Mego_Franks_Final_Project(1).ipynb

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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/megoeggo/colab/blob/main/Mego_Franks_Final_Project(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Final Project\n",
        "Choose one of the research briefs options below to focus on for this final project:\n",
        "\n",
        "1. [Public Awareness of Alcohol  as a Risk Factor for Cancer](https://hints.cancer.gov/docs/Briefs/HINTS_Brief_47.pdf)\n",
        "\n",
        "Use this dataset to complete the following steps:\n",
        "\n",
        "https://drive.google.com/file/d/1c7Nv3xTjGsYpHlT5oXPFfXurmDbl1AlB/view?usp=sharing\n",
        "\n",
        "If needed, you can find the codebook for this dataset here:\n",
        "\n",
        "https://hints.cancer.gov/data/survey-instruments.aspx#H5C4\n",
        "\n",
        "And you can download the original data here:\n",
        "\n",
        "https://hints.cancer.gov/data/Default.aspx\n"
      ],
      "metadata": {
        "id": "TGTe8AvZ1lXW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recreate a visualization\n",
        "\n",
        "Insert your code into the code box below to recreate a visualization from your brief. Make sure to comment your code!"
      ],
      "metadata": {
        "id": "h56cHFewiGBu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "6HFJk8Xy1fUv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e471c92-8338-4180-f36a-ed14d50b1a13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.12.2)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.23.5)\n",
            "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.5.3)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /usr/local/lib/python3.10/dist-packages (from seaborn) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.43.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->seaborn) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "# code for recreating a visualization\n",
        "\n",
        "# first import libraries\n",
        "\n",
        "!pip install seaborn\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "from matplotlib import style"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now import data\n",
        "\n",
        "hints_data_raw = pd.read_csv(\"/content/hints5_cycle4_final_project.csv\")"
      ],
      "metadata": {
        "id": "0Vpnyd_VWMBP"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a subset of the data to look at just the average drinks per week column\n",
        "\n",
        "hints_data_drinks = hints_data_raw[[\"AvgDrinksPerWeek\"]]"
      ],
      "metadata": {
        "id": "G1aevs7Z5otY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# replace all missing data with a negative number, so it will work with a numeric loop\n",
        "\n",
        "hints_data_drinks = hints_data_drinks.apply(lambda x: x.replace({\"^.*Missing Data.*$\":-1}, regex=True))\n",
        "\n"
      ],
      "metadata": {
        "id": "gAYMC8kdLTBB"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# also turn the data type to numeric so that the loop works\n",
        "\n",
        "hints_data_drinks = hints_data_drinks.apply(pd.to_numeric, errors='coerce')\n",
        "\n",
        "hints_data_drinks.dtypes"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "lvnU3cxKL8nx",
        "outputId": "3ce76f3d-c418-4f39-b91c-240bfa0ff054"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AvgDrinksPerWeek    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define the aforementioned loop - this will turn the data into the ranges seen in the infographic; 0 drinks a week, 1-10 drinks a week, etc.\n",
        "\n",
        "def drink_ranges(AvgDrinksPerWeek):\n",
        "  if AvgDrinksPerWeek == 0:\n",
        "    return \"0\"\n",
        "  if AvgDrinksPerWeek >= 1 and AvgDrinksPerWeek <= 10:\n",
        "    return \"1-10\"\n",
        "  if AvgDrinksPerWeek >= 11 and AvgDrinksPerWeek <= 20:\n",
