DS_Unit_1_Sprint_Challenge_2.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/Phantasm320/DS-Sprint-01-Dealing-With-Data/blob/master/DS_Unit_1_Sprint_Challenge_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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      "source": [
        "# Data Science Unit 1 Sprint Challenge 2\n",
        "\n",
        "# Storytelling with Data\n",
        "\n",
        "In this sprint challenge you'll work with a dataset from **FiveThirtyEight's article, [Every Guest Jon Stewart Ever Had On ‘The Daily Show’](https://fivethirtyeight.com/features/every-guest-jon-stewart-ever-had-on-the-daily-show/)**!"
      ]
    },
    {
      "metadata": {
        "id": "Thm2n5FF2Fnp",
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      "cell_type": "markdown",
      "source": [
        "# Part 0 — Run this starter code\n",
        "\n",
        "You don't need to add or change anything here. Just run this cell and it loads the data for you, into a dataframe named `df`.\n",
        "\n",
        "(You can explore the data if you want, but it's not required to pass the Sprint Challenge.)"
      ]
    },
    {
      "metadata": {
        "id": "0rTHgzJIuRS7",
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      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/daily-show-guests/daily_show_guests.csv')\n",
        "df.rename(columns={'YEAR': 'Year', 'Raw_Guest_List': 'Guest'}, inplace=True)\n",
        "\n",
        "def get_occupation(group):\n",
        "    if group in ['Acting', 'Comedy', 'Musician']:\n",
        "        return 'Acting, Comedy & Music'\n",
        "    elif group in ['Media', 'media']:\n",
        "        return 'Media'\n",
        "    elif group in ['Government', 'Politician', 'Political Aide']:\n",
        "        return 'Government and Politics'\n",
        "    else:\n",
        "        return 'Other'\n",
        "      \n",
        "df['Occupation'] = df['Group'].apply(get_occupation)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "# Part 1 — What's the breakdown of guests’ occupations per year?\n",
        "\n",
        "For example, in 1999, what percentage of guests were actors, comedians, or musicians? What percentage were in the media? What percentage were in politics? What percentage were from another occupation?\n",
        "\n",
        "Then, what about in 2000? In 2001? And so on, up through 2015.\n",
        "\n",
        "So, **for each year of _The Daily Show_, calculate the percentage of guests from each occupation:**\n",
        "- Acting, Comedy & Music\n",
        "- Government and Politics\n",
        "- Media\n",
        "- Other\n",
        "\n",
        "#### Hints:\n",
        "1. Use pandas to make a **crosstab** of **`Year`** & **`Occupation`**. ([This documentation](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#cross-tabulations) has examples and explanation.)\n",
        "2. To get percentages instead of counts, use crosstab's **`normalize`** parameter to normalize over each _row._ ([This documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.crosstab.html) describes the parameter and its options.)\n",
        "3. You'll know you've calculated the crosstab correctly when the percentage of \"Acting, Comedy & Music\" guests is 90.36% in 1999, and 45% in 2015."
      ]
    },
    {
      "metadata": {
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          "height": 194
        },
        "outputId": "1761f8c0-7f8b-4361-f51a-2cad64d4d79f"
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 101,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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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>GoogleKnowlege_Occupation</th>\n",
              "      <th>Show</th>\n",
              "      <th>Group</th>\n",
              "      <th>Guest</th>\n",
              "      <th>Occupation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1999</td>\n",
              "      <td>actor</td>\n",
              "      <td>1/11/99</td>\n",
              "      <td>Acting</td>\n",
              "      <td>Michael J. Fox</td>\n",
              "      <td>Acting, Comedy &amp; Music</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1999</td>\n",
              "      <td>Comedian</td>\n",
              "      <td>1/12/99</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>Sandra Bernhard</td>\n",
              "      <td>Acting, Comedy &amp; Music</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1999</td>\n",
              "      <td>television actress</td>\n",
              "      <td>1/13/99</td>\n",
              "      <td>Acting</td>\n",
              "      <td>Tracey Ullman</td>\n",
              "      <td>Acting, Comedy &amp; Music</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1999</td>\n",
              "      <td>film actress</td>\n",
              "      <td>1/14/99</td>\n",
              "      <td>Acting</td>\n",
              "      <td>Gillian Anderson</td>\n",
              "      <td>Acting, Comedy &amp; Music</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1999</td>\n",
              "      <td>actor</td>\n",
              "      <td>1/18/99</td>\n",
              "      <td>Acting</td>\n",
              "      <td>David Alan Grier</td>\n",
              "      <td>Acting, Comedy &amp; Music</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year GoogleKnowlege_Occupation     Show   Group             Guest  \\\n",
              "0  1999                     actor  1/11/99  Acting    Michael J. Fox   \n",
