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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()

# Extract hour from 'TIME OCC' and create a new column 'Hour'
crimes['Hour'] = crimes['TIME OCC'].str[:2]

# Count the occurrences of each hour
hourly_crime_counts = crimes['Hour'].value_counts().sort_index()

# Find the hour with the highest frequency of crimes
peak_crime_hour = hourly_crime_counts.idxmax()

print("The hour with the highest frequency of crimes is:", peak_crime_hour)
peak_crime_hour = int(peak_crime_hour)

# Assuming you have already executed the code to read the data and calculate hourly crime counts

# Create a bar plot using Seaborn
plt.figure(figsize=(12, 6))
sns.barplot(x=hourly_crime_counts.index, y=hourly_crime_counts.values, color='skyblue')

# Set plot labels and title
plt.xlabel('Hour of the Day')
plt.ylabel('Number of Crimes')
plt.title('Hourly Crime Counts')

# Rotate x-axis labels for better readability
plt.xticks(rotation=45, ha='right')

# Show plot
plt.tight_layout()
plt.show()
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset with date parsing for 'DATE OCC' and type conversion for 'TIME OCC'
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})

# Convert 'TIME OCC' to datetime to extract the hour. Assuming 'TIME OCC' is in HHMM format.
crimes['Hour'] = pd.to_datetime(crimes['TIME OCC'], format='%H%M').dt.hour

# Filter the dataset for night crimes
night_crimes = crimes[(crimes['Hour'] >= 22) | (crimes['Hour'] < 4)]

# Assuming the correct column name for location might be different, let's say it's 'Area' instead of 'Location'
# Find the location with the largest frequency of night crimes
peak_night_crime_location = night_crimes['AREA NAME'].value_counts().idxmax()

print("The area with the largest frequency of night crimes is:", peak_night_crime_location)

# Create a bar plot using Seaborn
plt.figure(figsize=(12, 6))
sns.countplot(data=night_crimes, x='AREA NAME', order=night_crimes['AREA NAME'].value_counts().index[:10], palette='viridis')

# Set plot labels and title
plt.xlabel('Area')
plt.ylabel('Number of Crimes')
plt.title('Top 10 Areas with Night Crimes')

# Rotate x-axis labels for better readability
plt.xticks(rotation=45, ha='right')

# Show plot
plt.tight_layout()
plt.show()
import pandas as pd
import numpy as np

# Load the dataset
crimes = pd.read_csv("crimes.csv")

# Define the age bins correctly
age_bins = [0, 17, 25, 34, 44, 54, 64, np.inf]

# Define the age group labels
age_labels = ['0-17', '18-25', '26-34', '35-44', '45-54', '55-64', '65+']

# Create a new column to categorize victim ages into age groups
crimes['Victim Age Group'] = pd.cut(crimes['Vict Age'], bins=age_bins, labels=age_labels)

# Count the occurrences of each age group
victim_ages = crimes['Victim Age Group'].value_counts()

# Sort the Series by index to maintain age group order
victim_ages = victim_ages.reindex(age_labels)

print(victim_ages)