Assignment 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# third party\n",
    "import numpy\n",
    "import pandas\n",
    "import numpy as np\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|       |   № Summer |      01 ! |     02 ! |      03 ! |    Total |   № Winter |   01 !.1 |   02 !.1 |   03 !.1 |   Total.1 |   № Games |    01 !.2 |   02 !.2 |    03 !.2 |   Combined total |\n",
      "|-------+------------+-----------+----------+-----------+----------+------------+----------+----------+----------+-----------+-----------+-----------+----------+-----------+------------------|\n",
      "| count |  147       |  147      |  147     |  147      |   147    |  147       | 147      | 147      | 147      |  147      |  147      |  147      |   147    |  147      |           147    |\n",
      "| mean  |   13.4762  |   65.4286 |   64.966 |   69.7959 |   200.19 |    6.70068 |  13.0476 |  13.034  |  12.898  |   38.9796 |   20.1769 |   78.4762 |    78    |   82.6939 |           239.17 |\n",
      "| std   |    7.07236 |  405.55   |  399.31  |  427.187  |  1231.31 |    7.43319 |  80.7992 |  80.6344 |  79.5884 |  240.917  |   13.257  |  485.013  |   478.86 |  505.855  |          1469.07 |\n",
      "| min   |    1       |    0      |    0     |    0      |     0    |    0       |   0      |   0      |   0      |    0      |    1      |    0      |     0    |    0      |             1    |\n",
      "| 25%   |    8       |    0      |    1     |    1      |     2    |    0       |   0      |   0      |   0      |    0      |   11      |    0      |     1    |    1      |             2.5  |\n",
      "| 50%   |   13       |    3      |    4     |    6      |    12    |    5       |   0      |   0      |   0      |    0      |   15      |    3      |     4    |    7      |            12    |\n",
      "| 75%   |   18.5     |   24      |   28     |   29      |    86    |   10       |   1      |   2      |   1      |    5      |   27      |   25.5    |    29    |   32.5    |            89    |\n",
      "| max   |   27       | 4809      | 4775     | 5130      | 14714    |   22       | 959      | 958      | 948      | 2865      |   49      | 5768      |  5733    | 6078      |         17579    |\n"
     ]
    }
   ],
   "source": [
    "data = pandas.read_csv('olympics.csv', index_col=0, skiprows=1)\n",
    "description = data.describe()\n",
    "print(tabulate(description, headers=\"keys\", tablefmt=\"orgtbl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>№ 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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>№ 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>Independent Olympic Participants (IOP) [IOP]</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia (ZAM) [ZAM]</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe (ZIM) [ZIM]</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed team (ZZX) [ZZX]</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Totals</th>\n",
       "      <td>27</td>\n",
       "      <td>4809</td>\n",
       "      <td>4775</td>\n",
       "      <td>5130</td>\n",
       "      <td>14714</td>\n",
       "      <td>22</td>\n",
       "      <td>959</td>\n",
       "      <td>958</td>\n",
       "      <td>948</td>\n",
       "      <td>2865</td>\n",
       "      <td>49</td>\n",
       "      <td>5768</td>\n",
       "      <td>5733</td>\n",
       "      <td>6078</td>\n",
       "      <td>17579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              № Summer  01 !  02 !  03 !  \\\n",
       "Independent Olympic Participants (IOP) [IOP]         1     0     1     2   \n",
       "Zambia (ZAM) [ZAM]                                  12     0     1     1   \n",
       "Zimbabwe (ZIM) [ZIM]                                12     3     4     1   \n",
       "Mixed team (ZZX) [ZZX]                               3     8     5     4   \n",
       "Totals                                              27  4809  4775  5130   \n",
       "\n",
       "                                              Total  № Winter  01 !.1  02 !.1  \\\n",
       "Independent Olympic Participants (IOP) [IOP]      3         0       0       0   \n",
       "Zambia (ZAM) [ZAM]                                2         0       0       0   \n",
       "Zimbabwe (ZIM) [ZIM]                              8         1       0       0   \n",
       "Mixed team (ZZX) [ZZX]                           17         0       0       0   \n",
       "Totals                                        14714        22     959     958   \n",
       "\n",
       "                                              03 !.1  Total.1  № Games  \\\n",
       "Independent Olympic Participants (IOP) [IOP]       0        0        1   \n",
       "Zambia (ZAM) [ZAM]                                 0        0       12   \n",
       "Zimbabwe (ZIM) [ZIM]                               0        0       13   \n",
       "Mixed team (ZZX) [ZZX]                             0        0        3   \n",
       "Totals                                           948     2865       49   \n",
       "\n",
       "                                              01 !.2  02 !.2  03 !.2  \\\n",
       "Independent Olympic Participants (IOP) [IOP]       0       1       2   \n",
       "Zambia (ZAM) [ZAM]                                 0       1       1   \n",
       "Zimbabwe (ZIM) [ZIM]                               3       4       1   \n",
       "Mixed team (ZZX) [ZZX]                             8       5       4   \n",
       "Totals                                          5768    5733    6078   \n",
       "\n",
       "                                              Combined total  \n",
       "Independent Olympic Participants (IOP) [IOP]               3  \n",
       "Zambia (ZAM) [ZAM]                                         2  \n",
       "Zimbabwe (ZIM) [ZIM]                                       8  \n",
       "Mixed team (ZZX) [ZZX]                                    17  \n",
       "Totals                                                 17579  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unexpected EOF while parsing (<ipython-input-67-85598e5eb101>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-67-85598e5eb101>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    print(data[\"Combined total\"]\u001b[0m\n\u001b[1;37m                                ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n"
