Exploring Crimes in Los Angeles Dataset
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
#Â Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Importing the crimes dataset and defining the date/ time columns
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()# In which hour of the day did the most crime occur?
# peak_crime_hour
# Rounding the time of crime column to the hour mark
crimes['HOUR OCC'] = crimes['TIME OCC'].str[:2].astype(int)
crimes.head()# peak_crime_hour
# Plotting the number of crimes per hour mark
crime_hour = sns.countplot(data=crimes, x='HOUR OCC')
plt.plot()# Defining the peak_crime_hour variable
peak_crime_hour = 12
print('\n The peak hour of crime is:', peak_crime_hour)# In which location did the most crime at night occur
# peak_night_crime_location
# Defining when night time is
night_time_list = [22, 23, 0, 1, 2, 3]
# Subsetting the crimes dataset with the night time filter
night_time = crimes[crimes["HOUR OCC"].isin(night_time_list)]
night_time# peak_night_crime_location
# Indentifying in which location the most crime occured at night
peak_night_crime_location = night_time.groupby('AREA NAME', as_index=False)['HOUR OCC'].count().sort_values('HOUR OCC', ascending=False).iloc[0]['AREA NAME']
# Presenting my findings
print(f'The area with the most crime at night is {peak_night_crime_location}')# Which age group conducting the most crime?
# victim_ages
# Making bins of the age brackets
age_bins = [0, 17, 25, 34, 44, 54, 64, np.inf]
age_labels = ['0-17', '18-25', '26-34', '35-44', '45-54', '55-64', '65+']
# Slicing crimes by the bins at labelled intervals
crimes["Age Bracket"] = pd.cut(crimes["Vict Age"], bins=age_bins, labels=age_labels)
crimes# victim_ages
# Counting the number of crimes conducted by the different age brackets
victim_ages = crimes["Age Bracket"].value_counts()
print(victim_ages)