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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.
# 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.columns = crimes.columns.str.replace(' ', '_')
#crimes.columns = [col.lower() for col in crimes.columns]
#crimes['hour_occ'] = crimes['time_occ'].str[:2].astype(int)
#crimes.head()
Hidden output 
#let's see Dataframe information
crimes.info() # huge dataset
#### Data Cleaning

crimes=pd.read_csv('crimes.csv', header=0, sep=",")

print(crimes.shape)
print(crimes)
#let's check null values
crimes.isna().sum().rename_axis('columns').reset_index(name = 'Missing Values Count')
# it looks like there are null values
# Replace spaces in column names with underscores
crimes.columns = crimes.columns.str.replace(' ', '_')

# Convert the 'TIME_OCC' column to datetime and extract the hour
crimes["HOUR_OCC"] = pd.to_datetime(crimes['TIME_OCC'], format='%H%M', errors='coerce').dt.hour
# Plotting the frequencies
plt.figure(figsize=(20, 12))
sns.countplot(data=crimes, x='HOUR_OCC')
plt.title('FREQUENCY OF CRIMES PER HOUR')
plt.show()
print(f'Peak crime hour is {12}.')
peak_crime_hour = 12 
# Filtering the DataFrame
peak_night_crime_filter = crimes[(crimes["HOUR_OCC"] > 22) | (crimes["HOUR_OCC"] < 4)]
# Corrected line: Use value_counts() to get the counts and then sort the values
peak_night_crime_location = peak_night_crime_filter.groupby("AREA_NAME", as_index=False)["HOUR_OCC"].count().sort_values("HOUR_OCC", ascending=False).iloc[0]["AREA_NAME"]

#print(peak_night_crime_location)
print(f'The area with the largest frequency of night crimes is {peak_night_crime_location}.')
#Crimes by age group, 
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)

# We also print the counts for each age group.
victim_ages = crimes['Age_Bracket'].value_counts()
print(victim_ages)
# Visualize the frequency of crime against victims' age
plt.figure(figsize=(20, 12))
sns.countplot(data=crimes, x="Vict_Age")
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()

################
Conclusion
Through this analysis, we have identified key patterns in the crime data:

1.The peak hour for crimes is 12:00.
2.The Central area experiences the highest number of night crimes.
3.Individuals aged 26-34 are the most frequent victims of crime.


These insights can help the LAPD allocate resources more effectively to address crime in Los Angeles.