How to Add Aggregated Columns to Grouped DataFrames in Pandas?

Adding Columns to Grouped DataFrames in Pandas

When working with dataframes in Pandas, it is often necessary to group data and aggregate values within those groups. Typically, this involves creating a separate dataframe with the aggregation results. However, it can also be useful to add these aggregated columns directly to the original dataframe.

Let's illustrate this with a simple example dataframe:

<code class="python">df = pd.DataFrame({'c': [1, 1, 1, 2, 2, 2, 2], 'type': ['m', 'n', 'o', 'm', 'm', 'n', 'n']})</code>

To count the values of 'type' for each value of 'c', we can use the following code:

<code class="python">g = df.groupby('c')['type'].value_counts().reset_index(name='t')</code>

This creates a new dataframe 'g' with three columns: 'c', 'type', and 't' representing the count of each 'type' within each 'c'.

Next, we can use the 'size()' method to count the number of rows in each group:

<code class="python">a = df.groupby('c').size().reset_index(name='size')</code>

This creates a new dataframe 'a' with two columns: 'c' and 'size' containing the number of rows in each 'c' group.

To add the 'size' column to the original dataframe, one option is to use the 'map()' function as shown in the question. However, a more straightforward approach is to use the 'transform()' method:

<code class="python">g['size'] = df.groupby('c')['type'].transform('size')</code>

The 'transform()' method returns a Series with its index aligned to the original dataframe. By assigning this Series to a new column in the grouped dataframe, we effectively add the aggregated values back to the original dataframe.

The resulting dataframe 'g' will now contain the additional 'size' column:

   c type  t  size
0  1    m  1     3
1  1    n  1     3
2  1    o  1     3
3  2    m  2     4
4  2    n  2     4

This approach provides a simple and efficient way to add aggregated columns to grouped dataframes in Pandas.

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