Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import pylab as plt\n",
    "import matplotlib\n",
    "\n",
    "%matplotlib inline \n",
    "#REVIEW THIS PART "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year       pop  lifeexp   gdppercap      country continent\n",
      "0  1952   8425333   28.801  779.445314  afghanistan      asia\n",
      "1  1957   9240934   30.332  820.853030  afghanistan      asia\n",
      "2  1962  10267083   31.997  853.100710  afghanistan      asia\n",
      "3  1967  11537966   34.020  836.197138  afghanistan      asia\n",
      "4  1972  13079460   36.088  739.981106  afghanistan      asia\n"
     ]
    }
   ],
   "source": [
    "VERBOSE = True \n",
    "\n",
    "cleaned_data_location = '../data/gapminder_cleaned.csv'\n",
    "try:\n",
    "    df = pd.read_csv (cleaned_data_location)\n",
    "    if VERBOSE:\n",
    "        print(df.head())\n",
    "except FileNotFoundError:\n",
    "    print('Could not find data file, check path? You tried', cleaned_data_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = df ['year'].unique()\n",
    "years.sort()\n",
    "assert years [-1] == 2007\n",
    "assert years [0] == 1952"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean for 1952 is 46.314393939393945\n"
     ]
    }
   ],
   "source": [
    "mask_asia = df['continent'] == 'asia' \n",
    "df_asia = df[mask_asia]\n",
    "mask_1952 = df_asia['year'] == 1952\n",
    "df_1952 = df_asia[mask_1952]\n",
    "value = np.mean(df_1952['lifeexp'])\n",
    "\n",
    "if VERBOSE:\n",
    "    print('mean for 1952 is', value)\n",
    "    \n",
    "result = []\n",
    "result.append (('asia', '1952', value))\n",
    "result = pd.DataFrame(result, columns=['continent', 'year', 'lifeexp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeexp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>46.314394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>49.318544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>51.563223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>54.663640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>57.319269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>asia</td>\n",
       "      <td>1977</td>\n",
       "      <td>59.610556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>asia</td>\n",
       "      <td>1982</td>\n",
       "      <td>62.617939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>asia</td>\n",
       "      <td>1987</td>\n",
       "      <td>64.851182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>66.537212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>asia</td>\n",
       "      <td>1997</td>\n",
       "      <td>68.020515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>69.233879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>70.728485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   continent  year    lifeexp\n",
       "0       asia  1952  46.314394\n",
       "1       asia  1957  49.318544\n",
       "2       asia  1962  51.563223\n",
       "3       asia  1967  54.663640\n",
       "4       asia  1972  57.319269\n",
       "5       asia  1977  59.610556\n",
       "6       asia  1982  62.617939\n",
       "7       asia  1987  64.851182\n",
       "8       asia  1992  66.537212\n",
       "9       asia  1997  68.020515\n",
       "10      asia  2002  69.233879\n",
