life_expectancy_by_country.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d033e85",
   "metadata": {},
   "source": [
    "# Life Expectancy By Country"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4403be",
   "metadata": {},
   "source": [
    "Over the course of the past few centuries, technological and medical advancements have helped increase the life expectancy of humans. However, as of now, the average life expectancy of humans varies depending on what country you live in.\n",
    "\n",
    "In this project, we will investigate a dataset containing information about the average life expectancy in 158 different countries. We will specifically look at how a country's economic success might impact the life expectancy in that area."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addfa720",
   "metadata": {},
   "source": [
    "## Access the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ba5eb2",
   "metadata": {},
   "source": [
    "1. We've imported a dataset containing the life expectancy in different countries. The data can be found in the variable named `data`.\n",
    "\n",
    "   To begin, let's get a sense of what this data looks like. Print `data.head()` to see the first 5 rows of the dataset.\n",
    "   \n",
    "   Look at the names of the columns. What other pieces of information does this dataset contain?\n",
    "   \n",
    "   You may want to comment out this print statement after looking at the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "551cb1ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.131199Z",
     "start_time": "2023-12-21T02:28:54.248317Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "data = pd.read_csv(\"country_data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23aa5a5b6440fa39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.143986Z",
     "start_time": "2023-12-21T02:28:55.140255Z"
    },
    "collapsed": false
   },
   "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>Country</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>58.19375</td>\n",
       "      <td>340.015425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>75.15625</td>\n",
       "      <td>2119.726679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>73.61875</td>\n",
       "      <td>2847.853392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>49.01875</td>\n",
       "      <td>1975.143045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>75.05625</td>\n",
       "      <td>9759.305728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Country  Life Expectancy          GDP\n",
       "0          Afghanistan         58.19375   340.015425\n",
       "1              Albania         75.15625  2119.726679\n",
       "2              Algeria         73.61875  2847.853392\n",
       "3               Angola         49.01875  1975.143045\n",
       "4  Antigua and Barbuda         75.05625  9759.305728"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0baea2",
   "metadata": {},
   "source": [
    "2. Let's isolate the column that contains the life expectancy and store it in a variable named `life_expectancy`. To get a single column from a Pandas DataFrame, use this syntax:\n",
    "\n",
    "   ```py\n",
    "   single_column = dataFrameName[\"columnName\"]\n",
    "   ```\n",
    "   \n",
    "   Make sure to pay attention to capitalization and spaces when using the column name!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "817d9a01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.153162Z",
     "start_time": "2023-12-21T02:28:55.144162Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "153    76.07500\n",
       "154    68.03125\n",
       "155    71.38750\n",
       "156    53.90625\n",
       "157    50.48750\n",
       "Name: Life Expectancy, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n",
    "life_exp = data['Life Expectancy']\n",
    "life_exp.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dbb4fb4",
   "metadata": {},
   "source": [
    "## Find the Quantiles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2bb4419",
   "metadata": {},
   "source": [
    "3. We can now use NumPy functions on that column! Let's use the `np.quantile()` function to find the quartiles of `life_expectancy`. Store the result in a variable named `life_expectancy_quartiles` and print the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "132424f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.195198Z",
     "start_time": "2023-12-21T02:28:55.155972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([62.325    , 72.525    , 75.4421875])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "life_exp_quartiles = np.quantile(life_exp, [.25, .5, .75])\n",
    "life_exp_quartiles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b6d31e",
   "metadata": {},
   "source": [
    "4. Nice work! By looking at those three values you can get a sense of the spread of the data. For example, it seems like some of the data is fairly close together &mdash; a quarter of the data is between `72.5` years and `75.4` years.\n",
    "\n",
    "   Could you predict what the histogram might look like from those three numbers? Plot the histogram by using the following two lines of code. Does it look how you expected?\n",
    "   \n",
    "   ```py\n",
    "   plt.hist(life_expectancy)\n",
    "   plt.show()\n",
    "   ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ae17988",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.556909Z",
     "start_time": "2023-12-21T02:28:55.170641Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4), facecolor='snow')\n",
    "fig = plt.figure()\n",
    "\n",
    "plt.hist(life_exp, bins=20)\n",
    "plt.title('Global Life Expectancy')\n",
    "plt.axvline(life_exp_quartiles[0], color='r', alpha=.5)\n",
    "plt.axvline(life_exp_quartiles[1], color='r', alpha=.5)\n",
    "plt.axvline(life_exp_quartiles[2], color='r', alpha=.5)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of Countries')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('global_life_expectancy.png', bbox_inches='tight', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8976cb",
   "metadata": {},
   "source": [