              "1  1999                  Comedian  1/12/99  Comedy   Sandra Bernhard   \n",
              "2  1999        television actress  1/13/99  Acting     Tracey Ullman   \n",
              "3  1999              film actress  1/14/99  Acting  Gillian Anderson   \n",
              "4  1999                     actor  1/18/99  Acting  David Alan Grier   \n",
              "\n",
              "               Occupation  \n",
              "0  Acting, Comedy & Music  \n",
              "1  Acting, Comedy & Music  \n",
              "2  Acting, Comedy & Music  \n",
              "3  Acting, Comedy & Music  \n",
              "4  Acting, Comedy & Music  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "metadata": {
        "id": "ZLFrnqfVTF7A",
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        },
        "outputId": "26d2cde8-d0c9-4cb0-b81c-69999b288cc0"
      },
      "cell_type": "code",
      "source": [
        "len(df['Occupation'].unique())"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "metadata": {
        "id": "fWUQ_ddSaIpf",
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        },
        "outputId": "6aa94deb-e0b2-41be-e5c2-257a79ed284e"
      },
      "cell_type": "code",
      "source": [
        "df['Occupation'].value_counts()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Acting, Comedy & Music     1203\n",
              "Media                       756\n",
              "Government and Politics     384\n",
              "Other                       350\n",
              "Name: Occupation, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "metadata": {
        "id": "N8--eBCpTF-U",
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        },
        "outputId": "77498b83-c42e-4c38-cecf-c88c6d7f0c64"
      },
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                          0\n",
              "GoogleKnowlege_Occupation    26\n",
              "Show                          0\n",
              "Group                        31\n",
              "Guest                         0\n",
              "Occupation                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "metadata": {
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        "outputId": "14977dba-301c-47f5-eeda-769e10b7b517"
      },
      "cell_type": "code",
      "source": [
        "len(df['Year'].unique())"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "metadata": {
        "id": "51hXZL_ETGDY",
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        "outputId": "fe171c4b-0664-4c2d-8f20-123823db86a8"
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      "cell_type": "code",
      "source": [
        "df_years = df.groupby(['Year', 'Occupation'])\n",
        "df_years.size()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year  Occupation             \n",
              "1999  Acting, Comedy & Music     150\n",
              "      Government and Politics      2\n",
              "      Media                       11\n",
              "      Other                        3\n",
              "2000  Acting, Comedy & Music     125\n",
              "      Government and Politics     14\n",
              "      Media                       21\n",
              "      Other                        9\n",
              "2001  Acting, Comedy & Music     114\n",
              "      Government and Politics      6\n",
              "      Media                       31\n",
              "      Other                        6\n",
              "2002  Acting, Comedy & Music      99\n",
              "      Government and Politics     11\n",
              "      Media                       42\n",
              "      Other                        7\n",
              "2003  Acting, Comedy & Music      93\n",
              "      Government and Politics     17\n",
              "      Media                       41\n",
              "      Other                       15\n",
              "2004  Acting, Comedy & Music      63\n",
              "      Government and Politics     37\n",
              "      Media                       45\n",
              "      Other                       19\n",
              "2005  Acting, Comedy & Music      60\n",
              "      Government and Politics     26\n",
              "      Media                       54\n",
              "      Other                       22\n",
              "2006  Acting, Comedy & Music      58\n",
              "      Government and Politics     31\n",
              "                                ... \n",
              "2008  Media                       77\n",
              "      Other                       20\n",
              "2009  Acting, Comedy & Music      34\n",
              "      Government and Politics     34\n",
              "      Media                       59\n",
              "      Other                       36\n",
              "2010  Acting, Comedy & Music      58\n",
              "      Government and Politics     30\n",
              "      Media                       50\n",
              "      Other                       27\n",
              "2011  Acting, Comedy & Music      55\n",
              "      Government and Politics     27\n",
              "      Media                       51\n",
              "      Other                       30\n",
              "2012  Acting, Comedy & Music      44\n",
              "      Government and Politics     33\n",
              "      Media                       52\n",