       "11      asia  2007  70.728485"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_asia = df['continent'] == 'asia' \n",
    "df_asia = df[mask_asia]\n",
    "years = df_asia['year'].unique()\n",
    "summary = []\n",
    "\n",
    "for year in years:\n",
    "    mask_year = df_asia['year'] == year\n",
    "    df_year = df_asia[mask_year]\n",
    "    value = np.mean(df_year['lifeexp'])\n",
    "    summary.append(('asia', year, value))\n",
    "\n",
    "summary = pd.DataFrame(summary,columns=['continent','year','lifeexp'])\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeexp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>39.135500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>41.266346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>43.319442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>africa</td>\n",
       "      <td>1967</td>\n",
       "      <td>45.334538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>47.450942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>africa</td>\n",
       "      <td>1977</td>\n",
       "      <td>49.580423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>africa</td>\n",
       "      <td>1982</td>\n",
       "      <td>51.592865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>53.344788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>53.629577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>53.598269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>53.325231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>54.806038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   continent  year    lifeexp\n",
       "0     africa  1952  39.135500\n",
       "1     africa  1957  41.266346\n",
       "2     africa  1962  43.319442\n",
       "3     africa  1967  45.334538\n",
       "4     africa  1972  47.450942\n",
       "5     africa  1977  49.580423\n",
       "6     africa  1982  51.592865\n",
       "7     africa  1987  53.344788\n",
       "8     africa  1992  53.629577\n",
       "9     africa  1997  53.598269\n",
       "10    africa  2002  53.325231\n",
       "11    africa  2007  54.806038"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continent = 'africa'\n",
    "\n",
    "mask_continent = df['continent'] == continent\n",
    "df_continent = df[mask_continent]\n",
    "years = df_continent['year'].unique()\n",
    "summary = []\n",
    "\n",
    "for year in years:\n",
    "    mask_year = df_continent['year'] == year\n",
    "    df_year = df_continent[mask_year]\n",
    "    value = np.mean(df_year['lifeexp'])\n",
    "    summary.append((continent, year, value))\n",
    "\n",
    "summary = pd.DataFrame(summary,columns=['continent','year','lifeexp'])\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "624\n"
     ]
    },
    {
     "data": {
      "image/png": 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vvosi/vArwJ1zyd5jipnNAXoCi8zsFmAw0M85V+g9p72t9ckAsbGxJXxfapHy5asNKdw3exWZR3L56287c02PZjq3W0pNkQFuZmcBIc65DG+6P/C4mQ3Ed9DyIudcZinXKVKuZefm8dQnG5n63U+0a1STV67vResGOrdbSpc/W+ANgTneVkQV4F3n3Kdmthmojm+XCsAS59xtpVapSDmVuOcgd72bwLqdBxhxQQsmXt5O53ZLmSgywJ1zW4CYQtpbl0pFIkHk0zU7ueeDVYRVDeHNm2O5tEPDQJcklYiuxBQ5Dc45/v7VZp5d8CNdm0fy6g3daVQ7LNBlSSWjABcppqycPP7w0WrmrUzmqq5N+ctvO2uXiQSEAlykGFIyshj7Vjwrf07jgQHnccfF5+osEwkYBbiIn9bsSGfMW3GkZebw2o3dGdhJY5lIYCnARfzw6Zpd3PPBSiJrVGX2bb3p1LR2oEsSUYCLnIpzjn8sTOSZzzbSpVkkk2/uToOaOlgp5YMCXOQksnLymPjRauauTGZolyY8dXW0DlZKuaIAFylESkYW496OJ2G7DlZK+aUAFznO2uR0xsyIY39mDq/d2I2BnRoHuiSRQinARQr4bO0u7n5fByslOCjARSjkYOVN3WlQSwcrpXxTgEull5WTx6SPf2BOwg6GxDTh6WE6WCnBQQEuldqejGzGvh1HwvY07u/vu+WZDlZKsFCAS6W1LvkAo2csZ1/mEV69oRuXd9bBSgkuCnCplBas3cXdH6ykVlhVPrztAh2slKCkAJdKxTnHa19v4enPNhDdtDZv3Byrg5UStBTgUmlk5/oOVn68YgdXxjThGR2slCDnV4Cb2VYgA8gDcp1zsWZWB/gAaAFsBX7nnNtfOmWKnJm9B7MZ93Y88dv2c+9lbbmrrw5WSvALKcaylzjnujjnYr3nE4EvnHNtgC+85yLlzvqdBxj6ynesTU7nHzd0Y3y/NgpvqRCKE+DHGwrM8KZnAL8583JEStbn63Zz9auLyct3zB53AVfoTBOpQPwNcAcsMLN4MxvrtTV0zu0E8B4bFLaimY01szgzi9uzZ8+ZVyziB9/BykTGvh1HmwYRzPt9HzpH6UwTqVj8PYjZxzmXbGYNgM/NbIO/b+CcmwxMBoiNjXWnUaNIsRQ8WDk4ujHPDo/RwUqpkPwKcOdcsveYYmZzgJ7AbjNr7JzbaWaNgZRSrFPEL3sPZnPb2/HEbdvPPZe2ZXw/HayUiqvIXShmdpaZ1Tw6DfQH1gDzgVu8xW4B5pVWkSL+2LDLd7ByTXI6f7++GxMu1cFKqdj82QJvCMzx/iNUAd51zn1qZsuBWWZ2K7AdGF56ZYqc2pcbdnPXuwlEhFVh1rjeREdFBrokkVJXZIA757YAMYW0pwL9SqMokeKYuXQbj8xdQ8cmvisrG9XWlZVSOehKTAlazjmeXbCRv3+VSN92DXjl+q7UqKZ/0lJ56F+7BKUjuflM/Gg1Hyfs4LqezXhiaCeqhJ7JZQ0iwUcBLkHnQFYOt78Tz3ebUzWGt1RqCnAJKrvSsxgxbRmbUw7y7PAYhnWPCnRJIgGjAJegsXFXBiOmLSMjK5dpI3vw6zb1A12SSEApwCUofJ+Yyti34wivGsoH43rRsYkuixdRgEu5N2/lDh6YvZpz6tZg+qieNI0MD3RJIuWCAlzKLecckxdt4S+fbKBnyzq8cVMstWtUDXRZIuWGAlzKpbx8x+P/XMuM77cxOLoxz/0uhupVNCCVSEEKcCl3snLyGP9eAgvW7Wbsha2YOLAdISE6TVDkeApwKVf2HTrC6BnLSfg5jUev7MDIPi0DXZJIuaUAl3JjW+ohRkxbTnLaYV69oRsDO+nuOSKnogCXcmHVz2mMmr6cPOd4d8z5dD+nTqBLEin3FOAScF+s383v302gXs1qTB/Zk3PrRwS6JJGgoACXgCo4FOzUET2oX7N6oEsSCRoKcAkI5xzPLfiRV77azCXn1eeV67txVnX9cxQpDr/H3zSzUDNLMLN/ec/7mdkKM1tpZt+aWevSK1MqkiO5+dw3exWvfLWZ63o2442bYxXeIqehOAMoTwDWF3j+KnCDc64L8C7wcEkWJhVTRlYOo6Yv5+MVO7jvsrb8+arOGsdb5DT59T/HzKKAQcCbBZodUMubrg0kl2xpUtHsSs9i+Gvfs2RLKs8Mi+aufrrpsMiZ8Pd764vAg0DNAm2jgf+Y2WHgANCrsBXNbCwwFqB58+anX6kEtR93ZzBi6jLSD+cwdUQPLmyroWBFzlSRW+BmNhhIcc7FHzfrHuAK51wUMA14vrD1nXOTnXOxzrnY+vX1n7Yy+j4xlatfXUxuvmPWbb0V3iIlxJ8t8D7AEDO7AggDapnZv4F2zrml3jIfAJ+WUo0SxOavSub+WatoXrcG00f2IOrsGoEuSaTCKHIL3Dk3yTkX5ZxrAVwLfAkMBWqbWVtvscv45QFOqeScc7z+dSLj30ugS/NIPrrtAoW3SAk7rXO3nHO5ZjYG+MjM8oH9wKgSrUyCVsGhYAdFN+a54TGEVdVQsCIlrVgB7pxbCCz0pucAc0q+JAlmBYeCHfPrlky6vL2GghUpJbp6QkqMhoIVKVsKcCkRW/ceYsS0ZexMz+If13fj8s4aClaktCnA5YwlbN/PrTPicBoKVqRMKcDljHy2dhcT3k+gQc0wpo/sQSsNBStSZhTgctpmLN7KY/9cS3RUJFNuiaVehIaCFSlLCnAptvx8x18/3cDkRVu4rENDXrq2K+HVdJqgSFlTgEuxZOXkcd/sVfx79U5u7n0Oj17ZkVCdJigSEApw8Vta5hHGvBXH8q37eeiK9oz+dUuNJigSQApw8cvP+zK5ZdoykvYd5uXrunJlTJNAlyRS6SnApUirk3x3jM/Jc7wz+nx6ttRpgiLlgQJcTunoHePrRlTj/bE9aN2gZtEriUiZUIDLSb2zZBt/nOe7Y/yUEbE0qBkW6JJEpAAFuJwgP9/x9Gcbee3rRPq2a8DL13XVTYdFyiH9r5RfyM7N44HZq5m/Kpnrz2/O40M66qbDIuWUAlyOSc/MYezbcSz9aR8PDjyP2y86V6cJipRjCnABIGl/JiOnLWdr6iH+dm0XhnZpGuiSRKQIfn83NrNQM0sws395z83MnjSzH81svZmNL70ypTSt2ZHOVf9YzK4DWbw16nyFt0iQKM4W+AR8972s5T0fATTDd3PjfDNrUMK1SRn4amMKd85cQWR4VWbefgFtG+o0QZFg4dcWuJlFAYOANws03w487pzLB3DOpZR8eVKa3l+2ndEz4mhR9yzm3NlH4S0SZPzdhfIi8CCQX6DtXOAaM4szs0/MrE1hK5rZWG+ZuD179pxhuVISnHM8t2AjEz/+gT6t6zHrtt40rKVzvEWCTZEBbmaDgRTnXPxxs6oDWc65WOANYGph6zvnJjvnYp1zsfXr1z/jguXMHMnN575Zq3j5y81cE9uMKbfEEqFzvEWCkj//c/sAQ8zsCiAMqGVm7wBJwEfeMnOAaaVTopSUA1k53PZ2PIsTU7nvsrb8vm9rnSYoEsSK3AJ3zk1yzkU551oA1wJfOuduBOYCfb3FLgJ+LLUq5Ywlpx1m+Kvfs+ynfTw3PIa7+rVReIsEuTP57vxXYKaZ3QMcBEaXTElS0tYlH2Dk9GVkZucxY1RP+rSuF+iSRKQEFCvAnXMLgYXedBq+M1OkHPtm0x5uf2cFEdWrMPv23rRrVKvolUQkKOjoVQU2O+5nJn38A60bRDBtZA8a1w4PdEkiUoIU4BWQc46/fbGJF/+7iV+1rsc/buxGrbCqgS5LREqYAryCyc7NY9LHP/Dxih1c3S2Kv/y2M9WqaDRBkYpIAV6B7D2YzW1vxxO3bT/3XNqW8f10mqBIRaYAryA27DrArdPjSD2Uzd+v78ag6MaBLklESpkCvAL477rdTHg/gYiwKswa15voqMhAlyQiZUABHsScc7zxzRb+8skGOjWpzRs3x9KotsY0EaksFOBBKjs3j4fnrGF2fBKDOjfm2eExhFcLDXRZIlKGFOBBKPVgNre/s4JlW/cxoV8bJvRrQ0iIDlaKVDYK8CCzcVcGt85Yzp6MbF66ritDYpoEuiQRCRAFeBD5akMKd72XQHi1UD4Y15suzXSwUqQyU4AHAeccU779iT//Zz3tG9fizVtidVm8iCjAy7sjufn8cd4a3l/+MwM7NuL5a2KoUU0fm4gowMu1fYeOcPs78Sz9aR+/v6Q1917WVgcrReQYBXg5tWl3BrfOiGPXgSz+dm0XhnZpGuiSRKScUYCXQws3pnDXuwlUrxrK+2N70a352YEuSUTKIb+HqTOzUDNLMLN/Hdf+spkdLPnSKh/nHNO++4lR05cTVacG837fR+EtIidVnC3wCcB64NgtXcwsFtC5bCUgJy+fP85by3vLttO/Q0NeuKYLZ+lu8SJyCn5tgZtZFL7bp71ZoC0UeAZ4sHRKqzzSMo9w85RlvLdsO3dcfC6v3dhd4S0iRfI3JV7EF9Q1C7T9HpjvnNupMadP3+aUg4yesZzktCye/10Mv+0WFeiSRCRIFBngZjYYSHHOxZvZxV5bE2A4cLEf648FxgI0b978TGqtcL7ZtIc7Zq6gepUQ3ht7Pt3PqRPokkQkiPizBd4HGGJmVwBh+PaBrwWygc3e1ncNM9vsnGt9/MrOucnAZIDY2FhXUoUHu7e+38qf/rmONg0iePOWWKLOrhHokkQkyBQZ4M65ScAkAG8L/H7n3OCCy5jZwcLCW06Uk5fP4/9cx9tLtnFp+4a8eG0XIrS/W0ROg5KjDKVn5nDnuyv4dvNexl3UigcHtCNUV1aKyGkqVoA75xYCCwtpjyiheiqsLXsOMnpGHD/vz+SZYdEMj20W6JJEJMhpC7wMfLd5L7e/E0+V0BDeHdOLHi10sFJEzpwCvJS9s2Qbj85fS+v6voOVzeroYKWIlAwFeCnJzcvn//69numLt9K3XQP+dm0XaoZVDXRZIlKBKMBLQUZWDne9l8DCjXsY8+uWTLy8vQ5WikiJU4CXsB1ph7l1+nI2pRzkz1d15vrzdfGSiJQOBXgJWvlzGqNnxJGdm8eMkT35VZt6gS5JRCowBXgJ+c8PO7nng5U0qFWd98eeT+sGNYteSUTkDCjAz5Bzjle/TuTpTzfS/ZyzmXxTd+pGVA90WSJSCSjAz8CR3HwemvMDs+OTGBLThKeHRRNWNTTQZYlIJaEAP01pmUe47Z14lmzZx4R+bbj70jZoWF0RKUsK8NPw095D3Dp9OUn7D/PiNV34TVfdcFhEyp4CvJiWbkll3DvxhJgxc8z5uixeRAJGAV4MH8UnMfHj1TSvU4OpI3pwTt2zAl2SiFRiCnA/5Oc7nv/8R175ajMXnFuXV2/oTu0auixeRAJLAV6ErJw87pu9in+v3sk1sc34v6s6UTXUr3tBi4iUKgX4KezJyGbMW3GsSkpj0uXtGHthK51pIiLlhgL8JDbuymDU9OWkHsrm1Ru6M7BTo0CXJCLyC37vCzCzUDNLMLN/ec9nmtlGM1tjZlPNrMLsFP76xz0Me3UxOXn5zBrXW+EtIuVScXbmTgDWF3g+E2gHdAbCgdElWFfAvL1kG6OmL6fp2eHMvbMP0VGRgS5JRKRQfgW4mUUBg4A3j7Y55/7jPMAyIKp0SiwbefmOx/+5jkfmruGitvX58PYLaBIZHuiyREROyt994C8CDwInDLHn7Tq5Cd8W+gnMbCwwFqB58/I5Nvah7FzGv5fAFxtSGNmnBQ8P6qAbMIhIuVfkFriZDQZSnHPxJ1nkH8Ai59w3hc10zk12zsU652Lr169/BqWWjp3phxn+2vd8tTGFx4d25NErOyq8RSQo+LMF3gcYYmZXAGFALTN7xzl3o5k9CtQHxpVmkaXlh6R0bp2xnMwjeUwd0YOLz2sQ6JJERPxW5Ba4c26Scy7KOdcCuBb40gvv0cAA4DrnXH4p11niPlu7i9+9/j1VQ0P48PbeCm8RCTpncknha0BD4HszW2lmfyyhmkqVc47JixK57Z142jaqyZw7L6Bdo1qBLktEpNiKdSGPc24hsNCbDrqLgHLy8nlk7hreX/4zgzo35rnfxegGDCIStPPqH1QAAAqCSURBVIIuhE9XemYOd7wbz3ebU7nzknO577LzCNHBShEJYpUiwLenZjJy+jK278vkmWHRDI9tFuiSRETOWIUP8Pht+xnzVhx5+Y63Rp1P73PrBrokEZESUaED/LO1uxj/XgKNaocxbUQPWtWPCHRJIiIlpsIG+Fvfb+XR+WuJjopk6i2x1I2oHuiSRERKVIUL8Px8x1OfbeD1r7dwafsGvHxdN8Kr6UwTEal4KlSAZ+fm8cDs1cxflcyNvZrz2JUdqaK754hIBVVhAjz9cA7j3o5jyZZ9PDjwPG6/6FzdPUdEKrQKEeDJaYcZMW0ZP+09xAvXxHBV16Ae2VZExC9BH+Drdx5gxLRlZGbnMX1kT/q0rhfokkREykRQB/h3m/cy7u14IqpXYdZtvWnfWGOaiEjlEbQBPichiQc/XE3LemcxfWRP3T1HRCqdoAtw5xz/WJjIM59tpFerOrx+Uyy1wyvM/ZRFRPwWVAGem5fPo/PXMnPpdobENOGZ4dFUr6JzvEWkcgqaAM884rtv5X/Xp3DbRefy4ACNJigilVtQBPjeg9ncOiOO1UlpPD60Izf3bhHokkREAs7vyxTNLNTMEszsX97zlma21Mw2mdkHZlatNArcuvcQV7+6mA07D/Dajd0V3iIinuJcZz4BWF/g+VPAC865NsB+4NaSLAwgYft+fvvqYg4czuHdMb0Y0LFRSb+FiEjQ8ivAzSwKGAS86T03oC/wobfIDOA3JVnY5+t2c90bS4ioXoWPbr+A7uecXZIvLyIS9PzdAn8ReBA4evf5ukCacy7Xe54ENC1sRTMba2ZxZha3Z88ev97snSXbGPd2HOc1rMnHd1ygcbxFRApRZICb2WAgxTkXX7C5kEVdYes75yY752Kdc7H169c/5Xs553j60w08PHcNl5zXgPfG9qKexvEWESmUP2eh9AGGmNkVQBhQC98WeaSZVfG2wqOA5DMp5EhuPn/4aDVzEnZwXc/mPDFUQ8GKiJxKkQnpnJvknItyzrUArgW+dM7dAHwFDPMWuwWYd7pFHMjKYeT0ZcxJ2MH9/dvy56s6KbxFRIpwJin5B+BeM9uMb5/4lNN5kZ3ph/