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I have two columns,

A         B         
2001     2003
2003     1999
1990     2001
1995     2010
2004     1996

I want to check if there are values similar between the two columns regardless of the rows and place it in a new column (SIMILAR)

This is the output that I would like to have 

A        B        SIMILAR
2001     2003     2003
2003     1999     2001
1990     2001
1995     2010
2004     1996

Thank you

kiyas
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4 Answers4

1

IIUC you can use isin:

df[df['A'].isin(df['B'])]['A'].values
It_is_Chris
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1

If by "similar" you mean equal, I'd solve this with the isin method. I'm also assuming that the order of values in the new column does not matter.

>>> df['SIMILAR'] = df.loc[df['A'].isin(df['B']), 'A']
>>> df
      A     B  SIMILAR
0  2001  2003   2001.0
1  2003  1999   2003.0
2  1990  2001      NaN
3  1995  2010      NaN
4  2004  1996      NaN
timgeb
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0

To find the duplicated values you can do something like this:

duplicateRowsDF = pdData[pdData.duplicated()]
print("Duplicate Rows except first occurrence based on all columns are :")
print(duplicateRowsDF)

The response should be somethin like this:

SIMILAR
2003
2001

Then you just use this new data to create a new colum

pdData["Similar"] = duplicateRowsDF
Vitor Albres
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0

Code-golfing with set intersection (assumes a standard range index):

df['C'] = pd.Series([*{*df.A} & {*df.B}])

      A     B       C
0  2001  2003  2001.0
1  2003  1999  2003.0
2  1990  2001     NaN
3  1995  2010     NaN
4  2004  1996     NaN
rafaelc
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