              "      Other                       35\n",
              "2013  Acting, Comedy & Music      71\n",
              "      Government and Politics     21\n",
              "      Media                       51\n",
              "      Other                       23\n",
              "2014  Acting, Comedy & Music      64\n",
              "      Government and Politics     21\n",
              "      Media                       53\n",
              "      Other                       25\n",
              "2015  Acting, Comedy & Music      45\n",
              "      Government and Politics     17\n",
              "      Media                       24\n",
              "      Other                       14\n",
              "Length: 68, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "metadata": {
        "id": "CBW4XJr5TGFZ",
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        },
        "outputId": "c8cf1215-1347-4545-8538-94430fef56b4"
      },
      "cell_type": "code",
      "source": [
        "df_year = df.groupby('Year')\n",
        "df_year.get_group(1999)['Occupation'].value_counts()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Acting, Comedy & Music     150\n",
              "Media                       11\n",
              "Other                        3\n",
              "Government and Politics      2\n",
              "Name: Occupation, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "metadata": {
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        "colab": {
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        },
        "outputId": "fd74714f-ab26-4b51-8397-9c2519ba728a"
      },
      "cell_type": "code",
      "source": [
        "for num in df_year.get_group(1999)['Occupation'].value_counts():\n",
        "  print(num / df_year.get_group(1999)['Occupation'].value_counts().sum() * 100)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90.36144578313254\n",
            "6.626506024096386\n",
            "1.8072289156626504\n",
            "1.2048192771084338\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "I9bEjuVJVIrf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "occupations = ['Acting, Comedy & Music', 'Government and Politics', 'Media', 'Other']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mY0iic4tVIuI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1528
        },
        "outputId": "fa1a5d9b-76b9-457c-ef84-95dd874d0a07"
      },
      "cell_type": "code",
      "source": [
        "for group, data in df_year:\n",
        "  print(group)\n",
        "  for num in data['Occupation'].value_counts():\n",
        "    print(num / data['Occupation'].value_counts().sum() * 100)\n",
        "  "
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1999\n",
            "90.36144578313254\n",
            "6.626506024096386\n",
            "1.8072289156626504\n",
            "1.2048192771084338\n",
            "2000\n",
            "73.96449704142012\n",
            "12.42603550295858\n",
            "8.284023668639055\n",
            "5.325443786982249\n",
            "2001\n",
            "72.61146496815286\n",
            "19.745222929936308\n",
            "3.821656050955414\n",
            "3.821656050955414\n",
            "2002\n",
            "62.264150943396224\n",
            "26.41509433962264\n",
            "6.918238993710692\n",
            "4.40251572327044\n",
            "2003\n",
            "56.024096385542165\n",
            "24.69879518072289\n",
            "10.240963855421686\n",
            "9.036144578313253\n",
            "2004\n",
            "38.41463414634146\n",
            "27.439024390243905\n",
            "22.5609756097561\n",
            "11.585365853658537\n",
            "2005\n",
            "37.03703703703704\n",
            "33.33333333333333\n",
            "16.049382716049383\n",
            "13.580246913580247\n",
            "2006\n",
            "36.024844720496894\n",
            "29.19254658385093\n",
            "19.25465838509317\n",
            "15.527950310559005\n",
            "2007\n",
            "33.33333333333333\n",
            "25.53191489361702\n",
            "24.113475177304963\n",
            "17.02127659574468\n",
            "2008\n",
            "46.95121951219512\n",
            "20.73170731707317\n",
            "20.121951219512198\n",
            "12.195121951219512\n",
            "2009\n",
            "36.19631901840491\n",
            "22.085889570552148\n",
            "20.858895705521473\n",
            "20.858895705521473\n",
            "2010\n",
            "35.15151515151515\n",
            "30.303030303030305\n",
            "18.181818181818183\n",
            "16.363636363636363\n",
            "2011\n",
            "33.74233128834356\n",
            "31.28834355828221\n",
            "18.404907975460123\n",
            "16.56441717791411\n",
            "2012\n",
            "31.70731707317073\n",
            "26.82926829268293\n",
            "21.341463414634145\n",
            "20.121951219512198\n",
            "2013\n",
            "42.77108433734939\n",
            "30.72289156626506\n",
            "13.855421686746988\n",
            "12.650602409638553\n",
            "2014\n",
            "39.263803680981596\n",
            "32.515337423312886\n",
            "15.337423312883436\n",
            "12.883435582822086\n",
            "2015\n",
            "45.0\n",
            "24.0\n",
            "17.0\n",
            "14.000000000000002\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gahQ3LPlVI67",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1137
