kd2718/Gun data Trial.ipynb

## Gun data Trial.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Gun Violence Data\n\n## Goal:\n\nmake a few plots about gun violence"
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n%matplotlib inline",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## load the data"
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "code",
      "source": "gtypes = {\"n_killed\": \"int8\", \"n_injured\": \"int8\"}\ngv = pd.read_csv(\"C:\\data\\gun_violence\\DATA_01-2013_03-2018.tar\\stage3.csv\", parse_dates=[\"date\"], dtype=gtypes)",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "code",
      "source": "gv.dtypes",
      "execution_count": 25,
      "outputs": [
        {
          "data": {
            "text/plain": "incident_id                             int64\ndate                           datetime64[ns]\nstate                                  object\ncity_or_county                         object\naddress                                object\nn_killed                                 int8\nn_injured                                int8\nincident_url                           object\nsource_url                             object\nincident_url_fields_missing              bool\ncongressional_district                float64\ngun_stolen                             object\ngun_type                               object\nincident_characteristics               object\nlatitude                              float64\nlocation_description                   object\nlongitude                             float64\nn_guns_involved                       float64\nnotes                                  object\nparticipant_age                        object\nparticipant_age_group                  object\nparticipant_gender                     object\nparticipant_name                       object\nparticipant_relationship               object\nparticipant_status                     object\nparticipant_type                       object\nsources                                object\nstate_house_district                  float64\nstate_senate_district                 float64\ndtype: object"
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Plotting time\n### first plot: histogram"
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "code",
      "source": "gv.describe()",
      "execution_count": 42,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>incident_id</th>\n      <th>n_killed</th>\n      <th>n_injured</th>\n      <th>congressional_district</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>n_guns_involved</th>\n      <th>state_house_district</th>\n      <th>state_senate_district</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>count</td>\n      <td>2.396770e+05</td>\n      <td>239677.000000</td>\n      <td>239677.000000</td>\n      <td>227733.000000</td>\n      <td>231754.000000</td>\n      <td>231754.000000</td>\n      <td>140226.000000</td>\n      <td>200905.000000</td>\n      <td>207342.00000</td>\n      <td>239677.000000</td>\n    </tr>\n    <tr>\n      <td>mean</td>\n      <td>5.593343e+05</td>\n      <td>0.252290</td>\n      <td>0.494007</td>\n      <td>8.001265</td>\n      <td>37.546598</td>\n      <td>-89.338348</td>\n      <td>1.372442</td>\n      <td>55.447132</td>\n      <td>20.47711</td>\n      <td>2015.711629</td>\n    </tr>\n    <tr>\n      <td>std</td>\n      <td>2.931287e+05</td>\n      <td>0.521779</td>\n      <td>0.729952</td>\n      <td>8.480835</td>\n      <td>5.130763</td>\n      <td>14.359546</td>\n      <td>4.678202</td>\n      <td>42.048117</td>\n      <td>14.20456</td>\n      <td>1.225870</td>\n    </tr>\n    <tr>\n      <td>min</td>\n      <td>9.211400e+04</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>19.111400</td>\n      <td>-171.429000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.00000</td>\n      <td>2013.000000</td>\n    </tr>\n    <tr>\n      <td>25%</td>\n      <td>3.085450e+05</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>33.903400</td>\n      <td>-94.158725</td>\n      <td>1.000000</td>\n      <td>21.000000</td>\n      <td>9.00000</td>\n      <td>2015.000000</td>\n    </tr>\n    <tr>\n      <td>50%</td>\n      <td>5.435870e+05</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>5.000000</td>\n      <td>38.570600</td>\n      <td>-86.249600</td>\n      <td>1.000000</td>\n      <td>47.000000</td>\n      <td>19.00000</td>\n      <td>2016.000000</td>\n    </tr>\n    <tr>\n      <td>75%</td>\n      <td>8.172280e+05</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>10.000000</td>\n      <td>41.437375</td>\n      <td>-80.048625</td>\n      <td>1.000000</td>\n      <td>84.000000</td>\n      <td>30.00000</td>\n      <td>2017.000000</td>\n    </tr>\n    <tr>\n      <td>max</td>\n      <td>1.083472e+06</td>\n      <td>50.000000</td>\n      <td>53.000000</td>\n      <td>53.000000</td>\n      <td>71.336800</td>\n      <td>97.433100</td>\n      <td>400.000000</td>\n      <td>901.000000</td>\n      <td>94.00000</td>\n      <td>2018.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        incident_id       n_killed      n_injured  congressional_district  \\\ncount  2.396770e+05  239677.000000  239677.000000           227733.000000   \nmean   5.593343e+05       0.252290       0.494007                8.001265   \nstd    2.931287e+05       0.521779       0.729952                8.480835   \nmin    9.211400e+04       0.000000       0.000000                0.000000   \n25%    3.085450e+05       0.000000       0.000000                2.000000   \n50%    5.435870e+05       0.000000       0.000000                5.000000   \n75%    8.172280e+05       0.000000       1.000000               10.000000   \nmax    1.083472e+06      50.000000      53.000000               53.000000   \n\n            latitude      longitude  n_guns_involved  state_house_district  \\\ncount  231754.000000  231754.000000    140226.000000         200905.000000   \nmean       37.546598     -89.338348         1.372442             55.447132   \nstd         5.130763      14.359546         4.678202             42.048117   \nmin        19.111400    -171.429000         1.000000              1.000000   \n25%        33.903400     -94.158725         1.000000             21.000000   \n50%        38.570600     -86.249600         1.000000             47.000000   \n75%        41.437375     -80.048625         1.000000             84.000000   \nmax        71.336800      97.433100       400.000000            901.000000   \n\n       state_senate_district           year  \ncount           207342.00000  239677.000000  \nmean                20.47711    2015.711629  \nstd                 14.20456       1.225870  \nmin                  1.00000    2013.000000  \n25%                  9.00000    2015.000000  \n50%                 19.00000    2016.000000  \n75%                 30.00000    2017.000000  \nmax                 94.00000    2018.000000  "
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "code",
      "source": "gv[\"year\"] = gv[\"date\"].dt.year\nplt.hist(gv[gv[\"year\"] == 2017][\"date\"], density=True);",
      "execution_count": 37,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### seconds plot: scatter"
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "code",
      "source": "xy = gv[gv[\"year\"] == 2017]\nplt.subplot(2,1,1)\nplt.scatter(xy[\"date\"], xy[\"n_killed\"], alpha=.33);\nplt.subplot(2,1,2)\nplt.scatter(xy[\"date\"], xy[\"n_injured\"], alpha=.33, color=\"red\");\n",
      "execution_count": 53,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.4",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Gun data Trial.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}