Build_Week_1_Vis_1_LE.ipynb

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      "name": "Build_Week_1_Vis_1_LE.ipynb",
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      "authorship_tag": "ABX9TyNSqOeDTbPOYgMnc0eAkL00",
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KristineYW/Unit-1-Build/blob/master/Build_Week_1_Vis_1_LE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96EDbWHG46gq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vahU_bJWAMIE",
        "colab_type": "code",
        "outputId": "755cdda2-b122-4b23-bb53-022876cc84d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dr_le = pd.read_csv('https://raw.githubusercontent.com/KristineYW/Unit-1-Build/master/NCHS_-_Death_rates_and_life_expectancy_at_birth.csv')\n",
        "\n",
        "dr_le.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "      <th>Age-adjusted Death Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.3</td>\n",
              "      <td>2,518.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1901</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>49.1</td>\n",
              "      <td>2,473.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1902</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>51.5</td>\n",
              "      <td>2,301.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1903</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>50.5</td>\n",
              "      <td>2,379.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.6</td>\n",
              "      <td>2,502.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year       Race  ... Average Life Expectancy (Years)  Age-adjusted Death Rate\n",
              "0  1900  All Races  ...                            47.3                  2,518.0\n",
              "1  1901  All Races  ...                            49.1                  2,473.1\n",
              "2  1902  All Races  ...                            51.5                  2,301.3\n",
              "3  1903  All Races  ...                            50.5                  2,379.0\n",
              "4  1904  All Races  ...                            47.6                  2,502.5\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 2
        }
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    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "dr_le_all = dr_le.drop(['Race', 'Age-adjusted Death Rate'], axis=1)\n",
        "\n",
        "dr_le_all.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1901</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>49.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1902</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>51.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1903</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>50.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1905</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>48.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1906</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>48.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1907</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1908</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>51.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1909</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>52.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year         Sex  Average Life Expectancy (Years)\n",
              "0  1900  Both Sexes                             47.3\n",
              "1  1901  Both Sexes                             49.1\n",
              "2  1902  Both Sexes                             51.5\n",
              "3  1903  Both Sexes                             50.5\n",
              "4  1904  Both Sexes                             47.6\n",
              "5  1905  Both Sexes                             48.7\n",
              "6  1906  Both Sexes                             48.7\n",
              "7  1907  Both Sexes                             47.6\n",
              "8  1908  Both Sexes                             51.1\n",
              "9  1909  Both Sexes                             52.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
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    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "dr_le_all = dr_le.drop(['Race', 'Age-adjusted Death Rate'], axis=1)\n",
        "\n",
        "dr_le_all.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1901</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>49.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1902</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>51.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1903</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>50.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1905</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>48.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1906</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>48.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1907</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1908</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>51.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1909</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>52.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year         Sex  Average Life Expectancy (Years)\n",
              "0  1900  Both Sexes                             47.3\n",
              "1  1901  Both Sexes                             49.1\n",
              "2  1902  Both Sexes                             51.5\n",
              "3  1903  Both Sexes                             50.5\n",
              "4  1904  Both Sexes                             47.6\n",
              "5  1905  Both Sexes                             48.7\n",
              "6  1906  Both Sexes                             48.7\n",
              "7  1907  Both Sexes                             47.6\n",
              "8  1908  Both Sexes                             51.1\n",
