{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Omaha Incidents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "\n",
    "You must download the data from the URL below first.\n",
    "\n",
    "https://police.cityofomaha.org/crime-information/incident-data-download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "\n",
    "Let's explore the data a little bit to see what kind of analysis and visualizations we want to implement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data\n",
    "df = pd.read_csv(\"../raw_data/Incidents_2015.csv\")\n",
    "\n",
    "# test to see what the dataframe looks like\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install \"matplotlib\"\n",
    "import numpy\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test plotting by sorting & plotting top 5 crime categories\n",
    "s = df.value_counts(subset=[\"Statute/Ordinance Description\"])\n",
    "t = s.nlargest(5)\n",
    "t.head()\n",
    "t.plot(kind=\"bar\", title=\"Top 5 Incident Categories\")"
   ]
  }
 ],
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