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

Answer 1

I also have another possible solution for dropping the columns with categorical value with 2 lines of code, defining a list with columns of categorical values (1st line) and dropping them with the second line. df is our DataFrame

df before dropping:

  to_be_dropped=pd.DataFrame(df.categorical).columns
  df= df.drop(to_be_dropped,axis=1)

df after dropping:

Answer 2

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]

Answer 3

This would remove each column which doesn't include float64 numerics.

df = pd.read_csv('sample.csv', index_col=0)
non_floats = []
for col in df:
    if df[col].dtypes != "float64":
        non_floats.append(col)
df = df.drop(columns=non_floats)

Answer 4

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