     ]
    }
   ],
   "source": [
    "print((data[\"Combined total\"].sum() - data.loc[\"Totals\"][\"Combined total\"]) * 2)\n",
    "print(data[\"Combined total\"].sum())\n",
    "print(data[\"Combined total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_columns = (\"summer_participations\",\n",
    "\t       \"summer_gold\",\n",
    "\t       \"summer_silver\",\n",
    "\t       \"summer_bronze\",\n",
    "\t       \"summer_total\",\n",
    "\t       \"winter_participations\",\n",
    "\t       \"winter_gold\",\n",
    "\t       \"winter_silver\",\n",
    "\t       \"winter_bronze\",\n",
    "\t       \"winter_total\",\n",
    "\t       \"total_participations\",\n",
    "\t       \"total_gold\",\n",
    "\t       \"total_silver\",\n",
    "\t       \"total_bronze\",\n",
    "\t       \"total_combined\")\n",
    "assert len(new_columns) == len(data.columns)\n",
    "\n",
    "column_remap = dict(zip(data.columns, new_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>summer_participations</th>\n",
       "      <th>summer_gold</th>\n",
       "      <th>summer_silver</th>\n",
       "      <th>summer_bronze</th>\n",
       "      <th>summer_total</th>\n",
       "      <th>winter_participations</th>\n",
       "      <th>winter_gold</th>\n",
       "      <th>winter_silver</th>\n",
       "      <th>winter_bronze</th>\n",
       "      <th>winter_total</th>\n",
       "      <th>total_participations</th>\n",
       "      <th>total_gold</th>\n",
       "      <th>total_silver</th>\n",
       "      <th>total_bronze</th>\n",
       "      <th>total_combined</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   summer_participations  summer_gold  summer_silver  \\\n",
       "Afghanistan (AFG)                     13            0              0   \n",
       "\n",
       "                   summer_bronze  summer_total  winter_participations  \\\n",
       "Afghanistan (AFG)              2             2                      0   \n",
       "\n",
       "                   winter_gold  winter_silver  winter_bronze  winter_total  \\\n",
       "Afghanistan (AFG)            0              0              0             0   \n",
       "\n",
       "                   total_participations  total_gold  total_silver  \\\n",
       "Afghanistan (AFG)                    13           0             0   \n",
       "\n",
       "                   total_bronze  total_combined  \n",
       "Afghanistan (AFG)             2               2  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Remaping\n",
    "if data.columns[0] != \"summer_participations\":\n",
    "    for column in data.columns:\n",
    "        data.rename(columns={column:column_remap[column]}, inplace=True)\n",
    "\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([          'AFG)',           'ALG)',           'ARG)',           'ARM)',\n",
      "           'ANZ) [ANZ]', 'AUS) [AUS] [Z]',           'AUT)',           'AZE)',\n",
      "                 'BAH)',           'BRN)',\n",
      "       ...\n",
      "                 'UZB)',           'VEN)',           'VIE)',           'ISV)',\n",
      "           'YUG) [YUG]',     'IOP) [IOP]',     'ZAM) [ZAM]',     'ZIM) [ZIM]',\n",
      "           'ZZX) [ZZX]',              nan],\n",
      "      dtype='object', length=147)\n",
      "Float64Index([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "              ...\n",
      "              nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
      "             dtype='float64', length=147)\n",
      "Float64Index([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "              ...\n",
      "              nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
      "             dtype='float64', length=147)\n"
     ]
    }
   ],
   "source": [
    "# split the index by '('\n",
    "names_ids = data.index.str.split('\\s\\(')\n",
    "data.index = names_ids.str[0]\n",
    "data['ID'] = names_ids.str[1].str[:3]\n",
    "data = data.drop('Totals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>summer_participations</th>\n",
       "      <th>summer_gold</th>\n",
       "      <th>summer_silver</th>\n",
       "      <th>summer_bronze</th>\n",
       "      <th>summer_total</th>\n",
       "      <th>winter_participations</th>\n",
       "      <th>winter_gold</th>\n",
       "      <th>winter_silver</th>\n",
       "      <th>winter_bronze</th>\n",
       "      <th>winter_total</th>\n",
       "      <th>total_participations</th>\n",
       "      <th>total_gold</th>\n",
       "      <th>total_silver</th>\n",
       "      <th>total_bronze</th>\n",
       "      <th>total_combined</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</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",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</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",
       "      <td>ALG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</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",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</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",
       "      <td>ARM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia</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",
       "      <td>ANZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             summer_participations  summer_gold  summer_silver  summer_bronze  \\\n",
       "Afghanistan                     13            0              0              2   \n",
       "Algeria                         12            5              2              8   \n",
       "Argentina                       23           18             24             28   \n",
       "Armenia                          5            1              2              9   \n",
       "Australasia                      2            3              4              5   \n",
       "\n",
       "             summer_total  winter_participations  winter_gold  winter_silver  \\\n",
       "Afghanistan             2                      0            0              0   \n",
       "Algeria                15                      3            0              0   \n",
