Pandas Advanced Grouping and Aggregation Exercises with Solutions

Last update on December 21 2024 07:51:53 (UTC/GMT +8 hours)

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Pandas Advanced Grouping and Aggregation [ 15 exercises with solution]

1. Grouping by Multiple columns:
Write a Pandas program to group data by multiple columns to perform complex data analysis and aggregations.
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2. Applying Multiple Aggregations:
Write a Pandas program to apply multiple aggregation functions to grouped data using for enhanced data insights.
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3. Custom Aggregation Functions:
Write a Pandas program to implement custom aggregation functions within groupby for tailored data analysis.
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4. Group by and Filter Groups:
Write a Pandas program that implements the technique of grouping and filtering groups to refine your data analysis and insights.
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5. Group by and Apply function:
Write a Pandas program to group data and apply custom functions to groups for flexible data transformations.
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6. Aggregating with different functions on different Columns:
Write a Pandas program to use different aggregation functions on different columns for versatile data analysis.
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7. Using GroupBy with Lambda functions:
Write a Pandas program to use lambda functions within groupby for flexible and efficient data transformations.
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8. Grouping and Aggregating with Multiple Index Levels:
Write a Pandas program to perform grouping and aggregation operations using multiple index levels.
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9. Applying different functions to different columns with GroupBy:
Write a Pandas program that applies different functions to different columns in Pandas GroupBy for tailored data analysis.
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10. Using named Aggregations:
Write a Pandas program to use named aggregations in GroupBy to make your data aggregation operations more readable and organized.
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11. Combining GroupBy with Transform:
Write a Pandas program to combine GroupBy with Transform to perform complex data transformations on grouped data.
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12. GroupBy and Applying Different Functions Using a Dictionary:
Write a Pandas program that uses dictionaries to apply different functions to different columns in GroupBy.
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13. GroupBy and Create a New Column with Aggregated Data:
Write a Pandas program to create a new column with aggregated data from a GroupBy operation for enriched data insights.
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14. GroupBy and Handling Missing data:
Write a Pandas program to handle missing data in GroupBy operations to ensure accurate and reliable data analysis.
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15. GroupBy and Applying Multiple Aggregations with Named Functions:
Write a Pandas program to apply multiple aggregations with named functions in GroupBy for detailed data analysis.
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