dropping infinite values from dataframes in pandas?

First replace() infs with NaN:

df.replace([np.inf, -np.inf], np.nan, inplace=True)

and then drop NaNs via dropna():

df.dropna(subset=["col1", "col2"], how="all", inplace=True)

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For example:

>>> df = pd.DataFrame({"col1": [1, np.inf, -np.inf], "col2": [2, 3, np.nan]})
>>> df
   col1  col2
0   1.0   2.0
1   inf   3.0
2  -inf   NaN

>>> df.replace([np.inf, -np.inf], np.nan, inplace=True)
>>> df
   col1  col2
0   1.0   2.0
1   NaN   3.0
2   NaN   NaN

>>> df.dropna(subset=["col1", "col2"], how="all", inplace=True)
>>> df
   col1  col2
0   1.0   2.0
1   NaN   3.0

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The same method also works for Series.

With option context, this is possible without permanently setting use_inf_as_na. For example:

with pd.option_context('mode.use_inf_as_na', True):
    df = df.dropna(subset=['col1', 'col2'], how='all')

Of course it can be set to treat inf as NaN permanently with

pd.set_option('use_inf_as_na', True)

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For older versions, replace use_inf_as_na with use_inf_as_null.