       "Argentina              70                     18            0              0   \n",
       "Armenia                12                      6            0              0   \n",
       "Australasia            12                      0            0              0   \n",
       "\n",
       "             winter_bronze  winter_total  total_participations  total_gold  \\\n",
       "Afghanistan              0             0                    13           0   \n",
       "Algeria                  0             0                    15           5   \n",
       "Argentina                0             0                    41          18   \n",
       "Armenia                  0             0                    11           1   \n",
       "Australasia              0             0                     2           3   \n",
       "\n",
       "             total_silver  total_bronze  total_combined   ID  \n",
       "Afghanistan             0             2               2  AFG  \n",
       "Algeria                 2             8              15  ALG  \n",
       "Argentina              24            28              70  ARG  \n",
       "Armenia                 2             9              12  ARM  \n",
       "Australasia             4             5              12  ANZ  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the first country in in the data-frame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Afghanistan'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_zero():\n",
    "    return data.iloc[0].name\n",
    "answer_zero()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which country has won the most gold medals in summer games? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simra\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: \n",
      "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
      "instead.\n",
      "The behavior of 'argmax' will be corrected to return the positional\n",
      "maximum in the future. For now, use 'series.values.argmax' or\n",
      "'np.argmax(np.array(values))' to get the position of the maximum\n",
      "row.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'United States'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_one():\n",
    "    return data.summer_gold.argmax()\n",
    "answer_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which country had the biggest difference between their summer and winter gold medal counts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simra\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: \n",
      "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
      "instead.\n",
      "The behavior of 'argmax' will be corrected to return the positional\n",
      "maximum in the future. For now, use 'series.values.argmax' or\n",
      "'np.argmax(np.array(values))' to get the position of the maximum\n",
      "row.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'United States'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    return (data.summer_gold - data.winter_gold).abs().argmax()\n",
    "\n",
    "answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which country has the biggest difference between their summer gold medal counts and winter gold medal counts relative to their total gold medal count?\n",
    "\n",
    "\n",
    "$$ \\frac{Summer~Gold - Winter~Gold}{Total~Gold} $$\n",
    "\n",
    "\n",
    "Only include countries that have won at least 1 gold in both summer and winter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simra\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\ipykernel_launcher.py:15: FutureWarning: \n",
      "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
      "instead.\n",
      "The behavior of 'argmax' will be corrected to return the positional\n",
      "maximum in the future. For now, use 'series.values.argmax' or\n",
      "'np.argmax(np.array(values))' to get the position of the maximum\n",
      "row.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Bulgaria'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_three():\n",
    "    elegible = data[(data.summer_gold>=1) & (data.winter_gold>=1)]\n",
    "    ratios = (elegible.summer_gold - elegible.winter_gold).abs()/elegible.total_gold\n",
    "    return ratios.argmax()\n",
    "\n",
    "answer_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function that creates a Series called \"Points\" which is a weighted value where each gold medal(Gold.2) counts for 3 points, silver medals (Silver.2) for 2 points, and bronze medals (Bronze.2) for 1 point. The function should return only the column (a Series object) which you created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def answer_four():\n",
    "    points = numpy.zeros(len(data))\n",
    "    points += data.total_gold * 3\n",
    "    points += data.total_silver * 2\n",
    "    points += data.total_bronze\n",
    "    return pandas.Series(points, index=data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Afghanistan                            2.0\n",
      "Algeria                               27.0\n",
      "Argentina                            130.0\n",
      "Armenia                               16.0\n",
      "Australasia                           22.0\n",
      "Australia                            923.0\n",
      "Austria                              569.0\n",
      "Azerbaijan                            43.0\n",
      "Bahamas                               24.0\n",
      "Bahrain                                1.0\n",
      "Barbados                               1.0\n",
      "Belarus                              154.0\n",
      "Belgium                              276.0\n",
      "Bermuda                                1.0\n",
      "Bohemia                                5.0\n",
      "Botswana                               2.0\n",
      "Brazil                               184.0\n",
      "British West Indies                    2.0\n",
      "Bulgaria                             411.0\n",
      "Burundi                                3.0\n",
      "Cameroon                              12.0\n",
      "Canada                               846.0\n",
      "Chile                                 24.0\n",
      "China                               1120.0\n",
