How do i create a function that scans multiple dataframe columns for a value. if any of those values are found the new column returns a given figure

Question

I’m trying to build a sheet whereby I have a new column (‘column x’).

This column would be populated by scanning over three already existing columns (a, b , c).

if a given value is found in any of those columns for the indexed figure the new column will read ‘Fail’ else it will read ‘pass.

When i try this on scanning a single column my code works

example:

df["Column x"] = df["Column a"].apply(lambda val: "Fail" if val == 'T' else "Pass")

When i try in more than one it fails no matter how i adjust.

df['Column x'] = df['Column a'].any(lambda val: 'Fail' if val == 0 else 'Pass') or df['Column b'].apply(lambda val: 'Fail' if val == 'False' else 'Pass')

any advise is incredibly helpful

Asked By: kiki833

||

Source

Answer 1

Check Below code with np.where, checking for J if present in any column than False else True

import pandas as pd
import numpy as np

df = pd.DataFrame({'col1':['A','B','C'],'col2':['E','F','G'],'col3':['H','I','J']})

df['column_x'] = np.where(((df['col1']=='J')|(df['col2']=='J')|(df['col3']=='J')),'Fail','Pass')

df

Output:

Answered By: Abhishek

Answer 2

Here is an all pandas code:

import pandas as pd
df = pd.DataFrame({
    'col1':['A','B','C'],
    'col2':['E','F','G'],
    'col3':['H','I','J']}
)

df["C"] = pd.Series(
    df['col1'].apply(lambda a: 0 if a == 'C' else 1) &
    df['col2'].apply(lambda a: 0 if a == 'C' else 1) &
    df['col3'].apply(lambda a: 0 if a == 'C' else 1)
).apply(lambda x: "Pass" if x else 'Fail')
print(df)

output

    col1 col2 col3     C
0    A    E    H     Pass
1    B    F    I     Pass
2    C    G    J     Fail

Handling empty data frame

if df.empty:
    print('DataFrame is empty!')

Answered By: Salahuddin

How do i create a function that scans multiple dataframe columns for a value. if any of those values are found the new column returns a given figure

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