        "    return \"11-20\"\n",
        "  if AvgDrinksPerWeek >= 21 and AvgDrinksPerWeek <=30:\n",
        "    return \"21-30\"\n",
        "  if AvgDrinksPerWeek >= 31:\n",
        "    return \"31+\"\n",
        "  else:\n",
        "    return \"Missing Data\"\n"
      ],
      "metadata": {
        "id": "tiQKfe1eCoGs"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# this for loop does the work of turning the data points into their assigned ranges\n",
        "\n",
        "ranges_list = []\n",
        "for categ in hints_data_drinks.AvgDrinksPerWeek.items():\n",
        "  ranges_list.append(drink_ranges(categ[1]))\n",
        "\n",
        "print(ranges_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqcgWBfBMDox",
        "outputId": "131cf75c-018d-4e18-8659-8c286ab48de5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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Data', '0', '1-10', '0', '21-30', '1-10', '1-10', '11-20', '0', '0', '1-10', '11-20', '1-10', '1-10', '0', '11-20', '0', '11-20', '1-10', '0', '1-10', '1-10', '0', '0', 'Missing Data', '1-10', 'Missing Data', '0', 'Missing Data', '1-10', 'Missing Data', '1-10', '0', '1-10', '0', '0', '1-10', '0', '0', '21-30', '31+', '0', 'Missing Data', '21-30', '0', '0', '0', 'Missing Data', '0', '1-10', '1-10', '0', '0', '0', '0', '1-10', '0', '0', '0', '0', '11-20', '0', '0', '0', '1-10', '11-20', '0', '0', '0', '0', '0', '0', '1-10', 'Missing Data', '1-10', '0', '1-10', '1-10', '1-10', '1-10', '0', '11-20', '0', '0', '1-10', '0', '0', '1-10', '0', '0', '0', '1-10', '11-20', '0', '1-10', '1-10', '0', '0', '11-20', '1-10', '0', '1-10', '0', '1-10', '0', '0', '0', '1-10', '11-20', '11-20', '1-10', '1-10', '1-10', '1-10', '1-10', 'Missing Data', '1-10', 'Missing Data', '0', '1-10', '1-10', '0', '11-20', '0', '1-10', '11-20', '0', '0', '0', '1-10', '11-20', '0', '0', '1-10', '0', '0', 'Missing Data', '1-10', '0', '0', '1-10', '0', '0', '0', '1-10', '0', '1-10', '0', '0', 'Missing Data', 'Missing Data', '11-20', '21-30', '11-20', '11-20', '1-10', '0', '0', '0', '0', '1-10', '1-10', '0', '0', '0', '21-30', '0', 'Missing Data', 'Missing Data', '1-10', '1-10', '0', '1-10', '0', 'Missing Data', '0', 'Missing Data', '31+', '0', '1-10', '0', '0', '1-10', '1-10', '0', '0', '1-10', '0', '0', '0', '0', '1-10', '11-20', '0', '31+', '0', '1-10', '1-10', '0', '0', '0', 'Missing Data', '1-10', '0', '0', '0', '0', '1-10', '1-10', '0', '0', '1-10', '1-10', '0', '1-10', '1-10', 'Missing Data', '0', '1-10', '11-20', '0', '0', '11-20', '1-10', '1-10', '0', '11-20', '1-10', 'Missing Data', '0', '1-10', '0', '0', 'Missing Data', 'Missing Data', '0', '0', '1-10', '0', '1-10', '0', '0', '0', '1-10', '0', '0', '0', '1-10', '0', '0', '0', '1-10', '31+', '0', '0', '1-10', '0', '0', '0', 'Missing Data', '1-10', '0', '0', '1-10', '0', '0', '0', '1-10', '0', '1-10', '0', '0', '11-20', 'Missing Data', '0', '0', '0', '11-20', '1-10', '31+', '0', 'Missing Data', '0', '0', '1-10', '1-10', 'Missing Data', '0', '1-10', '0', '0', '0', '11-20', 'Missing Data', '1-10', '1-10', '1-10', '0', 'Missing Data', '1-10', '0', '0', '0', '0', '0', '1-10', '1-10', '1-10', '0', '0', '1-10', '0', '0', '0', '1-10', '0', 'Missing Data', '21-30', '0', '0', '1-10', '0', '1-10', '1-10', '1-10', '1-10', '31+', '1-10', 'Missing Data', '11-20', '0', '11-20', '21-30', '11-20', '1-10', '0', '0', '0', '0', '1-10', '0', '1-10', '11-20', '0', '1-10', '0', '21-30', '0', '1-10', '0', '0', '0', '0', 'Missing Data', '0', '0', '11-20', '1-10', '0', '1-10', '0', '1-10', '0', '0', '11-20', '0', '0', '0', '1-10', '0', '0', '0', '11-20', '0', '1-10', '21-30', '0', '1-10', '0', 'Missing Data', '11-20', '1-10', '0', '0', '1-10', '11-20', '11-20', 'Missing Data', '21-30', '1-10', '1-10', '1-10', '1-10', '1-10', '1-10', '1-10', '1-10', '0', '0', '0', '1-10', '0', '0', '1-10', '0', '1-10', '1-10', '0', '1-10', '0', '0', '0', '0', '1-10', '1-10', '0', '1-10', '1-10', '0', '0', '1-10', '1-10', '0', '0', '21-30', 'Missing