        },
        "outputId": "fcf48522-412d-4160-87ce-c6a526a6df6a"
      },
      "cell_type": "code",
      "source": [
        "df_year['Occupation'].value_counts()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year  Occupation             \n",
              "1999  Acting, Comedy & Music     150\n",
              "      Media                       11\n",
              "      Other                        3\n",
              "      Government and Politics      2\n",
              "2000  Acting, Comedy & Music     125\n",
              "      Media                       21\n",
              "      Government and Politics     14\n",
              "      Other                        9\n",
              "2001  Acting, Comedy & Music     114\n",
              "      Media                       31\n",
              "      Government and Politics      6\n",
              "      Other                        6\n",
              "2002  Acting, Comedy & Music      99\n",
              "      Media                       42\n",
              "      Government and Politics     11\n",
              "      Other                        7\n",
              "2003  Acting, Comedy & Music      93\n",
              "      Media                       41\n",
              "      Government and Politics     17\n",
              "      Other                       15\n",
              "2004  Acting, Comedy & Music      63\n",
              "      Media                       45\n",
              "      Government and Politics     37\n",
              "      Other                       19\n",
              "2005  Acting, Comedy & Music      60\n",
              "      Media                       54\n",
              "      Government and Politics     26\n",
              "      Other                       22\n",
              "2006  Acting, Comedy & Music      58\n",
              "      Media                       47\n",
              "                                ... \n",
              "2008  Government and Politics     33\n",
              "      Other                       20\n",
              "2009  Media                       59\n",
              "      Other                       36\n",
              "      Acting, Comedy & Music      34\n",
              "      Government and Politics     34\n",
              "2010  Acting, Comedy & Music      58\n",
              "      Media                       50\n",
              "      Government and Politics     30\n",
              "      Other                       27\n",
              "2011  Acting, Comedy & Music      55\n",
              "      Media                       51\n",
              "      Other                       30\n",
              "      Government and Politics     27\n",
              "2012  Media                       52\n",
              "      Acting, Comedy & Music      44\n",
              "      Other                       35\n",
              "      Government and Politics     33\n",
              "2013  Acting, Comedy & Music      71\n",
              "      Media                       51\n",
              "      Other                       23\n",
              "      Government and Politics     21\n",
              "2014  Acting, Comedy & Music      64\n",
              "      Media                       53\n",
              "      Other                       25\n",
              "      Government and Politics     21\n",
              "2015  Acting, Comedy & Music      45\n",
              "      Media                       24\n",
              "      Government and Politics     17\n",
              "      Other                       14\n",
              "Name: Occupation, Length: 68, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "metadata": {
        "id": "oTROJl49VI9p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sGWcl-rZVJAL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Nqf9oJJDDu-d",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Part 2 — Recreate this explanatory visualization:"
      ]
    },
    {
      "metadata": {
        "id": "scozkHQc0_eD",
        "colab_type": "code",
        "outputId": "b7d09c45-3cd4-4a42-967a-c76b8ec34b60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Image\n",
        "url = 'https://fivethirtyeight.com/wp-content/uploads/2015/08/hickey-datalab-dailyshow.png'\n",
        "example = Image(url, width=500)\n",
        "display(example)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Nx1VBFMvhEasTu+oRpidrskpGQ6zEfj4pHxrY69C3v4xuiKYiEEsrYk12BEeVbwk2xVwi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\n4JhflzBydcIRb30AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 500
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "W7lw3JzAE6BJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Hint:** use the crosstab you calculated in part 1!\n",
        "\n",
        "**Expectations:** Your plot should include:\n",
        "- 3 lines visualizing \"occupation of guests, by year.\" The shapes of the lines should look roughly identical to 538's example. Each line should be a different color. (But you don't need to use the _same_ colors as 538.)\n",
        "- Legend or labels for the lines. (But you don't need each label positioned next to its line or colored like 538.)\n",
        "- Title in the upper left: _\"Who Got To Be On 'The Daily Show'?\"_ with more visual emphasis than the subtitle. (Bolder and/or larger font.)\n",
        "- Subtitle underneath the title: _\"Occupation of guests, by year\"_\n",
        "\n",
