Compare two pandas df columns with string values

Question

I have two pandas df colums with populated with some names, first df has more name enteries, and second has less, e.g.

id	names
1	John Doe
2	Jane Doe

id	names
1	John Doe

I need to create a column with True/False in the first df, if names are matching, e.g.

id	names	match
1	John Doe	True
2	Jane Doe	False

so far I have tried this two methodes:

df['match'] = (df1.names is df2.names) # always return False
df['match'] = df1.names.isin(df2.names) # compare only first names, eg. John, but doesn't the second

Additionally I have tried to lowercase and remove strings, but still not getting the result

What am I doing wrong?

there is no "first name" and "second name" here........ there is only "names".......... so do you want to compare "names" ? — Joshua
– Joshua, Commented Jul 15, 2021 at 11:50
@ThePyGuy thanks, but this also only compares the first name and not whole str. — Volume069
– Volume069, Commented Jul 15, 2021 at 11:52
@Joshua yes, I want to compare the whole str ('names'), but methode I used ither compares only the first part of the string and ignores the second or gives all false.... — Volume069
– Volume069, Commented Jul 15, 2021 at 11:54

ThePyGuy · Accepted Answer · 2021-07-15 11:56:48Z

1

I tested the solution I provided in comment and it works, here is the sample run:

>>> df1.names.isin(df2.names) #Additional call .tolist() isn't even required.
0     True
1    False
Name: names, dtype: bool

If above doesn't work, try this:

>>> df1['names'].apply(lambda x: x in df2.names.tolist())
0     True
1    False
Name: names, dtype: bool

edited Jul 15, 2021 at 11:56

answered Jul 15, 2021 at 11:55

ThePyGuy

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3 Comments

ThePyGuy Over a year ago

Which pandas version and python version are you using? Also update the question running the provided solution, and let us see if it's actually not working.

Volume069 Over a year ago

pandas version 1.3.0, python version 3.9.5

Volume069 Over a year ago

the second variant works perfect - many many many many thanks!

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