Project: Analyzing Crime in Los Angeles

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: `A` - Other Asian `B` - Black `C` - Chinese `D` - Cambodian `F` - Filipino `G` - Guamanian `H` - Hispanic/Latin/Mexican `I` - American Indian/Alaskan Native `J` - Japanese `K` - Korean `L` - Laotian `O` - Other `P` - Pacific Islander `S` - Samoan `U` - Hawaiian `V` - Vietnamese `W` - White `X` - Unknown `Z` - Asian Indian
`'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()

# inspect the index range of the dataframe
crimes.index

# inspect the shape of the dataframe
crimes.shape

# check the summery stastics of the numerical columns
crimes.describe()

# inspect the datatypes to check if there are some convertions needs for
# certain columns
crimes.dtypes

Finding the frequencies of crimes by the hour of occurrence

Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour.

# Extracting the hours from the TIME OCC column and converting it to integer data type

#peak_crime_hour
crimes['HOUR OCC'] = crimes['TIME OCC'].str[:2].astype(int)

# Plot the frequencies
sns.countplot(data=crimes,x='HOUR OCC',color='purple')
plt.show()

# peak_crime_hour
peak_crime_hour = 12

Identifying the area with the most night crime

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.

# Method 1: Filter the data for the relevant hours i.e. 10pm and 4am using 
# comparison operators
between_10_and_24 = (crimes['HOUR OCC'] >= 22) & (crimes['HOUR OCC'] < 24)
between_0_and_4 = (crimes['HOUR OCC'] >= 0) & (crimes['HOUR OCC'] < 5)
crimes_10pm_and_4am = crimes[between_10_and_24 | between_0_and_4]

# count the number of crimes by area
crimes_by_areaname = crimes_10pm_and_4am.groupby('AREA NAME')['AREA NAME'].count().sort_values(ascending=False)
print(crimes_by_areaname)

# Method 2: Filter the data for the relevant hours i.e. 10pm and 4am using 
# the isin function
crimes_10pm_and_4am_2 = crimes[crimes['HOUR OCC'].isin([22,23,0,1,2,3,4])]

# count the number of crimes by area
crimes_by_areaname_2 = crimes_10pm_and_4am_2.groupby('AREA NAME')['AREA NAME'].count().sort_values(ascending=False)
print(crimes_by_areaname_2)

peak_night_crime_location = "Central"

Crimes by age group

Identify the number of crimes committed against victims by age group (0-17, 18-25, 26-34, 35-44, 45-54, 55-64, 65+). Save as a pandas Series called victim_ages.