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", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()# Start coding here
# Use as many cells as you need
crimes.shapecrimes.info()# Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour.
temp = crimes.groupby('TIME OCC')['DR_NO'].size()
temp = int(temp.idxmax())
# I have to convert 1200 to 12 because DataCamp make it an obligation 😜
peak_crime_hour = int(temp / 100)
print(peak_crime_hour)# Which area has the largest frequency of night crimes (crimes committed between 10pm and 3:59am)? Save as a string variable called peak_night_crime_location.
night_crimes_2200 = crimes[crimes['TIME OCC'] >= '2200']
night_crimes_0359 = crimes[crimes['TIME OCC'] <= '0359']
night_crimes = pd.concat([night_crimes_2200, night_crimes_0359])
number_crime_hour = night_crimes.groupby('AREA NAME')['DR_NO'].size()
peak_night_crime_location = str(number_crime_hour.idxmax())
print(peak_night_crime_location)# Identify the number of crimes committed against victims of different age groups. Save 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
# defining the bins edge. The 150 value is the number that I believe the oldest person could live.
bins = [0, 17, 25, 34, 44, 54, 64, 99]
labels = ['0-17', '18-25', '26-34', '35-44', '45-54', '55-64', '65+']
# Assuming 'crimes' DataFrame is already defined
crimes['group_age'] = pd.cut(crimes['Vict Age'], bins=bins, labels=labels)
number_crimes_by_age_group = crimes.groupby('group_age').size()
victim_ages = number_crimes_by_age_group
print(victim_ages)This project analyzed crime data from Los Angeles to identify patterns and assist the LAPD in resource allocation. By examining various attributes of the data, the following insights were revealed:
The peak hour for crimes in Los Angeles is 12 PM, highlighting midday as a high-risk time. The Central area experiences the highest frequency of crimes at night (from 10 PM to 3:59 AM), indicating a possible need for increased nighttime policing in this area. Crimes are distributed across age groups, with ages 26-34 experiencing the highest frequency (47,470 incidents), followed by the 35-44 age group (42,157 incidents).