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": [
    "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = [1, 2, 3]\n",
    "pd.Series(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Tiger\n",
       "1     Bear\n",
       "2     None\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals = ['Tiger', 'Bear', None]\n",
    "pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = [1, 2, None]\n",
    "pd.Series(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.nan == None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Archery', 'Golf', 'Sumo', 'Taekwondo'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India      Tiger\n",
       "America     Bear\n",
       "Canada     Moose\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['Tiger', 'Bear', 'Moose'], index=['India', 'America', 'Canada'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golf      Scotland\n",
       "Sumo         Japan\n",
       "Hockey         NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\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)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'South Korea'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Scotland'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc['Golf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'South Korea'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Scotland'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['Golf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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-19-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/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    601\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    602\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 603\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    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\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/python3.6/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   2167\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2168\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 2169\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   2170\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   2171\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:3557)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3240)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4279)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8564)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8508)\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([100.00, 120.00, 101.00, 3.00])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = 0\n",
    "for item in s:\n",
    "    total+=item\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "total = np.sum(s)\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit -n 100\n",
    "summary = 0\n",
    "for item in s:\n",
    "    summary+=item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit -n 100\n",
    "summary = np.sum(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s+=2 #adds two to each item in s using broadcasting\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label, value in s.iteritems():\n",
    "    s.set_value(label, value+2)\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit -n 10\n",
    "s = pd.Series(np.random.randint(0,1000,10000))\n",
    "s+=2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1, 2, 3])\n",
    "s.loc['Animal'] = 'Bears'\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_sports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cricket    Australia\n",
       "Cricket     Barbados\n",
       "Cricket     Pakistan\n",
       "Cricket      England\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cricket_loving_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cricket    Australia\n",
       "Cricket     Barbados\n",
       "Cricket     Pakistan\n",
       "Cricket      England\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cost                      5\n",
       "Item Purchased    Bird Seed\n",
       "Name                  Vinod\n",
       "Name: Store 2, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.loc['Store 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 1', 'Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T.loc['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 1']['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['Name', 'Cost']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Store 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 37,
