how can I set a pandas dataframe with the first value is what stays?-CodePudding

I got the following table with repeated values per person:

Costumer	Jan	Feb	Mar	Apr	Jun	Jul
Adam	345	345	345	345	345
Susan			645	645	645	645
Paul		153	153	153	153

You can see that the first value is actual value. So that table should be like that:

Costumer	Jan	Feb	Mar
Adam	345
Susan			645
Paul		153

What's the better approach to solve this table?

Data:

import numpy as np
import pandas as pd

data = {'Costumer': ['Adam', 'Susan', 'Paul'],
             'Jan': [345.0, np.NaN, np.NaN],
             'Feb': [345.0, np.NaN, 153.0],
             'Mar': [345.0, 645.0, 153.0],
             'Apr': [345.0, 645.0, 153.0],
             'Jun': [345.0, 645.0, 153.0],
             'Jul': [np.NaN, 645.0, np.NaN]}

df = pd.DataFrame(data)

CodePudding user response：

You could mask the duplicate values:

out = (df.mask(df.apply(lambda x: x.duplicated(), axis=1)).fillna(''))

Output:

  Costumer    Jan    Feb    Mar Apr Jun Jul
0     Adam  345.0                          
1    Susan                645.0            
2     Paul         153.0