Week+2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Series Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip freeze > requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.Series?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Tiger\n",
       "1     Bear\n",
       "2    Moose\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals = ['Tiger', 'Bear', 'Moose']\n",
    "pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = [1, 2, 3]\n",
    "pd.Series(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Tiger\n",
       "1     Bear\n",
       "2     None\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals = ['Tiger', 'Bear', None]\n",
    "pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = [1, 2, None]\n",
    "pd.Series(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.nan == None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = {'Archery': 'Bhutan',\n",
    "          'Golf': 'Scotland',\n",
    "          'Sumo': 'Japan',\n",
    "          'Taekwondo': 'South Korea'}\n",
    "s = pd.Series(sports)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Archery', 'Golf', 'Sumo', 'Taekwondo'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India      Tiger\n",
       "America     Bear\n",
       "Canada     Moose\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['Tiger', 'Bear', 'Moose'], index=['India', 'America', 'Canada'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golf      Scotland\n",
       "Sumo         Japan\n",
       "Hockey         NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = {'Archery': 'Bhutan',\n",
    "          'Golf': 'Scotland',\n",
    "          'Sumo': 'Japan',\n",
    "          'Taekwondo': 'South Korea'}\n",
    "s = pd.Series(sports, index=['Golf', 'Sumo', 'Hockey'])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = {'Archery': 'Bhutan',\n",
    "          'Golf': 'Scotland',\n",
    "          'Sumo': 'Japan',\n",
    "          'Taekwondo': 'South Korea'}\n",
    "s = pd.Series(sports)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'South Korea'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loc, iloc de truy van hang`\n",
    "#[] de truy van cot, neu can truy van nhieu cot thi su dung 2 ngoac [] VD df2 = df[['col1','col2']]\n",
    "s.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Scotland'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc['Golf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'South Korea'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Scotland'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['Golf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sports = {99: 'Bhutan',\n",
    "          100: 'Scotland',\n",
    "          101: 'Japan',\n",
    "          102: 'South Korea'}\n",
    "s = pd.Series(sports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-a5f43d492595>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#This won't call s.iloc[0] as one might expect, it generates an error instead\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    581\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 583\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   1978\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1979\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 1980\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   1981\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1982\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3332)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3035)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4018)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6610)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6554)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "s[0] #This won't call s.iloc[0] as one might expect, it generates an error instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100.0\n",
       "1    120.0\n",
       "2    101.0\n",
       "3      3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([100.00, 120.00, 101.00, 3.00])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "324.0\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "for item in s:\n",
    "    total+=item\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "324.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "total = np.sum(s)\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    151\n",
       "1    787\n",
       "2     25\n",
       "3    353\n",
       "4    640\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this creates a big series of random numbers\n",
    "s = pd.Series(np.random.randint(0,1000,10000))\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 1.75 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 100\n",
    "summary = 0\n",
    "for item in s:\n",
    "    summary+=item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 155 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 100\n",
    "summary = np.sum(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    153\n",
       "1    789\n",
       "2     27\n",
       "3    355\n",
       "4    642\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s+=2 #adds two to each item in s using broadcasting\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    155\n",
       "1    791\n",
       "2     29\n",
       "3    357\n",
       "4    644\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for label, value in s.iteritems():\n",
    "    s.set_value(label, value+2)\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 1.59 s per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 10\n",
    "s = pd.Series(np.random.randint(0,1000,10000))\n",
    "for label, value in s.iteritems():\n",
    "    s.loc[label]= value+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 482 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 10\n",
    "s = pd.Series(np.random.randint(0,1000,10000))\n",
    "s+=2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             1\n",
       "1             2\n",
       "2             3\n",
       "Animal    Bears\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 2, 3])\n",
    "s.loc['Animal'] = 'Bears'\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#thêm 1 hàng vào dataframe\n",
    "original_sports = pd.Series({'Archery': 'Bhutan',\n",
    "                             'Golf': 'Scotland',\n",
    "                             'Sumo': 'Japan',\n",
    "                             'Taekwondo': 'South Korea'})\n",
    "cricket_loving_countries = pd.Series(['Australia',\n",
    "                                      'Barbados',\n",
    "                                      'Pakistan',\n",
    "                                      'England'], \n",
    "                                   index=['Cricket',\n",
    "                                          'Cricket',\n",
    "                                          'Cricket',\n",
    "                                          'Cricket'])\n",
    "all_countries = original_sports.append(cricket_loving_countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_sports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cricket    Australia\n",
       "Cricket     Barbados\n",
       "Cricket     Pakistan\n",
       "Cricket      England\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cricket_loving_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "Cricket        Australia\n",
       "Cricket         Barbados\n",
       "Cricket         Pakistan\n",
       "Cricket          England\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cricket    Australia\n",
       "Cricket     Barbados\n",
       "Cricket     Pakistan\n",
       "Cricket      England\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries.loc['Cricket']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# The DataFrame Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>Chris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>Vinod</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name\n",
       "Store 1  22.5       Dog Food  Chris\n",
       "Store 1   2.5   Kitty Litter  Kevyn\n",
       "Store 2   5.0      Bird Seed  Vinod"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tạo df\n",
    "import pandas as pd\n",
    "purchase_1 = pd.Series({'Name': 'Chris',\n",
    "                        'Item Purchased': 'Dog Food',\n",
    "                        'Cost': 22.50})\n",
    "purchase_2 = pd.Series({'Name': 'Kevyn',\n",
    "                        'Item Purchased': 'Kitty Litter',\n",
    "                        'Cost': 2.50})\n",
