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.