drop dataframe rows that contain entries being either -inf or NaN

In a pandas data frame, if there have entries being equal to -inf or NaN, how to remove all the related columns.

>Solution :

If it doesn’t matter that you retain other null like values (e.g. None, NaT) then replace -inf with nan and drop columns with any null values (default value of how arg of dropna is "any")

import numpy as np
import pandas as pd

df = pd.DataFrame({
    'a': [1,2,np.nan],
    'b': [1,-np.inf,3],
    'c': [1,2,3]
})
df = df.replace({-np.inf: np.nan}).dropna(axis=1)
df

Output:

    c
0   1
1   2
2   3

Otherwise I’d do something like this:

import numpy as np
import pandas as pd

TARGET_VALUES = [-np.inf, np.nan]

df = pd.DataFrame({
    'a': [1,2,np.nan],
    'b': [1,-np.inf,3],
    'c': ['a','b',None]
})
column_value_counts = df.isin(TARGET_VALUES).sum(axis=0)
df = df[column_value_counts[column_value_counts == 0].index.values].copy()
df

Output:

    c
0   a
1   b
2   None