nda9+zdMs+nh0ew+/7ttE43iIifijWhTzOuYXAQm96C9DzTN58464MRkxbRkZWLtNG9uDXbU69j1xERP4nYFdifp+Yyti34wivGsoH43rRsUntQJUiIhKUAhLg81bu4IHZqzmnbg2mj+pJUw0FKyJSbGUe4K9/nchfPtlAz5Z1eOOmWGrX0FCwIiKno0wDPDntMH/5ZAODoxvz3O9iNBSsiMgZKNNz9VIPHWHsha146dquCm8RkTNUplvgjWuH8f+uaF+WbykiUmGV6Ra4LosXESk5utxRRCRIKcBFRIKUAlxEJEgpwEVEgpQCXEQkSCnARUSClAJcRCRIKcBFRIKUOVforSxL583M9gDbyuwNS0c9YG+giyhjla3Pla2/oD6Xd+c45064YUKZBnhFYGZxzrnYQNdRlipbnytbf0F9DlbahSIiEqQU4CIiQUoBXnyTA11AAFS2Ple2/oL6HJS0D1xEJEhpC1xEJEgpwEVEglSlD3Azm2pmKWa2pkBbjJl9b2Y/mNk/zaxWgXnR3ry13vwwr72793yzmb1kZhaI/vijOH02sxvMbGWBn3wz6+LNq6h9rmpmM7z29WY2qcA6A81so9fniYHoi7+K2edqZjbNa19lZhcXWCcoPmcza2ZmX3mf2Vozm+C11zGzz81sk/d4ttduXn82m9lqM+tW4LVu8ZbfZGa3BKpPRXLOVeof4EKgG7CmQNty4CJvehTwhDddBVgNxHjP6wKh3vQyoDdgwCfA5YHuW0n0+bj1OgNbCjyvkH0Grgfe96ZrAFuBFkAokAi0AqoBq4AOge5bCfX5TmCaN90AiAdCgulzBhoD3bzpmsCPQAfgaWCi1z4ReMqbvsLrjwG9gKVeex1gi/d4tjd9dqD7V9hPpd8Cd84tAvYd13wesMib/hy42pvuD6x2zq3y1k11zuWZWWOglnPue+f7F/AW8JvSr/70FLPPBV0HvAdQwfvsgLPMrAoQDhwBDgA9gc3OuS3OuSPA+8DQ0q79dBWzzx2AL7z1UoA0IDaYPmfn3E7n3ApvOgNYDzTF9xnN8Babwf/qHwq85XyWAJFefwcAnzvn9jnn9uP7PQ0sw674rdIH+EmsAYZ408OBZt50W8CZ2WdmtsLMHvTamwJJBdZP8tqCycn6XNA1eAFOxe7zh8AhYCewHXjWObcPX/9+LrB+RerzKmComVUxs5ZAd29eUH7OZtYC6AosBRo653aCL+TxfcOAk3+eQfM5K8ALNwq408zi8X0VO+K1VwF+BdzgPV5lZv3wfQU7XrCdn3myPgNgZucDmc65o/tTK3KfewJ5QBOgJXCfmbWiYvd5Kr6gigNeBBYDuQRhn80sAvgIuNs5d+BUixbS5k7RXu5UCXQB5ZFzbgO+3SWYWVtgkDcrCfjaObfXm/cffPsY3wGiCrxEFJBcZgWXgFP0+ahr+d/WN/h+FxW1z9cDnzrncoAUM/sOiMW3VVbwm0mF6bNzLhe45+hyZrYY2ATsJ4g+ZzOrii+8ZzrnPvaad5tZY+fcTm8XSYrXnkThn2cScPFx7QtLs+7TpS3wQphZA+8xBHgYeM2b9RkQbWY1vP2jFwHrvK9lGWbWyztCfzMwLwCln7ZT9Plo23B8+3yBY19FK2qftwN9vbMUzsJ3gGsDvgOAbcyspZlVw/dHbX7ZV376TtZn79/0Wd70ZUCucy6o/m179U0B1jvnni8waz5w9EySW/hf/fOBm73PuReQ7vX3M6C/mZ3tnbHS32srfwJ9FDXQP/i2KncCOfj+8t4KTMB3BPtH4K94V6x6y98IrMW3L/HpAu2xXlsi8ErBdcrbz2n0+WJgSSGvUyH7DEQAs73PeR3wQIHXucJbPhF4KND9KsE+twA24jvw9198w5cG1eeMb7emw3em2Erv5wp8Z4t9ge8bxRdAHW95A/7u9esHILbAa40CNns/IwPdt5P96FJ6EZEgpV0oIiJBSgEuIhKkFOAiIkFKAS4iEqQU4CIiQUoBLiISpBTgIsVkZqGBrkEEFOBSwZnZE0fHhfaeP2lm483sATNb7o0D/acC8+eaWbw3nvTYAu0HzexxM1uKb2hVkYBTgEtFNwXvMmrv8vFrgd1AG3yDVnUBupvZhd7yo5xz3fFdfTjezOp67WfhG1f7fOfct2XZAZGT0WBWUqE557aaWaqZdQUaAglAD3zjWyR4i0XgC/RF+EL7Kq+9mdeeim90wo/KsnaRoijApTJ4ExgBNMI3bGo/4C/OudcLLuTdRuxSoLdzLtPMFgJh3uws51xeWRUs4g/tQpHKYA6+O6r0wDeq3GfAKG/caMysqTdKX21gvxfe7fCNQihSbmkLXCo859wRM/sKSPO2oheYWXvge+/+vAfxjTL5KXCbma3GNzLfkkDVLOIPjUYoFZ538HIFMNw5tynQ9YiUFO1CkQrNzDrgG9P5C4W3VDTaAhcRCVLaAhcRCVIKcBGRIKUAFxEJUgpwEZEgpQAXEQlS/x+p2DDwju+mUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary.plot.line('year','lifeexp', label='life expectancy')\n",
    "print(len(df_continent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_func_over_time(df,category,continent, verbose=False):\n",
    "\n",
    "    mask_continent = df['continent'] == continent\n",
    "    df_continent = df[mask_continent]\n",
    "    years = df_continent['year'].unique()\n",
    "    summary = []\n",
    "\n",
    "    for year in years:\n",
    "        if verbose:\n",
    "            print (year)\n",
    "        mask_year = df_continent['year'] == year\n",
    "        df_year = df_continent[mask_year]\n",
    "        value = func(df_year[category])\n",
    "        summary.append((continent, year, value\n",
