Finding common elements between multiple dataframe columns

Simplest way is to use set intersection

list(set(df1.A) & set(df2.A) & set(df3.A))

['dog']

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However if you have a long list of these things, I'd use reduce from functools. This same technique can be used with @cᴏʟᴅsᴘᴇᴇᴅ's use of np.intersect1d as well.

from functools import reduce

list(reduce(set.intersection, map(set, [df1.A, df2.A, df3.A])))

['dog']

The problem with your current approach is that you need to chain multiple isin calls. What's worse is that you'd need to keep track of which dataframe is the largest, and you call isin on that one. Otherwise, it doesn't work.

To make things easy, you can use np.intersect1d:

>>> np.intersect1d(df3.A, np.intersect1d(df1.A, df2.A))
array(['dog'], dtype=object)

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Similar method using functools.reduce + intersect1d by piRSquared:

>>> from functools import reduce # python 3 only
>>> reduce(np.intersect1d, [df1.A, df2.A, df3.A])
array(['dog'], dtype=object)