What is the primary purpose of the “pivot_table” function in Pandas when aggregating data?

The correct answer is: A. Aggregate and summarize data.

The pivot_table function in Pandas is used to aggregate and summarize data. It takes a DataFrame as input and returns a new DataFrame with aggregated values. The aggregation can be done on a column-wise or row-wise basis.

For example, if you have a DataFrame with columns A, B, and C, you can use the pivot_table function to aggregate the values in column A by the values in column B. The result will be a new DataFrame with columns B and A_sum, where A_sum is the sum of the values in column A for each value in column B.

The pivot_table function can also be used to group data. For example, if you have a DataFrame with columns A, B, and C, you can use the pivot_table function to group the data by the values in column B. The result will be a new DataFrame with columns B and C_count, where C_count is the count of the number of times each value in column C appears for each value in column B.

The pivot_table function is a powerful tool that can be used to aggregate, summarize, and group data. It is a versatile function that can be used in a variety of ways.

Here are some additional details about each option:

• Option B: Reshape data. The pivot_table function can be used to reshape data, but this is not its primary purpose. The primary purpose of the pivot_table function is to aggregate and summarize data.
• Option C: Sort data. The pivot_table function cannot be used to sort data.
• Option D: Group data. The pivot_table function can be used to group data, but this is not its primary purpose. The primary purpose of the pivot_table function is to aggregate and summarize data.