Pandas Function Series.notna() Detects existing (not missing) values. This function returns a boolean that is the same size as the object, indicating whether the values ​​are missing or not. Values ​​not missing are displayed on. Characters such as blank lines “or numpy.inf are not considered NA values ​​(unless pandas.options.mode.use_inf_as_na = True is set). NA values ​​such as None or numpy.NaN are mapped to false values.

Syntax: Series.notna ()

Parameter: None

Returns: Series

Example # 1: Use the Series.notna () to find all non-missing values ​​in a given series object.

# import pandas as pd import pandas as pd   # Create series sr = pd.Series ([ 10 , 25 , 3 , 11 , 24 , 6 ])    # Create index index_ = [ `Coca Cola` , ` Sprite` , `Coke` , `Fanta` , ` Dew` , ` ThumbsUp` ]    # set index sr.index = index_    # Print series print (sr)

Exit :

We will now use the Series function. notna () to detect non-missing values ​​in a series object.

# detect not missing value result = sr.notna ()   # Print result print (result)

Output:

As we can see from the output, the Series.notna () function returned a boolean.  True means that the corresponding value is not missing. The False value indicates that there is no value. All values ​​are true in this series, since there are no missing values.

Example # 2: Use the Series.notna () function to find all not missing values ​​in this series object.

# import pandas as pd import pandas as pd   # Create series sr = pd.Series ([ 19.5 , 16.8 , None , 22.78 , None , 2 0.124 , None , 18.1002 , None ])   # Print series print (sr)

Output:

Now we will use the Series.notna () function to detect non-missing values ​​in the series object.

# detect not missing value result = sr.notna ()    # Print result print (result)

Exit:

As we can see from the output, the Series .notna () returned a boolean.  True means that the corresponding value is not missing. The False value indicates that there is no value.