    "purchase_3 = pd.Series({'Name': 'Vinod',\n",
    "                        'Item Purchased': 'Bird Seed',\n",
    "                        'Cost': 5.00})\n",
    "df = pd.DataFrame([purchase_1, purchase_2, purchase_3], index=['Store 1', 'Store 1', 'Store 2'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cost                      5\n",
       "Item Purchased    Bird Seed\n",
       "Name                  Vinod\n",
       "Name: Store 2, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.loc['Store 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>Chris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name\n",
       "Store 1  22.5       Dog Food  Chris\n",
       "Store 1   2.5   Kitty Litter  Kevyn"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 1', 'Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store 1</th>\n",
       "      <th>Store 1</th>\n",
       "      <th>Store 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cost</th>\n",
       "      <td>22.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Purchased</th>\n",
       "      <td>Dog Food</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Bird Seed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Kevyn</td>\n",
       "      <td>Vinod</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Store 1       Store 1    Store 2\n",
       "Cost                22.5           2.5          5\n",
       "Item Purchased  Dog Food  Kitty Litter  Bird Seed\n",
       "Name               Chris         Kevyn      Vinod"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Store 2       5\n",
       "Name: Cost, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T.loc['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Store 2     5.0\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#đưa ra dữ liệu hàng và cột\n",
    "df.loc['Store 1']['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Kevyn</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name  Cost\n",
       "Store 1  Chris  22.5\n",
       "Store 1  Kevyn   2.5\n",
       "Store 2  Vinod   5.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#đưa ra dữ liệu 2 cột\n",
    "df.loc[:,['Name', 'Cost']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>Vinod</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name\n",
       "Store 2   5.0      Bird Seed  Vinod"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#xóa hàng\n",
    "df.drop('Store 1')\n",
    "\n",
    "\n",
    "#xóa hàng có điều kiện (lấy ở week3)\n",
    "#xóa các hàng có Quantity ==0\n",
    "# df.drop(df[df['Quantity']==0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>Chris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>Vinod</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name\n",
       "Store 1  22.5       Dog Food  Chris\n",
       "Store 1   2.5   Kitty Litter  Kevyn\n",
       "Store 2   5.0      Bird Seed  Vinod"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>Vinod</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name\n",
       "Store 2   5.0      Bird Seed  Vinod"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_df = df.copy()\n",
    "copy_df = copy_df.drop('Store 1')\n",
    "copy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_df.drop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Bird Seed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased\n",
       "Store 2   5.0      Bird Seed"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del copy_df['Name']\n",
    "copy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>Chris</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>Vinod</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name Location\n",
       "Store 1  22.5       Dog Food  Chris     None\n",
       "Store 1   2.5   Kitty Litter  Kevyn     None\n",
       "Store 2   5.0      Bird Seed  Vinod     None"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Location'] = None\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Dataframe Indexing and Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Store 2     5.0\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs = df['Cost']\n",
    "costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    24.5\n",
       "Store 1     4.5\n",
       "Store 2     7.0\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs+=2\n",
    "costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>24.5</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>Chris</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>4.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>Vinod</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name Location\n",
       "Store 1  24.5       Dog Food  Chris     None\n",
       "Store 1   4.5   Kitty Litter  Kevyn     None\n",
       "Store 2   7.0      Bird Seed  Vinod     None"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'cat' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!cat olympics.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>№ Summer</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
       "      <td>03 !</td>\n",
       "      <td>Total</td>\n",
       "      <td>№ Winter</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
       "      <td>03 !</td>\n",
       "      <td>Total</td>\n",
       "      <td>№ Games</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
       "      <td>03 !</td>\n",
       "      <td>Combined total</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria (ALG)</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina (ARG)</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia (ARM)</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1     2     3     4      5         6     7     8  \\\n",
       "0                NaN  № Summer  01 !  02 !  03 !  Total  № Winter  01 !  02 !   \n",
       "1  Afghanistan (AFG)        13     0     0     2      2         0     0     0   \n",
       "2      Algeria (ALG)        12     5     2     8     15         3     0     0   \n",
       "3    Argentina (ARG)        23    18    24    28     70        18     0     0   \n",
       "4      Armenia (ARM)         5     1     2     9     12         6     0     0   \n",
       "\n",
       "      9     10       11    12    13    14              15  \n",
       "0  03 !  Total  № Games  01 !  02 !  03 !  Combined total  \n",
       "1     0      0       13     0     0     2               2  \n",
       "2     0      0       15     5     2     8              15  \n",
       "3     0      0       41    18    24    28              70  \n",
       "4     0      0       11     1     2     9              12  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('olympics.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>№ Summer</th>\n",
       "      <th>01 !</th>\n",
       "      <th>02 !</th>\n",
       "      <th>03 !</th>\n",
       "      <th>Total</th>\n",
       "      <th>№ Winter</th>\n",
       "      <th>01 !.1</th>\n",
       "      <th>02 !.1</th>\n",
       "      <th>03 !.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th>№ Games</th>\n",
       "      <th>01 !.2</th>\n",
       "      <th>02 !.2</th>\n",
       "      <th>03 !.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         № Summer  01 !  02 !  03 !  Total  № Winter  01 !.1  \\\n",
       "Afghanistan (AFG)              13     0     0     2      2         0       0   \n",
       "Algeria (ALG)                  12     5     2     8     15         3       0   \n",
       "Argentina (ARG)                23    18    24    28     70        18       0   \n",
       "Armenia (ARM)                   5     1     2     9     12         6       0   \n",
       "Australasia (ANZ) [ANZ]         2     3     4     5     12         0       0   \n",
       "\n",
       "                         02 !.1  03 !.1  Total.1  № Games  01 !.2  02 !.2  \\\n",
       "Afghanistan (AFG)             0       0        0       13       0       0   \n",
       "Algeria (ALG)                 0       0        0       15       5       2   \n",
       "Argentina (ARG)               0       0        0       41      18      24   \n",
       "Armenia (ARM)                 0       0        0       11       1       2   \n",
       "Australasia (ANZ) [ANZ]       0       0        0        2       3       4   \n",
       "\n",
       "                         03 !.2  Combined total  \n",
       "Afghanistan (AFG)             2               2  \n",
       "Algeria (ALG)                 8              15  \n",
       "Argentina (ARG)              28              70  \n",
