517 Electric\n",
      "518 Fire\n",
      "519 Fairy\n",
      "520 Bug\n",
      "521 Grass\n",
      "522 Ice\n",
      "523 Ground\n",
      "524 Ice\n",
      "525 Normal\n",
      "526 Psychic\n",
      "527 Psychic\n",
      "528 Rock\n",
      "529 Ghost\n",
      "530 Ice\n",
      "531 Electric\n",
      "532 Electric\n",
      "533 Electric\n",
      "534 Electric\n",
      "535 Electric\n",
      "536 Electric\n",
      "537 Psychic\n",
      "538 Psychic\n",
      "539 Psychic\n",
      "540 Steel\n",
      "541 Water\n",
      "542 Fire\n",
      "543 Normal\n",
      "544 Ghost\n",
      "545 Ghost\n",
      "546 Psychic\n",
      "547 Water\n",
      "548 Water\n",
      "549 Dark\n",
      "550 Grass\n",
      "551 Grass\n",
      "552 Normal\n",
      "553 Psychic\n",
      "554 Grass\n",
      "555 Grass\n",
      "556 Grass\n",
      "557 Fire\n",
      "558 Fire\n",
      "559 Fire\n",
      "560 Water\n",
      "561 Water\n",
      "562 Water\n",
      "563 Normal\n",
      "564 Normal\n",
      "565 Normal\n",
      "566 Normal\n",
      "567 Normal\n",
      "568 Dark\n",
      "569 Dark\n",
      "570 Grass\n",
      "571 Grass\n",
      "572 Fire\n",
      "573 Fire\n",
      "574 Water\n",
      "575 Water\n",
      "576 Psychic\n",
      "577 Psychic\n",
      "578 Normal\n",
      "579 Normal\n",
      "580 Normal\n",
      "581 Electric\n",
      "582 Electric\n",
      "583 Rock\n",
      "584 Rock\n",
      "585 Rock\n",
      "586 Psychic\n",
      "587 Psychic\n",
      "588 Ground\n",
      "589 Ground\n",
      "590 Normal\n",
      "591 Normal\n",
      "592 Fighting\n",
      "593 Fighting\n",
      "594 Fighting\n",
      "595 Water\n",
      "596 Water\n",
      "597 Water\n",
      "598 Fighting\n",
      "599 Fighting\n",
      "600 Bug\n",
      "601 Bug\n",
      "602 Bug\n",
      "603 Bug\n",
      "604 Bug\n",
      "605 Bug\n",
      "606 Grass\n",
      "607 Grass\n",
      "608 Grass\n",
      "609 Grass\n",
      "610 Water\n",
      "611 Ground\n",
      "612 Ground\n",
      "613 Ground\n",
      "614 Fire\n",
      "615 Fire\n",
      "616 Fire\n",
      "617 Grass\n",
      "618 Bug\n",
      "619 Bug\n",
      "620 Dark\n",
      "621 Dark\n",
      "622 Psychic\n",
      "623 Ghost\n",
      "624 Ghost\n",
      "625 Water\n",
      "626 Water\n",
      "627 Rock\n",
      "628 Rock\n",
      "629 Poison\n",
      "630 Poison\n",
      "631 Dark\n",
      "632 Dark\n",
      "633 Normal\n",
      "634 Normal\n",
      "635 Psychic\n",
      "636 Psychic\n",
      "637 Psychic\n",
      "638 Psychic\n",
      "639 Psychic\n",
      "640 Psychic\n",
      "641 Water\n",
      "642 Water\n",
      "643 Ice\n",
      "644 Ice\n",
      "645 Ice\n",
      "646 Normal\n",
      "647 Normal\n",
      "648 Electric\n",
      "649 Bug\n",
      "650 Bug\n",
      "651 Grass\n",
      "652 Grass\n",
      "653 Water\n",
      "654 Water\n",
      "655 Water\n",
      "656 Bug\n",
      "657 Bug\n",
      "658 Grass\n",
      "659 Grass\n",
      "660 Steel\n",
      "661 Steel\n",
      "662 Steel\n",
      "663 Electric\n",
      "664 Electric\n",
      "665 Electric\n",
      "666 Psychic\n",
      "667 Psychic\n",
      "668 Ghost\n",
      "669 Ghost\n",
      "670 Ghost\n",
      "671 Dragon\n",
      "672 Dragon\n",
      "673 Dragon\n",
      "674 Ice\n",
      "675 Ice\n",
      "676 Ice\n",
      "677 Bug\n",
      "678 Bug\n",
      "679 Ground\n",
      "680 Fighting\n",
      "681 Fighting\n",
      "682 Dragon\n",
      "683 Ground\n",
      "684 Ground\n",
      "685 Dark\n",
      "686 Dark\n",
      "687 Normal\n",
      "688 Normal\n",
      "689 Normal\n",
      "690 Dark\n",
      "691 Dark\n",
      "692 Fire\n",
      "693 Bug\n",
      "694 Dark\n",
      "695 Dark\n",
      "696 Dark\n",
      "697 Bug\n",
      "698 Bug\n",
      "699 Steel\n",
      "700 Rock\n",
      "701 Grass\n",
      "702 Flying\n",
      "703 Flying\n",
      "704 Electric\n",
      "705 Electric\n",
      "706 Dragon\n",
      "707 Dragon\n",
      "708 Ground\n",
      "709 Ground\n",
      "710 Dragon\n",
      "711 Dragon\n",
      "712 Dragon\n",
      "713 Water\n",
      "714 Water\n",
      "715 Normal\n",
      "716 Normal\n",
      "717 Bug\n",
      "718 Grass\n",
      "719 Grass\n",
      "720 Grass\n",
      "721 Fire\n",
      "722 Fire\n",
      "723 Fire\n",
      "724 Water\n",
      "725 Water\n",
      "726 Water\n",
      "727 Normal\n",
      "728 Normal\n",
      "729 Normal\n",
      "730 Fire\n",
      "731 Fire\n",
      "732 Bug\n",
      "733 Bug\n",
      "734 Bug\n",
      "735 Fire\n",