     "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": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_df.drop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del copy_df['Name']\n",
    "copy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "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": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs = df['Cost']\n",
    "costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs+=2\n",
    "costs"
   ]
  },
  {
   "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>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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15\r\n",
      ",№ Summer,01 !,02 !,03 !,Total,№ Winter,01 !,02 !,03 !,Total,№ Games,01 !,02 !,03 !,Combined total\r\n",
      "Afghanistan (AFG),13,0,0,2,2,0,0,0,0,0,13,0,0,2,2\r\n",
      "Algeria (ALG),12,5,2,8,15,3,0,0,0,0,15,5,2,8,15\r\n",
      "Argentina (ARG),23,18,24,28,70,18,0,0,0,0,41,18,24,28,70\r\n",
      "Armenia (ARM),5,1,2,9,12,6,0,0,0,0,11,1,2,9,12\r\n",
      "Australasia (ANZ) [ANZ],2,3,4,5,12,0,0,0,0,0,2,3,4,5,12\r\n",
      "Australia (AUS) [AUS] [Z],25,139,152,177,468,18,5,3,4,12,43,144,155,181,480\r\n",
      "Austria (AUT),26,18,33,35,86,22,59,78,81,218,48,77,111,116,304\r\n",
      "Azerbaijan (AZE),5,6,5,15,26,5,0,0,0,0,10,6,5,15,26\r\n",
      "Bahamas (BAH),15,5,2,5,12,0,0,0,0,0,15,5,2,5,12\r\n",
      "Bahrain (BRN),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1\r\n",
      "Barbados (BAR) [BAR],11,0,0,1,1,0,0,0,0,0,11,0,0,1,1\r\n",
      "Belarus (BLR),5,12,24,39,75,6,6,4,5,15,11,18,28,44,90\r\n",
      "Belgium (BEL),25,37,52,53,142,20,1,1,3,5,45,38,53,56,147\r\n",
      "Bermuda (BER),17,0,0,1,1,7,0,0,0,0,24,0,0,1,1\r\n",
      "Bohemia (BOH) [BOH] [Z],3,0,1,3,4,0,0,0,0,0,3,0,1,3,4\r\n",
      "Botswana (BOT),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1\r\n",
      "Brazil (BRA),21,23,30,55,108,7,0,0,0,0,28,23,30,55,108\r\n",
      "British West Indies (BWI) [BWI],1,0,0,2,2,0,0,0,0,0,1,0,0,2,2\r\n",
      "Bulgaria (BUL) [H],19,51,85,78,214,19,1,2,3,6,38,52,87,81,220\r\n",
      "Burundi (BDI),5,1,0,0,1,0,0,0,0,0,5,1,0,0,1\r\n",
      "Cameroon (CMR),13,3,1,1,5,1,0,0,0,0,14,3,1,1,5\r\n",
      "Canada (CAN),25,59,99,121,279,22,62,56,52,170,47,121,155,173,449\r\n",
      "Chile (CHI) [I],22,2,7,4,13,16,0,0,0,0,38,2,7,4,13\r\n",
      "China (CHN) [CHN],9,201,146,126,473,10,12,22,19,53,19,213,168,145,526\r\n",
      "Colombia (COL),18,2,6,11,19,1,0,0,0,0,19,2,6,11,19\r\n",
      "Costa Rica (CRC),14,1,1,2,4,6,0,0,0,0,20,1,1,2,4\r\n",
      "Ivory Coast (CIV) [CIV],12,0,1,0,1,0,0,0,0,0,12,0,1,0,1\r\n",
      "Croatia (CRO),6,6,7,10,23,7,4,6,1,11,13,10,13,11,34\r\n",
      "Cuba (CUB) [Z],19,72,67,70,209,0,0,0,0,0,19,72,67,70,209\r\n",
      "Cyprus (CYP),9,0,1,0,1,10,0,0,0,0,19,0,1,0,1\r\n",
      "Czech Republic (CZE) [CZE],5,14,15,15,44,6,7,9,8,24,11,21,24,23,68\r\n",
      "Czechoslovakia (TCH) [TCH],16,49,49,45,143,16,2,8,15,25,32,51,57,60,168\r\n",
      "Denmark (DEN) [Z],26,43,68,68,179,13,0,1,0,1,39,43,69,68,180\r\n",
      "Djibouti (DJI) [B],7,0,0,1,1,0,0,0,0,0,7,0,0,1,1\r\n",
      "Dominican Republic (DOM),13,3,2,1,6,0,0,0,0,0,13,3,2,1,6\r\n",
      "Ecuador (ECU),13,1,1,0,2,0,0,0,0,0,13,1,1,0,2\r\n",
      "Egypt (EGY) [EGY] [Z],21,7,9,10,26,1,0,0,0,0,22,7,9,10,26\r\n",
      "Eritrea (ERI),4,0,0,1,1,0,0,0,0,0,4,0,0,1,1\r\n",
      "Estonia (EST),11,9,9,15,33,9,4,2,1,7,20,13,11,16,40\r\n",
      "Ethiopia (ETH),12,21,7,17,45,2,0,0,0,0,14,21,7,17,45\r\n",
      "Finland (FIN),24,101,84,117,302,22,42,62,57,161,46,143,146,174,463\r\n",
      "France (FRA) [O] [P] [Z],27,202,223,246,671,22,31,31,47,109,49,233,254,293,780\r\n",
      "Gabon (GAB),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1\r\n",
      "Georgia (GEO),5,6,5,14,25,6,0,0,0,0,11,6,5,14,25\r\n",
      "Germany (GER) [GER] [Z],15,174,182,217,573,11,78,78,53,209,26,252,260,270,782\r\n",
      "United Team of Germany (EUA) [EUA],3,28,54,36,118,3,8,6,5,19,6,36,60,41,137\r\n",
      "East Germany (GDR) [GDR],5,153,129,127,409,6,39,36,35,110,11,192,165,162,519\r\n",
      "West Germany (FRG) [FRG],5,56,67,81,204,6,11,15,13,39,11,67,82,94,243\r\n",
      "Ghana (GHA) [GHA],13,0,1,3,4,1,0,0,0,0,14,0,1,3,4\r\n",
