ajsbsd/hj

2025 Homicide Trend Analysis

📊 This project provides Python-based data visualization examples for analyzing homicide trends over time.

Source: HeyJackass.com

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📊 Overview

This repository includes Python scripts to visualize homicide data using:

• Bar graphs
• Pie charts
• DataFrames from pandas

All visualizations are based on real-world data spanning from 2015 to 2024.

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📈 Sample Data

Year	Homicides
2024	219
2023	257
2022	261
2021	276
2020	254
2019	211
2018	211
2017	257
2016	262
2015	174

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📊 Visualization Examples

1. Bar Graph: Homicides Per Year

import matplotlib.pyplot as plt
import pandas as pd

# Data from your CSV
data = {
    'Year': [2024, 2023, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015],
    'Homicides': [219, 257, 261, 276, 254, 211, 211, 257, 262, 174]
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Plot bar graph
plt.figure(figsize=(10, 6))
plt.bar(df['Year'].astype(str), df['Homicides'], color='skyblue')
plt.title('Homicides Per Year')
plt.xlabel('Year')
plt.ylabel('Number of Homicides')
plt.xticks(rotation=45)
plt.tight_layout()

# Show the plot
plt.show()

2. Pie Chart: Distribution of Incidents by Year

from datasets import load_dataset
import matplotlib.pyplot as plt

# Load the dataset
ds = load_dataset("ajsbsd/hj")

# Count year occurrences
year_counts = {}
for entry in ds['train']:
    year = entry.get('Year', None)
    if year is not None:
        year_counts[year] = year_counts.get(year, 0) + 1

# Sort years
sorted_years = sorted(year_counts.items())
years, counts = zip(*sorted_years)

# Plot pie chart
plt.figure(figsize=(8, 8))
plt.pie(counts, labels=years, autopct='%1.1f%%', startangle=140)
plt.title('Distribution of Incidents by Year')
plt.axis('equal')
plt.show()

BSD-3-Clause-Clear License

See LICENSE file for full text.