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(10)# Extract the first two characters and convert to integer
crimes['HOUR OCC'] = crimes['TIME OCC'].str[:2].astype(int)
# Plotting the frequency of hours
sns.countplot(data=crimes, x='HOUR OCC',)
plt.xlabel('Hour')
plt.ylabel('Frequency')
plt.title('Frequency of Crimes by Hour')
plt.show()
#Peak crime hour
peak_crime_hour = crimes['HOUR OCC'].mode()[0]
print(f"The peak crime hour is: {peak_crime_hour}")# List of relevant hours (e.g., 22 for 10pm to 10:59pm)
relevant_hours = [22, 23, 0, 1, 2, 3, 4]
# Filter the DataFrame
filtered_crimes = crimes[crimes['HOUR OCC'].isin(relevant_hours)]
# Group by 'AREA NAME' and count the occurrences
crimes_by_area = filtered_crimes.groupby('AREA NAME').size().reset_index(name='Crime Count')
# Sort by 'Crime Count' in descending order
crimes_by_area = crimes_by_area.sort_values(by='Crime Count', ascending=False)
print(crimes_by_area)
# Isolate the first row and extract the 'AREA NAME'
peak_night_crime_location = crimes_by_area.iloc[0]['AREA NAME']
print(f"The area with the highest number of crimes during the night is: {peak_night_crime_location}")# Define the bins and labels
bins = [0, 18, 26, 35, 45, 55, 65, np.inf]
labels = ['0-17', '18-25', '26-34', '35-44', '45-54', '55-64', '65+']
# Bin and label of the victim ages
crimes['Age Group'] = pd.cut(crimes['Vict Age'], bins=bins, labels=labels, right=False)
victim_ages = crimes['Age Group'].value_counts().sort_index()
print(victim_ages)