pandas: how to check if a column value is in other columns in the same row

Question

I have a pandas data frame in the following format:

col1 col2 col3 col4 col5
A    A    B    Z    X
B    A    Z    Z    X
A    A    C    Z    X
C    A    C    D    X
D    A    B    D    X

How can I filter the rows where the value in col1 is in col2 , col3 or col4 (disregarding col5 )?

I tried among other things:

df = df[df[['col2', 'col3', 'col4']].isin(['col1'])]

but get an empty data frame.

The expected output would be:

col1 col2 col3 col4 col5
A    A    B    Z    X
A    A    C    Z    X
C    A    C    D    X
D    A    B    D    X

Answer 1

Broadcasting is an option to get equality row-wise then check if any True on axis 1 withany :

import pandas as pd

df = pd.DataFrame({
    'col1': {0: 'A', 1: 'B', 2: 'A', 3: 'C', 4: 'D'},
    'col2': {0: 'A', 1: 'A', 2: 'A', 3: 'A', 4: 'A'},
    'col3': {0: 'B', 1: 'Z', 2: 'C', 3: 'C', 4: 'B'},
    'col4': {0: 'Z', 1: 'Z', 2: 'Z', 3: 'D', 4: 'D'},
    'col5': {0: 'X', 1: 'X', 2: 'X', 3: 'X', 4: 'X'}
})

m = (df['col1'].values[:, None] == df[['col2', 'col3', 'col4']].values).any(1)

filtered = df[m]
print(filtered)

filtered :

  col1 col2 col3 col4 col5
0    A    A    B    Z    X
2    A    A    C    Z    X
3    C    A    C    D    X
4    D    A    B    D    X

Answer 2

Check if any of the values are equal ( eq ) the value in column 1. DataFrame.eq supports an axis argument.

m = df[['col2', 'col3', 'col4']].eq(df['col1'], axis=0).any(1)
df[m]

  col1 col2 col3 col4 col5
0    A    A    B    Z    X
2    A    A    C    Z    X
3    C    A    C    D    X
4    D    A    B    D    X

Answer 3

Fix your code by adding any

df = df[df[['col2', 'col3', 'col4']].isin(df['col1']).any(1)]
df
Out[135]: 
  col1 col2 col3 col4 col5
0    A    A    B    Z    X
2    A    A    C    Z    X
3    C    A    C    D    X
4    D    A    B    D    X