Difference between isna() and isnull() in pandas

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I have been using pandas for quite some time. But, I don't understand what's the difference between isna() and isnull(). And, more importantly, which one to use when identifying missing values in a dataframe.

What is the basic underlying difference of how a value is detected as either na or null?

Vaibhav Thakur

Posted 2018-09-06T10:14:01.593

Reputation: 1 783

2The two functions are same. Both give the missing values. – Ankit Seth – 2018-09-06T11:37:46.537

Answers

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Pandas isna() vs isnull().

I'm assuming you are referring to pandas.DataFrame.isna() vs pandas.DataFrame.isnull(). Not to confuse with pandas.isnull(), which in contrast to the two above isn't a method of the DataFrame class.

These two DataFrame methods do exactly the same thing! Even their docs are identical. You can even confirm this in pandas' code.

But why have two methods with different names do the same thing?

This is because pandas' DataFrames are based on R's DataFrames. In R na and null are two separate things. Read this post for more information.

However, in python, pandas is built on top of numpy, which has neither na nor null values. Instead numpy has NaN values (which stands for "Not a Number"). Consequently, pandas also uses NaN values.

In short

  • To detect NaN values numpy uses np.isnan().

  • To detect NaN values pandas uses either .isna() or .isnull().
    The NaN values are inherited from the fact that pandas is built on top of numpy, while the two functions' names originate from R's DataFrames, whose structure and functionality pandas tried to mimic.

Djib2011

Posted 2018-09-06T10:14:01.593

Reputation: 6 495

7This explains everything and yes i wanted to infer 'pandas.DataFrame.isna()' vs 'pandas.DataFrame.isnull()' . Thanks for such a detailed explanation. – Vaibhav Thakur – 2018-09-07T06:45:46.690

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Do note that in Pandas 1.0.0 a new experimental pd.NA value has been introduced and it might behave differently in certain operations from np.nan.

– Bruno Lubascher – 2020-04-21T14:27:33.723