      "736 Fire\n",
      "737 Fairy\n",
      "738 Fairy\n",
      "739 Fairy\n",
      "740 Grass\n",
      "741 Grass\n",
      "742 Fighting\n",
      "743 Fighting\n",
      "744 Normal\n",
      "745 Psychic\n",
      "746 Psychic\n",
      "747 Psychic\n",
      "748 Steel\n",
      "749 Steel\n",
      "750 Steel\n",
      "751 Steel\n",
      "752 Fairy\n",
      "753 Fairy\n",
      "754 Fairy\n",
      "755 Fairy\n",
      "756 Dark\n",
      "757 Dark\n",
      "758 Rock\n",
      "759 Rock\n",
      "760 Poison\n",
      "761 Poison\n",
      "762 Water\n",
      "763 Water\n",
      "764 Electric\n",
      "765 Electric\n",
      "766 Rock\n",
      "767 Rock\n",
      "768 Rock\n",
      "769 Rock\n",
      "770 Fairy\n",
      "771 Fighting\n",
      "772 Electric\n",
      "773 Rock\n",
      "774 Dragon\n",
      "775 Dragon\n",
      "776 Dragon\n",
      "777 Steel\n",
      "778 Ghost\n",
      "779 Ghost\n",
      "780 Ghost\n",
      "781 Ghost\n",
      "782 Ghost\n",
      "783 Ghost\n",
      "784 Ghost\n",
      "785 Ghost\n",
      "786 Ghost\n",
      "787 Ghost\n",
      "788 Ice\n",
      "789 Ice\n",
      "790 Flying\n",
      "791 Flying\n",
      "792 Fairy\n",
      "793 Dark\n",
      "794 Dragon\n",
      "795 Rock\n",
      "796 Rock\n",
      "797 Psychic\n",
      "798 Psychic\n",
      "799 Fire\n"
     ]
    }
   ],
   "source": [
    "# print each roe\n",
    "for index, row in df.iterrows():\n",
    "    print(index, row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check how many Fire are there in the ['Type 1'] column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>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>4</th>\n",
       "      <td>4</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>5</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>6</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>6</td>\n",
       "      <td>CharizardMega 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>6</td>\n",
       "      <td>CharizardMega 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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #                       Name Type 1  Type 2  HP  Attack  Defense  Sp. Atk  \\\n",
       "4  4                 Charmander   Fire     NaN  39      52       43       60   \n",
       "5  5                 Charmeleon   Fire     NaN  58      64       58       80   \n",
       "6  6                  Charizard   Fire  Flying  78      84       78      109   \n",
       "7  6  CharizardMega Charizard X   Fire  Dragon  78     130      111      130   \n",
       "8  6  CharizardMega Charizard Y   Fire  Flying  78     104       78      159   \n",
       "\n",
       "   Sp. Def  Speed  Generation  Legendary  \n",
       "4       50     65           1      False  \n",
       "5       65     80           1      False  \n",
       "6       85    100           1      False  \n",
       "7       85    100           1      False  \n",
       "8      115    100           1      False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Type 1']=='Fire'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sorting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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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>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>794</th>\n",
       "      <td>718</td>\n",
       "      <td>Zygarde50% Forme</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>Ground</td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "      <td>121</td>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>634</td>\n",
       "      <td>Zweilous</td>\n",
       "      <td>Dark</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>72</td>\n",
       "      <td>85</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>41</td>\n",
       "      <td>Zubat</td>\n",
       "      <td>Poison</td>\n",
       "      <td>Flying</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>570</td>\n",
       "      <td>Zorua</td>\n",
       "      <td>Dark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>571</td>\n",
       "      <td>Zoroark</td>\n",
       "      <td>Dark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>105</td>\n",
       "      <td>60</td>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>105</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>359</td>\n",
       "      <td>AbsolMega Absol</td>\n",
       "      <td>Dark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>115</td>\n",
       "      <td>60</td>\n",
       "      <td>115</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>359</td>\n",
