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. |
# 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()Hour with the highest frequency of crimes.
import pandas as pd
# Load the data
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
# Extract the hour from TIME OCC
crimes['Hour Occ'] = crimes['TIME OCC'].str[:2].astype(int)
# Count the number of crimes per hour
crime_counts_per_hour = crimes['Hour Occ'].value_counts()
# Identify the hour with the highest count
peak_crime_hour = crime_counts_per_hour.idxmax()
print(peak_crime_hour)
Area with the largest frequency of night crimes (crimes committed between 10pm and 3:59am)? Saved as a string called peak_night_crime_location.
import pandas as pd
# Load the data
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
# Extract the hour from TIME OCC
crimes['Hour Occ'] = crimes['TIME OCC'].str[:2].astype(int)
# Filter crimes committed between 10 PM and 3:59 AM
night_crimes = crimes[(crimes['Hour Occ'] >= 22) | (crimes['Hour Occ'] < 4)]
# Count the number of night crimes per area
night_crime_counts_per_area = night_crimes['AREA NAME'].value_counts()
# Identify the area with the largest frequency of night crimes
peak_night_crime_location = night_crime_counts_per_area.idxmax()
print(peak_night_crime_location)
Identifying the number of crimes committed against victims of different age groups. Saved as a pandas Series called victim_ages, with age group labels "0-17", "18-25", "26-34", "35-44", "45-54", "55-64", and "65+" as the index and the frequency of crimes as the values.
import pandas as pd
# Load the data
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
# Define age groups
bins = [0, 17, 25, 34, 44, 54, 64, float('inf')]
labels = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"]
# Assign each victim's age to an age group
crimes['Age Group'] = pd.cut(crimes['Vict Age'].dropna().astype(int), bins=bins, labels=labels)
# Count the number of crimes in each age group
victim_ages = crimes['Age Group'].value_counts().sort_index()
print(victim_ages)