Mohammed Bashar

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Pandas Series.dropna() function is used to remove NaN values from the pandas Series. It returns new series with the same values as the original but without any NaN values. In this article, I will explain how to remove NaN from Series in Pandas by using dropna() and other methods with examples.

If you are in hurry below are some quick examples of how to remove NaN from the pandas series.

Following is the syntax of Series.dropna() function.

Following are the parameters of the dropna().

It returns the pandas Series without NaN values.

Pandas Series is a one-dimensional, Index-labeled data structure that is available only in the Pandas library. It can store all the datatypes such as strings, integers, float, and other python objects. We can access each element in the Series with the help of corresponding default indices.

Note : Series data structure is the same as the NumPy array data structure but only one difference is that array indices are integers and start with 0, whereas in series, the index can be anything even strings. The labels do not need to be unique but they must be of hashable type.

Now, let’s create pandas series using a list. Note that NaN in pandas and represent by using NumPy np.nan.

Yields below output.

Using dropna() method we can remove the NaN values from the series. Let’s use Series.dropna() method to remove NaN (missing) values from the original Series to get a new series. This method returns a new Series after removing all NAN values.

Yields below output.

We can also use Series.isnull() on the original Series to get a new Series with only boolean values and the same dimensions as the original. The boolean Series contains True if the value in the original is NaN and False otherwise for each element and use this Series on the original series to remove all NaN values.

Yields the same output as above.

In this article, you have learned how to remove NaN values from Series in Pandas by using Series.dropna(), and Series.isnull() functions with examples.

Happy Learning !!