       "      <td>Absol</td>\n",
       "      <td>Dark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>130</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>63</td>\n",
       "      <td>Abra</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>105</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>460</td>\n",
       "      <td>AbomasnowMega Abomasnow</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Ice</td>\n",
       "      <td>90</td>\n",
       "      <td>132</td>\n",
       "      <td>105</td>\n",
       "      <td>132</td>\n",
       "      <td>105</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>460</td>\n",
       "      <td>Abomasnow</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Ice</td>\n",
       "      <td>90</td>\n",
       "      <td>92</td>\n",
       "      <td>75</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                     Name   Type 1  Type 2   HP  Attack  Defense  \\\n",
       "794  718         Zygarde50% Forme   Dragon  Ground  108     100      121   \n",
       "695  634                 Zweilous     Dark  Dragon   72      85       70   \n",
       "46    41                    Zubat   Poison  Flying   40      45       35   \n",
       "631  570                    Zorua     Dark     NaN   40      65       40   \n",
       "632  571                  Zoroark     Dark     NaN   60     105       60   \n",
       "..   ...                      ...      ...     ...  ...     ...      ...   \n",
       "393  359          AbsolMega Absol     Dark     NaN   65     150       60   \n",
       "392  359                    Absol     Dark     NaN   65     130       60   \n",
       "68    63                     Abra  Psychic     NaN   25      20       15   \n",
       "511  460  AbomasnowMega Abomasnow    Grass     Ice   90     132      105   \n",
       "510  460                Abomasnow    Grass     Ice   90      92       75   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "794       81       95     95           6       True  \n",
       "695       65       70     58           5      False  \n",
       "46        30       40     55           1      False  \n",
       "631       80       40     65           5      False  \n",
       "632      120       60    105           5      False  \n",
       "..       ...      ...    ...         ...        ...  \n",
       "393      115       60    115           3      False  \n",
       "392       75       60     75           3      False  \n",
       "68       105       55     90           1      False  \n",
       "511      132      105     30           4      False  \n",
       "510       92       85     60           4      False  \n",
       "\n",
       "[800 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['Name'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a new column Total and add hp - speed value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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</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",
       "      <td>318</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",
       "      <td>405</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",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>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>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>309</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  3  VenusaurMega Venusaur  Grass  Poison  80     100      123      122   \n",
       "4  4             Charmander   Fire     NaN  39      52       43       60   \n",
       "\n",
       "   Sp. Def  Speed  Generation  Legendary  Total  \n",
       "0       65     45           1      False    318  \n",
       "1       80     60           1      False    405  \n",
       "2      100     80           1      False    525  \n",
       "3      120     80           1      False    625  \n",
       "4       50     65           1      False    309  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total'] = df.iloc[:, 4:10].sum(axis=1)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Legendary</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </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>False</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </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>False</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>False</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #                   Name Type 1  Type 2  Legendary  Total  HP  Attack  \\\n",
       "0  1              Bulbasaur  Grass  Poison      False    318  45      49   \n",