              "9  1909  Both Sexes                             52.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzRB4Y693UXa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create new dataframe with just total population information\n",
        "\n",
        "dr_le_pop = dr_le.loc[dr_le['Race'] == 'All Races']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSgsbIBZ4jj9",
        "colab_type": "code",
        "outputId": "db9feb63-8e45-4a41-d28e-846eab66a94f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Delete gendered rows\n",
        "\n",
        "dr_le_pop = dr_le_pop.loc[dr_le_pop['Sex'] == 'Both Sexes']\n",
        "\n",
        "dr_le_pop.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "      <th>Age-adjusted Death Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.3</td>\n",
              "      <td>2,518.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1901</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>49.1</td>\n",
              "      <td>2,473.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1902</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>51.5</td>\n",
              "      <td>2,301.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1903</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>50.5</td>\n",
              "      <td>2,379.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.6</td>\n",
              "      <td>2,502.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year       Race  ... Average Life Expectancy (Years)  Age-adjusted Death Rate\n",
              "0  1900  All Races  ...                            47.3                  2,518.0\n",
              "1  1901  All Races  ...                            49.1                  2,473.1\n",
              "2  1902  All Races  ...                            51.5                  2,301.3\n",
              "3  1903  All Races  ...                            50.5                  2,379.0\n",
              "4  1904  All Races  ...                            47.6                  2,502.5\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwdTTZ0Z49Jr",
        "colab_type": "code",
        "outputId": "0d37c708-33fd-435e-be4a-02119ad2b2e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Delete Race, Sex, and Age-adjusted Death Rate columns now that they contain only one value\n",
        "\n",
        "dr_le_pop = dr_le_pop.drop(['Sex','Race','Age-adjusted Death Rate'], axis=1)\n",
        "\n",
        "dr_le_pop.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>47.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1901</td>\n",
              "      <td>49.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1902</td>\n",
              "      <td>51.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1903</td>\n",
              "      <td>50.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Average Life Expectancy (Years)\n",
              "0  1900                             47.3\n",
              "1  1901                             49.1\n",
              "2  1902                             51.5\n",
              "3  1903                             50.5\n",
              "4  1904                             47.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mx0BqCs6DMq",
        "colab_type": "code",
        "outputId": "9c85b94f-82bc-4259-d82b-5a6f24f57057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# Check the dataset for NaNs\n",
        "\n",
        "dr_le_pop.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>118.000000</td>\n",
              "      <td>118.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1958.500000</td>\n",
              "      <td>66.855085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>34.207699</td>\n",
              "      <td>9.697904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1900.000000</td>\n",
              "      <td>39.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1929.250000</td>\n",
              "      <td>59.775000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1958.500000</td>\n",
              "      <td>69.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1987.750000</td>\n",
              "      <td>74.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2017.000000</td>\n",
              "      <td>78.900000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Year  Average Life Expectancy (Years)\n",
              "count   118.000000                       118.000000\n",
              "mean   1958.500000                        66.855085\n",
              "std      34.207699                         9.697904\n",
              "min    1900.000000                        39.100000\n",
              "25%    1929.250000                        59.775000\n",
              "50%    1958.500000                        69.700000\n",
              "75%    1987.750000                        74.900000\n",
              "max    2017.000000                        78.900000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJcI5nf_5aQq",
        "colab_type": "code",
        "outputId": "f347beff-2a56-4066-9de1-c767365ddf1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        }
      },
      "source": [
        "# Create Visualization\n",
        "\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(18, 10))\n",
        "\n",
        "ax.set_title(\"Average Life Expectancy from 1900-2017\", color='black', weight='bold', fontsize=24, x=.475, y=1.05)\n",
        "\n",
        "ax.set_yticklabels([35,40,45,50,55,60,65,70,75,80], color='#616161', fontsize=15)\n",
        "ax.set_xticklabels([1880,1900,1920,1940,1960,1980,2000,2020], color='#616161', fontsize=15)\n",
        "\n",
        "ax.grid()\n",
        "a = ax.get_ygridlines()\n",
        "\n",
        "fig.set_facecolor('#f0f0f0')\n",
        "ax.set_facecolor('#f0f0f0')\n",
        "\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['top'].set_visible(False)\n",
        "\n",
        "ax.plot(dr_le_pop['Year'],dr_le_pop['Average Life Expectancy (Years)'], color='green',linewidth=3);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnnoTu3BHkj-",