    "5. Let's take a moment to think about the meaning of these quartiles. If your country has a life expectancy of `70` years, does that fall in the first, second, third, or final quarter of the data?\n",
    "\n",
    "   View **Life Expectancy By Country_Solution.ipynb** to see the answer!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc83db64",
   "metadata": {},
   "source": [
    "## Splitting the Data by GDP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dab91c1",
   "metadata": {},
   "source": [
    "6. GDP is a measure of a country's wealth. Let's now use the GDP data to see if life expectancy is affected by this value.\n",
    "\n",
    "   Let's split the data into two groups based on GDP. If we find the median GDP, we can create two datasets for \"low GDP countries\" and \"high GDP countries\".\n",
    "   \n",
    "   To start, let's isolate the GDP column and store it in a variable named `gdp`. This should be similar to how you isolated the life expectancy column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa544862",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.571924Z",
     "start_time": "2023-12-21T02:28:55.557840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       340.015425\n",
       "1      2119.726679\n",
       "2      2847.853392\n",
       "3      1975.143045\n",
       "4      9759.305728\n",
       "          ...     \n",
       "153    7192.584875\n",
       "154     651.092359\n",
       "155    2000.245518\n",
       "156     811.811841\n",
       "157     410.980194\n",
       "Name: GDP, Length: 158, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp = data.GDP\n",
    "gdp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7e433e",
   "metadata": {},
   "source": [
    "7. We now want to find the median GDP. You can use NumPy's `np.median()` function, but since the median is also a quantile, we can call `np.quantile()` using `0.5` as the second parameter.\n",
    "\n",
    "   Store the median in a variable named `median_gdp`. Print that variable to see the median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a3e21b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.604018Z",
     "start_time": "2023-12-21T02:28:55.570022Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2938.0781155"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_gdp = np.median(gdp)\n",
    "median_gdp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7581520c",
   "metadata": {},
   "source": [
    "8. Let's now grab all of the rows from our original dataset that have a GDP less than or equal to the median. The following code will do that for you:\n",
    "\n",
    "   ```py\n",
    "   low_gdp = data[data['GDP'] <= median_gdp]\n",
    "   ```\n",
    "   \n",
    "   Do the same for all of the rows that have a GDP higher than the median. Store those rows in a variable named `high_gdp`.\n",
    "   \n",
    "   The line of code should look almost identical to the one above, but you should change the `<=` to `>`.\n",
    "   \n",
    "   Remember to change the name of the variable!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29c6e2fde7bf475e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.606995Z",
     "start_time": "2023-12-21T02:28:55.586624Z"
    }
   },
   "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>Country</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>58.19375</td>\n",
       "      <td>340.015425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>75.15625</td>\n",
       "      <td>2119.726679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>73.61875</td>\n",
       "      <td>2847.853392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>49.01875</td>\n",
       "      <td>1975.143045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>73.40000</td>\n",
       "      <td>1999.986318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>69.93750</td>\n",
       "      <td>1577.293329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>68.03125</td>\n",
       "      <td>651.092359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>71.38750</td>\n",
       "      <td>2000.245518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>53.90625</td>\n",
       "      <td>811.811841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>50.48750</td>\n",
       "      <td>410.980194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Life Expectancy          GDP\n",
       "0    Afghanistan         58.19375   340.015425\n",
       "1        Albania         75.15625  2119.726679\n",
       "2        Algeria         73.61875  2847.853392\n",
       "3         Angola         49.01875  1975.143045\n",
       "6        Armenia         73.40000  1999.986318\n",
       "..           ...              ...          ...\n",
       "151      Ukraine         69.93750  1577.293329\n",
       "154   Uzbekistan         68.03125   651.092359\n",
       "155      Vanuatu         71.38750  2000.245518\n",
       "156       Zambia         53.90625   811.811841\n",
       "157     Zimbabwe         50.48750   410.980194\n",
       "\n",
       "[79 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_gdp = data[data.GDP <= median_gdp]\n",
    "low_gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12067c34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.624339Z",
     "start_time": "2023-12-21T02:28:55.595284Z"
    },
    "collapsed": false
   },
   "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>Country</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>75.05625</td>\n",
       "      <td>9759.305728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>75.15625</td>\n",
       "      <td>6998.575103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Australia</td>\n",
       "      <td>81.81250</td>\n",
       "      <td>34637.565050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Austria</td>\n",
       "      <td>81.48125</td>\n",
       "      <td>33827.476310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>70.73125</td>\n",
       "      <td>3302.947807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>71.06875</td>\n",
       "      <td>7741.748090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>74.35625</td>\n",
       "      <td>3044.081488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>73.91250</td>\n",
