Search for columns in a pandas DataFrame

Question

I need to get the column names of a pandas DataFrame where the columns match those in a numpy array.

Example

import numpy as np
import pandas as pd

x = pd.DataFrame( data=[[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]], columns=list('abc') )

y = np.array( x[['b','c']] )
y

y has then the second and third columns from the DataFrame:

array([[0, 1],
       [1, 0],
       [0, 0],
       [1, 1],
       [1, 0],
       [1, 1]])

How can I get the column names where y is in x? (In this case b, c)

I am looking for something like:

x[ x==y ].columns

or

pd.DataFrame(y).isin(x)

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The example is motivated by a feature selection problem, and was taken from the sklearn page.

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I am using numpy 1.11.1 and pandas 0.18.1.

Answer 1

Here's an approach with NumPy broadcasting -

x.columns[(x.values[...,None] == y[:,None]).all(0).any(1)]

Answer 2

Maybe this?

import numpy as np
import pandas as pd

x = pd.DataFrame( data=[[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]], columns=list('abc') )

y = np.array( x[['b','c']] )

for yj in y.T:
    for xj in x:
        if (all(x[xj] == yj)):
            print(xj)