Los Angeles, California 😎. The City of Angels. Tinseltown. The Entertainment Capital of the World!
Known for its warm weather, palm trees, sprawling coastline, and Hollywood, along with producing some of the most iconic films and songs. However, as with any highly populated city, it isn't always glamorous and there can be a large volume of crime. That's where you can help!
You have been asked to support the Los Angeles Police Department (LAPD) by analyzing crime data to identify patterns in criminal behavior. They plan to use your insights to allocate resources effectively to tackle various crimes in different areas.
The Data
They have provided you with a single dataset to use. A summary and preview are provided below.
It is a modified version of the original data, which is publicly available from Los Angeles Open Data.
crimes.csv
| Column | Description |
|---|---|
'DR_NO' | Division of Records Number: Official file number made up of a 2-digit year, area ID, and 5 digits. |
'Date Rptd' | Date reported - MM/DD/YYYY. |
'DATE OCC' | Date of occurrence - MM/DD/YYYY. |
'TIME OCC' | In 24-hour military time. |
'AREA NAME' | The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example, the 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles. |
'Crm Cd Desc' | Indicates the crime committed. |
'Vict Age' | Victim's age in years. |
'Vict Sex' | Victim's sex: F: Female, M: Male, X: Unknown. |
'Vict Descent' | Victim's descent:
|
'Weapon Desc' | Description of the weapon used (if applicable). |
'Status Desc' | Crime status. |
'LOCATION' | Street address of the crime. |
# Re-run this cell
#Â Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()crimes.dtypes# convert the 'TIME OCC' column to datetime format
crimes['TIME OCC'] = pd.to_datetime(crimes['TIME OCC'], format='%H%M')
# extract the hour component from the 'TIME OCC' column
crimes['hour'] = crimes['TIME OCC'].dt.hour
# count the number of crimes that occurred in each hour
hourly_crime_count = crimes['hour'].value_counts()
# get the hour with the highest frequency of crimes
peak_crime_hour = int(hourly_crime_count.idxmax())
type(peak_crime_hour)# filter the rows where the hour is between 22 and 3
night_crimes = crimes[(crimes['hour'] >= 22) | (crimes['hour'] < 4)]
# count the number of crimes that occurred in each area
area_crime_count = night_crimes['AREA NAME'].value_counts()
# get the area with the largest frequency of night crimes
peak_night_crime_location = area_crime_count.idxmax()
peak_night_crime_location# Define the age groups
bins = [0, 17, 25, 34, 44, 54, 64, 200]
labels = ['<18', '18-25', '26-34', '35-44', '45-54', '55-64', '65+']
# Categorize the age groups
crimes['Age Group'] = pd.cut(crimes['Vict Age'], bins=bins, labels=labels)
# Count the number of crimes by age group
victim_ages = crimes['Age Group'].value_counts()
victim_ages