      "Colombia                              29.0\n",
      "Costa Rica                             7.0\n",
      "Ivory Coast                            2.0\n",
      "Croatia                               67.0\n",
      "Cuba                                 420.0\n",
      "Cyprus                                 2.0\n",
      "                                     ...  \n",
      "Spain                                268.0\n",
      "Sri Lanka                              4.0\n",
      "Sudan                                  2.0\n",
      "Suriname                               4.0\n",
      "Sweden                              1217.0\n",
      "Switzerland                          630.0\n",
      "Syria                                  6.0\n",
      "Chinese Taipei                        32.0\n",
      "Tajikistan                             4.0\n",
      "Tanzania                               4.0\n",
      "Thailand                              44.0\n",
      "Togo                                   1.0\n",
      "Tonga                                  2.0\n",
      "Trinidad and Tobago                   27.0\n",
      "Tunisia                               19.0\n",
      "Turkey                               191.0\n",
      "Uganda                                14.0\n",
      "Ukraine                              220.0\n",
      "United Arab Emirates                   3.0\n",
      "United States                       5684.0\n",
      "Uruguay                               16.0\n",
      "Uzbekistan                            38.0\n",
      "Venezuela                             18.0\n",
      "Vietnam                                4.0\n",
      "Virgin Islands                         2.0\n",
      "Yugoslavia                           171.0\n",
      "Independent Olympic Participants       4.0\n",
      "Zambia                                 3.0\n",
      "Zimbabwe                              18.0\n",
      "Mixed team                            38.0\n",
      "Name: total_gold, Length: 146, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "points = answer_four()\n",
    "print(points)\n",
    "assert points.loc[\"United States\"] == 5684"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>...</th>\n",
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       "      <th>RDOMESTICMIG2012</th>\n",
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       "      <td>182265</td>\n",
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       "      <td>17.647293</td>\n",
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       "      <td>15.844176</td>\n",
       "      <td>18.559627</td>\n",
       "      <td>22.727626</td>\n",
       "      <td>20.317142</td>\n",
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       "      <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  ...  RDOMESTICMIG2011  \\\n",
       "0        4779736            4780127          4785161  ...          0.002295   \n",
       "1          54571              54571            54660  ...          7.242091   \n",
       "2         182265             182265           183193  ...         14.832960   \n",
       "3          27457              27457            27341  ...         -4.728132   \n",
       "4          22915              22919            22861  ...         -5.527043   \n",
       "\n",
       "   RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  RDOMESTICMIG2015  \\\n",
       "0         -0.193196          0.381066          0.582002         -0.467369   \n",
       "1         -2.915927         -3.012349          2.265971         -2.530799   \n",
       "2         17.647293         21.845705         19.243287         17.197872   \n",
       "3         -2.500690         -7.056824         -3.904217        -10.543299   \n",
       "4         -5.068871         -6.201001         -0.177537          0.177258   \n",
       "\n",
       "   RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  RNETMIG2015  \n",
       "0     1.030015     0.826644     1.383282     1.724718     0.712594  \n",
       "1     7.606016    -2.626146    -2.722002     2.592270    -2.187333  \n",
       "2    15.844176    18.559627    22.727626    20.317142    18.293499  \n",
       "3    -4.874741    -2.758113    -7.167664    -3.978583   -10.543299  \n",
       "4    -5.088389    -4.363636    -5.403729     0.754533     1.107861  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_data = pandas.read_csv('census.csv')\n",
    "census_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CensusVariables:\n",
    "    state_name = \"STNAME\"\n",
    "    county_name = \"CTYNAME\"\n",
    "    census_population = \"CENSUS2010POP\"\n",
    "    region = \"REGION\"\n",
    "    population_2014 = \"POPESTIMATE2014\"\n",
    "    population_2015 = \"POPESTIMATE2015\"\n",
    "    population_estimates = [\"POPESTIMATE2010\",\n",
    "\t\t\t    \"POPESTIMATE2011\",\n",
    "\t\t\t    \"POPESTIMATE2012\",\n",
    "\t\t\t    \"POPESTIMATE2013\",\n",
    "\t\t\t    population_2014,\n",
    "\t\t\t    population_2015]\n",
    "    county_level = 50\n",
    "    summary_level = \"SUMLEV\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      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",
      "6         50       3         6      1      11    Alabama      Bullock County   \n",
      "7         50       3         6      1      13    Alabama       Butler County   \n",
      "8         50       3         6      1      15    Alabama      Calhoun County   \n",
      "9         50       3         6      1      17    Alabama     Chambers County   \n",
      "10        50       3         6      1      19    Alabama     Cherokee County   \n",
      "11        50       3         6      1      21    Alabama      Chilton County   \n",
      "12        50       3         6      1      23    Alabama      Choctaw County   \n",
      "13        50       3         6      1      25    Alabama       Clarke County   \n",
      "14        50       3         6      1      27    Alabama         Clay County   \n",
      "15        50       3         6      1      29    Alabama     Cleburne County   \n",
      "16        50       3         6      1      31    Alabama       Coffee County   \n",
      "17        50       3         6      1      33    Alabama      Colbert County   \n",