       "Armenia (ARM)                 9              12  \n",
       "Australasia (ANZ) [ANZ]       5              12  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('olympics.csv', index_col = 0, skiprows=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['№ Summer', '01 !', '02 !', '03 !', 'Total', '№ Winter', '01 !.1',\n",
       "       '02 !.1', '03 !.1', 'Total.1', '№ Games', '01 !.2', '02 !.2', '03 !.2',\n",
       "       'Combined total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Afghanistan (AFG)              13     0       0       2      2         0   \n",
       "Algeria (ALG)                  12     5       2       8     15         3   \n",
       "Argentina (ARG)                23    18      24      28     70        18   \n",
       "Armenia (ARM)                   5     1       2       9     12         6   \n",
       "Australasia (ANZ) [ANZ]         2     3       4       5     12         0   \n",
       "\n",
       "                         Gold.1  Silver.1  Bronze.1  Total.1  # Games  Gold.2  \\\n",
       "Afghanistan (AFG)             0         0         0        0       13       0   \n",
       "Algeria (ALG)                 0         0         0        0       15       5   \n",
       "Argentina (ARG)               0         0         0        0       41      18   \n",
       "Armenia (ARM)                 0         0         0        0       11       1   \n",
       "Australasia (ANZ) [ANZ]       0         0         0        0        2       3   \n",
       "\n",
       "                         Silver.2  Bronze.2  Combined total  \n",
       "Afghanistan (AFG)               0         2               2  \n",
       "Algeria (ALG)                   2         8              15  \n",
       "Argentina (ARG)                24        28              70  \n",
       "Armenia (ARM)                   2         9              12  \n",
       "Australasia (ANZ) [ANZ]         4         5              12  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if col[:2]=='01':\n",
    "        df.rename(columns={col:'Gold' + col[4:]}, inplace=True)\n",
    "    if col[:2]=='02':\n",
    "        df.rename(columns={col:'Silver' + col[4:]}, inplace=True)\n",
    "    if col[:2]=='03':\n",
    "        df.rename(columns={col:'Bronze' + col[4:]}, inplace=True)\n",
    "    if col[:1]=='№':\n",
    "        df.rename(columns={col:'#' + col[1:]}, inplace=True) \n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan (AFG)                               False\n",
       "Algeria (ALG)                                    True\n",
       "Argentina (ARG)                                  True\n",
       "Armenia (ARM)                                    True\n",
       "Australasia (ANZ) [ANZ]                          True\n",
       "Australia (AUS) [AUS] [Z]                        True\n",
       "Austria (AUT)                                    True\n",
       "Azerbaijan (AZE)                                 True\n",
       "Bahamas (BAH)                                    True\n",
       "Bahrain (BRN)                                   False\n",
       "Barbados (BAR) [BAR]                            False\n",
       "Belarus (BLR)                                    True\n",
       "Belgium (BEL)                                    True\n",
       "Bermuda (BER)                                   False\n",
       "Bohemia (BOH) [BOH] [Z]                         False\n",
       "Botswana (BOT)                                  False\n",
       "Brazil (BRA)                                     True\n",
       "British West Indies (BWI) [BWI]                 False\n",
       "Bulgaria (BUL) [H]                               True\n",
       "Burundi (BDI)                                    True\n",
       "Cameroon (CMR)                                   True\n",
       "Canada (CAN)                                     True\n",
       "Chile (CHI) [I]                                  True\n",
       "China (CHN) [CHN]                                True\n",
       "Colombia (COL)                                   True\n",
       "Costa Rica (CRC)                                 True\n",
       "Ivory Coast (CIV) [CIV]                         False\n",
       "Croatia (CRO)                                    True\n",
       "Cuba (CUB) [Z]                                   True\n",
       "Cyprus (CYP)                                    False\n",
       "                                                ...  \n",
       "Sri Lanka (SRI) [SRI]                           False\n",
       "Sudan (SUD)                                     False\n",
       "Suriname (SUR) [E]                               True\n",
       "Sweden (SWE) [Z]                                 True\n",
       "Switzerland (SUI)                                True\n",
       "Syria (SYR)                                      True\n",
       "Chinese Taipei (TPE) [TPE] [TPE2]                True\n",
       "Tajikistan (TJK)                                False\n",
       "Tanzania (TAN) [TAN]                            False\n",
       "Thailand (THA)                                   True\n",
       "Togo (TOG)                                      False\n",
       "Tonga (TGA)                                     False\n",
       "Trinidad and Tobago (TRI) [TRI]                  True\n",
       "Tunisia (TUN)                                    True\n",
       "Turkey (TUR)                                     True\n",
       "Uganda (UGA)                                     True\n",
       "Ukraine (UKR)                                    True\n",
       "United Arab Emirates (UAE)                       True\n",
       "United States (USA) [P] [Q] [R] [Z]              True\n",
       "Uruguay (URU)                                    True\n",
       "Uzbekistan (UZB)                                 True\n",
       "Venezuela (VEN)                                  True\n",
       "Vietnam (VIE)                                   False\n",
       "Virgin Islands (ISV)                            False\n",
       "Yugoslavia (YUG) [YUG]                           True\n",
       "Independent Olympic Participants (IOP) [IOP]    False\n",
       "Zambia (ZAM) [ZAM]                              False\n",
       "Zimbabwe (ZIM) [ZIM]                             True\n",
       "Mixed team (ZZX) [ZZX]                           True\n",
       "Totals                                           True\n",
       "Name: Gold, dtype: bool"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gold'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Afghanistan (AFG)             NaN   NaN     NaN     NaN    NaN       NaN   \n",
       "Algeria (ALG)                12.0   5.0     2.0     8.0   15.0       3.0   \n",
       "Argentina (ARG)              23.0  18.0    24.0    28.0   70.0      18.0   \n",
       "Armenia (ARM)                 5.0   1.0     2.0     9.0   12.0       6.0   \n",
       "Australasia (ANZ) [ANZ]       2.0   3.0     4.0     5.0   12.0       0.0   \n",
       "\n",
       "                         Gold.1  Silver.1  Bronze.1  Total.1  # Games  Gold.2  \\\n",
       "Afghanistan (AFG)           NaN       NaN       NaN      NaN      NaN     NaN   \n",
       "Algeria (ALG)               0.0       0.0       0.0      0.0     15.0     5.0   \n",
       "Argentina (ARG)             0.0       0.0       0.0      0.0     41.0    18.0   \n",
       "Armenia (ARM)               0.0       0.0       0.0      0.0     11.0     1.0   \n",
       "Australasia (ANZ) [ANZ]     0.0       0.0       0.0      0.0      2.0     3.0   \n",
       "\n",
       "                         Silver.2  Bronze.2  Combined total  \n",
       "Afghanistan (AFG)             NaN       NaN             NaN  \n",
       "Algeria (ALG)                 2.0       8.0            15.0  \n",
       "Argentina (ARG)              24.0      28.0            70.0  \n",
       "Armenia (ARM)                 2.0       9.0            12.0  \n",
       "Australasia (ANZ) [ANZ]       4.0       5.0            12.0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = df.where(df['Gold'] > 0)\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold['Gold'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gold'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia (AUS) [AUS] [Z]</th>\n",
       "      <td>25.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           # Summer   Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Algeria (ALG)                  12.0    5.0     2.0     8.0   15.0       3.0   \n",
