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 needWhich hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour
#Finding the frequencies of crimes by the hour of occurrence
crimes['HOUR OCC']= crimes['TIME OCC'].str[:2].astype(int)
#Plotting the frequencies
sns.countplot(data=crimes, x= "HOUR OCC")
plt.show()
# From the chart we can see that the hour that has the highest frequency of crimes is the 12 hour
# Storing the hour as a variable
peak_crime_hour= 12Which 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
# Identifying the area with the most night crime
# Subsetting for night hours
night_hour = crimes[crimes['HOUR OCC'].isin([22, 23, 0, 1, 2, 3])]
# Counting crime by area
night_crime_location = night_hour.groupby('AREA NAME')['HOUR OCC'].count().sort_values(ascending=False)
print(night_crime_location)
# The result from night_crime_location shows that "Central" has the largest frequency of night crimes
peak_night_crime_location = "Central"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.
# Creating bins and labels
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+']
# Adding a new column to the crimes DataFrame containing binned age bracket values
crimes['Age Bracket'] = pd.cut(crimes['Vict Age'], bins=age_bins, labels=age_labels)
# Counting crimes by victim age group
victim_ages = crimes['Age Bracket'].value_counts()
print(victim_ages)
# The result shows that the most victimized age group is 26-34