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
# 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", dtype={"TIME OCC": str})
crimes.head()SELECT *
FROM 'crimes.csv'
LIMIT 5crimes.dtypescrimes['TIME OCC'] = pd.to_datetime(crimes['TIME OCC'], format='%H%M')
crimes# Extract the hour from the 'TIME OCC' column and count the occurrences of each hour
hourly_counts = crimes['TIME OCC'].dt.hour.value_counts()
# Sort the counts in descending order and get the top 1 hour with the highest count
peak_crime_hour = hourly_counts.sort_values(ascending=False).nlargest(1)
# Extract the hour value from the index of the resulting Series
peak_crime_hour = peak_crime_hour.index[0]
# Display the peak crime hour
peak_crime_hourSELECT extract(hour from "TIME OCC") as peak_crime_hour, count(*) as count
FROM crimes
GROUP BY peak_crime_hour
ORDER BY count DESC
LIMIT 1;# The code below filters the crimes that occurred between 10 PM and 4 AM,
# then groups these filtered crimes by 'AREA NAME' and finds the area with the highest number of crimes during these hours.
# Create a boolean mask for crimes that occurred between 10 PM and 4 AM
hours_between = (crimes['TIME OCC'].dt.hour <= 4) | (crimes['TIME OCC'].dt.hour >= 22)
# Filter the crimes dataframe using the boolean mask, group by 'AREA NAME',
# count the number of crimes in each area, and find the area with the highest count
peak_night_crime_location = crimes[hours_between].groupby('AREA NAME').size().nlargest(1)
# Display the result
peak_night_crime_location = peak_night_crime_location.index[0]
peak_night_crime_locationbins = [0, 17, 25, 34, 44, 54, 64, np.inf]
labels = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"]
crimes['ages'] = pd.cut(crimes['Vict Age'], bins=bins, labels=labels)
victim_ages = crimes.groupby('ages').size()
victim_ages