       "1  2                Ivysaur  Grass  Poison      False    405  60      62   \n",
       "2  3               Venusaur  Grass  Poison      False    525  80      82   \n",
       "3  3  VenusaurMega Venusaur  Grass  Poison      False    625  80     100   \n",
       "4  4             Charmander   Fire     NaN      False    309  39      52   \n",
       "\n",
       "   Defense  Sp. Atk  Sp. Def  Speed  Generation  \n",
       "0       49       65       65     45           1  \n",
       "1       63       80       80     60           1  \n",
       "2       83      100      100     80           1  \n",
       "3      123      122      120     80           1  \n",
       "4       43       60       50     65           1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = list(df.columns.values)\n",
    "\n",
    "df = df[cols[0:4] + [cols[-1]]+cols[4:12]]\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving modified file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv(\"modified.csv\", index=False)\n",
    "\n",
    "# df.to_excel(\"modified.xlsx\", index=False)\n",
    "\n",
    "# df.to_csv(\"modified.txt\", index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
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       "      <th>3</th>\n",
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       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>45</td>\n",
       "      <td>Vileplume</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>False</td>\n",
       "      <td>490</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>110</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>71</td>\n",
       "      <td>Victreebel</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>False</td>\n",
       "      <td>490</td>\n",
       "      <td>80</td>\n",
       "      <td>105</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>591</td>\n",
       "      <td>Amoonguss</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>False</td>\n",
       "      <td>464</td>\n",
       "      <td>114</td>\n",
       "      <td>85</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                   Name Type 1  Type 2  Legendary  Total   HP  Attack  \\\n",
       "2      3               Venusaur  Grass  Poison      False    525   80      82   \n",
       "3      3  VenusaurMega Venusaur  Grass  Poison      False    625   80     100   \n",
       "50    45              Vileplume  Grass  Poison      False    490   75      80   \n",
       "77    71             Victreebel  Grass  Poison      False    490   80     105   \n",
       "652  591              Amoonguss  Grass  Poison      False    464  114      85   \n",
       "\n",
       "     Defense  Sp. Atk  Sp. Def  Speed  Generation  \n",
       "2         83      100      100     80           1  \n",
       "3        123      122      120     80           1  \n",
       "50        85      110       90     50           1  \n",
       "77        65      100       70     70           1  \n",
       "652       70       85       80     30           5  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['Type 1'] == 'Grass') & (df['Type 2'] == 'Poison') & (df['HP'] > 70)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset_index\n",
    "# df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Legendary</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </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>False</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </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>False</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>405</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>718</td>\n",
       "      <td>Zygarde50% Forme</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>Ground</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "      <td>121</td>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>719</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Confined</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Unbound</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Dark</td>\n",
       "      <td>True</td>\n",
       "      <td>680</td>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>170</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>721</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>751 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                 Name   Type 1  Type 2  Legendary  Total   HP  Attack  \\\n",
       "0      1            Bulbasaur    Grass  Poison      False    318   45      49   \n",
       "1      2              Ivysaur    Grass  Poison      False    405   60      62   \n",
