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()# Start coding here
# Use as many cells as you need
import pandas as pd
# Load the data
df = pd.read_csv("crimes.csv")
# Ensure TIME OCC is treated as a string and extract the hour (first 2 digits)
df['HOUR'] = df['TIME OCC'].astype(str).str.zfill(4).str[:2].astype(int)
# Find the hour with the most crimes
peak_crime_hour = df['HOUR'].value_counts().idxmax()
import pandas as pd
# Load the data
df = pd.read_csv("crimes.csv")
# Extract hour from TIME OCC (pad with zeros and slice first two characters)
df['HOUR'] = df['TIME OCC'].astype(str).str.zfill(4).str[:2].astype(int)
# Filter for night crime hours (22 to 23 and 0 to 3)
night_crimes = df[(df['HOUR'] >= 22) | (df['HOUR'] <= 3)]
# Find the area with the highest number of night crimes
peak_night_crime_location = night_crimes['AREA NAME'].value_counts().idxmax()
import pandas as pd
# Load the data
df = pd.read_csv("crimes.csv")
# Define age bins and labels with correct upper boundary
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 'Vict Age' column based on the defined bins and labels
df['Age Group'] = pd.cut(df['Vict Age'], bins=age_bins, labels=age_labels, right=True)
# Count the frequency of crimes for each age group
victim_ages = df['Age Group'].value_counts().sort_index()
# Display the result as a pandas Series
print(victim_ages)