CODEWITHSUNDEEP
pythonpandasdataframe
Dheeraj
Dheeraj

Reputation: 1202

Replace values from one column to another using pandas?

I have a dataframe:

       subtracted      Subs        hrs  
0      281871.120   450878.77      0.00    
1      221343.432   229535.34      0.00     
2      197454.408    32080.93      0.00    
3      41934.000    -9853.07     32080.93

i want to replace positive values (With respect to Subs column) from subtracted column to hrs column.

Expected output:

       subtracted      Subs        hrs  
0      281871.120   450878.77      281871.120   
1      221343.432   229535.34      221343.432    
2      197454.408    32080.93      197454.408    
3      41934.000    -9853.07       32080.93

Can anybody suggest me the correct way to do it?

Upvotes: 1

Views: 46

Answers (1)

jezrael
jezrael

Reputation: 862611

You can replace by mask created by positive values with loc, where or numpy.where:

m = df['Subs'] > 0
df.loc[m, 'hrs'] = df['subtracted']

df['hrs'] = df['subtracted'].where(m, df['hrs'])

df['hrs'] = np.where(m, df['subtracted'], df['hrs'])

print (df)
   subtracted       Subs         hrs
0  281871.120  450878.77  281871.120
1  221343.432  229535.34  221343.432
2  197454.408   32080.93  197454.408
3   41934.000   -9853.07   32080.930

For mask created by negative values is possible use mask:

m = df['Subs'] < 0
df['hrs'] = np.where(m, df['hrs'], df['subtracted'])

df['hrs'] = df['subtracted'].mask(m, df['hrs'])

Upvotes: 1

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