       "Argentina (ARG)                23.0   18.0    24.0    28.0   70.0      18.0   \n",
       "Armenia (ARM)                   5.0    1.0     2.0     9.0   12.0       6.0   \n",
       "Australasia (ANZ) [ANZ]         2.0    3.0     4.0     5.0   12.0       0.0   \n",
       "Australia (AUS) [AUS] [Z]      25.0  139.0   152.0   177.0  468.0      18.0   \n",
       "\n",
       "                           Gold.1  Silver.1  Bronze.1  Total.1  # Games  \\\n",
       "Algeria (ALG)                 0.0       0.0       0.0      0.0     15.0   \n",
       "Argentina (ARG)               0.0       0.0       0.0      0.0     41.0   \n",
       "Armenia (ARM)                 0.0       0.0       0.0      0.0     11.0   \n",
       "Australasia (ANZ) [ANZ]       0.0       0.0       0.0      0.0      2.0   \n",
       "Australia (AUS) [AUS] [Z]     5.0       3.0       4.0     12.0     43.0   \n",
       "\n",
       "                           Gold.2  Silver.2  Bronze.2  Combined total  \n",
       "Algeria (ALG)                 5.0       2.0       8.0            15.0  \n",
       "Argentina (ARG)              18.0      24.0      28.0            70.0  \n",
       "Armenia (ARM)                 1.0       2.0       9.0            12.0  \n",
       "Australasia (ANZ) [ANZ]       3.0       4.0       5.0            12.0  \n",
       "Australia (AUS) [AUS] [Z]   144.0     155.0     181.0           480.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = only_gold.dropna()\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia (AUS) [AUS] [Z]</th>\n",
       "      <td>25</td>\n",
       "      <td>139</td>\n",
       "      <td>152</td>\n",
       "      <td>177</td>\n",
       "      <td>468</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>43</td>\n",
       "      <td>144</td>\n",
       "      <td>155</td>\n",
       "      <td>181</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Algeria (ALG)                    12     5       2       8     15         3   \n",
       "Argentina (ARG)                  23    18      24      28     70        18   \n",
       "Armenia (ARM)                     5     1       2       9     12         6   \n",
       "Australasia (ANZ) [ANZ]           2     3       4       5     12         0   \n",
       "Australia (AUS) [AUS] [Z]        25   139     152     177    468        18   \n",
       "\n",
       "                           Gold.1  Silver.1  Bronze.1  Total.1  # Games  \\\n",
       "Algeria (ALG)                   0         0         0        0       15   \n",
       "Argentina (ARG)                 0         0         0        0       41   \n",
       "Armenia (ARM)                   0         0         0        0       11   \n",
       "Australasia (ANZ) [ANZ]         0         0         0        0        2   \n",
       "Australia (AUS) [AUS] [Z]       5         3         4       12       43   \n",
       "\n",
       "                           Gold.2  Silver.2  Bronze.2  Combined total  \n",
       "Algeria (ALG)                   5         2         8              15  \n",
       "Argentina (ARG)                18        24        28              70  \n",
       "Armenia (ARM)                   1         2         9              12  \n",
       "Australasia (ANZ) [ANZ]         3         4         5              12  \n",
       "Australia (AUS) [AUS] [Z]     144       155       181             480  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = df[df['Gold'] > 0]\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['Gold'] > 0) | (df['Gold.1'] > 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Liechtenstein (LIE)</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     # Summer  Gold  Silver  Bronze  Total  # Winter  Gold.1  \\\n",
       "Liechtenstein (LIE)        16     0       0       0      0        18       2   \n",
       "\n",
       "                     Silver.1  Bronze.1  Total.1  # Games  Gold.2  Silver.2  \\\n",
       "Liechtenstein (LIE)         2         5        9       34       2         2   \n",
       "\n",
       "                     Bronze.2  Combined total  \n",
       "Liechtenstein (LIE)         5               9  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Gold.1'] > 0) & (df['Gold'] == 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indexing Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Afghanistan (AFG)              13     0       0       2      2         0   \n",
       "Algeria (ALG)                  12     5       2       8     15         3   \n",
       "Argentina (ARG)                23    18      24      28     70        18   \n",
       "Armenia (ARM)                   5     1       2       9     12         6   \n",
       "Australasia (ANZ) [ANZ]         2     3       4       5     12         0   \n",
       "\n",
       "                         Gold.1  Silver.1  Bronze.1  Total.1  # Games  Gold.2  \\\n",
       "Afghanistan (AFG)             0         0         0        0       13       0   \n",
       "Algeria (ALG)                 0         0         0        0       15       5   \n",
       "Argentina (ARG)               0         0         0        0       41      18   \n",
       "Armenia (ARM)                 0         0         0        0       11       1   \n",
       "Australasia (ANZ) [ANZ]       0         0         0        0        2       3   \n",
       "\n",
       "                         Silver.2  Bronze.2  Combined total  \n",
       "Afghanistan (AFG)               0         2               2  \n",
       "Algeria (ALG)                   2         8              15  \n",
       "Argentina (ARG)                24        28              70  \n",
       "Armenia (ARM)                   2         9              12  \n",
       "Australasia (ANZ) [ANZ]         4         5              12  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>Algeria (ALG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>Argentina (ARG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>Armenia (ARM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Australasia (ANZ) [ANZ]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      # Summer  Silver  Bronze  Total  # Winter  Gold.1  Silver.1  Bronze.1  \\\n",
       "Gold                                                                          \n",
       "0           13       0       2      2         0       0         0         0   \n",
       "5           12       2       8     15         3       0         0         0   \n",
       "18          23      24      28     70        18       0         0         0   \n",
       "1            5       2       9     12         6       0         0         0   \n",
       "3            2       4       5     12         0       0         0         0   \n",
       "\n",
       "      Total.1  # Games  Gold.2  Silver.2  Bronze.2  Combined total  \\\n",
       "Gold                                                                 \n",
       "0           0       13       0         0         2               2   \n",
       "5           0       15       5         2         8              15   \n",
       "18          0       41      18        24        28              70   \n",
       "1           0       11       1         2         9              12   \n",
       "3           0        2       3         4         5              12   \n",
       "\n",
       "                      country  \n",
       "Gold                           \n",
       "0           Afghanistan (AFG)  \n",
       "5               Algeria (ALG)  \n",
       "18            Argentina (ARG)  \n",
       "1               Armenia (ARM)  \n",
       "3     Australasia (ANZ) [ANZ]  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['country'] = df.index\n",
    "df = df.set_index('Gold')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gold</th>\n",
       "      <th># Summer</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>Algeria (ALG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>Argentina (ARG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>Armenia (ARM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Australasia (ANZ) [ANZ]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gold  # Summer  Silver  Bronze  Total  # Winter  Gold.1  Silver.1  \\\n",
       "0     0        13       0       2      2         0       0         0   \n",
       "1     5        12       2       8     15         3       0         0   \n",
       "2    18        23      24      28     70        18       0         0   \n",
       "3     1         5       2       9     12         6       0         0   \n",