        "colab_type": "code",
        "outputId": "fe3cc278-d797-421c-af85-27538ba61621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "# Create race-specific dataset\n",
        "\n",
        "dr_le_by_race = dr_le.loc[dr_le['Sex'] == 'Both Sexes']\n",
        "\n",
        "dr_le_by_race.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "      <th>Age-adjusted Death Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.3</td>\n",
              "      <td>2,518.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1901</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>49.1</td>\n",
              "      <td>2,473.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1902</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>51.5</td>\n",
              "      <td>2,301.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1903</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>50.5</td>\n",
              "      <td>2,379.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.6</td>\n",
              "      <td>2,502.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1905</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>48.7</td>\n",
              "      <td>2,423.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1906</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>48.7</td>\n",
              "      <td>2,399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1907</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>47.6</td>\n",
              "      <td>2,494.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1908</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>51.1</td>\n",
              "      <td>2,298.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1909</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Both Sexes</td>\n",
              "      <td>52.1</td>\n",
              "      <td>2,249.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year       Race  ... Average Life Expectancy (Years)  Age-adjusted Death Rate\n",
              "0  1900  All Races  ...                            47.3                  2,518.0\n",
              "1  1901  All Races  ...                            49.1                  2,473.1\n",
              "2  1902  All Races  ...                            51.5                  2,301.3\n",
              "3  1903  All Races  ...                            50.5                  2,379.0\n",
              "4  1904  All Races  ...                            47.6                  2,502.5\n",
              "5  1905  All Races  ...                            48.7                  2,423.7\n",
              "6  1906  All Races  ...                            48.7                  2,399.0\n",
              "7  1907  All Races  ...                            47.6                  2,494.4\n",
              "8  1908  All Races  ...                            51.1                  2,298.9\n",
              "9  1909  All Races  ...                            52.1                  2,249.2\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPIFGbyQ_Fir",
        "colab_type": "code",
        "outputId": "916c5e91-eab2-41aa-d6e8-6cd27c6ea029",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "# Drop Sex & Age-adjusted Death Rate column\n",
        "\n",
        "dr_le_by_race = dr_le_by_race.drop(['Sex','Age-adjusted Death Rate'], axis=1)\n",
        "\n",
        "dr_le_by_race.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Race</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>All Races</td>\n",
              "      <td>47.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1901</td>\n",
              "      <td>All Races</td>\n",
              "      <td>49.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1902</td>\n",
              "      <td>All Races</td>\n",
              "      <td>51.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1903</td>\n",
              "      <td>All Races</td>\n",
              "      <td>50.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1904</td>\n",
              "      <td>All Races</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1905</td>\n",
              "      <td>All Races</td>\n",
              "      <td>48.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1906</td>\n",
              "      <td>All Races</td>\n",
              "      <td>48.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1907</td>\n",
              "      <td>All Races</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1908</td>\n",
              "      <td>All Races</td>\n",
              "      <td>51.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1909</td>\n",
              "      <td>All Races</td>\n",
              "      <td>52.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year       Race  Average Life Expectancy (Years)\n",
              "0  1900  All Races                             47.3\n",
              "1  1901  All Races                             49.1\n",
              "2  1902  All Races                             51.5\n",
              "3  1903  All Races                             50.5\n",
              "4  1904  All Races                             47.6\n",
              "5  1905  All Races                             48.7\n",
              "6  1906  All Races                             48.7\n",
              "7  1907  All Races                             47.6\n",
              "8  1908  All Races                             51.1\n",
              "9  1909  All Races                             52.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmZ29F8OAztj",
        "colab_type": "code",
        "outputId": "1a206dde-e78a-45ba-e0d3-1e80a7b929aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "source": [
        "# Filter out data for Race: white\n",
        "\n",
        "dr_le_white = dr_le_by_race[dr_le['Race'] == 'White']\n",
        "\n",
        "dr_le_white.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Race</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>708</th>\n",
