Removing infinite values ​​from data in pandas?

Question

what is the fastest / easiest way to reset nan and inf / -inf values ​​from pandas DataFrame without resetting mode.use_inf_as_null ? I would like to be able to use the arguments subset and how dropna , with the exception of those that are considered missing inf , for example:

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

Is it possible? Is there a way to tell dropna to include inf in the definition of missing values?

Answer 1

The easiest way is to replace infs to NaN first:

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

and then use dropna :

 df.replace([np.inf, -np.inf], np.nan).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) Out[12]: 0 0 1 1 2 2 NaN 3 NaN 

The same method will work for the series.

Answer 2

In the context of the context, this is possible without constantly 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, you can configure continuous processing of inf as NaN 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 .

Answer 3

Here is another method using .loc to replace inf with nan on the 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.inf df ABC 0 inf 1.000000 1.000000 1 1.000000 inf 1.000000 2 1.000000 1.000000 inf df.sum() A inf B inf C inf dtype: float64 df.apply(lambda s: s[np.isfinite(s)].dropna()).sum() A 2 B 2 C 2 dtype: float64 

Answer 4

The above solution will change inf that is not in the target columns. To fix this,

 lst = [np.inf, -np.inf] to_replace = {v: lst for v in ['col1', 'col2']} df.replace(to_replace, np.nan) 

Answer 5

Another solution is to use the isin method. Use it to determine whether each value is infinite or absent, and then link the all method to determine whether all values ​​in the strings are infinite or absent.

Finally, use the negation of this result to select rows that do not have all infinite or missing values ​​using Boolean indexing.

 all_inf_or_nan = df.isin([np.inf, -np.inf, np.nan]).all(axis='columns') df[~all_inf_or_nan] 

Answer 6

Use (quick and easy):

 df = df[np.isfinite(df).all(1)] 

This answer is based on Dougr's answer to another question. Here is a sample code:

 import pandas as pd import numpy as np df=pd.DataFrame([1,2,3,np.nan,4,np.inf,5,-np.inf,6]) print('Input:\n',df,sep='') df = df[np.isfinite(df).all(1)] print('\nDropped:\n',df,sep='') 

Result:

 Input: 0 0 1.0000 1 2.0000 2 3.0000 3 NaN 4 4.0000 5 inf 6 5.0000 7 -inf 8 6.0000 Dropped: 0 0 1.0 1 2.0 2 3.0 4 4.0 6 5.0 8 6.0 

Answer 7

You can use pd.DataFrame.mask with np.isinf . First you need to make sure that all of your data series are of type float . Then use dropna with your existing logic.

 print(df) col1 col2 0 -0.441406 inf 1 -0.321105 -inf 2 -0.412857 2.223047 3 -0.356610 2.513048 df = df.mask(np.isinf(df)) print(df) col1 col2 0 -0.441406 NaN 1 -0.321105 NaN 2 -0.412857 2.223047 3 -0.356610 2.513048