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", dtype={"TIME OCC": str})
crimes# Task 1: Calculate which hour has the highest frequency of crimes
# Format the time of the crime in integer
crimes["TIME OCC2"] = crimes["TIME OCC"].astype(int)
# Calculate the hour component by floor dividing the time of the crime by 100
crimes["TIME OCC3"] = crimes["TIME OCC2"] // 100
# Get the peak crime hour by using the mode function
peak_crime_hour = int(crimes["TIME OCC3"].mode())
print(peak_crime_hour)# Task 2: Which area has the largest frequency of night crimes for those committed between 10:00PM and 3:59 PM
# subset for the time from 10:00PM to 3:59 AM
night_crimes = crimes[(crimes["TIME OCC3"]<4) | (crimes["TIME OCC3"]>=22)]
# Get the most frequent crime areausing mode function
peak_night_crime_location = night_crimes["AREA NAME"].mode()[0]
print(peak_night_crime_location)# Define labels for each age group for the bins
age_group_labels = ["0-17","18-25","26-34","35-44","45-54","55-64","65+"]
# Define the boundaries for each bins, with the maximum age set as the last boundary
age_ranges = [0,17,25,34,44,54,64,crimes["Vict Age"].max()]
# Segment the victim ages into the sorted bins
crimes["victim_age_range"] = pd.cut(crimes["Vict Age"], bins=age_ranges, labels=age_group_labels, right = True)
# Plot the victim age range by the age groups
sns.countplot(data=crimes, x="victim_age_range")
plt.show()
# Save the aggregated count of victim age by age group into a panda series
victim_ages = crimes["victim_age_range"].value_counts()print(victim_ages)