How to Add a Column with Group Counts to a Grouped Dataframe in Pandas?

How to Add a Column to a Grouped Dataframe in Pandas

In data analysis, it is often necessary to group data and perform calculations on each group. Pandas offers a convenient way to do this through its groupby function. One common task is to count the values of a column within each group and add a column containing these counts to the dataframe.

Consider the dataframe df:

<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 c, we can use the value_counts function on the grouped dataframe:

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

This creates a new dataframe g with the group counts. To add a column to g with the size of each group, we can use the transform function:

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

transform applies a function to each group in the original dataframe and returns a Series with its index aligned to the original dataframe. In this case, we use the size function to count the number of elements in each group and assign it to the new column size. The resulting dataframe g will now look like this:

<code class="python">   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</code>

This demonstrates a straightforward way to add a new column to a grouped dataframe based on the results of a groupby aggregation.

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