How Can I Effectively Aggregate Data Using Pandas?

Aggregation in Pandas

Question 1: How can I perform aggregation with Pandas?

Answer:

• Pandas provides various aggregation functions, such as sum(), mean(), count(), etc.
• Group by specific columns before applying aggregation to summarize data across groups.

Question 2: No DataFrame after aggregation! What happened?

Answer:

• If the aggregation results in a Series, use reset_index().
• If it's a MultiIndex Series, use map() or str.replace() to flatten the columns.

Question 3: How can I aggregate mainly strings columns (to lists, tuples, strings with separator)?

Answer:

• Pass a list, tuple, or set to the aggregation function.
• Use GroupBy.apply() for custom aggregation.
• Use .join() on string columns to create a string with a separator.

Question 4: How can I aggregate counts?

Answer:

• Use GroupBy.size() for the number of items in each group.
• Use GroupBy.count() for the number of non-missing values in each group.
• Use Series.value_counts() to count unique values in a Series.

Question 5: How can I create a new column filled by aggregated values?

Answer:

• Use GroupBy.transform() to apply an aggregation function to each group and generate a new column based on the results.

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