In Pandas, grouped mean refers to the average value of a variable calculated over different groups of data. Here are different methods to compute grouped mean on a Pandas DataFrame and keep the grouped column as another column (not index):
1. Using "groupby()" and "mean()":
You can use the groupby() method to group the data by the desired column and then use the mean() method to compute the mean of each group. Finally, use the reset_index() method to keep the grouped column as another column.
Example:
2. Using "pivot_table()":
Another way to compute the grouped mean and keep the grouped column as another column is to use the pivot_table() method. This method creates a new DataFrame with the values in the specified columns, grouped by the specified rows and columns, and aggregated using a specified function.
Example:
3. Using "agg()" and "reset_index()":
Another way to compute the grouped mean and keep the grouped column as another column is to use the agg() method with a dictionary specifying the column to group by and the function to apply. Finally, use the reset_index() method to keep the grouped column as another column.
Overall, grouped mean is a powerful and flexible tool in Pandas that allows us to perform complex analysis and gain insights from our data. By grouping data and calculating the mean value of a variable for each group, we can easily compare different subsets of data and identify patterns and trends that may not be immediately apparent from the raw data.