Fill sparse row of a dataframe with the existing values in the column

Question

I have a dataframe of the following types:

CurrentDf = pd.DataFrame(np.array([[5, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [7, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0], [8, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0], [7, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0], [5, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0], [5, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0]]),
                   columns=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10'])
'''
 and I would like to transform it in this one: 

'''
DesiredDf = pd.DataFrame(np.array([[5, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0], [7, 0, 0, 0, 1, 1, 2, 2, 0, 0, 0], [8, 0, 0, 0, 1, 1, 3, 3, 3, 0, 0], [7, 0, 1, 1, 4, 4, 4, 4, 0, 0, 0], [5, 0, 1, 1, 5, 5, 0, 0, 0, 0, 0], [5, 1, 1, 1, 3, 3, 0, 0, 0, 0, 0]]),
                   columns=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10'])

For each row, the next column take the value of the previous column if the next column is zero, ’till the column number which is given in the column 0.

Asked By: Pachita

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Source

Answer 1

NaNs are easier to work with than 0s:

df = df.replace(0, np.nan)
df = df.ffill(axis=1).fillna(0).astype(int)
print(df)

Output:

   1  2  3  4  5  6
0  0  0  0  0  1  1
1  0  1  1  1  1  1
2  1  1  2  2  2  2
3  0  0  1  1  2  2
4  2  1  1  3  3  3
5  0  0  2  2  5  5

Answered By: BeRT2me

Fill sparse row of a dataframe with the existing values in the column

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