在 pandas 中相对于其他行的值创建一个新列

Question

I have a sample data as:

column1 column2 column3 column4
  0.       1.      1.      0
  1.       1.      1.      1
  0.       0.      0.      0
  1.       1.      1.      0
  1.       1.      1.      1

I would like to create a new column(output) which shows 1 if all the row values of the dataframe are 1, otherwise 0.

The sample output is shown below:

column1 column2 column3 column4. output
  0.       1.      1.      0.     0
  1.       1.      1.      1.     1
  0.       0.      0.      0.     0
  1.       1.      1.      0.     0
  1.       1.      1.      1.     1

Answer 1

You can use the numpy select()

import pandas as pd 
import numpy as np



condition = [(df.column1==1) & (df.column2==1) & (df.column3==1) & (df.column4==1)]
choices = [1]
df['output'] =np.select(condition, choices, default= 0)

if you have multiple columns you can use the np.apply_along_axis()

def ex(x):
    a = 0
    if x.all() == 1.0:
        a = 1
    return a

df['output'] = np.apply_along_axis(ex,1,df)

Answer 2

If there is only 0, 1 values use DataFrame.all , because 0 is processing like False and 1 like True :

df['new'] = df.all(axis=1).astype(int)
#alternative
#df['new'] = np.where(df.all(axis=1), 1, 0)
print (df)
   column1  column2  column3  column4  new
0      0.0      1.0      1.0        0    0
1      1.0      1.0      1.0        1    1
2      0.0      0.0      0.0        0    0
3      1.0      1.0      1.0        0    0
4      1.0      1.0      1.0        1    1

If there are also another values compare by 1 :

df['new'] = df.eq(1).all(axis=1).astype(int)

If need choose only some columns:

cols = ['column1', 'column2', 'column3', 'column4']
df['new'] = df[cols].eq(1).all(axis=1).astype(int)