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+# Pandas + Matplotlib Example
+
+This example demonstrates a simple workflow using **pandas** and **matplotlib**:
+
+- Create a small dataset using pandas
+- Perform a simple data transformation (moving average)
+- Visualize the results with matplotlib (line chart)
+
+## Files
+
+- `example.py` — main script demonstrating the workflow
+
+## Requirements
+
+```bash
+pip install pandas matplotlib
diff --git a/examples/library_examples/pandas_matplotlib/example.py b/examples/library_examples/pandas_matplotlib/example.py
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+"""
+Example: Using pandas and matplotlib together
+
+This script demonstrates how to:
+1. Create a DataFrame using pandas
+2. Perform a simple transformation
+3. Visualize the results using matplotlib
+
+Requirements:
+    pip install pandas matplotlib
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Step 1: Create a sample DataFrame
+data = {
+    "Month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"],
+    "Sales": [250, 300, 280, 350, 400, 380],
+}
+df = pd.DataFrame(data)
+
+# Step 2: Add a moving average column
+df["Moving_Avg"] = df["Sales"].rolling(window=2).mean()
+
+# Step 3: Plot the data
+plt.figure(figsize=(8, 5))
+plt.plot(df["Month"], df["Sales"], marker="o", label="Sales", color="blue")
+plt.plot(
+    df["Month"],
+    df["Moving_Avg"],
+    marker="s",
+    label="Moving Avg",
+    linestyle="--",
+    color="orange",
+)
+
+plt.title("Monthly Sales with Moving Average")
+plt.xlabel("Month")
+plt.ylabel("Sales")
+plt.legend()
+plt.grid(True, linestyle="--", alpha=0.6)
+plt.tight_layout()
+plt.show()
+
+# Optional: Print the DataFrame for reference
+print(df)