Dropping infinite values from dataframes in pandas? Dropping infinite values from dataframes in pandas? python python

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