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(10)# Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour.
crimes['HOUR OCC'] = crimes['TIME OCC'].str[:2].astype('int64')
peak_crime_hour = crimes['HOUR OCC'].value_counts().idxmax()
peak_crime_hoursns.histplot(x='HOUR OCC', data=crimes, bins=10)
plt.ylabel("Crime count")
plt.xlabel("Hour of Crime")# To change the dtype of the 'HOUR OCC' column to 'category', you need to assign the result back to the dataframe
crimes['HOUR OCC'] = crimes['HOUR OCC'].astype('category')
crimes['HOUR OCC'].describe()# List of hours to retain
hours_to_retain = [22, 23, 0, 1, 2, 3]
# Create a new DataFrame using boolean indexing with .isin() method
filtered_crimes = crimes[crimes['HOUR OCC'].isin(hours_to_retain)]
# Display the first few rows of the new DataFrame
filtered_crimes.head()crime_counts_by_area = filtered_crimes['AREA NAME'].value_counts(ascending=False)
peak_night_crime_location = crime_counts_by_area.idxmax()
peak_night_crime_location # Define age ranges and labels
age_range = [0, 17, 25, 34, 44, 54, 64, np.Inf]
age_labels = ['0-17', '18-25', '26-34', '35-44', '45-54', '55-64', '65+']
# Exclude negative age values
crimes = crimes[crimes['Vict Age'] >= 0]
# Categorize ages into groups
crimes['age_group'] = pd.cut(crimes['Vict Age'], bins=age_range, labels=age_labels, right=True, include_lowest=True)
# Count the number of crimes for each age group
victim_ages = crimes['age_group'].value_counts().sort_index()
victim_ages