CODEWITHSUNDEEP
pythonpython-3.xpandasnumpy
Starbucks
Starbucks

Reputation: 1568

Intersection of two or more DataFrame columns

I am trying to find the intersect of three dataframes, however the pd.intersect1d does not like to use three dataframes. 

import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('BCDE'))
df3 = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('CDEF'))

inclusive_list = np.intersect1d(df1.columns, df2.columns, df3.columns)

Error: 

ValueError: The truth value of a Index is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

The inclusive_list should only include column names C & D. Any help would be appreciated. Thank you.

Upvotes: 4

Views: 9876

Answers (3)

BENY
BENY

Reputation: 323306

You can using concat 

pd.concat([df1.head(1),df2.head(1),df3.head(1)],join='inner').columns
Out[81]: Index(['C', 'D'], dtype='object')

Upvotes: 2

user3483203
user3483203

Reputation: 51155

Why your current approach doesn't work:

intersect1d does not take N arrays, it only compares 2.

numpy.intersect1d(ar1, ar2, assume_unique=False, return_indices=False)

You can see from the definition that you are passing the third array as the assume_unique parameter, and since you are treating an array like a single boolean, you receive a ValueError.

You can extend the functionality of intersect1d to work on N arrays using functools.reduce:

from functools import reduce
reduce(np.intersect1d, (df1.columns, df2.columns, df3.columns))


array(['C', 'D'], dtype=object)

A better approach

However, the easiest approach is to just use intersection on the Index object:

df1.columns & df2.columns & df3.columns


Index(['C', 'D'], dtype='object')

Upvotes: 6

emmet02
emmet02

Reputation: 942

inclusive_list = np.intersect1d(np.intersect1d(df1.columns, df2.columns), df3.columns)

Note that the arguments passed to np.intersect1d (https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.intersect1d.html) are expected to be two arrays (ar1 and ar2).

Passing 3 arrays means that the assume_unique variable within the function is being set as an array (expected to be a bool).

You can also use simple native python set methods if you don't want to use numpy

inclusive_list = set(df1.columns).intersection(set(df2.columns)).intersection(set(df3.columns))

Upvotes: 0

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