        "Any visual element not specifically mentioned in the expectations is an optional bonus, but it's _not_ required to pass the Sprint Challenge.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "E8XBAr8rz_Na",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "1e3761ad-8a43-4876-8d83-1eb0783b8789"
      },
      "cell_type": "code",
      "source": [
        "years = [x for x in range(1999, 2016)]\n",
        "percent_acm = [90.36, 73.96, 72.61, 62.26, 56.02, 38.41, 37.03, 36.02, 33.33, 20.73, 36.19, 35.15, 33.74, 31.70, 42.77, 39.26, 45]\n",
        "percent_m = [np.random.randint(20, 40) for x in range(17)]\n",
        "percent_gp = [np.random.randint(5, 20) for x in range(17)]\n",
        "plt.plot(final.index, percent_acm, color = 'blue')\n",
        "plt.plot(final.index, percent_m, color = 'purple')\n",
        "plt.plot(final.index, percent_gp, color = 'red');\n",
        "plt.legend()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7efc762b5b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GDSXCwiQ2b04mNNQjDo95TE5ku5G3JcnChdVnHR39tl1GskvIVXKKti2BvnsZ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\nWHwm/ghKJUkffkbKK689MdLN8rrsdlR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ZqvuM5EXzVtUWVW32vp0BbARKVDXujR0E+gP9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\nWfM2xpgIsuZtjDER9C/8xK/MvgcopQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7efc762fe7f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "LuacMjSf2ses",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Part 3 — Who were the top 10 guests on _The Daily Show_?\n",
        "\n",
        "**Make a plot** that shows their names and number of appearances.\n",
        "\n",
        "**Hint:** you can use the pandas `value_counts` method.\n",
        "\n",
        "**Expectations:** This can be a simple, quick plot: exploratory, not explanatory. \n",
        "\n",
        "If you want, you can add titles and change aesthetics, but it's _not_ required to pass the Sprint Challenge."
      ]
    },
    {
      "metadata": {
        "id": "tbwfBN3HsFlh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "d137bbe9-05d6-4839-ae8e-e3e0a176539a"
      },
      "cell_type": "code",
      "source": [
        "top_10 = df['Guest'].value_counts().head(10)\n",
        "top_10"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fareed Zakaria    19\n",
              "Denis Leary       17\n",
              "Brian Williams    16\n",
              "Ricky Gervais     13\n",
              "Paul Rudd         13\n",
              "Tom Brokaw        12\n",
              "Bill O'Reilly     10\n",
              "Richard Lewis     10\n",
              "Reza Aslan        10\n",
              "Will Ferrell      10\n",
              "Name: Guest, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "metadata": {
        "id": "BwEBnwGdqInG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "a7ae972d-7b2b-470e-c8f1-123aa56ce20e"
      },
      "cell_type": "code",
      "source": [
        "plt.barh(y = top_10.index, width = top_10.values)\n",
        "plt.title(\"Top 10 Guests on 'The Daily Show'\")\n",
        "plt.xlabel('Number of apperances');"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O7CRpDdK03pMR0QJsR0pkkEZ4+wP3kkZyGwNvRcQHXYzXzMzaqSHJKSImAlPy/aAbgdHt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\n20cDewBbAjtJGtjIoNrQnuv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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7efc762d1ef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rMdQwhv0qwen",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plt.barh?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MBhpvukXrW3Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "outputId": "ffd9bc01-e748-4986-8fa3-eb7dcba23942"
      },
      "cell_type": "code",
      "source": [
        "final = pd.crosstab(index = df['Year'], columns = df['Occupation'], normalize = 'index')\n",
        "final"
      ],
      "execution_count": 125,
      "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>Occupation</th>\n",
              "      <th>Acting, Comedy &amp; Music</th>\n",
              "      <th>Government and Politics</th>\n",
              "      <th>Media</th>\n",
              "      <th>Other</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>0.903614</td>\n",
              "      <td>0.012048</td>\n",
              "      <td>0.066265</td>\n",
              "      <td>0.018072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>0.739645</td>\n",
              "      <td>0.082840</td>\n",
              "      <td>0.124260</td>\n",
              "      <td>0.053254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>0.726115</td>\n",
              "      <td>0.038217</td>\n",
              "      <td>0.197452</td>\n",
              "      <td>0.038217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>0.622642</td>\n",
              "      <td>0.069182</td>\n",
              "      <td>0.264151</td>\n",
              "      <td>0.044025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>0.560241</td>\n",
              "      <td>0.102410</td>\n",
              "      <td>0.246988</td>\n",
              "      <td>0.090361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>0.384146</td>\n",
              "      <td>0.225610</td>\n",
              "      <td>0.274390</td>\n",
              "      <td>0.115854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>0.370370</td>\n",
              "      <td>0.160494</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.135802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>0.360248</td>\n",
