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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:
|
'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()# I will start by exploring my data to ensure I understand how it is coded:
crimes.info()
#Note.
# All are object except age and dr_no which are integers (int64)
# N=185,714
# There are missing values for
# Vict Sex
# Vict Descent
# Weapon Desc# Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour.
# First I will group each of these by hour:
crimes["hour"] = crimes["TIME OCC"].str[:2].astype(int)
#look at all categories of "hour" to ensure it's correct:
print("These are the categories of the new variable hour with counts")
print(crimes["hour"].value_counts())
# This looks great. I will now find the max and store this as an integer variable called peak_crime_hour.
peak_crime_hour = crimes["hour"].value_counts().idxmax()
print ("The Peak Crime Hour Is:")
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.
# First I am converting TIME OCC to an integer so I can work with it
crimes["TIME OCC"] = crimes["TIME OCC"].astype(int)
# Creating a variable called night_crimes
crimes["night_crimes"] = (crimes["TIME OCC"] >= 2200) | (crimes["TIME OCC"] < 400)
# Checking this briefly in the dataset
# print(crimes.head(20)) # This looks correct
# Now I will group by AREA NAME and count the number of crimes that occurred at night,
# then identify the area with the largest frequency of night crimes.
peak_night_crime_location = (
crimes[crimes["night_crimes"]]
.groupby("AREA NAME")
.size()
.sort_values(ascending=False)
.index[0]
)
print("The Peak Night (10PM to 3:59AM) Crime Location is:")
print(peak_night_crime_location)
# Checking the data type
# print(type(peak_night_crime_location))# 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
# Removing all rows with missing data for age
crimes=crimes.dropna(subset=["Vict Age"])
# Creating age groups
age_group_labels = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+" ]
conditions = [(crimes["Vict Age"] <= 17),
(crimes["Vict Age"] <= 25),
(crimes["Vict Age"] <= 34),
(crimes["Vict Age"] <= 44),
(crimes["Vict Age"] <= 54),
(crimes["Vict Age"] <= 64),
(crimes["Vict Age"] >= 65)]
crimes["victim_ages"] = np.select(conditions, age_group_labels)
# Looking at the number by age
victim_ages = crimes["victim_ages"].value_counts().reindex(age_group_labels)
print(victim_ages)