Grouped EDA Using pandas groupby
Segmenting your retail data by group—such as by store, region, or product category—lets you:
- Compare performance across different segments;
- Spot trends and patterns that single-feature analysis might miss;
- Identify opportunities or issues at a more granular level.
The pandas library provides the powerful groupby operation. With groupby, you can:
- Split your dataset into groups based on one or more categorical variables;
- Perform aggregations (such as sum, mean, or count) on each group;
- Analyze the aggregated results to draw actionable insights.
Common retail use cases include:
- Summarizing sales by product category;
- Comparing revenue across store locations;
- Tracking average order value by customer segment.
By grouping your data, you can answer questions like:
- Which product categories have the highest average sales?
- Which stores generate the most revenue?
Using grouped EDA, you make data-driven decisions tailored to specific segments.
123456789101112import pandas as pd # Sample retail sales data data = { "product_category": ["Electronics", "Clothing", "Electronics", "Groceries", "Clothing", "Groceries"], "sales": [1200, 300, 900, 400, 600, 700] } df = pd.DataFrame(data) # Calculate average sales per product category avg_sales_per_category = df.groupby("product_category")["sales"].mean() print(avg_sales_per_category)
123456789101112import pandas as pd # Sample retail sales data with store location data = { "store_location": ["North", "South", "North", "East", "South", "East"], "revenue": [5000, 7000, 6000, 4000, 8000, 4500] } df = pd.DataFrame(data) # Aggregate total revenue by store location total_revenue_by_store = df.groupby("store_location")["revenue"].sum() print(total_revenue_by_store)
After running groupby operations, use the results to drive business decisions for each retail segment:
- If average sales for "Electronics" are much higher than for "Groceries" or "Clothing", prioritize inventory or marketing investments in electronics;
- If total revenue by store location shows the "South" location consistently outperforms others, investigate what drives its success—such as customer demographics or local promotions—and consider replicating those strategies in other locations.
By breaking down key metrics by meaningful groups, you can:
- Identify top-performing categories or stores;
- Pinpoint underperforming segments that need attention;
- Tailor your actions and strategies to maximize impact in specific areas of your business.
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Can you explain how to group by multiple columns at once?
How can I visualize the results of these groupby operations?
What other aggregation functions can I use with groupby?
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Completion rate improved to 5.56Grouped EDA Using pandas groupby
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Segmenting your retail data by group—such as by store, region, or product category—lets you:
- Compare performance across different segments;
- Spot trends and patterns that single-feature analysis might miss;
- Identify opportunities or issues at a more granular level.
The pandas library provides the powerful groupby operation. With groupby, you can:
- Split your dataset into groups based on one or more categorical variables;
- Perform aggregations (such as sum, mean, or count) on each group;
- Analyze the aggregated results to draw actionable insights.
Common retail use cases include:
- Summarizing sales by product category;
- Comparing revenue across store locations;
- Tracking average order value by customer segment.
By grouping your data, you can answer questions like:
- Which product categories have the highest average sales?
- Which stores generate the most revenue?
Using grouped EDA, you make data-driven decisions tailored to specific segments.
123456789101112import pandas as pd # Sample retail sales data data = { "product_category": ["Electronics", "Clothing", "Electronics", "Groceries", "Clothing", "Groceries"], "sales": [1200, 300, 900, 400, 600, 700] } df = pd.DataFrame(data) # Calculate average sales per product category avg_sales_per_category = df.groupby("product_category")["sales"].mean() print(avg_sales_per_category)
123456789101112import pandas as pd # Sample retail sales data with store location data = { "store_location": ["North", "South", "North", "East", "South", "East"], "revenue": [5000, 7000, 6000, 4000, 8000, 4500] } df = pd.DataFrame(data) # Aggregate total revenue by store location total_revenue_by_store = df.groupby("store_location")["revenue"].sum() print(total_revenue_by_store)
After running groupby operations, use the results to drive business decisions for each retail segment:
- If average sales for "Electronics" are much higher than for "Groceries" or "Clothing", prioritize inventory or marketing investments in electronics;
- If total revenue by store location shows the "South" location consistently outperforms others, investigate what drives its success—such as customer demographics or local promotions—and consider replicating those strategies in other locations.
By breaking down key metrics by meaningful groups, you can:
- Identify top-performing categories or stores;
- Pinpoint underperforming segments that need attention;
- Tailor your actions and strategies to maximize impact in specific areas of your business.
¡Gracias por tus comentarios!
