if else operation on pandas dataframe python

Question

I have the following dataframe

import pandas as pd

data = {
  "a": [0, 1, 0,1],
  "b": [0,0,1,1]
}

#load data into a DataFrame object:
df = pd.DataFrame(data)

The expected output is df2

data = {
  "a": [1, 0.75, 0.25,1],
  "b": [1,0.25,0.75,1]
}

#load data into a DataFrame object:
df2 = pd.DataFrame(data)

When both a and b are same, the output dataframe should have 1. If a =0 and b=1 then output dataframe should be 0.25 and 0.75. If a =1 and b=0 then output dataframe should be 0.75 and 0.25. How to do this without for loop? Thanks in advance.

jezrael · Accepted Answer · 2021-06-08 07:15:57Z

1

Use DataFrame.replace with DataFrame.mask for set 1 if same values:

df = df.replace({0:.25, 1:.75}).mask(df.std(1).eq(0), 1)
print (df)

      a     b
0  1.00  1.00
1  0.75  0.25
2  0.25  0.75
3  1.00  1.00

Another idea with broadcasting in numpy.select:

m1 = df['a'] == df['b']
m2 = (df['a'] == 0) & (df['b'] == 1)
m3 = (df['a'] == 1) & (df['b'] == 0)

masks = [m1.to_numpy()[:, None],
         m2.to_numpy()[:, None],
         m3.to_numpy()[:, None]]

df[['a','b']] = np.select(masks, [[1,1], [0.25,0.75], [0.75,0.25]])

print (df)
      a     b
0  1.00  1.00
1  0.75  0.25
2  0.25  0.75
3  1.00  1.00

edited Jun 8, 2021 at 7:15

answered Jun 8, 2021 at 6:50

jezrael

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