       "4     3         2       4       5     12         0       0         0   \n",
       "\n",
       "   Bronze.1  Total.1  # Games  Gold.2  Silver.2  Bronze.2  Combined total  \\\n",
       "0         0        0       13       0         0         2               2   \n",
       "1         0        0       15       5         2         8              15   \n",
       "2         0        0       41      18        24        28              70   \n",
       "3         0        0       11       1         2         9              12   \n",
       "4         0        0        2       3         4         5              12   \n",
       "\n",
       "                   country  \n",
       "0        Afghanistan (AFG)  \n",
       "1            Algeria (ALG)  \n",
       "2          Argentina (ARG)  \n",
       "3            Armenia (ARM)  \n",
       "4  Australasia (ANZ) [ANZ]  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUMLEV</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>...</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4779736</td>\n",
       "      <td>4780127</td>\n",
       "      <td>4785161</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002295</td>\n",
       "      <td>-0.193196</td>\n",
       "      <td>0.381066</td>\n",
       "      <td>0.582002</td>\n",
       "      <td>-0.467369</td>\n",
       "      <td>1.030015</td>\n",
       "      <td>0.826644</td>\n",
       "      <td>1.383282</td>\n",
       "      <td>1.724718</td>\n",
       "      <td>0.712594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>54571</td>\n",
       "      <td>54571</td>\n",
       "      <td>54660</td>\n",
       "      <td>...</td>\n",
       "      <td>7.242091</td>\n",
       "      <td>-2.915927</td>\n",
       "      <td>-3.012349</td>\n",
       "      <td>2.265971</td>\n",
       "      <td>-2.530799</td>\n",
       "      <td>7.606016</td>\n",
       "      <td>-2.626146</td>\n",
       "      <td>-2.722002</td>\n",
       "      <td>2.592270</td>\n",
       "      <td>-2.187333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>182265</td>\n",
       "      <td>182265</td>\n",
       "      <td>183193</td>\n",
       "      <td>...</td>\n",
       "      <td>14.832960</td>\n",
       "      <td>17.647293</td>\n",
       "      <td>21.845705</td>\n",
       "      <td>19.243287</td>\n",
       "      <td>17.197872</td>\n",
       "      <td>15.844176</td>\n",
       "      <td>18.559627</td>\n",
       "      <td>22.727626</td>\n",
       "      <td>20.317142</td>\n",
       "      <td>18.293499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27341</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.728132</td>\n",
       "      <td>-2.500690</td>\n",
       "      <td>-7.056824</td>\n",
       "      <td>-3.904217</td>\n",
       "      <td>-10.543299</td>\n",
       "      <td>-4.874741</td>\n",
       "      <td>-2.758113</td>\n",
       "      <td>-7.167664</td>\n",
       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>22915</td>\n",
       "      <td>22919</td>\n",
       "      <td>22861</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.527043</td>\n",
       "      <td>-5.068871</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
       "      <td>1.107861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SUMLEV  REGION  DIVISION  STATE  COUNTY   STNAME         CTYNAME  \\\n",
       "0      40       3         6      1       0  Alabama         Alabama   \n",
       "1      50       3         6      1       1  Alabama  Autauga County   \n",
       "2      50       3         6      1       3  Alabama  Baldwin County   \n",
       "3      50       3         6      1       5  Alabama  Barbour County   \n",
       "4      50       3         6      1       7  Alabama     Bibb County   \n",
       "\n",
       "   CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010     ...       \\\n",
       "0        4779736            4780127          4785161     ...        \n",
       "1          54571              54571            54660     ...        \n",
       "2         182265             182265           183193     ...        \n",
       "3          27457              27457            27341     ...        \n",
       "4          22915              22919            22861     ...        \n",
       "\n",
       "   RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  \\\n",
       "0          0.002295         -0.193196          0.381066          0.582002   \n",
       "1          7.242091         -2.915927         -3.012349          2.265971   \n",
       "2         14.832960         17.647293         21.845705         19.243287   \n",
       "3         -4.728132         -2.500690         -7.056824         -3.904217   \n",
       "4         -5.527043         -5.068871         -6.201001         -0.177537   \n",
       "\n",
       "   RDOMESTICMIG2015  RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  \\\n",
       "0         -0.467369     1.030015     0.826644     1.383282     1.724718   \n",
       "1         -2.530799     7.606016    -2.626146    -2.722002     2.592270   \n",
       "2         17.197872    15.844176    18.559627    22.727626    20.317142   \n",
       "3        -10.543299    -4.874741    -2.758113    -7.167664    -3.978583   \n",
       "4          0.177258    -5.088389    -4.363636    -5.403729     0.754533   \n",
       "\n",
       "   RNETMIG2015  \n",
       "0     0.712594  \n",
       "1    -2.187333  \n",
       "2    18.293499  \n",
       "3   -10.543299  \n",
       "4     1.107861  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('census.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([40, 50])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SUMLEV'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>SUMLEV</th>\n",
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       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
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       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
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       "      <td>50</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>182265</td>\n",
       "      <td>182265</td>\n",
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       "      <td>18.293499</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Alabama</td>\n",
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       "      <td>27457</td>\n",
       "      <td>27341</td>\n",
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       "      <td>-4.874741</td>\n",
       "      <td>-2.758113</td>\n",
       "      <td>-7.167664</td>\n",
       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>22915</td>\n",
       "      <td>22919</td>\n",
       "      <td>22861</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.527043</td>\n",
       "      <td>-5.068871</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
       "      <td>1.107861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>57322</td>\n",
       "      <td>57322</td>\n",
       "      <td>57373</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807375</td>\n",
       "      <td>-1.177622</td>\n",
       "      <td>-1.748766</td>\n",
       "      <td>-2.062535</td>\n",
       "      <td>-1.369970</td>\n",
       "      <td>1.859511</td>\n",
       "      <td>-0.848580</td>\n",
       "      <td>-1.402476</td>\n",
       "      <td>-1.577232</td>\n",
       "      <td>-0.884411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SUMLEV  REGION  DIVISION  STATE  COUNTY   STNAME         CTYNAME  \\\n",
       "1      50       3         6      1       1  Alabama  Autauga County   \n",
       "2      50       3         6      1       3  Alabama  Baldwin County   \n",
       "3      50       3         6      1       5  Alabama  Barbour County   \n",
       "4      50       3         6      1       7  Alabama     Bibb County   \n",
       "5      50       3         6      1       9  Alabama   Blount County   \n",
       "\n",
       "   CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010     ...       \\\n",
       "1          54571              54571            54660     ...        \n",
       "2         182265             182265           183193     ...        \n",
       "3          27457              27457            27341     ...        \n",
       "4          22915              22919            22861     ...        \n",
       "5          57322              57322            57373     ...        \n",
       "\n",
       "   RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  \\\n",