              "      <td>1900</td>\n",
              "      <td>White</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>709</th>\n",
              "      <td>1901</td>\n",
              "      <td>White</td>\n",
              "      <td>49.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>710</th>\n",
              "      <td>1902</td>\n",
              "      <td>White</td>\n",
              "      <td>51.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>1903</td>\n",
              "      <td>White</td>\n",
              "      <td>50.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>1904</td>\n",
              "      <td>White</td>\n",
              "      <td>48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>713</th>\n",
              "      <td>1905</td>\n",
              "      <td>White</td>\n",
              "      <td>49.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>714</th>\n",
              "      <td>1906</td>\n",
              "      <td>White</td>\n",
              "      <td>49.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>1907</td>\n",
              "      <td>White</td>\n",
              "      <td>48.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>716</th>\n",
              "      <td>1908</td>\n",
              "      <td>White</td>\n",
              "      <td>51.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>717</th>\n",
              "      <td>1909</td>\n",
              "      <td>White</td>\n",
              "      <td>52.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year   Race  Average Life Expectancy (Years)\n",
              "708  1900  White                             47.6\n",
              "709  1901  White                             49.4\n",
              "710  1902  White                             51.9\n",
              "711  1903  White                             50.9\n",
              "712  1904  White                             48.0\n",
              "713  1905  White                             49.1\n",
              "714  1906  White                             49.3\n",
              "715  1907  White                             48.1\n",
              "716  1908  White                             51.5\n",
              "717  1909  White                             52.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6ohVrehBF0L",
        "colab_type": "code",
        "outputId": "a021eb68-c9fd-46c2-a306-82bbb81e0823",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "source": [
        "# Filter out data for Race: black\n",
        "\n",
        "dr_le_black = dr_le_by_race[dr_le['Race'] == 'Black']\n",
        "\n",
        "dr_le_black.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Race</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>1900</td>\n",
              "      <td>Black</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>1901</td>\n",
              "      <td>Black</td>\n",
              "      <td>33.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>1902</td>\n",
              "      <td>Black</td>\n",
              "      <td>34.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>357</th>\n",
              "      <td>1903</td>\n",
              "      <td>Black</td>\n",
              "      <td>33.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358</th>\n",
              "      <td>1904</td>\n",
              "      <td>Black</td>\n",
              "      <td>30.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>359</th>\n",
              "      <td>1905</td>\n",
              "      <td>Black</td>\n",
              "      <td>31.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>1906</td>\n",
              "      <td>Black</td>\n",
              "      <td>32.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>1907</td>\n",
              "      <td>Black</td>\n",
              "      <td>32.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>1908</td>\n",
              "      <td>Black</td>\n",
              "      <td>34.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>1909</td>\n",
              "      <td>Black</td>\n",
              "      <td>35.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year   Race  Average Life Expectancy (Years)\n",
              "354  1900  Black                             33.0\n",
              "355  1901  Black                             33.7\n",
              "356  1902  Black                             34.6\n",
              "357  1903  Black                             33.1\n",
              "358  1904  Black                             30.8\n",
              "359  1905  Black                             31.3\n",
              "360  1906  Black                             32.9\n",
              "361  1907  Black                             32.5\n",
              "362  1908  Black                             34.9\n",
              "363  1909  Black                             35.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JH6JhOGZuKoH",
        "colab_type": "code",
        "outputId": "df33356d-3436-42e4-9bdd-ee279340c1fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "# Create gendered dataframe\n",
        "\n",
        "\n",
        "dr_le_by_sex = dr_le.loc[dr_le['Race'] == 'All Races']\n",
        "\n",
        "dr_le_by_sex.tail(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "      <th>Age-adjusted Death Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>344</th>\n",
              "      <td>2008</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>75.6</td>\n",
              "      <td>918.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>345</th>\n",
              "      <td>2009</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.0</td>\n",
              "      <td>890.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>346</th>\n",
              "      <td>2010</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.2</td>\n",
              "      <td>887.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>2011</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.3</td>\n",
