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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

ColumnDescription
'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.

Preview

# 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()

Questions

1. Which hour has the highest frequency of crimes?

  • Store as an integer variable called peak_crime_hour.
crimes.dtypes
# Time is currently a string "XXXX" and must be sliced to get the first 2 letters
crimes["crime_hour"] = crimes["TIME OCC"].str.slice(stop=2)

# Count the occurences of different crime_hours
crimes_count = crimes["crime_hour"].value_counts()

# Find the maximum occurence max() but safe corresponding value "crime_hour" using idxmax() instead and make it an int
peak_crime_hour = int(crimes_count.idxmax())

print(peak_crime_hour)

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 (Invalid URL)
crimes.dtypes
# Extract Time from Datetime 

crimes["crime_hour"] = crimes["crime_hour"].astype(int)

crimes.dtypes
# Create a new column with daycrime/nightcrime
crimes['crime_category'] = np.where(crimes['crime_hour'] >= 22, "night", "day")

# Test if it worked
crimes["crime_category"].value_counts()
# Safe a DataFrame with only night-crimes 
crimes_night = crimes[crimes["crime_category"] == "night"]

# remove unneeded data
crimes_night = crimes_night[["AREA NAME","crime_category"]]

# Test
crimes_night["crime_category"].value_counts()
# Group by Area 
crimes_night_area = crimes_night.groupby("AREA NAME").value_counts()

# Test
crimes_night_area.head()
# Show highest occurrence of night crimes area and use index 0 because there are more columns
peak_night_crime_location = crimes_night_area.idxmax()[0]

# Test
peak_night_crime_location

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.

# Create bins
bins = [0,17,25,34,44,54,64,crimes["Vict Age"].max()]

# Create Labels
labels = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"]

# Zippy
crimes["age_category"] = pd.cut(crimes["Vict Age"],
                              labels=labels,
                              bins=bins)
# Test
crimes.head()
# Count the crimes per age_category
victim_ages = crimes.groupby("age_category")["age_category"].count()

# Test
victim_ages