Year int64 2.02k 2.02k	Homicides int64 174 276	Wounded int64 746 1.3k
2,024	219	866
2,023	257	892
2,022	261	996
2,021	276	1,191
2,020	254	942
2,019	211	746
2,018	211	879
2,017	257	1,137
2,016	262	1,296
2,015	174	843

2025 Homicide Trend Analysis

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

📊 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.

📈 Sample Data

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

📊 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.