Data', '1-10', '0', '1-10', 'Missing Data', '1-10', '0', '0', '0', '1-10', '0', '1-10', '1-10', '0', '0', '11-20', '1-10', '1-10', '1-10', '0', '1-10', '1-10', '0', 'Missing Data', '1-10', '1-10', '11-20', '1-10', '21-30', '1-10', '1-10', '0', '0', '1-10', '0', '0', '1-10', '0', '0', '0', '11-20', '0', 'Missing Data', '21-30', '0', '0', '0', '0', '31+', '1-10', '1-10', '0', 'Missing Data', '1-10', '11-20', '0', '1-10', '11-20', '0', 'Missing Data', '11-20', 'Missing Data', '31+', '0', '0', '1-10', '11-20', '1-10', '1-10', '0', '1-10', '0', '0', '11-20', '0', '21-30', '1-10', 'Missing Data', 'Missing Data', '0', '0', '0', '0', 'Missing Data', 'Missing Data', 'Missing Data', '0', '0', '0', 'Missing Data', '0', '1-10', '0', '1-10', '1-10', '0', '0', '1-10', '0', '0', '0', '1-10', '0', 'Missing Data', '1-10', 'Missing Data', '0', '21-30', '0', '0', '0', '0', '0', 'Missing Data', '1-10', '0', '1-10', '1-10', '0', '1-10', '0', 'Missing Data', 'Missing Data', '0', '1-10', 'Missing Data', '1-10', '0', '11-20', 'Missing Data', '1-10', '0', '0', '1-10', 'Missing Data', '1-10', '1-10', '1-10', '0', '0', '0', '0', '0', '0', '1-10', '11-20', '1-10', '1-10', 'Missing Data', 'Missing Data', 'Missing Data', '0', '0', 'Missing Data', 'Missing Data', '0', 'Missing Data', '0', '1-10', '0', '0', '0', '0', '11-20', '11-20', '21-30', '11-20', '1-10', '0', '1-10', '0', 'Missing Data', '11-20', 'Missing Data', 'Missing Data', '0', '1-10', '0', '0', 'Missing Data', '0', '0', '0', '0', 'Missing Data', '0', '0', '0', '21-30', '0', 'Missing Data', 'Missing Data', '0', 'Missing Data', '0', '0', 'Missing Data', '0', '0', '0', 'Missing Data', 'Missing Data', 'Missing Data', '0', 'Missing Data', 'Missing Data', '0', '0', 'Missing Data', 'Missing Data', '0', 'Missing Data', '1-10', '0', 'Missing Data', '0', '0', '0', '0', '0', '0', '1-10', '1-10', '1-10', '0', 'Missing Data', '0', '0', '0', 'Missing Data', '0', '1-10', '1-10', 'Missing Data', 'Missing Data', 'Missing Data', '0', 'Missing Data', 'Missing Data', '1-10', 'Missing Data', '0', 'Missing Data', '0', '11-20', '0', '0', '0', '0', '0', 'Missing Data', '0', 'Missing Data', '1-10', '21-30', '0', '0', '0', 'Missing Data', 'Missing Data', 'Missing Data', 'Missing Data', 'Missing Data', 'Missing Data', 'Missing Data', '0', '0', '0', '0', '0', '0', '21-30', '1-10', '0', '0', 'Missing Data', '0', '0', '0', '0', 'Missing Data', 'Missing Data', 'Missing Data', '0', '1-10', '0', '1-10', 'Missing Data', 'Missing Data', '0', '1-10', '1-10', '1-10', 'Missing Data', '0', '0', 'Missing Data', '0', 'Missing Data', 'Missing Data', '0', '0', 'Missing Data', 'Missing Data', 'Missing Data', '1-10', 'Missing Data', 'Missing Data', '1-10', '1-10', '0', '1-10', '1-10', 'Missing Data', 'Missing Data', '0', 'Missing Data', '0', 'Missing Data', '31+', 'Missing Data', '0', '1-10', '1-10', '0', '0', '1-10', '0', '11-20', '0', 'Missing Data', '0', '1-10', 'Missing Data', '1-10', '1-10', '0', '1-10', 'Missing Data', '0', 'Missing Data', 'Missing Data', '21-30', 'Missing Data', 'Missing Data', '0', '1-10', 'Missing Data', 'Missing Data', 'Missing Data', '0', '0', '0', '0', 'Missing Data', 'Missing Data', '1-10', '0', '0', '0', '0', '0', '0', 'Missing Data', '0', '0', '0', 'Missing Data', 'Missing Data', '1-10', '21-30', '0', '1-10', '0', '0', '0', '0', 'Missing Data', '0', 'Missing Data', '21-30', '0', '0', 'Missing Data', 'Missing Data', '0', '0', '1-10', '0', 'Missing Data', '0', '21-30', 'Missing Data', '0', '0', '0', '0', '0', '0', '1-10', '0', '0', 'Missing Data', 'Missing Data', '0', '0', 'Missing Data', '1-10', '0', 'Missing Data', '1-10', '0', '0', '0', '1-10', 'Missing Data', 'Missing Data', 'Missing Data', 'Missing Data', 'Missing Data', 'Missing Data', '0', '0', 'Missing Data', '0', '0', '1-10', '0', 'Missing Data', '1-10', '0', '0', '1-10', '0', 'Missing Data', '0', '0', '1-10', '0', '1-10', '11-20', '0', 'Missing Data', '0', '1-10', '0', 'Missing Data', '0', '0', '0', 'Missing Data', '0', 'Missing Data', '0']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# this for loop shows the counts for each range\n",
