Compute number of occurance of each value and Sum another column in Pandas

Question

I have a pandas dataframe with some columns in it. The column I am interested in is something like this,

df['col'] = ['A', 'A', 'B', 'C', 'B', 'A']

I want to make another column say, col_count such that it shows count value in col from that index to the end of the column.

The first A in the column should have a value 3 because there is 3 occurrence of A in the column from that index. The second A will have value 2 and so on.

Finally, I want to get the following result,

    col     col_count
0   A   3
1   A   2
2   B   2
3   C   1
4   B   1
5   A   1

How can I do this effectively in pandas.? I was able to do this by looping through the dataframe and taking a unique count of that value for a sliced dataframe.

Is there an efficient method to do this.? Something without loops preferable.

Another part of the question is, I have another column like this along with col ,

df['X'] = [10, 40, 10, 50, 30, 20]

I want to sum up this column in the same fashion I wanted to count the column col .

For instance, At index 0, I will have 10 + 40 + 20 as the sum. At index 1, the sum will be 40 + 20. In short, instead of counting, I want to sum up another column.

The result will be like this,

    col     col_count   X   X_sum
0   A   3   10  70
1   A   2   40  60
2   B   2   10  40
3   C   1   50  50
4   B   1   30  30
5   A   1   20  20

Answer 1

Use pandas.Series.groupby with cumcount and cumsum .

g = df[::-1].groupby('col')
df['col_count'] = g.cumcount().add(1)
df['X_sum'] = g['X'].cumsum()
print(df)

Output:

  col   X  col_count  X_sum
0   A  10          3     70
1   A  40          2     60
2   B  10          2     40
3   C  50          1     50
4   B  30          1     30
5   A  20          1     20