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{
"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\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
