✅ Python Project Data Aggregation with Pandas

 

Python Project Data Aggregation with Pandas 📊🧠

📌 Task:
Create a Python script that summarizes structured data — perfect for calculating totals, averages, counts, and other metrics for business insights.

🧠 What You’ll Learn

  • Using groupby() for data grouping

  • Applying aggregate functions like sum and mean

  • Sorting and exporting summarized data

  • Multi-level aggregation with multiple columns

✅ Python Code

import pandas as pd  

# 1. Load cleaned dataset
data = pd.read_csv("cleaned_data.csv")  

# 2. Total sales by region
sales_by_region = data.groupby("Region")["Sales"].sum().reset_index()  

# 3. Average sales by product
avg_sales_by_product = data.groupby("Product")["Sales"].mean().reset_index()  

# 4. Total sales by region & product
region_product_sales = data.groupby(["Region", "Product"])["Sales"].sum().reset_index()  

# 5. Export all summaries to Excel
with pd.ExcelWriter("aggregated_report.xlsx", engine="openpyxl") as writer:
    sales_by_region.to_excel(writer, index=False, sheet_name="Region_Sales")
    avg_sales_by_product.to_excel(writer, index=False, sheet_name="Product_Avg")
    region_product_sales.to_excel(writer, index=False, sheet_name="Region_Product")

📤 What It Does

  1. Groups and summarizes your data

  2. Computes total sales and average sales

  3. Exports results into a multi-sheet Excel report

🛠️ Requirements

pip install pandas openpyxl

🔧 Try Improving

  • Use .agg() to compute multiple metrics at once

  • Filter data (e.g., by year or quarter) before aggregation

  • Visualize results with matplotlib or seaborn

💡 Real-World Use Cases

✅ Monthly or quarterly sales summaries
✅ Product and region performance analysis
✅ Building dashboards and executive reports

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