How to check one rows value present in any of the other column row value

Question

I have excel like below

A B
1A 100 
2A  1A 
3A  101
5A  1A

Expected out is

A B   Bool
1A 100  
2A  1A  True
3A  101 
5A  1A   True

Here you can see that 1A present in df[‘A’] is present in df[‘B]

I tried like

import pandas as pd
df = pdf.read_excel('test.xlsx')
df['A'].isin(df['B'])

but its not working

Asked By: abd

||

Source

Answer 1

import pandas as pd
import numpy as np

data = {
    'A':['1A','2A','3A','5A'],
    'B':[100,'1A',101,'1A'],
    'Bool': False
}
df = pd.DataFrame(data)

With this code, you will have the following dataframe:

    A    B   Bool
0  1A  100  False
1  2A   1A  False
2  3A  101  False
3  5A   1A  False

You can correct the third column in this way by using the iterrows()

list_A = np.array(df['A'])
for index, row in df.iterrows():
  if df['B'][index] in list_A:
    df['Bool'][index] = True

The output is:

    A    B   Bool
0  1A  100  False
1  2A   1A   True
2  3A  101  False
3  5A   1A   True

But this is not a good solution. Without iterrows() you can do as follows:

data = {
    'A':['1A','2A','3A','5A'],
    'B':[100,'1A',101,'1A']
}
df = pd.DataFrame(data)

df['bool'] = df['B'].isin(df['A'])

The output is the same:

    A    B   bool
0  1A  100  False
1  2A   1A   True
2  3A  101  False
3  5A   1A   True

Why is the second way better than using iterrows()?

SettingWithCopyWarning:
Usually, when you run iterrows() on the rows in pandas, and at the same time change a value in the dataframe, you get the following warning:

SettingWithCopyWarning:A value is trying to be set on a copy of a slice from a DataFrame

RunTime:

df['B'].isin(df['A']) Time:  0.002069372000050862
df.iterrows()         Time:  0.010589972999696329

Answered By: Shahriyar Shamsipour

How to check one rows value present in any of the other column row value

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