    "    summary = pd.DataFrame(summary,columns=['continent','year',category])\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>4.228356e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>4.735699e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>5.140476e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>5.774736e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>6.518098e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>asia</td>\n",
       "      <td>1977</td>\n",
       "      <td>7.225799e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>asia</td>\n",
       "      <td>1982</td>\n",
       "      <td>7.909502e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>asia</td>\n",
       "      <td>1987</td>\n",
       "      <td>8.700669e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>9.494825e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>asia</td>\n",
       "      <td>1997</td>\n",
       "      <td>1.025238e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.091455e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>1.155138e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   continent  year           pop\n",
       "0       asia  1952  4.228356e+07\n",
       "1       asia  1957  4.735699e+07\n",
       "2       asia  1962  5.140476e+07\n",
       "3       asia  1967  5.774736e+07\n",
       "4       asia  1972  6.518098e+07\n",
       "5       asia  1977  7.225799e+07\n",
       "6       asia  1982  7.909502e+07\n",
       "7       asia  1987  8.700669e+07\n",
       "8       asia  1992  9.494825e+07\n",
       "9       asia  1997  1.025238e+08\n",
       "10      asia  2002  1.091455e+08\n",
       "11      asia  2007  1.155138e+08"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_mean_over_time(df,'pop', 'asia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_func_over_time(df,category,continent, verbose=False):\n",
    "\n",
    "    mask_continent = df['continent'] == continent\n",
    "    df_continent = df[mask_continent]\n",
    "    years = df_continent['year'].unique()\n",
    "    summary = []\n",
    "\n",
    "    for year in years:\n",
    "        if verbose:\n",
    "            print (year)\n",
    "        mask_year = df_continent['year'] == year\n",
    "        df_year = df_continent[mask_year]\n",
    "        value = func(df_year[category])\n",
    "        summary.append((continent, year, value))\n",
    "    summary = pd.DataFrame(summary,columns=['continent','year',category])\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1952\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'func' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-51-8b1938ab72c0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcalculate_func_over_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'life exp'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'asia'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-46-bf1d591c299f>\u001b[0m in \u001b[0;36mcalculate_func_over_time\u001b[1;34m(df, category, continent, verbose)\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0mmask_year\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_continent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'year'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0myear\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[0mdf_year\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_continent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask_year\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m         \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_year\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcategory\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m         \u001b[0msummary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontinent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myear\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0msummary\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'continent'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'year'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcategory\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'func' is not defined"
     ]
    }
   ],
   "source": [
    "calculate_func_over_time(df,'life exp','asia',len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function calculate_func_over_time in module __main__:\n",
      "\n",
      "calculate_func_over_time(df, category, continent, verbose=False)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(calculate_func_over_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_func_over_time(df,category,continent, verbose=False):\n",
    "    \"\"\" Calculates values of a statistic function over time \n",
    "    Args: df: A pandas DataFrame \n",
    "        category: One of the column headers in your Dataframe df e.g 'lifeexp'\n",
    "        continent: possible value of continent value in your dataframe e.g 'asia'\n",
    "        func: A function to apply on the data values (e.g np.mean)\n",
    "    \n",
    "    Returns:\n",
    "        a summary table of value per year (DataFrame)\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    mask_continent = df['continent'] == continent\n",
    "    df_continent = df[mask_continent]\n",
    "    years = df_continent['year'].unique()\n",
    "    summary = []\n",
    "\n",
    "    for year in years:\n",
    "        if verbose:\n",
    "            print (year)\n",
    "        mask_year = df_continent['year'] == year\n",
    "        df_year = df_continent[mask_year]\n",
    "        value = func(df_year[category])\n",
    "        summary.append((continent, year, value))\n",
    "    summary = pd.DataFrame(summary,columns=['continent','year',category])\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function calculate_func_over_time in module __main__:\n",
      "\n",
      "calculate_func_over_time(df, category, continent, verbose=False)\n",
      "    Calculates values of a statistic function over time \n",
      "    Args: df: A pandas DataFrame \n",
      "        category: One of the column headers in your Dataframe df e.g 'lifeexp'\n",
      "        continent: possible value of continent value in your dataframe e.g 'asia'\n",
      "        func: A function to apply on the data values (e.g np.mean)\n",
      "    \n",
      "    Returns:\n",
      "        a summary table of value per year (DataFrame)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(calculate_func_over_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_func_over_time(data, category, continent, func, verbose=False):\n",
    "    \"\"\" Calculates values of a statistic function over time \n",
    "    Args: df: A pandas DataFrame \n",
    "        category: One of the column headers in your Dataframe df e.g 'lifeexp'\n",