              "      <td>875.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>2012</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.4</td>\n",
              "      <td>865.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349</th>\n",
              "      <td>2013</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.4</td>\n",
              "      <td>863.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>2014</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.5</td>\n",
              "      <td>855.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351</th>\n",
              "      <td>2015</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.3</td>\n",
              "      <td>863.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>2016</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.2</td>\n",
              "      <td>861.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>2017</td>\n",
              "      <td>All Races</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.1</td>\n",
              "      <td>864.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year       Race  ... Average Life Expectancy (Years)  Age-adjusted Death Rate\n",
              "344  2008  All Races  ...                            75.6                    918.8\n",
              "345  2009  All Races  ...                            76.0                    890.9\n",
              "346  2010  All Races  ...                            76.2                    887.1\n",
              "347  2011  All Races  ...                            76.3                    875.3\n",
              "348  2012  All Races  ...                            76.4                    865.1\n",
              "349  2013  All Races  ...                            76.4                    863.6\n",
              "350  2014  All Races  ...                            76.5                    855.1\n",
              "351  2015  All Races  ...                            76.3                    863.2\n",
              "352  2016  All Races  ...                            76.2                    861.0\n",
              "353  2017  All Races  ...                            76.1                    864.5\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UejVkKhjozS0",
        "colab_type": "code",
        "outputId": "85d72dd4-a3a9-4473-9c15-fb824bf17f85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "\n",
        "dr_le_by_sex = dr_le_by_sex.drop(['Race','Age-adjusted Death Rate'], axis=1)\n",
        "\n",
        "dr_le_by_sex.tail(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>344</th>\n",
              "      <td>2008</td>\n",
              "      <td>Male</td>\n",
              "      <td>75.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>345</th>\n",
              "      <td>2009</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>346</th>\n",
              "      <td>2010</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>2011</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>2012</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349</th>\n",
              "      <td>2013</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>2014</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351</th>\n",
              "      <td>2015</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>2016</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>2017</td>\n",
              "      <td>Male</td>\n",
              "      <td>76.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year   Sex  Average Life Expectancy (Years)\n",
              "344  2008  Male                             75.6\n",
              "345  2009  Male                             76.0\n",
              "346  2010  Male                             76.2\n",
              "347  2011  Male                             76.3\n",
              "348  2012  Male                             76.4\n",
              "349  2013  Male                             76.4\n",
              "350  2014  Male                             76.5\n",
              "351  2015  Male                             76.3\n",
              "352  2016  Male                             76.2\n",
              "353  2017  Male                             76.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5_tpNOQpkEu",
        "colab_type": "code",
        "outputId": "c64bb07a-49c0-4ad5-e911-f49559771393",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "source": [
        "dr_le_m = dr_le_by_sex[dr_le['Sex'] == 'Male']\n",
        "\n",
        "dr_le_m.head(100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>1900</td>\n",
              "      <td>Male</td>\n",
              "      <td>46.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>1901</td>\n",
              "      <td>Male</td>\n",
              "      <td>47.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>1902</td>\n",
              "      <td>Male</td>\n",
              "      <td>49.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>1903</td>\n",
              "      <td>Male</td>\n",
              "      <td>49.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>1904</td>\n",
              "      <td>Male</td>\n",
              "      <td>46.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>331</th>\n",
              "      <td>1995</td>\n",
              "      <td>Male</td>\n",
              "      <td>72.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>332</th>\n",
              "      <td>1996</td>\n",
              "      <td>Male</td>\n",
              "      <td>73.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>1997</td>\n",
              "      <td>Male</td>\n",
              "      <td>73.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334</th>\n",
              "      <td>1998</td>\n",
              "      <td>Male</td>\n",
              "      <td>73.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335</th>\n",
              "      <td>1999</td>\n",
