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. |
Import required libraries
Pass the archive to a variable a read the data. Parse_dates and set dtype.
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()Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour??
Extract the hour string from the column ['TIME OCC]'' and set it as integer and create a new column['HOUR OCC']
Count the values for each hour and find the maximum value.
Plot the data with seaborn and countplot.
crimes['HOUR OCC']=crimes['TIME OCC'].str[:2].astype(int)
peak_crime_hour = crimes['HOUR OCC'].value_counts().idxmax()
print("The hour with highest frequency of crimes is at " + str(peak_crime_hour) + " pm")
sns.countplot(data = crimes,x='HOUR OCC')Which area has the largest frequency of night crimes (crimes committed between 10pm and 3:59am)?
For this is needed to create a list of the required hours and subset the dataframe by that hour list.
Then groupby by area and count the hours, sort the values on descending mode and extract the first area.
boolean_index = crimes[crimes['HOUR OCC'].isin([22,23,0,1,2,3])]
peak_night_crime_location = boolean_index.groupby("AREA NAME",as_index=False)['HOUR OCC'].count().sort_values("HOUR OCC",ascending=False).iloc[0]["AREA NAME"]
print( " The are with the largest frequency of crimes at night is : " + peak_night_crime_location)Identify the number of crimes committed against victims by age group (0-17, 18-25, 26-34, 35-44, 45-54, 55-64, 65+)
Two lists are created storing the bin ranges and the labels separately.
Add a new column by cutting the ['Vict Age'] column and asign the bins and labels.
Count the values, sort them and asign to a new pandas series.
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+']
crimes['Age Bracket'] = pd.cut(crimes['Vict Age'],bins=age_bins,labels=age_labels)
crimes['Age Bracket'].value_counts()
victim_ages= crimes['Age Bracket'].value_counts().sort_index()
victim_ages = pd.Series(crimes['Age Bracket'].value_counts(), index=age_labels)
print(victim_ages)