      "Great Britain (GBR) [GBR] [Z],27,236,272,272,780,22,10,4,12,26,49,246,276,284,806\r\n",
      "Greece (GRE) [Z],27,30,42,39,111,18,0,0,0,0,45,30,42,39,111\r\n",
      "Grenada (GRN),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1\r\n",
      "Guatemala (GUA),13,0,1,0,1,1,0,0,0,0,14,0,1,0,1\r\n",
      "Guyana (GUY) [GUY],16,0,0,1,1,0,0,0,0,0,16,0,0,1,1\r\n",
      "Haiti (HAI) [J],14,0,1,1,2,0,0,0,0,0,14,0,1,1,2\r\n",
      "Hong Kong (HKG) [HKG],15,1,1,1,3,4,0,0,0,0,19,1,1,1,3\r\n",
      "Hungary (HUN),25,167,144,165,476,22,0,2,4,6,47,167,146,169,482\r\n",
      "Iceland (ISL),19,0,2,2,4,17,0,0,0,0,36,0,2,2,4\r\n",
      "India (IND) [F],23,9,6,11,26,9,0,0,0,0,32,9,6,11,26\r\n",
      "Indonesia (INA),14,6,10,11,27,0,0,0,0,0,14,6,10,11,27\r\n",
      "Iran (IRI) [K],15,15,20,25,60,10,0,0,0,0,25,15,20,25,60\r\n",
      "Iraq (IRQ),13,0,0,1,1,0,0,0,0,0,13,0,0,1,1\r\n",
      "Ireland (IRL),20,9,8,12,29,6,0,0,0,0,26,9,8,12,29\r\n",
      "Israel (ISR),15,1,1,5,7,6,0,0,0,0,21,1,1,5,7\r\n",
      "Italy (ITA) [M] [S],26,198,166,185,549,22,37,34,43,114,48,235,200,228,663\r\n",
      "Jamaica (JAM) [JAM],16,17,30,20,67,7,0,0,0,0,23,17,30,20,67\r\n",
      "Japan (JPN),21,130,126,142,398,20,10,17,18,45,41,140,143,160,443\r\n",
      "Kazakhstan (KAZ),5,16,17,19,52,6,1,3,3,7,11,17,20,22,59\r\n",
      "Kenya (KEN),13,25,32,29,86,3,0,0,0,0,16,25,32,29,86\r\n",
      "North Korea (PRK),9,14,12,21,47,8,0,1,1,2,17,14,13,22,49\r\n",
      "South Korea (KOR),16,81,82,80,243,17,26,17,10,53,33,107,99,90,296\r\n",
      "Kuwait (KUW),12,0,0,2,2,0,0,0,0,0,12,0,0,2,2\r\n",
      "Kyrgyzstan (KGZ),5,0,1,2,3,6,0,0,0,0,11,0,1,2,3\r\n",
      "Latvia (LAT),10,3,11,5,19,10,0,4,3,7,20,3,15,8,26\r\n",
      "Lebanon (LIB),16,0,2,2,4,16,0,0,0,0,32,0,2,2,4\r\n",
      "Liechtenstein (LIE),16,0,0,0,0,18,2,2,5,9,34,2,2,5,9\r\n",
      "Lithuania (LTU),8,6,5,10,21,8,0,0,0,0,16,6,5,10,21\r\n",
      "Luxembourg (LUX) [O],22,1,1,0,2,8,0,2,0,2,30,1,3,0,4\r\n",
      "Macedonia (MKD),5,0,0,1,1,5,0,0,0,0,10,0,0,1,1\r\n",
      "Malaysia (MAS) [MAS],12,0,3,3,6,0,0,0,0,0,12,0,3,3,6\r\n",
      "Mauritius (MRI),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1\r\n",
      "Mexico (MEX),22,13,21,28,62,8,0,0,0,0,30,13,21,28,62\r\n",
      "Moldova (MDA),5,0,2,5,7,6,0,0,0,0,11,0,2,5,7\r\n",
      "Mongolia (MGL),12,2,9,13,24,13,0,0,0,0,25,2,9,13,24\r\n",
      "Montenegro (MNE),2,0,1,0,1,2,0,0,0,0,4,0,1,0,1\r\n",
      "Morocco (MAR),13,6,5,11,22,6,0,0,0,0,19,6,5,11,22\r\n",
      "Mozambique (MOZ),9,1,0,1,2,0,0,0,0,0,9,1,0,1,2\r\n",
      "Namibia (NAM),6,0,4,0,4,0,0,0,0,0,6,0,4,0,4\r\n",
      "Netherlands (NED) [Z],25,77,85,104,266,20,37,38,35,110,45,114,123,139,376\r\n",
      "Netherlands Antilles (AHO) [AHO] [I],13,0,1,0,1,2,0,0,0,0,15,0,1,0,1\r\n",
      "New Zealand (NZL) [NZL],22,42,18,39,99,15,0,1,0,1,37,42,19,39,100\r\n",
      "Niger (NIG),11,0,0,1,1,0,0,0,0,0,11,0,0,1,1\r\n",
      "Nigeria (NGR),15,3,8,12,23,0,0,0,0,0,15,3,8,12,23\r\n",
      "Norway (NOR) [Q],24,56,49,43,148,22,118,111,100,329,46,174,160,143,477\r\n",
      "Pakistan (PAK),16,3,3,4,10,2,0,0,0,0,18,3,3,4,10\r\n",
      "Panama (PAN),16,1,0,2,3,0,0,0,0,0,16,1,0,2,3\r\n",
      "Paraguay (PAR),11,0,1,0,1,1,0,0,0,0,12,0,1,0,1\r\n",
      "Peru (PER) [L],17,1,3,0,4,2,0,0,0,0,19,1,3,0,4\r\n",
      "Philippines (PHI),20,0,2,7,9,4,0,0,0,0,24,0,2,7,9\r\n",
      "Poland (POL),20,64,82,125,271,22,6,7,7,20,42,70,89,132,291\r\n",
      "Portugal (POR),23,4,8,11,23,7,0,0,0,0,30,4,8,11,23\r\n",
      "Puerto Rico (PUR),17,0,2,6,8,6,0,0,0,0,23,0,2,6,8\r\n",
      "Qatar (QAT),8,0,0,4,4,0,0,0,0,0,8,0,0,4,4\r\n",
      "Romania (ROU),20,88,94,119,301,20,0,0,1,1,40,88,94,120,302\r\n",
      "Russia (RUS) [RUS],5,132,121,142,395,6,49,40,35,124,11,181,161,177,519\r\n",
      "Russian Empire (RU1) [RU1],3,1,4,3,8,0,0,0,0,0,3,1,4,3,8\r\n",
      "Soviet Union (URS) [URS],9,395,319,296,1010,9,78,57,59,194,18,473,376,355,1204\r\n",
      "Unified Team (EUN) [EUN],1,45,38,29,112,1,9,6,8,23,2,54,44,37,135\r\n",
      "Saudi Arabia (KSA),10,0,1,2,3,0,0,0,0,0,10,0,1,2,3\r\n",
      "Senegal (SEN),13,0,1,0,1,5,0,0,0,0,18,0,1,0,1\r\n",
      "Serbia (SRB) [SRB],3,1,2,4,7,2,0,0,0,0,5,1,2,4,7\r\n",
      "Serbia and Montenegro (SCG) [SCG],3,2,4,3,9,3,0,0,0,0,6,2,4,3,9\r\n",
      "Singapore (SIN),15,0,2,2,4,0,0,0,0,0,15,0,2,2,4\r\n",