      "18        50       3         6      1      35    Alabama      Conecuh County   \n",
      "19        50       3         6      1      37    Alabama        Coosa County   \n",
      "20        50       3         6      1      39    Alabama    Covington County   \n",
      "21        50       3         6      1      41    Alabama     Crenshaw County   \n",
      "22        50       3         6      1      43    Alabama      Cullman County   \n",
      "23        50       3         6      1      45    Alabama         Dale County   \n",
      "24        50       3         6      1      47    Alabama       Dallas County   \n",
      "25        50       3         6      1      49    Alabama       DeKalb County   \n",
      "26        50       3         6      1      51    Alabama       Elmore County   \n",
      "27        50       3         6      1      53    Alabama     Escambia County   \n",
      "28        50       3         6      1      55    Alabama       Etowah County   \n",
      "29        50       3         6      1      57    Alabama      Fayette County   \n",
      "30        50       3         6      1      59    Alabama     Franklin County   \n",
      "...      ...     ...       ...    ...     ...        ...                 ...   \n",
      "3162      50       2         3     55     129  Wisconsin     Washburn County   \n",
      "3163      50       2         3     55     131  Wisconsin   Washington County   \n",
      "3164      50       2         3     55     133  Wisconsin     Waukesha County   \n",
      "3165      50       2         3     55     135  Wisconsin      Waupaca County   \n",
      "3166      50       2         3     55     137  Wisconsin     Waushara County   \n",
      "3167      50       2         3     55     139  Wisconsin    Winnebago County   \n",
      "3168      50       2         3     55     141  Wisconsin         Wood County   \n",
      "3170      50       4         8     56       1    Wyoming       Albany County   \n",
      "3171      50       4         8     56       3    Wyoming     Big Horn County   \n",
      "3172      50       4         8     56       5    Wyoming     Campbell County   \n",
      "3173      50       4         8     56       7    Wyoming       Carbon County   \n",
      "3174      50       4         8     56       9    Wyoming     Converse County   \n",
      "3175      50       4         8     56      11    Wyoming        Crook County   \n",
      "3176      50       4         8     56      13    Wyoming      Fremont County   \n",
      "3177      50       4         8     56      15    Wyoming       Goshen County   \n",
      "3178      50       4         8     56      17    Wyoming  Hot Springs County   \n",
      "3179      50       4         8     56      19    Wyoming      Johnson County   \n",
      "3180      50       4         8     56      21    Wyoming      Laramie County   \n",
      "3181      50       4         8     56      23    Wyoming      Lincoln County   \n",
      "3182      50       4         8     56      25    Wyoming      Natrona County   \n",
      "3183      50       4         8     56      27    Wyoming     Niobrara County   \n",
      "3184      50       4         8     56      29    Wyoming         Park County   \n",
      "3185      50       4         8     56      31    Wyoming       Platte County   \n",
      "3186      50       4         8     56      33    Wyoming     Sheridan County   \n",
      "3187      50       4         8     56      35    Wyoming     Sublette County   \n",
      "3188      50       4         8     56      37    Wyoming   Sweetwater County   \n",
      "3189      50       4         8     56      39    Wyoming        Teton County   \n",
      "3190      50       4         8     56      41    Wyoming        Uinta County   \n",
      "3191      50       4         8     56      43    Wyoming     Washakie County   \n",
      "3192      50       4         8     56      45    Wyoming       Weston 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",
      "6             10914              10915            10887  ...   \n",
      "7             20947              20946            20944  ...   \n",
      "8            118572             118586           118437  ...   \n",
      "9             34215              34170            34098  ...   \n",
      "10            25989              25986            25976  ...   \n",
      "11            43643              43631            43665  ...   \n",
      "12            13859              13858            13841  ...   \n",
      "13            25833              25840            25767  ...   \n",
      "14            13932              13932            13880  ...   \n",
      "15            14972              14972            14973  ...   \n",
      "16            49948              49948            50177  ...   \n",
      "17            54428              54428            54514  ...   \n",
      "18            13228              13228            13208  ...   \n",
      "19            11539              11758            11758  ...   \n",
      "20            37765              37765            37796  ...   \n",
      "21            13906              13906            13853  ...   \n",
      "22            80406              80410            80473  ...   \n",
      "23            50251              50251            50358  ...   \n",
      "24            43820              43820            43803  ...   \n",
      "25            71109              71115            71142  ...   \n",
      "26            79303              79296            79465  ...   \n",
      "27            38319              38319            38309  ...   \n",
      "28           104430             104427           104442  ...   \n",
      "29            17241              17241            17231  ...   \n",
      "30            31704              31709            31734  ...   \n",
      "...             ...                ...              ...  ...   \n",
      "3162          15911              15911            15930  ...   \n",
      "3163         131887             131885           131967  ...   \n",
      "3164         389891             389938           390076  ...   \n",
      "3165          52410              52410            52422  ...   \n",
