Drop non-numeric columns from a pandas DataFrame [duplicate]

To avoid using a private method you can also use select_dtypes, where you can either include or exclude the dtypes you want.

Ran into it on this post on the exact same thing.

Or in your case, specifically:
source.select_dtypes(['number']) or source.select_dtypes([np.number]

It`s a private method, but it will do the trick: source._get_numeric_data()

In [2]: import pandas as pd

In [3]: source = pd.DataFrame({'A': ['foo', 'bar'], 'B': [1, 2], 'C': [(1,2), (3,4)]})

In [4]: source
Out[4]:
     A  B       C
0  foo  1  (1, 2)
1  bar  2  (3, 4)

In [5]: source._get_numeric_data()
Out[5]:
   B
0  1
1  2