       "1          7.242091         -2.915927         -3.012349          2.265971   \n",
       "2         14.832960         17.647293         21.845705         19.243287   \n",
       "3         -4.728132         -2.500690         -7.056824         -3.904217   \n",
       "4         -5.527043         -5.068871         -6.201001         -0.177537   \n",
       "5          1.807375         -1.177622         -1.748766         -2.062535   \n",
       "\n",
       "   RDOMESTICMIG2015  RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  \\\n",
       "1         -2.530799     7.606016    -2.626146    -2.722002     2.592270   \n",
       "2         17.197872    15.844176    18.559627    22.727626    20.317142   \n",
       "3        -10.543299    -4.874741    -2.758113    -7.167664    -3.978583   \n",
       "4          0.177258    -5.088389    -4.363636    -5.403729     0.754533   \n",
       "5         -1.369970     1.859511    -0.848580    -1.402476    -1.577232   \n",
       "\n",
       "   RNETMIG2015  \n",
       "1    -2.187333  \n",
       "2    18.293499  \n",
       "3   -10.543299  \n",
       "4     1.107861  \n",
       "5    -0.884411  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[df['SUMLEV'] == 50]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>BIRTHS2010</th>\n",
       "      <th>BIRTHS2011</th>\n",
       "      <th>BIRTHS2012</th>\n",
       "      <th>BIRTHS2013</th>\n",
       "      <th>BIRTHS2014</th>\n",
       "      <th>BIRTHS2015</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
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       "      <th>POPESTIMATE2012</th>\n",
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       "      <th>POPESTIMATE2014</th>\n",
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       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>151</td>\n",
       "      <td>636</td>\n",
       "      <td>615</td>\n",
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       "      <td>623</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>517</td>\n",
       "      <td>2187</td>\n",
       "      <td>2092</td>\n",
       "      <td>2160</td>\n",
       "      <td>2186</td>\n",
       "      <td>2240</td>\n",
       "      <td>183193</td>\n",
       "      <td>186659</td>\n",
       "      <td>190396</td>\n",
       "      <td>195126</td>\n",
       "      <td>199713</td>\n",
       "      <td>203709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>70</td>\n",
       "      <td>335</td>\n",
       "      <td>300</td>\n",
       "      <td>283</td>\n",
       "      <td>260</td>\n",
       "      <td>269</td>\n",
       "      <td>27341</td>\n",
       "      <td>27226</td>\n",
       "      <td>27159</td>\n",
       "      <td>26973</td>\n",
       "      <td>26815</td>\n",
       "      <td>26489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>44</td>\n",
       "      <td>266</td>\n",
       "      <td>245</td>\n",
       "      <td>259</td>\n",
       "      <td>247</td>\n",
       "      <td>253</td>\n",
       "      <td>22861</td>\n",
       "      <td>22733</td>\n",
       "      <td>22642</td>\n",
       "      <td>22512</td>\n",
       "      <td>22549</td>\n",
       "      <td>22583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>183</td>\n",
       "      <td>744</td>\n",
       "      <td>710</td>\n",
       "      <td>646</td>\n",
       "      <td>618</td>\n",
       "      <td>603</td>\n",
       "      <td>57373</td>\n",
       "      <td>57711</td>\n",
       "      <td>57776</td>\n",
       "      <td>57734</td>\n",
       "      <td>57658</td>\n",
       "      <td>57673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STNAME         CTYNAME  BIRTHS2010  BIRTHS2011  BIRTHS2012  BIRTHS2013  \\\n",
       "1  Alabama  Autauga County         151         636         615         574   \n",
       "2  Alabama  Baldwin County         517        2187        2092        2160   \n",
       "3  Alabama  Barbour County          70         335         300         283   \n",
       "4  Alabama     Bibb County          44         266         245         259   \n",
       "5  Alabama   Blount County         183         744         710         646   \n",
       "\n",
       "   BIRTHS2014  BIRTHS2015  POPESTIMATE2010  POPESTIMATE2011  POPESTIMATE2012  \\\n",
       "1         623         600            54660            55253            55175   \n",
       "2        2186        2240           183193           186659           190396   \n",
       "3         260         269            27341            27226            27159   \n",
       "4         247         253            22861            22733            22642   \n",
       "5         618         603            57373            57711            57776   \n",
       "\n",
       "   POPESTIMATE2013  POPESTIMATE2014  POPESTIMATE2015  \n",
       "1            55038            55290            55347  \n",
       "2           195126           199713           203709  \n",
       "3            26973            26815            26489  \n",
       "4            22512            22549            22583  \n",
       "5            57734            57658            57673  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_keep = ['STNAME',\n",
    "                   'CTYNAME',\n",
    "                   'BIRTHS2010',\n",
    "                   'BIRTHS2011',\n",
    "                   'BIRTHS2012',\n",
    "                   'BIRTHS2013',\n",
    "                   'BIRTHS2014',\n",
    "                   'BIRTHS2015',\n",
    "                   'POPESTIMATE2010',\n",
    "                   'POPESTIMATE2011',\n",
    "                   'POPESTIMATE2012',\n",
    "                   'POPESTIMATE2013',\n",
    "                   'POPESTIMATE2014',\n",
    "                   'POPESTIMATE2015']\n",
    "df = df[columns_to_keep]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>BIRTHS2010</th>\n",
       "      <th>BIRTHS2011</th>\n",
       "      <th>BIRTHS2012</th>\n",
       "      <th>BIRTHS2013</th>\n",
       "      <th>BIRTHS2014</th>\n",
       "      <th>BIRTHS2015</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2012</th>\n",
       "      <th>POPESTIMATE2013</th>\n",
       "      <th>POPESTIMATE2014</th>\n",
       "      <th>POPESTIMATE2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Alabama</th>\n",
       "      <th>Autauga County</th>\n",
       "      <td>151</td>\n",
       "      <td>636</td>\n",
       "      <td>615</td>\n",
       "      <td>574</td>\n",
       "      <td>623</td>\n",
       "      <td>600</td>\n",
       "      <td>54660</td>\n",
       "      <td>55253</td>\n",
       "      <td>55175</td>\n",
       "      <td>55038</td>\n",
       "      <td>55290</td>\n",
       "      <td>55347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baldwin County</th>\n",
       "      <td>517</td>\n",
       "      <td>2187</td>\n",
       "      <td>2092</td>\n",
       "      <td>2160</td>\n",
       "      <td>2186</td>\n",
       "      <td>2240</td>\n",
       "      <td>183193</td>\n",
       "      <td>186659</td>\n",
       "      <td>190396</td>\n",
       "      <td>195126</td>\n",
       "      <td>199713</td>\n",
       "      <td>203709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbour County</th>\n",
       "      <td>70</td>\n",
       "      <td>335</td>\n",
       "      <td>300</td>\n",
       "      <td>283</td>\n",
       "      <td>260</td>\n",
       "      <td>269</td>\n",
       "      <td>27341</td>\n",
       "      <td>27226</td>\n",
       "      <td>27159</td>\n",
       "      <td>26973</td>\n",
       "      <td>26815</td>\n",
       "      <td>26489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bibb County</th>\n",
       "      <td>44</td>\n",
       "      <td>266</td>\n",
       "      <td>245</td>\n",
       "      <td>259</td>\n",
       "      <td>247</td>\n",
       "      <td>253</td>\n",
       "      <td>22861</td>\n",
       "      <td>22733</td>\n",
       "      <td>22642</td>\n",
       "      <td>22512</td>\n",
       "      <td>22549</td>\n",
       "      <td>22583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blount County</th>\n",
       "      <td>183</td>\n",
       "      <td>744</td>\n",
       "      <td>710</td>\n",
       "      <td>646</td>\n",
       "      <td>618</td>\n",
       "      <td>603</td>\n",
       "      <td>57373</td>\n",
       "      <td>57711</td>\n",
       "      <td>57776</td>\n",
       "      <td>57734</td>\n",