      "3166          24496              24496            24506  ...   \n",
      "3167         166994             166994           167059  ...   \n",
      "3168          74749              74749            74807  ...   \n",
      "3170          36299              36299            36428  ...   \n",
      "3171          11668              11668            11672  ...   \n",
      "3172          46133              46133            46244  ...   \n",
      "3173          15885              15885            15837  ...   \n",
      "3174          13833              13833            13826  ...   \n",
      "3175           7083               7083             7114  ...   \n",
      "3176          40123              40123            40222  ...   \n",
      "3177          13249              13247            13408  ...   \n",
      "3178           4812               4812             4813  ...   \n",
      "3179           8569               8569             8581  ...   \n",
      "3180          91738              91881            92271  ...   \n",
      "3181          18106              18106            18091  ...   \n",
      "3182          75450              75450            75472  ...   \n",
      "3183           2484               2484             2492  ...   \n",
      "3184          28205              28205            28259  ...   \n",
      "3185           8667               8667             8678  ...   \n",
      "3186          29116              29116            29146  ...   \n",
      "3187          10247              10247            10244  ...   \n",
      "3188          43806              43806            43593  ...   \n",
      "3189          21294              21294            21297  ...   \n",
      "3190          21118              21118            21102  ...   \n",
      "3191           8533               8533             8545  ...   \n",
      "3192           7208               7208             7181  ...   \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",
      "6           -30.953709         -5.180127         -1.130263         14.354290   \n",
      "7           -14.032727        -11.684234         -5.655413          1.085428   \n",
      "8            -6.155670         -4.611706         -5.524649         -4.463211   \n",
      "9            -2.731639          3.849092          2.872721         -2.287222   \n",
      "10            6.339327          1.113180          5.488706         -0.076806   \n",
      "11           -1.372935         -2.653369          0.480044          0.456017   \n",
      "12          -15.455274         -0.737028         -8.766391         -1.274984   \n",
      "13           -6.194363        -17.667705         -0.318345         -8.686428   \n",
      "14          -10.744102        -13.345130          4.902871          5.702648   \n",
      "15           -3.673524         -5.151880          7.345821          3.654485   \n",
      "16            0.377640          7.675579        -13.146535         -3.602859   \n",
      "17           -0.073423          1.065051          1.762390          1.835688   \n",
      "18           -4.861559         -7.504690         -6.107224        -14.645416   \n",
      "19          -33.930581        -10.291443         -4.313831        -22.958017   \n",
      "20            6.696899         -4.612668          0.740271          3.697932   \n",
      "21            1.729792          3.950156         -1.864936          3.084648   \n",
      "22           -1.404233         -1.019628          4.071247          5.087142   \n",
      "23          -10.749798         -5.277150        -15.236079        -11.979785   \n",
      "24          -15.635599        -11.308243        -16.745678         -9.344789   \n",
      "25            0.294677         -9.302391         -1.748807          0.267830   \n",
      "26            3.235576          0.822717          1.760531         -1.507057   \n",
      "27           -3.449988         -3.855889         -4.822706         -1.189831   \n",
      "28           -1.015919          2.062637         -1.931884         -1.726932   \n",
      "29           -5.015601         -0.646640         -3.725937          0.296745   \n",
      "30           -1.638750         -5.459394         -8.043702         -1.267849   \n",
      "...                ...               ...               ...               ...   \n",
      "3162         -6.873936          7.338289         -6.732724          3.510452   \n",
      "3163         -0.794876          0.785279         -2.215465          1.601149   \n",
      "3164         -0.765799          2.128860          0.038132          0.760109   \n",
      "3165          3.111756         -2.241873          6.292687         -0.441031   \n",
      "3166          4.930022         -2.404973         -4.097017         -4.906711   \n",
      "3167          0.316712          2.889873          0.833819         -2.406192   \n",
      "3168         -4.081523         -5.019090         -6.901200         -5.596471   \n",
      "3170          3.708956          2.637812         -3.544634         -3.334877   \n",
      "3171          4.868258          2.804930         16.815908         -8.026420   \n",
      "3172         -2.843479         15.601020         -5.895711         -8.550911   \n",
      "3173         -7.581980        -13.081441          3.178134         -2.970641   \n",
      "3174        -12.847499         15.493820         19.035533        -20.550587   \n",
      "3175         -1.544618         -4.202564          1.397819          6.378258   \n",
      "3176          2.747083          7.782673         -4.990688        -12.331633   \n",
      "3177         14.293649          3.961413         -8.079028         -7.017803   \n",
      "3178          3.322604          6.208609          3.095336         -6.017222   \n",
      "3179          4.995063         -4.058912         -0.812583        -10.715742   \n",
      "3180         -1.200428         15.547274          4.787847         -1.226133   \n",
      "3181         -9.802564        -11.566801         13.564556          6.125989   \n",
      "3182          7.189319         23.066162         24.322042         -0.958472   \n",
      "3183         -0.401849          0.806452         29.066295        -12.603387   \n",
