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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

ColumnDescription
'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:
  • A - Other Asian
  • B - Black
  • C - Chinese
  • D - Cambodian
  • F - Filipino
  • G - Guamanian
  • H - Hispanic/Latin/Mexican
  • I - American Indian/Alaskan Native
  • J - Japanese
  • K - Korean
  • L - Laotian
  • O - Other
  • P - Pacific Islander
  • S - Samoan
  • U - Hawaiian
  • V - Vietnamese
  • W - White
  • X - Unknown
  • Z - Asian Indian
'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()


# Extract hour from TIME OCC and create additional column in df crimes:

crimes['CRM_HOUR'] = pd.to_datetime(crimes['TIME OCC'], format="%H%M").dt.hour

# Create a dictionary to count how many crimes are commited in this hour:
crime_counts = {}
for i in crimes['CRM_HOUR']:
    if i in crime_counts:
        crime_counts[i] +=1
    else:
        crime_counts[i] = 1

# Find out which hour accounts for the most crimes (on average):        
peak_crime_hour = max(crime_counts, key=crime_counts.get)

# Correct filtering: Parentheses for proper logical evaluation
night_crimes = crimes[((crimes['CRM_HOUR'] >= 22) & (crimes['CRM_HOUR'] <= 23)) | ((crimes['CRM_HOUR'] >= 0) & (crimes['CRM_HOUR'] < 4))]

# Define the largest crime district
peak_night_crime_location = night_crimes.groupby('AREA NAME').size().idxmax()

# Group by district and count crimes
crime_counts_per_district = night_crimes.groupby('AREA NAME').size().reset_index(name="TOTAL CRIMES")

# Sort by total crimes (descending)
crime_counts_per_district = crime_counts_per_district.sort_values(by="TOTAL CRIMES", ascending=False)

# Plot
plt.figure(figsize=(8, 10))
sns.barplot(data=crime_counts_per_district, y="AREA NAME", x="TOTAL CRIMES", palette="magma")

plt.ylabel("District")
plt.xlabel("Total Night Crimes")
plt.title("Night Crimes per District (10 PM - 3:59 AM)")

plt.show()

print(f"The district with the most night crimes is: {peak_night_crime_location}")
# Define age bins and labels
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+"]

# Bin the victim ages into categories
crimes["AGE_GROUP"] = pd.cut(crimes["Vict Age"], bins=age_bins, labels=age_labels, right=True)

# Count occurrences per age group
victim_ages = crimes["AGE_GROUP"].value_counts().sort_index()

# Display the Series
print(victim_ages)

# Plot the victim age distribution
plt.figure(figsize=(10, 6))
sns.barplot(x=victim_ages.index, y=victim_ages.values, palette="viridis")

# Labels and title
plt.xlabel("Victim Age Group")
plt.ylabel("Number of Crimes")
plt.title("Number of Crimes by Victim Age Group")

# Show the plot
plt.show()