      "Slovakia (SVK) [SVK],5,7,9,8,24,6,2,2,1,5,11,9,11,9,29\r\n",
      "Slovenia (SLO),6,4,6,9,19,7,2,4,9,15,13,6,10,18,34\r\n",
      "South Africa (RSA),18,23,26,27,76,6,0,0,0,0,24,23,26,27,76\r\n",
      "Spain (ESP) [Z],22,37,59,35,131,19,1,0,1,2,41,38,59,36,133\r\n",
      "Sri Lanka (SRI) [SRI],16,0,2,0,2,0,0,0,0,0,16,0,2,0,2\r\n",
      "Sudan (SUD),11,0,1,0,1,0,0,0,0,0,11,0,1,0,1\r\n",
      "Suriname (SUR) [E],11,1,0,1,2,0,0,0,0,0,11,1,0,1,2\r\n",
      "Sweden (SWE) [Z],26,143,164,176,483,22,50,40,54,144,48,193,204,230,627\r\n",
      "Switzerland (SUI),27,47,73,65,185,22,50,40,48,138,49,97,113,113,323\r\n",
      "Syria (SYR),12,1,1,1,3,0,0,0,0,0,12,1,1,1,3\r\n",
      "Chinese Taipei (TPE) [TPE] [TPE2],13,2,7,12,21,11,0,0,0,0,24,2,7,12,21\r\n",
      "Tajikistan (TJK),5,0,1,2,3,4,0,0,0,0,9,0,1,2,3\r\n",
      "Tanzania (TAN) [TAN],12,0,2,0,2,0,0,0,0,0,12,0,2,0,2\r\n",
      "Thailand (THA),15,7,6,11,24,3,0,0,0,0,18,7,6,11,24\r\n",
      "Togo (TOG),9,0,0,1,1,1,0,0,0,0,10,0,0,1,1\r\n",
      "Tonga (TGA),8,0,1,0,1,1,0,0,0,0,9,0,1,0,1\r\n",
      "Trinidad and Tobago (TRI) [TRI],16,2,5,11,18,3,0,0,0,0,19,2,5,11,18\r\n",
      "Tunisia (TUN),13,3,3,4,10,0,0,0,0,0,13,3,3,4,10\r\n",
      "Turkey (TUR),21,39,25,24,88,16,0,0,0,0,37,39,25,24,88\r\n",
      "Uganda (UGA),14,2,3,2,7,0,0,0,0,0,14,2,3,2,7\r\n",
      "Ukraine (UKR),5,33,27,55,115,6,2,1,4,7,11,35,28,59,122\r\n",
      "United Arab Emirates (UAE),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1\r\n",
      "United States (USA) [P] [Q] [R] [Z],26,976,757,666,2399,22,96,102,84,282,48,1072,859,750,2681\r\n",
      "Uruguay (URU),20,2,2,6,10,1,0,0,0,0,21,2,2,6,10\r\n",
      "Uzbekistan (UZB),5,5,5,10,20,6,1,0,0,1,11,6,5,10,21\r\n",
      "Venezuela (VEN),17,2,2,8,12,4,0,0,0,0,21,2,2,8,12\r\n",
      "Vietnam (VIE),14,0,2,0,2,0,0,0,0,0,14,0,2,0,2\r\n",
      "Virgin Islands (ISV),11,0,1,0,1,7,0,0,0,0,18,0,1,0,1\r\n",
      "Yugoslavia (YUG) [YUG],16,26,29,28,83,14,0,3,1,4,30,26,32,29,87\r\n",
      "Independent Olympic Participants (IOP) [IOP],1,0,1,2,3,0,0,0,0,0,1,0,1,2,3\r\n",
      "Zambia (ZAM) [ZAM],12,0,1,1,2,0,0,0,0,0,12,0,1,1,2\r\n",
      "Zimbabwe (ZIM) [ZIM],12,3,4,1,8,1,0,0,0,0,13,3,4,1,8\r\n",
      "Mixed team (ZZX) [ZZX],3,8,5,4,17,0,0,0,0,0,3,8,5,4,17\r\n",
      "Totals,27,4809,4775,5130,14714,22,959,958,948,2865,49,5768,5733,6078,17579\r\n"
     ]
    }
   ],
   "source": [
    "!cat olympics.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('olympics.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>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": 46,
     "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": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "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># 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": 48,
     "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": 49,
   "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gold'] > 0"
   ]
  },
  {
   "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># 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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = df.where(df['Gold'] > 0)\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold['Gold'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gold'].count()"
   ]
  },
  {
   "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># 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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = only_gold.dropna()\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = df[df['Gold'] > 0]\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['Gold'] > 0) | (df['Gold.1'] > 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 56,
     "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": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['country'] = df.index\n",
    "df = df.set_index('Gold')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
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       "      <th>CTYNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
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       "      <th>...</th>\n",
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       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4779736</td>\n",
       "      <td>4780127</td>\n",
       "      <td>4785161</td>\n",
       "      <td>...</td>\n",