              "      <td>Male</td>\n",
              "      <td>73.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year   Sex  Average Life Expectancy (Years)\n",
              "236  1900  Male                             46.3\n",
              "237  1901  Male                             47.6\n",
              "238  1902  Male                             49.8\n",
              "239  1903  Male                             49.1\n",
              "240  1904  Male                             46.2\n",
              "..    ...   ...                              ...\n",
              "331  1995  Male                             72.5\n",
              "332  1996  Male                             73.1\n",
              "333  1997  Male                             73.6\n",
              "334  1998  Male                             73.8\n",
              "335  1999  Male                             73.9\n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BxT26hFpqgM",
        "colab_type": "code",
        "outputId": "b7e341f7-cb88-4500-8d4c-a257312ff37d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "source": [
        "dr_le_f = dr_le_by_sex[dr_le['Sex'] == 'Female']\n",
        "\n",
        "dr_le_f.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Average Life Expectancy (Years)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>1900</td>\n",
              "      <td>Female</td>\n",
              "      <td>48.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>1901</td>\n",
              "      <td>Female</td>\n",
              "      <td>50.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>1902</td>\n",
              "      <td>Female</td>\n",
              "      <td>53.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>1903</td>\n",
              "      <td>Female</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>1904</td>\n",
              "      <td>Female</td>\n",
              "      <td>49.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>1905</td>\n",
              "      <td>Female</td>\n",
              "      <td>50.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>1906</td>\n",
              "      <td>Female</td>\n",
              "      <td>50.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>1907</td>\n",
              "      <td>Female</td>\n",
              "      <td>49.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>1908</td>\n",
              "      <td>Female</td>\n",
              "      <td>52.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>1909</td>\n",
              "      <td>Female</td>\n",
              "      <td>53.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year     Sex  Average Life Expectancy (Years)\n",
              "118  1900  Female                             48.3\n",
              "119  1901  Female                             50.6\n",
              "120  1902  Female                             53.4\n",
              "121  1903  Female                             52.0\n",
              "122  1904  Female                             49.1\n",
              "123  1905  Female                             50.2\n",
              "124  1906  Female                             50.8\n",
              "125  1907  Female                             49.9\n",
              "126  1908  Female                             52.8\n",
              "127  1909  Female                             53.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YAFBP3N4_ygQ",
        "colab_type": "code",
        "outputId": "12c92dd6-17b7-44e7-c9d0-d5d084150238",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        }
      },
      "source": [
        "# Create Visualization\n",
        "\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(18, 10))\n",
        "\n",
        "ax.set_title(\"Average Life Expectancy (Years) from 1900-2017\", color='black', weight='bold', fontsize=24, x=.475, y=1.05)\n",
        "fig.text(x=.28,y=.17,s=\"*WWI\", fontsize=15)\n",
        "\n",
        "ax.set_yticklabels([20,30,40,50,60,70,80], color='#000000', fontsize=20)\n",
        "ax.set_xticklabels([1880,1900,1920,1940,1960,1980,2000,2020], color='#000000', fontsize=20)\n",
        "\n",
        "\n",
        "fig.set_facecolor('#FFFFFF')\n",
        "ax.set_facecolor('#FFFFFF')\n",
        "\n",
        "ax.set_xlabel('Year',fontsize=25,labelpad=30)\n",
        "ax.set_ylabel('Life Expectancy',fontsize=25,labelpad=20)\n",
        "\n",
        "l1 = ax.plot(dr_le_pop['Year'],dr_le_pop['Average Life Expectancy (Years)'], color='#000000',linewidth=5);\n",
        "l2 = ax.plot(dr_le_white['Year'],dr_le_white['Average Life Expectancy (Years)'], color='#189ad3',linewidth=3);\n",
        "l3 = ax.plot(dr_le_black['Year'],dr_le_black['Average Life Expectancy (Years)'], color='#107dac',linewidth=3);\n",
        "l4 =ax.plot(dr_le_m['Year'],dr_le_m['Average Life Expectancy (Years)'], color='#A270A7',linewidth=3);\n",
        "l5 = ax.plot(dr_le_f['Year'],dr_le_f['Average Life Expectancy (Years)'], color='#85478B',linewidth=3);\n",
        "\n",
        "\n",
        "ax.legend((l1,l2,l3,l4,l5),labels=('All','White','Black','Male','Female'), \n",
        "          fontsize=22,bbox_to_anchor=(.95,.5),shadow=True,facecolor='#f1f1f1',borderpad=1)\n",
        "\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['top'].set_visible(False)\n",
        "\n",
        "ax.plot(dr_le_white['Year'],dr_le_white['Average Life Expectancy (Years)'], color='#189ad3',linewidth=3);\n",
        "ax.plot(dr_le_black['Year'],dr_le_black['Average Life Expectancy (Years)'], color='#107dac',linewidth=3);\n",
        "ax.plot(dr_le_m['Year'],dr_le_m['Average Life Expectancy (Years)'], color='#A270A7',linewidth=3);\n",
        "ax.plot(dr_le_f['Year'],dr_le_f['Average Life Expectancy (Years)'], color='#85478B',linewidth=3);\n",
        "ax.plot(dr_le_pop['Year'],dr_le_pop['Average Life Expectancy (Years)'], color='#000000',linewidth=5);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:27: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}