    "        continent: possible value of continent value in your dataframe e.g 'asia'\n",
    "        func: A function to apply on the data values (e.g np.mean)\n",
    "    \n",
    "    Returns:\n",
    "        a summary table of value per year (DataFrame)\n",
    "    \n",
    "    \"\"\"\n",
    "    #how we check the values provided\n",
    "    assert category in df.columns.values \n",
    "    assert 'continent' in df.columns.values\n",
    "    assert continent in df['continent'].unique()\n",
    "    \n",
    "    mask_continent = df['continent'] == continent\n",
    "    df_continent = df[mask_continent]\n",
    "    years = df_continent['year'].unique()\n",
    "    summary = []\n",
    "\n",
    "    for year in years:\n",
    "        if verbose:\n",
    "            print (year)\n",
    "        mask_year = df_continent['year'] == year\n",
    "        df_year = df_continent[mask_year]\n",
    "        value = func(df_year[category])\n",
    "        summary.append((continent, year, value))\n",
    "    summary = pd.DataFrame(summary,columns=['continent','year',category])\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>4.228356e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>4.735699e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>5.140476e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>5.774736e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>6.518098e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>asia</td>\n",
       "      <td>1977</td>\n",
       "      <td>7.225799e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>asia</td>\n",
       "      <td>1982</td>\n",
       "      <td>7.909502e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>asia</td>\n",
       "      <td>1987</td>\n",
       "      <td>8.700669e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>9.494825e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>asia</td>\n",
       "      <td>1997</td>\n",
       "      <td>1.025238e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.091455e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>1.155138e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   continent  year           pop\n",
       "0       asia  1952  4.228356e+07\n",
       "1       asia  1957  4.735699e+07\n",
       "2       asia  1962  5.140476e+07\n",
       "3       asia  1967  5.774736e+07\n",
       "4       asia  1972  6.518098e+07\n",
       "5       asia  1977  7.225799e+07\n",
       "6       asia  1982  7.909502e+07\n",
       "7       asia  1987  8.700669e+07\n",
       "8       asia  1992  9.494825e+07\n",
       "9       asia  1997  1.025238e+08\n",
       "10      asia  2002  1.091455e+08\n",
       "11      asia  2007  1.155138e+08"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_func_over_time(df,'pop','asia',np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continents = df['continent'].unique()\n",
    "VERBOSE = False\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for continent in continents:\n",
    "    output = calculate_func_over_time(df,\"lifeexp\",continent, np.median)\n",
    "    output.plot.line ('year','lifeexp', ax=ax, label = continent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python is not strong in version control \n",
    "#Skill in finding the important part of error messages"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}