Update README.md

README.md

@@ -25,3 +25,63 @@ plt.tight_layout()
 
 # Show the plot
 plt.show()
+
+# Python3 Examples for Bar/Pie Chart
+### 📊 Python Code to Create a Pie Chart Based on Year Distribution
+
+``python
+
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+
+# Load the dataset
+ds = load_dataset("ajsbsd/hj")
+
+# Assuming the dataset is in the 'train' split and contains a 'Year' column
+year_counts = {}
+
+# Count occurrences per year
+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 by year for consistent ordering
+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')  # Equal aspect ratio ensures the pie is circular
+plt.show()
+
+### 📊 Python Code to Create a Bar Graph of 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()
+
+```