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", dtype={"TIME OCC": str})
crimes.head()Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour.
# Extracting the hours
crimes["HOUR OCC"] = crimes["TIME OCC"].str[:2].astype(int)
# preview
crimes.head()# Plotting the frequencies
sns.countplot(data=crimes,
x="HOUR OCC"
)
plt.show()# Storing the hour as a variable
peak_crime_hour = crimes["HOUR OCC"].value_counts().idxmax()
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.
# Subsetting for night hours
# time period
night = [22, 23, 0, 1, 2, 3]
# new night crime df
night_crime = crimes[crimes["HOUR OCC"].isin(night)]
# check
night_crime.head()# Counting crime by area
crime_cnt = night_crime.groupby("AREA NAME").count()
# sort values
crime_cnt = crime_cnt.sort_values(by=crime_cnt.columns[0], ascending=False)
# extract the value for the first row
peak_night_crime_location = crime_cnt.index[0]
# check
print(peak_night_crime_location)Crimes by age group
Bin and label victim age into the provided groups, then produce a pandas Series detailing how many crimes were committed against each age group.
# creating bins and labels
# age bins
max_age = crimes["Vict Age"].max()
age_range = [0, 17, 25, 34, 44, 54, 64, max_age]
# age labels
age_labels = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"]
# brackets of ages
age_bracket = pd.cut(crimes["Vict Age"], age_range, labels=age_labels)
# check
print(age_bracket)# Adding a new column to the crimes DataFrame containing binned age bracket values
crimes["Age Bracket"] = age_bracket
# check
crimes.head()# Counting crimes by victim age group
victim_ages = crimes["Age Bracket"].value_counts()
# check
print(victim_ages)