Aggregate sets of Pandas DataFrames columns

Question

I have a pandas DataFrame with some independent columns, and I'm looking for an efficient way to unwind / aggregate them.

So, let's say I have the table:

+-----+-----+-------+------+-------+
| One | Two | Three | Four | Count |
+-----+-----+-------+------+-------+
| a   | x   | y     | y    | 3     |
+-----+-----+-------+------+-------+
| b   | z   | x     | x    | 5     |
+-----+-----+-------+------+-------+
| c   | y   | x     | y    | 1     |
+-----+-----+-------+------+-------+

Where rows Two, Three and Four are independent.

I would like to end up with the table:

+-----+-------+-------+
| One | Other | Count |
+-----+-------+-------+
| a   | x     | 3     |
+-----+-------+-------+
| a   | y     | 6     |
+-----+-------+-------+
| b   | x     | 10    |
+-----+-------+-------+
| b   | z     | 5     |
+-----+-------+-------+
| c   | x     | 1     |
+-----+-------+-------+
| c   | y     | 2     |
+-----+-------+-------+

How would be the best way to achieve this?

Answer 1

You can use melt function from pandas to reshape your data frame from wide to long format then groupby the One and Other columns and sum the Count column:

import pandas as pd
pd.melt(df, id_vars = ['One', 'Count'], value_name = 'Other').groupby(['One', 'Other'])['Count'].sum().reset_index()

  One Other Count
0   a   x   3
1   a   y   6
2   b   x   10
3   b   z   5
4   c   x   1
5   c   y   2