Dropping infinite values from dataframes in pandas?

The simplest way would be to first replace() infs to NaN:

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

and then use the dropna():

df.replace([np.inf, -np.inf], np.nan, inplace=True) \    .dropna(subset=["col1", "col2"], how="all")

For example:

In [11]: df = pd.DataFrame([1, 2, np.inf, -np.inf])In [12]: df.replace([np.inf, -np.inf], np.nan, inplace=True)Out[12]:    00   11   22 NaN3 NaN

The same method would work for a 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)

For older versions, replace use_inf_as_na with use_inf_as_null.

Here is another method using .loc to replace inf with nan on a Series:

s.loc[(~np.isfinite(s)) & s.notnull()] = np.nan

So, in response to the original question:

df = pd.DataFrame(np.ones((3, 3)), columns=list('ABC'))for i in range(3):     df.iat[i, i] = np.infdf          A         B         C0       inf  1.000000  1.0000001  1.000000       inf  1.0000002  1.000000  1.000000       infdf.sum()A    infB    infC    infdtype: float64df.apply(lambda s: s[np.isfinite(s)].dropna()).sum()A    2B    2C    2dtype: float64