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()# Start coding here
# Use as many cells as you need1- Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour.
# Exctracting Hour as integer
crimes['Hour'] = crimes['TIME OCC'].str[0:2].astype(int)
# Determining the hour with the highest frequency of crime occurances
peak_crime_hour = crimes['Hour'].value_counts().idxmax()
# Answer
print(f"The answer is: {peak_crime_hour} o'clock")
# Visualizing crime distribution per hour
sns.countplot(x='Hour', data=crimes)
plt.show()2- 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
# Creating a subset of the dataframe that only has night crimes
night_crimes = crimes[crimes['Hour'].isin([22, 23, 0, 1, 2, 3])]
# Grouping crimes by area
area_crimes = night_crimes.groupby('AREA NAME')['DR_NO'].count()
# Determining the area with the largest night crime count
peak_night_crime_location = area_crimes.idxmax()
# Answer
print(f"The answer is: {peak_night_crime_location}")
# Visualizing total number of crimes occurances per area
df = pd.DataFrame(area_crimes)
g = sns.barplot(data=df, x='AREA NAME', y='DR_NO')
g.set_xticklabels(df.index, rotation=90)
g.set_ylabel('COUNT')
plt.show()3- 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 Column to segment and sort age values into bins
age_groups = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"]
crimes['age groups'] = pd.cut(crimes['Vict Age'], labels=age_groups, bins=[0, 17, 25, 34, 44, 54, 64, crimes['Vict Age'].max()])
# Counting crimes by age group
victim_ages = crimes['age groups'].value_counts(sort=False)
# Answer
print(victim_ages)