       "      <td>57658</td>\n",
       "      <td>57673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        BIRTHS2010  BIRTHS2011  BIRTHS2012  BIRTHS2013  \\\n",
       "STNAME  CTYNAME                                                          \n",
       "Alabama Autauga County         151         636         615         574   \n",
       "        Baldwin County         517        2187        2092        2160   \n",
       "        Barbour County          70         335         300         283   \n",
       "        Bibb County             44         266         245         259   \n",
       "        Blount County          183         744         710         646   \n",
       "\n",
       "                        BIRTHS2014  BIRTHS2015  POPESTIMATE2010  \\\n",
       "STNAME  CTYNAME                                                   \n",
       "Alabama Autauga County         623         600            54660   \n",
       "        Baldwin County        2186        2240           183193   \n",
       "        Barbour County         260         269            27341   \n",
       "        Bibb County            247         253            22861   \n",
       "        Blount County          618         603            57373   \n",
       "\n",
       "                        POPESTIMATE2011  POPESTIMATE2012  POPESTIMATE2013  \\\n",
       "STNAME  CTYNAME                                                             \n",
       "Alabama Autauga County            55253            55175            55038   \n",
       "        Baldwin County           186659           190396           195126   \n",
       "        Barbour County            27226            27159            26973   \n",
       "        Bibb County               22733            22642            22512   \n",
       "        Blount County             57711            57776            57734   \n",
       "\n",
       "                        POPESTIMATE2014  POPESTIMATE2015  \n",
       "STNAME  CTYNAME                                           \n",
       "Alabama Autauga County            55290            55347  \n",
       "        Baldwin County           199713           203709  \n",
       "        Barbour County            26815            26489  \n",
       "        Bibb County               22549            22583  \n",
       "        Blount County             57658            57673  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(['STNAME', 'CTYNAME'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BIRTHS2010            977\n",
       "BIRTHS2011           3826\n",
       "BIRTHS2012           3780\n",
       "BIRTHS2013           3662\n",
       "BIRTHS2014           3683\n",
       "BIRTHS2015           3709\n",
       "POPESTIMATE2010    345563\n",
       "POPESTIMATE2011    349048\n",
       "POPESTIMATE2012    351213\n",
       "POPESTIMATE2013    354289\n",
       "POPESTIMATE2014    357029\n",
       "POPESTIMATE2015    358880\n",
       "Name: (Michigan, Washtenaw County), dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Michigan', 'Washtenaw County']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>BIRTHS2010</th>\n",
       "      <th>BIRTHS2011</th>\n",
       "      <th>BIRTHS2012</th>\n",
       "      <th>BIRTHS2013</th>\n",
       "      <th>BIRTHS2014</th>\n",
       "      <th>BIRTHS2015</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2012</th>\n",
       "      <th>POPESTIMATE2013</th>\n",
       "      <th>POPESTIMATE2014</th>\n",
       "      <th>POPESTIMATE2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Michigan</th>\n",
       "      <th>Washtenaw County</th>\n",
       "      <td>977</td>\n",
       "      <td>3826</td>\n",
       "      <td>3780</td>\n",
       "      <td>3662</td>\n",
       "      <td>3683</td>\n",
       "      <td>3709</td>\n",
       "      <td>345563</td>\n",
       "      <td>349048</td>\n",
       "      <td>351213</td>\n",
       "      <td>354289</td>\n",
       "      <td>357029</td>\n",
       "      <td>358880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wayne County</th>\n",
       "      <td>5918</td>\n",
       "      <td>23819</td>\n",
       "      <td>23270</td>\n",
       "      <td>23377</td>\n",
       "      <td>23607</td>\n",
       "      <td>23586</td>\n",
       "      <td>1815199</td>\n",
       "      <td>1801273</td>\n",
       "      <td>1792514</td>\n",
       "      <td>1775713</td>\n",
       "      <td>1766008</td>\n",
       "      <td>1759335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           BIRTHS2010  BIRTHS2011  BIRTHS2012  BIRTHS2013  \\\n",
       "STNAME   CTYNAME                                                            \n",
       "Michigan Washtenaw County         977        3826        3780        3662   \n",
       "         Wayne County            5918       23819       23270       23377   \n",
       "\n",
       "                           BIRTHS2014  BIRTHS2015  POPESTIMATE2010  \\\n",
       "STNAME   CTYNAME                                                     \n",
       "Michigan Washtenaw County        3683        3709           345563   \n",
       "         Wayne County           23607       23586          1815199   \n",
       "\n",
       "                           POPESTIMATE2011  POPESTIMATE2012  POPESTIMATE2013  \\\n",
       "STNAME   CTYNAME                                                               \n",
       "Michigan Washtenaw County           349048           351213           354289   \n",
       "         Wayne County              1801273          1792514          1775713   \n",
       "\n",
       "                           POPESTIMATE2014  POPESTIMATE2015  \n",
       "STNAME   CTYNAME                                             \n",
       "Michigan Washtenaw County           357029           358880  \n",
       "         Wayne County              1766008          1759335  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[ [('Michigan', 'Washtenaw County'),\n",
    "         ('Michigan', 'Wayne County')] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>1469974424</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
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       "      <td>10.0</td>\n",
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       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1469974544</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>1469974574</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1469977514</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1469977544</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1469977574</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1469977604</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1469974604</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1469974694</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1469974724</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1469974454</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1469974524</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1469974424</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1469974554</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1469974624</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1469974654</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1469974724</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1469974484</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1469974514</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1469974754</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1469974824</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1469974854</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1469974924</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1469977424</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1469977454</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1469977484</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1469977634</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1469977664</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1469974634</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1469974664</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1469977694</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1469977724</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          time    user          video  playback position paused  volume\n",
       "0   1469974424  cheryl     intro.html                  5  False    10.0\n",
       "1   1469974454  cheryl     intro.html                  6    NaN     NaN\n",
