Census_2010_Diff_Privacy_Demo_State_Extract.ipynb

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      "source": [
        "# Differential Privacy 2010 \n",
        "\n",
        "Modified version of notebook copied from: https://github.com/CT-Data-Collaborative/dp2010\n",
        "\n",
        "Inspired by https://shiny.demog.berkeley.edu/eddieh/DP2010DemoData_CA/ (also on GitHub: https://github.com/edyhsgr/DP2010DemoDataReview)\n",
        "\n",
        "Data Source: https://www.nhgis.org/differentially-private-2010-census-data "
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "FA_l-UF_8Uig",
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      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "Wy0JldVm8Ui6",
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      "source": [
        "### Read and filter DP1 table (people)\n",
        "Enter value in place of ### for CSV that corresponds to geography you would like to filter (eg... /wide_dp1_050.csv for county)\n",
        "*   050 County\n",
        "*   060 County Subdivision (towns)\n",
        "*   610 State Legislative District (Upper)\n",
        "*   620 State Legislative District (Lower)\n",
        "*   970 School District - Unified \n",
        "\n",
        "Enter two digit [State Code](https://www.census.gov/govs/www/gidstate.html) in 'G##' for State you would like to extract from data. (eg. Vermont is 50, so set as >'G50' & <'G51'.)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1s_XziC-8Ui9",
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      "source": [
        "dp1 = pd.read_csv('https://vcgi.nyc3.cdn.digitaloceanspaces.com/census2010dp/wide_dp1_###.csv', encoding='ISO-8859-1')\n",
        "dp1 = dp1[(dp1.gisjoin > 'G50') & (dp1.gisjoin < 'G51')]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_koIwbJ8UjK",
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      },
      "source": [
        "### Read and filter DP14 table (housing units)\n",
        "Enter value in place of ### for CSV that corresponds to geography you would like to filter & enter state code (see above instructions for DP1 table)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGhxC_Qt8UjP",
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      "source": [
        "dp14 = pd.read_csv('https://vcgi.nyc3.cdn.digitaloceanspaces.com/census2010dp/wide_dp14_###.csv', encoding='ISO-8859-1')\n",
        "dp14 = dp14[(dp14.gisjoin > 'G50') & (dp14.gisjoin < 'G51')]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-cNmyyC8Uje",
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      },
      "source": [
        "### Join tables and save"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-6i2rPG8Ujh",
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      "source": [
        "dp1.drop(columns=['gisjoin', 'name_sf']).merge(\n",
        "    dp14,\n",
        "    left_on='name_dp',\n",
        "    right_on='name_dp',\n",
        "    suffixes=(False, False)\n",
        ").sort_values(['name_dp']).to_csv('data.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    }
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