      "3184          4.582951          8.057765          7.641997         -9.252437   \n",
      "3185          4.373094          5.392073          2.634593          6.055759   \n",
      "3186          0.958559          8.425487          4.546373          3.678069   \n",
      "3187        -23.741784         15.272374        -40.870074        -16.596273   \n",
      "3188          1.072643         16.243199         -5.339774        -14.252889   \n",
      "3189         -1.589565          0.972695         19.525929         14.143021   \n",
      "3190        -17.755986         -4.916350         -6.902954        -14.215862   \n",
      "3191        -11.637475         -0.827815         -2.013502        -17.781491   \n",
      "3192        -11.752361         -8.040059         12.372583          1.533635   \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",
      "6           -16.167247   -29.001673    -2.825524     1.507017    17.243790   \n",
      "7            -6.529805   -13.936612   -11.586865    -5.557058     1.184103   \n",
      "8            -3.376322    -5.791579    -4.092677    -5.062836    -3.912834   \n",
      "9             1.349468    -1.821092     4.701181     3.781439    -1.290228   \n",
      "10           -3.239866     6.416167     1.420264     5.757384     0.230419   \n",
      "11           -2.253483    -0.823761    -2.447504     0.868651     0.957636   \n",
      "12           -5.291205   -15.528177    -0.737028    -8.766391    -1.274984   \n",
      "13           -5.613667    -6.077488   -17.509958    -0.159172    -8.486280   \n",
      "14            3.912450   -10.816697   -13.345130     4.977157     5.776708   \n",
      "15           -3.123961    -3.673524    -5.151880     7.345821     3.654485   \n",
      "16            2.214774     2.166460    11.513368   -10.438741    -0.767822   \n",
      "17           -0.110260     0.513964     1.469035     2.276420     2.533249   \n",
      "18            2.684140    -4.861559    -7.504690    -6.107224   -14.645416   \n",
      "19           -5.387581   -34.017138   -10.380162    -4.403703   -23.049483   \n",
      "20           -0.316945     6.881460    -4.559952     0.793147     3.750759   \n",
      "21            3.439504     2.666763     5.099293    -0.502098     4.734577   \n",
      "22            7.915406    -1.031427    -0.634159     4.542916     5.593387   \n",
      "23           -5.107706    -9.575283    -0.776637   -12.640155    -9.503292   \n",
      "24          -14.687232   -15.727573   -11.378047   -16.792849    -9.368689   \n",
      "25            0.028141     1.375159    -8.656001    -1.029539     1.198187   \n",
      "26            2.067820     3.674511     1.558176     2.306047    -0.951175   \n",
      "27            1.190902    -3.397716    -3.803428    -4.769999    -1.136950   \n",
      "28           -2.082234    -0.632554     2.446383    -1.518596    -1.234901   \n",
      "29           -2.797536    -5.132243    -0.705426    -3.785079     0.237396   \n",
      "30           -2.401719     0.063029    -3.471291    -5.700261     1.553115   \n",
      "...                ...          ...          ...          ...          ...   \n",
      "3162         -5.123279    -6.747809     7.464811    -6.605691     3.638104   \n",
      "3163         -0.434498    -0.431504     1.162817    -1.763330     2.104796   \n",
      "3164         -0.719858     0.102448     3.180527     1.189727     2.077633   \n",
      "3165         -0.480617     3.359933    -2.011937     6.561277    -0.134227   \n",
      "3166         -4.397793     5.174486    -2.160399    -3.810226    -4.535615   \n",
      "3167         -4.557985     0.842573     3.502335     1.531624    -1.545153   \n",
      "3168         -3.958322    -3.733590    -4.562809    -6.442917    -5.040889   \n",
      "3170         -9.911169     6.736119     6.433032     0.719587     1.429233   \n",
      "3171          5.095861     4.868258     3.144921    17.236306    -7.608378   \n",
      "3172         10.916963    -2.649606    15.558684    -5.916543    -8.509402   \n",
      "3173        -23.300971    -7.392431   -12.636926     3.623073    -2.338590   \n",
      "3174         -0.070403   -12.774915    16.502720    20.093063   -19.358233   \n",
      "3175         18.629317    -0.982939    -3.642222     2.096729     7.071547   \n",
      "3176        -13.673610     3.093562     8.027411    -4.747240   -12.013555   \n",
      "3177        -11.899450    14.886132     4.841727    -6.903896    -5.761986   \n",
      "3178         -5.454164     5.191569     6.001656     2.888981    -6.224712   \n",
      "3179          0.933652     5.227392    -4.058912    -0.812583   -10.715742   \n",
      "3180          0.278940    -0.973320    17.914554     6.003143    -0.207819   \n",
      "3181          1.555544    -9.691801   -11.566801    13.619696     6.234414   \n",
      "3182         -0.061057     7.689674    23.749508    25.085233    -0.110593   \n",
      "3183          7.492114    -0.401849     0.806452    29.066295   -12.603387   \n",
      "3184         -2.878980     6.486639    11.127389    10.877797    -5.585731   \n",
      "3185          4.662270     4.373094     4.933173     2.176403     5.598720   \n",
      "3186         -3.298406     2.122524     9.342778     5.523001     4.781489   \n",
      "3187        -22.870900   -21.092907    16.828794   -39.211861   -14.409938   \n",
      "3188        -14.248864     1.255221    16.243199    -5.295460   -14.075283   \n",
      "3189         -0.564849     0.654527     2.408578    21.160658    16.308671   \n",
      "3190        -12.127022   -18.136812    -5.536861    -7.521840   -14.740608   \n",
      "3191          1.682288   -11.990126    -1.182592    -2.250385   -18.020168   \n",
      "3192          6.935294   -12.032179    -8.040059    12.372583     1.533635   \n",
      "\n",
      "      RNETMIG2015  \n",
      "1       -2.187333  \n",
      "2       18.293499  \n",
      "3      -10.543299  \n",
      "4        1.107861  \n",
      "5       -0.884411  \n",
      "6      -13.193961  \n",
      "7       -6.430868  \n",
      "8       -2.806406  \n",
      "9        2.346901  \n",
      "10      -2.931307  \n",
      "11      -1.752709  \n",
      "12      -5.291205  \n",
      "13      -5.411736  \n",
      "14       3.986270  \n",