        "\n",
        "range_freq_dict = {}\n",
        "for category in ranges_list:\n",
        "  if category in range_freq_dict:\n",
        "    range_freq_dict[category] += 1\n",
        "  else:\n",
        "    range_freq_dict[category] = 1\n",
        "\n",
        "range_freq_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyF2EBQbM5Ir",
        "outputId": "4b5dbeb2-de1a-4a19-dd0c-67b305d4215e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'11-20': 207,\n",
              " '0': 1866,\n",
              " '1-10': 1239,\n",
              " 'Missing Data': 431,\n",
              " '21-30': 87,\n",
              " '31+': 35}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now we need to make our counts and ranges into variables so we can make a new dataframe\n",
        "\n",
        "bar_range_vals = []\n",
        "bar_count_vals = []\n",
        "for key, value in range_freq_dict.items():\n",
        "  bar_range_vals.append(key)\n",
        "  bar_count_vals.append(value)\n",
        "\n",
        "print(bar_range_vals)\n",
        "print(bar_count_vals)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "knS1eQ7VN6Mk",
        "outputId": "5c88391d-12de-40ae-c154-ce8bd408a649"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['11-20', '0', '1-10', 'Missing Data', '21-30', '31+']\n",
            "[207, 1866, 1239, 431, 87, 35]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make the new dataframe from ranges and counts\n",
        "\n",
        "barchart = pd.DataFrame({\"ranges\": bar_range_vals, \"counts\": bar_count_vals})\n",
        "\n"
      ],
      "metadata": {
        "id": "Nu5afrRrcU8h"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define the ranges as the category, and turn the counts into percentages\n",
        "\n",
        "barchart[\"ranges\"] = barchart[\"ranges\"].astype(\"category\")\n",
        "\n",
        "barchart[\"counts\"] = (100. * barchart[\"counts\"] / barchart[\"counts\"].sum()).round(1)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Let9rdzJdfeg"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reorder the range categories into a logical order\n",
        "\n",
        "barchart[\"ranges\"] = barchart[\"ranges\"].cat.reorder_categories([\"0\", \"1-10\", \"11-20\", \"21-30\", \"31+\", \"Missing Data\"], ordered=True)\n",
        "\n"
      ],
      "metadata": {
        "id": "iKrvM-c4eGf3"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for our chart we want all the bars except the last one to be the same color, so define the color palette now\n",
        "\n",
        "color = [\"#0a49c7\", \"#0a49c7\", \"#0a49c7\", \"#0a49c7\", \"#0a49c7\", \"gray\"]"
      ],
      "metadata": {
        "id": "PY5AHRfpgGaS"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create the barplot and add styling\n",
        "\n",
        "qc = sns.barplot(y=\"ranges\", x=\"counts\", data=barchart,\n",
        "            errorbar=None,\n",
        "            palette=color,\n",
        "               width=.5,\n",
        "                );\n",
        "plt.bar_label(qc.containers[0], fmt=\"%.1f%%\")\n",
        "plt.suptitle(\"Alcohol Consumption by American Adults (2020)\", color=\"#0a49c7\")\n",
        "plt.title(\"Number of drinks* per week\", color=\"gray\", size=\"small\")\n",
        "plt.ylabel(\" \")\n",
        "plt.xlabel(\" \")\n",
        "plt.tick_params(left = False)\n",
        "qc.axes.get_xaxis().set_visible(False)\n",
        "qc.axes.spines[\"top\"].set_visible(False)\n",
        "qc.axes.spines[\"bottom\"].set_visible(False)\n",
        "qc.axes.spines[\"right\"].set_visible(False)\n",
        "plt.figtext(0.01,0.01, \"*One drink can be 12 fl oz of regular beer; 8-9 fl oz of malt liquor; 5 fl oz of table wine; or 1.5 oz of 80-proof distilled spirits. Source: HINTS 5 Cycle 4, 2020\", wrap=True, fontsize=8)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "beOZeK3c8Ney",
