Python | Pandas Series.notnull()

Last Updated : 11 Feb, 2019

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.notnull() function Detect existing (non-missing) values. This function return a boolean object having the size same as the object, indicating if the values are missing values or not. Non-missing values get mapped to True. Characters such as empty strings '' 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, get mapped to False values.

Syntax: Series.notnull() Parameter : None Returns : Series

Example #1: Use Series.notnull() function to detect all the non-missing values in the given series object.

Python3

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

Output :

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

Python3

# detect non-missing value result = sr.notnull() # Print the result print(result)

Output :

As we can see in the output, the Series.notnull() function has returned a boolean object. True indicates that the corresponding value is not missing. False value indicates that the value is missing. All the values are True in this series as there is no missing values. Example #2: Use Series.notnull() function to detect all the non-missing values in the given series object.

Python3

# importing pandas as pd import pandas as pd  # Creating the Series sr = pd.Series([19.5, 16.8, None, 22.78, None, 20.124, None, 18.1002, None])  # Print the series print(sr)