              "      <td>0.192547</td>\n",
              "      <td>0.291925</td>\n",
              "      <td>0.155280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>0.255319</td>\n",
              "      <td>0.170213</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.241135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>0.207317</td>\n",
              "      <td>0.201220</td>\n",
              "      <td>0.469512</td>\n",
              "      <td>0.121951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>0.208589</td>\n",
              "      <td>0.208589</td>\n",
              "      <td>0.361963</td>\n",
              "      <td>0.220859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>0.351515</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.303030</td>\n",
              "      <td>0.163636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>0.337423</td>\n",
              "      <td>0.165644</td>\n",
              "      <td>0.312883</td>\n",
              "      <td>0.184049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>0.268293</td>\n",
              "      <td>0.201220</td>\n",
              "      <td>0.317073</td>\n",
              "      <td>0.213415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>0.427711</td>\n",
              "      <td>0.126506</td>\n",
              "      <td>0.307229</td>\n",
              "      <td>0.138554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>0.392638</td>\n",
              "      <td>0.128834</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.153374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>0.450000</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>0.140000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Occupation  Acting, Comedy & Music  Government and Politics     Media  \\\n",
              "Year                                                                    \n",
              "1999                      0.903614                 0.012048  0.066265   \n",
              "2000                      0.739645                 0.082840  0.124260   \n",
              "2001                      0.726115                 0.038217  0.197452   \n",
              "2002                      0.622642                 0.069182  0.264151   \n",
              "2003                      0.560241                 0.102410  0.246988   \n",
              "2004                      0.384146                 0.225610  0.274390   \n",
              "2005                      0.370370                 0.160494  0.333333   \n",
              "2006                      0.360248                 0.192547  0.291925   \n",
              "2007                      0.255319                 0.170213  0.333333   \n",
              "2008                      0.207317                 0.201220  0.469512   \n",
              "2009                      0.208589                 0.208589  0.361963   \n",
              "2010                      0.351515                 0.181818  0.303030   \n",
              "2011                      0.337423                 0.165644  0.312883   \n",
              "2012                      0.268293                 0.201220  0.317073   \n",
              "2013                      0.427711                 0.126506  0.307229   \n",
              "2014                      0.392638                 0.128834  0.325153   \n",
              "2015                      0.450000                 0.170000  0.240000   \n",
              "\n",
              "Occupation     Other  \n",
              "Year                  \n",
              "1999        0.018072  \n",
              "2000        0.053254  \n",
              "2001        0.038217  \n",
              "2002        0.044025  \n",
              "2003        0.090361  \n",
              "2004        0.115854  \n",
              "2005        0.135802  \n",
              "2006        0.155280  \n",
              "2007        0.241135  \n",
              "2008        0.121951  \n",
              "2009        0.220859  \n",
              "2010        0.163636  \n",
              "2011        0.184049  \n",
              "2012        0.213415  \n",
              "2013        0.138554  \n",
              "2014        0.153374  \n",
              "2015        0.140000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "metadata": {
        "id": "90w-s1sQ3Lv4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xEc9byOU3L0M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6c578449-c944-4f03-8910-32df6c462237"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "percent_acm = final['Acting, Comedy & Music']\n",
        "percent_m = final['Government and Politics']\n",
        "percent_gp = final['Media']\n",
        "plt.plot(final.index, percent_acm, color = 'blue')\n",
        "plt.plot(final.index, percent_m, color = 'purple')\n",
        "plt.plot(final.index, percent_gp, color = 'red')\n",
        "plt.legend(final.columns);"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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djSAI7iASeSaTJ49Cy5Y2Ll1SieXhBCGDEIk8E+rZ04pG42qVOxyejkYQhJQS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\nhPByUsiFEMLLSSEXQggvJ4VcCCG83P8DzXIMYBmv7dEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7efc76b23828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_Of4sN1w3L2q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K5fTGy2R1VUe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MLgTu8eE2MwT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}