      "15      -3.123961  \n",
      "16       5.350738  \n",
      "17       0.588052  \n",
      "18       2.684140  \n",
      "19      -5.387581  \n",
      "20      -0.264121  \n",
      "21       5.087600  \n",
      "22       8.417777  \n",
      "23      -1.998668  \n",
      "24     -14.711389  \n",
      "25       0.956790  \n",
      "26       2.757093  \n",
      "27       1.243830  \n",
      "28      -1.588308  \n",
      "29      -2.857058  \n",
      "30       0.442422  \n",
      "...           ...  \n",
      "3162    -4.995197  \n",
      "3163     0.059931  \n",
      "3164     0.593567  \n",
      "3165    -0.173022  \n",
      "3166    -4.024395  \n",
      "3167    -3.685304  \n",
      "3168    -3.414223  \n",
      "3170    -5.166460  \n",
      "3171     5.513554  \n",
      "3172    10.978525  \n",
      "3173   -22.600668  \n",
      "3174     1.126443  \n",
      "3175    19.309219  \n",
      "3176   -13.352750  \n",
      "3177   -10.635133  \n",
      "3178    -5.663940  \n",
      "3179     0.933652  \n",
      "3180     1.673640  \n",
      "3181     1.662823  \n",
      "3182     0.793743  \n",
      "3183     7.492114  \n",
      "3184     0.856839  \n",
      "3185     4.207414  \n",
      "3186    -2.198937  \n",
      "3187   -20.664059  \n",
      "3188   -14.070195  \n",
      "3189     1.520747  \n",
      "3190   -12.606351  \n",
      "3191     1.441961  \n",
      "3192     6.935294  \n",
      "\n",
      "[3142 rows x 100 columns]\n"
     ]
    }
   ],
   "source": [
    "counties = census_data[census_data[\n",
    "    CensusVariables.summary_level]==CensusVariables.county_level]\n",
    "#print(counties)\n",
    "# this throws off the numeric index for the argmax method so reset it\n",
    "counties = counties.reset_index()\n",
    "#print(counties)\n",
    "# but the last question wants the original index\n",
    "counties_original_index = census_data[census_data[\n",
    "    CensusVariables.summary_level]==CensusVariables.county_level]\n",
    "print(counties_original_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which state has the most counties in it? (hint: consider the sumlevel key carefully! You'll need this for future questions too…)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def answer_five():\n",
    "\n",
    "    return counties.groupby(\n",
    "\tCensusVariables.state_name).count().COUNTY.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simra\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: \n",
      "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
      "instead.\n",
      "The behavior of 'argmax' will be corrected to return the positional\n",
      "maximum in the future. For now, use 'series.values.argmax' or\n",
      "'np.argmax(np.array(values))' to get the position of the maximum\n",
      "row.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Texas'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer_five()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only looking at the three most populous counties for each state, what are the three most populous states (in order of highest population to lowest population)? Use CENSUS2010POP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['California', 'Texas', 'Illinois']"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_six():\n",
    "    top_threes = counties.groupby(\n",
    "\tCensusVariables.state_name\n",
    "    )[CensusVariables.census_population].nlargest(3)\n",
    "    states = top_threes.groupby(level=0).sum()\n",
    "    return list(states.nlargest(3).index)\n",
    "\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which county has had the largest absolute change in population within the period 2010-2015? (Hint: population values are stored in columns POPESTIMATE2010 through POPESTIMATE2015, you need to consider all six columns.)\n",
    "\n",
    "e.g. If County Population in the 5 year period is 100, 120, 80, 105, 100, 130, then its largest change in the period would be |130-80| = 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simra\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: \n",
      "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
      "instead.\n",
      "The behavior of 'argmax' will be corrected to return the positional\n",
      "maximum in the future. For now, use 'series.values.argmax' or\n",
      "'np.argmax(np.array(values))' to get the position of the maximum\n",
      "row.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Harris County'"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_seven():\n",
    "    seven = counties.iloc[(counties[CensusVariables.population_estimates].max(axis=1) - counties[CensusVariables.population_estimates].min(axis=1)).argmax()]\n",
    "    return seven[CensusVariables.county_name]\n",
    "\n",
    "answer_seven()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this datafile, the United States is broken up into four regions using the \"REGION\" column.\n",
    "\n",
    "Create a query that finds the counties that belong to regions 1 or 2, whose name starts with 'Washington', and whose POPESTIMATE2015 was greater than their POPESTIMATE2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            STNAME            CTYNAME\n",
      "896           Iowa  Washington County\n",
      "1419     Minnesota  Washington County\n",
      "2345  Pennsylvania  Washington County\n",
      "2355  Rhode Island  Washington County\n",
      "3163     Wisconsin  Washington County\n"
     ]
    }
   ],
   "source": [
    "def answer_eight():\n",
    "    regions = counties_original_index[\n",
    "\t(counties_original_index[CensusVariables.region]==1) |\n",
    "\t(counties_original_index[CensusVariables.region]==2)]\n",
    "    washingtons = regions[\n",
    "\tregions[CensusVariables.county_name].str.startswith(\"Washington\")]\n",
    "    grew = washingtons[washingtons[CensusVariables.population_2015] >\n",
    "\t\t       washingtons[CensusVariables.population_2014]]\n",
    "    return grew[[CensusVariables.state_name,\n",
    "\t\t CensusVariables.county_name]]\n",
    "\n",
    "outcome = answer_eight()\n",
    "print(outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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