       "2      3             Venusaur    Grass  Poison      False    525   80      82   \n",
       "4      4           Charmander     Fire     NaN      False    309   39      52   \n",
       "5      5           Charmeleon     Fire     NaN      False    405   58      64   \n",
       "..   ...                  ...      ...     ...        ...    ...  ...     ...   \n",
       "794  718     Zygarde50% Forme   Dragon  Ground       True    600  108     100   \n",
       "795  719              Diancie     Rock   Fairy       True    600   50     100   \n",
       "797  720  HoopaHoopa Confined  Psychic   Ghost       True    600   80     110   \n",
       "798  720   HoopaHoopa Unbound  Psychic    Dark       True    680   80     160   \n",
       "799  721            Volcanion     Fire   Water       True    600   80     110   \n",
       "\n",
       "     Defense  Sp. Atk  Sp. Def  Speed  Generation  \n",
       "0         49       65       65     45           1  \n",
       "1         63       80       80     60           1  \n",
       "2         83      100      100     80           1  \n",
       "4         43       60       50     65           1  \n",
       "5         58       80       65     80           1  \n",
       "..       ...      ...      ...    ...         ...  \n",
       "794      121       81       95     95           6  \n",
       "795      150      100      150     50           6  \n",
       "797       60      150      130     70           6  \n",
       "798       60      170      130     80           6  \n",
       "799      120      130       90     70           6  \n",
       "\n",
       "[751 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[~df['Name'].str.contains('Mega')] # ~ exclude name which contain 'Mega'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
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       "      <th>Generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>Pidgey</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>False</td>\n",
       "      <td>251</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>17</td>\n",
       "      <td>Pidgeotto</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>False</td>\n",
       "      <td>349</td>\n",
       "      <td>63</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>18</td>\n",
       "      <td>Pidgeot</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>False</td>\n",
       "      <td>479</td>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18</td>\n",
       "      <td>PidgeotMega Pidgeot</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>False</td>\n",
       "      <td>579</td>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>135</td>\n",
       "      <td>80</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>25</td>\n",
       "      <td>Pikachu</td>\n",
       "      <td>Electric</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>320</td>\n",
       "      <td>35</td>\n",
       "      <td>55</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>127</td>\n",
       "      <td>Pinsir</td>\n",
       "      <td>Bug</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>500</td>\n",
       "      <td>65</td>\n",
       "      <td>125</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>127</td>\n",
       "      <td>PinsirMega Pinsir</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Flying</td>\n",
       "      <td>False</td>\n",
       "      <td>600</td>\n",
       "      <td>65</td>\n",
       "      <td>155</td>\n",
       "      <td>120</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>172</td>\n",
       "      <td>Pichu</td>\n",
       "      <td>Electric</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>205</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>204</td>\n",
       "      <td>Pineco</td>\n",
       "      <td>Bug</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>290</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>221</td>\n",
       "      <td>Piloswine</td>\n",
       "      <td>Ice</td>\n",
       "      <td>Ground</td>\n",
       "      <td>False</td>\n",
       "      <td>450</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>393</td>\n",
       "      <td>Piplup</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>314</td>\n",
       "      <td>53</td>\n",
       "      <td>51</td>\n",
       "      <td>53</td>\n",
       "      <td>61</td>\n",
       "      <td>56</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>499</td>\n",
       "      <td>Pignite</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>False</td>\n",