       "      <td>3983.917722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>75.70000</td>\n",
       "      <td>22110.366990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>76.07500</td>\n",
       "      <td>7192.584875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country  Life Expectancy           GDP\n",
       "4     Antigua and Barbuda         75.05625   9759.305728\n",
       "5               Argentina         75.15625   6998.575103\n",
       "7               Australia         81.81250  34637.565050\n",
       "8                 Austria         81.48125  33827.476310\n",
       "9              Azerbaijan         70.73125   3302.947807\n",
       "..                    ...              ...           ...\n",
       "146   Trinidad and Tobago         71.06875   7741.748090\n",
       "147               Tunisia         74.35625   3044.081488\n",
       "148                Turkey         73.91250   3983.917722\n",
       "152  United Arab Emirates         75.70000  22110.366990\n",
       "153               Uruguay         76.07500   7192.584875\n",
       "\n",
       "[79 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_gdp = data[data.GDP > median_gdp]\n",
    "high_gdp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ad7103",
   "metadata": {},
   "source": [
    "9. Now that we've split the data based on the GDP, let's see how the life expectancy of each group compares to each other.\n",
    "\n",
    "   Find the quartiles of the `\"Life Expectancy\"` column of `low_gdp`. Store the quartiles in a variable named `low_gdp_quartiles`. Print the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a06ada2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.624900Z",
     "start_time": "2023-12-21T02:28:55.613323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[56.3375  64.34375 71.7375 ]\n"
     ]
    }
   ],
   "source": [
    "low_gdp_quartiles = np.quantile(low_gdp['Life Expectancy'], [0.25, 0.5, 0.75])\n",
    "print(low_gdp_quartiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e900d679",
   "metadata": {},
   "source": [
    "10. Find the quartiles of the high GDP countries and store them in a variable named `high_gdp_quartiles`. This should look very similar to the last line of code you wrote. Print the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "091c76c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:55.680836Z",
     "start_time": "2023-12-21T02:28:55.621090Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[72.965625 75.15625  80.521875]\n"
     ]
    }
   ],
   "source": [
    "high_gdp_quartiles = np.quantile(high_gdp['Life Expectancy'], [0.25, 0.5, 0.75])\n",
    "print(high_gdp_quartiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbc2a78",
   "metadata": {},
   "source": [
    "## Histogram and Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3472ae2",
   "metadata": {},
   "source": [
    "11. By looking at the quantiles, you should get a sense of the spread and central tendency of these two datasets. But let's plot a histogram of each dataset to really compare them.\n",
    "\n",
    "    At the bottom of your code, add these four lines:\n",
    "    ```py\n",
    "    plt.hist(high_gdp[\"Life Expectancy\"], alpha = 0.5, label = \"High GDP\")\n",
    "    plt.hist(low_gdp[\"Life Expectancy\"], alpha = 0.5, label = \"Low GDP\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87077d84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:56.076579Z",
     "start_time": "2023-12-21T02:28:55.633027Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4), facecolor='snow')\n",
    "fig = plt.figure()\n",
    "\n",
    "plt.hist(high_gdp['Life Expectancy'], alpha=.5, label=\"High GDP\")\n",
    "plt.axvline(low_gdp_quartiles[0], dashes=(4,2), color='coral')\n",
    "plt.annotate('Q1', xy=(54, 15), color='coral', size=11)\n",
    "plt.axvline(low_gdp_quartiles[1], dashes=(4,2), color='coral')\n",
    "plt.annotate('Q2', xy=(62, 15), color='coral', size=11)\n",
    "plt.axvline(low_gdp_quartiles[2], dashes=(4,2), color='coral')\n",
    "plt.annotate('Q3', xy=(70, 15), color='coral', size=11)\n",
    "\n",
    "plt.hist(low_gdp['Life Expectancy'], alpha=.5, label='Low GDP')\n",
    "plt.axvline(high_gdp_quartiles[0], dashes=(4,2), color='steelblue')\n",
    "plt.annotate('Q1', xy=(73, 18), color='steelblue', size=11)\n",
    "plt.axvline(high_gdp_quartiles[1], dashes=(4,2), color='steelblue')\n",
    "plt.annotate('Q2', xy=(76, 18), color='steelblue', size=11)\n",
    "plt.axvline(high_gdp_quartiles[2], dashes=(4,2), color='steelblue')\n",
    "plt.annotate('Q3', xy=(81, 18), color='steelblue', size=11)\n",
    "plt.title('Global Life Expectancy by Low and High GDP Countries')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of Countries')\n",
    "plt.legend()\n",
    "\n",
    "plt.axvline(70, dashes=(4,2), color='darkseagreen')\n",
    "plt.annotate('life exp 70\\nexpectation', xy=(69.5, 20), size=10, color='darkseagreen', ha='right')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('life_expectancy_by_gdp_high_and_low.png', bbox_inches='tight', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2903763d",
   "metadata": {},
   "source": [
    "12. We can now truly see the impact GDP has on life expectancy.\n",
    "\n",
    "    Once again, consider a country that has a life expectancy of `70` years. If that country is in the top half of GDP countries, is it in the first, second, third, or fourth quarter of the data with respect to life expectancy? What if the country is in the bottom half of the countries? View **Life Expectancy By Country_Solution.ipynb** to see our thoughts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a28e056e08bc384f",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "'70' is below the first quartile of the high GDP dataset, so it falls in the first quarter of that dataset.\n",
    "'70' is between the second and third quartile of the low GDP dataset, so it falls in the third quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e146bca80511896c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-21T02:28:56.083112Z",
     "start_time": "2023-12-21T02:28:56.077194Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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