        "outputId": "bb5d95e3-6aee-45d1-b3ec-8fe2da0116a6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Your explanation for the new visualization\n",
        "\n",
        "**What does your new visualization show?**\n",
        "\n",
        "The relationship between how many drinks someone consumes per week, and how worried they are about getting cancer."
      ],
      "metadata": {
        "id": "eL5d9AoO6oZv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Your code for the new visualization\n",
        "\n",
        "Insert your code in the code block below to create a new visualization based on a different demographic variable of your choice. Make sure to comment your code!"
      ],
      "metadata": {
        "id": "_QQfSwNWhZt7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# new visualization code\n",
        "\n",
        "#use the original dataset to create a new dataframe with just our variables of interest\n",
        "\n",
        "worrydata = hints_data_raw[[\"FreqWorryCancer\", \"AvgDrinksPerWeek\"]]\n",
        "\n",
        "#replace all missing values with NaN\n",
        "worrydata = worrydata.replace(\"Missing Data (Not Ascertained)\", np.NaN)\n",
        "\n",
        "worrydata = worrydata.replace(\"Missing data (Not Ascertained)\", np.NaN)\n",
        "\n",
        "worrydata = worrydata.replace(\"Unreadable or Nonconforming Numeric Response\", np.NaN)\n",
        "\n",
        "worrydata = worrydata.replace(\"Multiple responses selected in error\", np.NaN)\n",
        "\n",
        "# now drop the NaNs so our data is clean\n",
        "worrydata = worrydata.dropna()\n",
        "\n",
        "worrydata"
      ],
      "metadata": {
        "id": "YqNgZo6k2wnq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "017b5690-f9ce-435d-8bab-a7e205976bd9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     FreqWorryCancer AvgDrinksPerWeek\n",
              "0           Somewhat               14\n",
              "1           Somewhat                0\n",
              "2           Slightly                2\n",
              "3           Slightly                1\n",
              "4         Moderately                0\n",
              "...              ...              ...\n",
              "3858        Somewhat                0\n",
              "3859        Somewhat                0\n",
              "3860        Slightly                0\n",
              "3862        Somewhat                0\n",
              "3864      Not at all                0\n",
              "\n",
              "[3397 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d69a5f55-0c39-40c2-9114-441e75915c3d\" class=\"colab-df-container\">\n",
              "    <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>FreqWorryCancer</th>\n",
              "      <th>AvgDrinksPerWeek</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Somewhat</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Somewhat</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Slightly</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Slightly</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Moderately</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3858</th>\n",
              "      <td>Somewhat</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3859</th>\n",
              "      <td>Somewhat</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3860</th>\n",
              "      <td>Slightly</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3862</th>\n",
              "      <td>Somewhat</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3864</th>\n",