       "      <td>418</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>519</td>\n",
       "      <td>Pidove</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Flying</td>\n",
       "      <td>False</td>\n",
       "      <td>264</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                 Name    Type 1    Type 2  Legendary  Total   HP  \\\n",
       "20    16               Pidgey    Normal    Flying      False    251   40   \n",
       "21    17            Pidgeotto    Normal    Flying      False    349   63   \n",
       "22    18              Pidgeot    Normal    Flying      False    479   83   \n",
       "23    18  PidgeotMega Pidgeot    Normal    Flying      False    579   83   \n",
       "30    25              Pikachu  Electric       NaN      False    320   35   \n",
       "136  127               Pinsir       Bug       NaN      False    500   65   \n",
       "137  127    PinsirMega Pinsir       Bug    Flying      False    600   65   \n",
       "186  172                Pichu  Electric       NaN      False    205   20   \n",
       "219  204               Pineco       Bug       NaN      False    290   50   \n",
       "239  221            Piloswine       Ice    Ground      False    450  100   \n",
       "438  393               Piplup     Water       NaN      False    314   53   \n",
       "558  499              Pignite      Fire  Fighting      False    418   90   \n",
       "578  519               Pidove    Normal    Flying      False    264   50   \n",
       "\n",
       "     Attack  Defense  Sp. Atk  Sp. Def  Speed  Generation  \n",
       "20       45       40       35       35     56           1  \n",
       "21       60       55       50       50     71           1  \n",
       "22       80       75       70       70    101           1  \n",
       "23       80       80      135       80    121           1  \n",
       "30       55       40       50       50     90           1  \n",
       "136     125      100       55       70     85           1  \n",
       "137     155      120       65       90    105           1  \n",
       "186      40       15       35       35     60           2  \n",
       "219      65       90       35       35     15           2  \n",
       "239     100       80       60       60     50           2  \n",
       "438      51       53       61       56     40           4  \n",
       "558      93       55       70       55     55           5  \n",
       "578      55       50       36       30     43           5  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "df.loc[df['Type 1'].str.contains('Grass|Fire', flags=re.I, regex=True)]\n",
    "\n",
    "# Name starting with 'pi'\n",
    "df.loc[df['Name'].str.contains('^pi[a-z]*', flags=re.I, regex=True)]  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditional Changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </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>False</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </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>False</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>False</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #                   Name Type 1  Type 2 Legendary  Total  HP  Attack  \\\n",
       "0  1              Bulbasaur  Grass  Poison     False    318  45      49   \n",
       "1  2                Ivysaur  Grass  Poison     False    405  60      62   \n",
       "2  3               Venusaur  Grass  Poison     False    525  80      82   \n",
       "3  3  VenusaurMega Venusaur  Grass  Poison     False    625  80     100   \n",
       "4  4             Charmander   Fire     NaN      True    309  39      52   \n",
       "\n",
       "   Defense  Sp. Atk  Sp. Def  Speed  Generation  \n",
       "0       49       65       65     45           1  \n",
       "1       63       80       80     60           1  \n",
       "2       83      100      100     80           1  \n",
       "3      123      122      120     80           1  \n",
       "4       43       60       50     65           1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change 'Fire' to 'Flamer' in Type 1 column\n",
    "df.loc[df['Type 1'] == 'Fire', 'Type 1'] = 'Flamer'\n",
    "df\n",
    "# revert changes\n",
    "df.loc[df['Type 1'] == 'Flamer', 'Type 1'] = 'Fire'\n",
    "df.head(5)\n",
    "\n",
    "# change 'Fire' in 'Type 1' column should be 'true' in 'Legendary' column\n",
    "df.loc[df['Type 1'] == 'Fire', 'Legendary'] = 'True'\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.cut"
   ]
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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