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", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()# Start coding here
# Use as many cells as you needimport pandas as pd
# Load the dataset
crimes = pd.read_csv("crimes.csv")
# Convert 'TIME OCC' to string to handle the extraction of the hour
crimes['TIME OCC'] = crimes['TIME OCC'].astype(str)
# Extract the hour of the crime (first two characters) and convert to integer
crimes['Hour'] = crimes['TIME OCC'].str[:2].astype(int)
# Q1: Find the hour with the highest frequency of crimes
peak_crime_hour = crimes['Hour'].value_counts().idxmax()
# Q2: Identify the area with the largest frequency of night crimes (10 PM to 3:59 AM)
# Filter for night crimes (from 10 PM to 3:59 AM)
night_crimes = crimes[(crimes['Hour'] >= 22) | (crimes['Hour'] <= 3)]
# Find the area with the highest frequency of night crimes
peak_night_crime_location = night_crimes['AREA NAME'].value_counts().idxmax()
# Q3: Count crimes by victim age groups
# Adjusting the bins for pd.cut() where the upper value of each bin is inclusive
age_bins = [0, 17, 25, 34, 44, 54, 64, float('inf')]
age_labels = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"]
# Create a new column for age groups
crimes['Age Group'] = pd.cut(crimes['Vict Age'], bins=age_bins, labels=age_labels, right=True)
# Count the number of crimes for each age group
victim_ages = crimes['Age Group'].value_counts()
# Display results
print(f"Peak crime hour: {peak_crime_hour}")
print(f"Peak night crime location: {peak_night_crime_location}")
print("Number of crimes by victim age groups:")
print(victim_ages)