              "      <td>Not at all</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3397 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d69a5f55-0c39-40c2-9114-441e75915c3d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d69a5f55-0c39-40c2-9114-441e75915c3d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d69a5f55-0c39-40c2-9114-441e75915c3d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-00d9baf0-a512-4a77-9d02-915bcad00d70\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-00d9baf0-a512-4a77-9d02-915bcad00d70')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-00d9baf0-a512-4a77-9d02-915bcad00d70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#turn the number of drinks variable into an actual numeric datatype\n",
        "worrydata[\"AvgDrinksPerWeek\"] = worrydata[\"AvgDrinksPerWeek\"].astype(\"int\")\n",
        "#and turn the worry scale into an ordered category datatype so it displays in the logical order on our chart\n",
        "worrydata[\"FreqWorryCancer\"] = worrydata[\"FreqWorryCancer\"].astype(\"category\")\n",
        "worrydata[\"FreqWorryCancer\"] = worrydata[\"FreqWorryCancer\"].cat.reorder_categories([\"Not at all\", \"Slightly\", \"Somewhat\", \"Moderately\", \"Extremely\"], ordered=True)"
      ],
      "metadata": {
        "id": "fzZLmTpdVWWS"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#build a plot showing each data point (like so many tiny beer bubbles)\n",
        "fig = sns.stripplot(x=\"FreqWorryCancer\", y=\"AvgDrinksPerWeek\", data=worrydata,\n",
        "                    palette=\"YlOrBr\");\n",
        "#add some context to the plot\n",
        "\n",
        "plt.suptitle(\"Worry of Getting Cancer & Number of Drinks Per Week\")\n",
        "plt.ylabel(\"Average Drinks Per Week\")\n",
        "plt.xlabel(\"Level of Worry of Getting Cancer\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "LAxs_eu5V8VT",
        "outputId": "b8933fc6-fb5b-455b-e0de-73929cc8cbc8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-d7bd5d54d8ac>:2: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
            "  fig = sns.stripplot(x=\"FreqWorryCancer\", y=\"AvgDrinksPerWeek\", data=worrydata,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reflections\n",
        "Edit this code block to answer the following questions.\n",
        "\n",
        "**1. Was the original visualization that you reproduced from your brief useful for the data that were being represented? Why or why not? How could it have been improved?**\n",
        "\n",
        "I think  the original visualization was a really useful way to represent the data. HOW much Americans are drinking is a base statistic for a lot of the rest of the survey questions i.e. does alcohol cause cancer, should alcohol ads be further regulated, etc. I think it was smart too to visualize ranges of drinks instead of each indvidual number, as there was a lot of variation. The only improvement I would make would be to remove the missing data bar - it's a small number that I don't think really adds anything useful to the visualization.\n",
        "\n",
        "**2. For the new visualization you created, what type of visualization did you choose and why did you choose that type?**\n",
        "\n",
        "Frankly, I looked up different seaborn plot types, and tried different ones with the data I wanted to look at until I found something I thought looked nice. I like the strip-plot because I think it represents the same idea as a barplot, but shows the wide range of the data - which I like for this visualization as there is not a super clear correlation between level of worry and number of drinks a week.\n",
        "\n",
        "\n",
        "**3. Were you able to understand the data you were working with based on the brief you chose? Were there any additional data you wish you had to enhance your analysis / visualization?**\n",
        "\n",
        "Yes, using the codebook made it a lot easier to understand the data. I was very happy to find the AvgDrinksPerWeek variable that was already calculated by the researchers, as that was initially my first roadblock on how to clean up this data. I think this dataset is very thorough and can't think of any other particular variables I would want to see."
      ],
      "metadata": {
        "id": "07PL70A53VPO"
      }
    }
  ]
}