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.info()#Hour with the highest frequency of crimes
#Subset the first two strings in the TIME OCC column and store to HOUR OCC
crimes["HOUR OCC"] = crimes["TIME OCC"].str[:2]
#Create countplot to visualize hour with highest crime frequency
sns.countplot(data = crimes, x="HOUR OCC")
plt.show()
#Store the most frequent hour in the peak_crime_hour
peak_crime_hour = crimes["HOUR OCC"].value_counts().sort_values(ascending=False).index[0]
peak_crime_hour=int(peak_crime_hour)#Areas with the largest frequency of night crimes between 10pm and 3:59am
#Create Dataframe crimes_10pm_3am with crimes committed at 10pm and 3am
crimes_10pm_3am = crimes[crimes["HOUR OCC"].astype(int).isin([22, 23, 0, 1, 2, 3])]
#Group by the AREA NAME and count, then sort in descending order
#Store to peak_night_crime_location and subset the first Index
peak_night_crime_location = crimes_10pm_3am.groupby("AREA NAME").count().sort_values("TIME OCC", ascending=False).index[0]# Create age bins
age_bins = [0, 17, 25, 34, 44, 54, 64, np.inf]
# Create labels or categories for age ranges
age_label = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"]
# Creating new column for categorization
crimes["Age Bracket"] = pd.cut(crimes["Vict Age"], bins=age_bins, labels=age_label)
victim_ages=crimes.value_counts("Age Bracket")