       "2   1469974544  cheryl     intro.html                  9    NaN     NaN\n",
       "3   1469974574  cheryl     intro.html                 10    NaN     NaN\n",
       "4   1469977514     bob     intro.html                  1    NaN     NaN\n",
       "5   1469977544     bob     intro.html                  1    NaN     NaN\n",
       "6   1469977574     bob     intro.html                  1    NaN     NaN\n",
       "7   1469977604     bob     intro.html                  1    NaN     NaN\n",
       "8   1469974604  cheryl     intro.html                 11    NaN     NaN\n",
       "9   1469974694  cheryl     intro.html                 14    NaN     NaN\n",
       "10  1469974724  cheryl     intro.html                 15    NaN     NaN\n",
       "11  1469974454     sue  advanced.html                 24    NaN     NaN\n",
       "12  1469974524     sue  advanced.html                 25    NaN     NaN\n",
       "13  1469974424     sue  advanced.html                 23  False    10.0\n",
       "14  1469974554     sue  advanced.html                 26    NaN     NaN\n",
       "15  1469974624     sue  advanced.html                 27    NaN     NaN\n",
       "16  1469974654     sue  advanced.html                 28    NaN     5.0\n",
       "17  1469974724     sue  advanced.html                 29    NaN     NaN\n",
       "18  1469974484  cheryl     intro.html                  7    NaN     NaN\n",
       "19  1469974514  cheryl     intro.html                  8    NaN     NaN\n",
       "20  1469974754     sue  advanced.html                 30    NaN     NaN\n",
       "21  1469974824     sue  advanced.html                 31    NaN     NaN\n",
       "22  1469974854     sue  advanced.html                 32    NaN     NaN\n",
       "23  1469974924     sue  advanced.html                 33    NaN     NaN\n",
       "24  1469977424     bob     intro.html                  1   True    10.0\n",
       "25  1469977454     bob     intro.html                  1    NaN     NaN\n",
       "26  1469977484     bob     intro.html                  1    NaN     NaN\n",
       "27  1469977634     bob     intro.html                  1    NaN     NaN\n",
       "28  1469977664     bob     intro.html                  1    NaN     NaN\n",
       "29  1469974634  cheryl     intro.html                 12    NaN     NaN\n",
       "30  1469974664  cheryl     intro.html                 13    NaN     NaN\n",
       "31  1469977694     bob     intro.html                  1    NaN     NaN\n",
       "32  1469977724     bob     intro.html                  1    NaN     NaN"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('log.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>video</th>\n",
       "      <th>playback position</th>\n",
       "      <th>paused</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>1469974574</th>\n",
       "      <td>cheryl</td>\n",
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       "      <th>1469974624</th>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1469974634</th>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1469974654</th>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469974664</th>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469974694</th>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469974724</th>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1469974724</th>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1469974754</th>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1469974824</th>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1469974854</th>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1469974924</th>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977424</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977454</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977484</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1469977514</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977544</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977574</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977604</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977634</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977664</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977694</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469977724</th>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              user          video  playback position paused  volume\n",
       "time                                                               \n",
       "1469974424  cheryl     intro.html                  5  False    10.0\n",
       "1469974424     sue  advanced.html                 23  False    10.0\n",
       "1469974454  cheryl     intro.html                  6    NaN     NaN\n",
       "1469974454     sue  advanced.html                 24    NaN     NaN\n",
       "1469974484  cheryl     intro.html                  7    NaN     NaN\n",
       "1469974514  cheryl     intro.html                  8    NaN     NaN\n",
       "1469974524     sue  advanced.html                 25    NaN     NaN\n",
       "1469974544  cheryl     intro.html                  9    NaN     NaN\n",
       "1469974554     sue  advanced.html                 26    NaN     NaN\n",
       "1469974574  cheryl     intro.html                 10    NaN     NaN\n",
       "1469974604  cheryl     intro.html                 11    NaN     NaN\n",
       "1469974624     sue  advanced.html                 27    NaN     NaN\n",
       "1469974634  cheryl     intro.html                 12    NaN     NaN\n",
       "1469974654     sue  advanced.html                 28    NaN     5.0\n",
       "1469974664  cheryl     intro.html                 13    NaN     NaN\n",
       "1469974694  cheryl     intro.html                 14    NaN     NaN\n",
       "1469974724  cheryl     intro.html                 15    NaN     NaN\n",
       "1469974724     sue  advanced.html                 29    NaN     NaN\n",
       "1469974754     sue  advanced.html                 30    NaN     NaN\n",
       "1469974824     sue  advanced.html                 31    NaN     NaN\n",
       "1469974854     sue  advanced.html                 32    NaN     NaN\n",
       "1469974924     sue  advanced.html                 33    NaN     NaN\n",
       "1469977424     bob     intro.html                  1   True    10.0\n",
       "1469977454     bob     intro.html                  1    NaN     NaN\n",
       "1469977484     bob     intro.html                  1    NaN     NaN\n",
       "1469977514     bob     intro.html                  1    NaN     NaN\n",
       "1469977544     bob     intro.html                  1    NaN     NaN\n",
       "1469977574     bob     intro.html                  1    NaN     NaN\n",
       "1469977604     bob     intro.html                  1    NaN     NaN\n",
       "1469977634     bob     intro.html                  1    NaN     NaN\n",
       "1469977664     bob     intro.html                  1    NaN     NaN\n",
       "1469977694     bob     intro.html                  1    NaN     NaN\n",
       "1469977724     bob     intro.html                  1    NaN     NaN"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('time')\n",
    "df = df.sort_index()\n",
    "df"
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      "text/plain": [
       "                           video  playback position paused  volume\n",
       "time       user                                                   \n",
       "1469974424 cheryl     intro.html                  5  False    10.0\n",
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       "1469974524 sue     advanced.html                 25    NaN     NaN\n",
       "1469974544 cheryl     intro.html                  9    NaN     NaN\n",
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       "1469974634 cheryl     intro.html                 12    NaN     NaN\n",
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       "1469974854 sue     advanced.html                 32    NaN     NaN\n",
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       "1469977424 bob        intro.html                  1   True    10.0\n",
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     },
     "execution_count": 86,
     "metadata": {},
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   ],
   "source": [
    "df = df.reset_index()\n",
    "df = df.set_index(['time', 'user'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
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