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       "      <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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('census.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([40, 50])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SUMLEV'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>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",
       "    <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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[df['SUMLEV'] == 50]\n",
    "df.head()"
   ]
  },
  {
   "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>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",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2012</th>\n",
       "      <th>POPESTIMATE2013</th>\n",
       "      <th>POPESTIMATE2014</th>\n",
       "      <th>POPESTIMATE2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\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>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": 63,
     "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": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th></th>\n",
       "      <th>BIRTHS2010</th>\n",
       "      <th>BIRTHS2011</th>\n",
       "      <th>BIRTHS2012</th>\n",
       "      <th>BIRTHS2013</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">Alabama</th>\n",
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       "      <td>151</td>\n",
       "      <td>636</td>\n",
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       "      <td>574</td>\n",
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       "      <td>55347</td>\n",
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       "    <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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(['STNAME', 'CTYNAME'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Michigan', 'Washtenaw County']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 66,
     "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": 67,
   "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>time</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1469974424</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1469974454</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1469974544</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('log.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "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",
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       "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",
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       "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('time')\n",
    "df = df.sort_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
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      "text/plain": [
       "                           video  playback position paused  volume\n",
       "time       user                                                   \n",
       "1469974424 cheryl     intro.html                  5  False    10.0\n",
       "           sue     advanced.html                 23  False    10.0\n",
       "1469974454 cheryl     intro.html                  6    NaN     NaN\n",
       "           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",
       "           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",
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       "1469977634 bob        intro.html                  1    NaN     NaN\n",
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       "1469977694 bob        intro.html                  1    NaN     NaN\n",
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      ]
     },
     "execution_count": 70,
     "metadata": {},
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    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df = df.set_index(['time', 'user'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "                           video  playback position paused  volume\n",
       "time       user                                                   \n",
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     },
     "execution_count": 71,
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   "source": [
    "df = df.fillna(method='ffill')\n",
    "df.head()"
   ]
  }
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