Index.notnull() Pandas Index.notnull() detects existing (not missing) values. This function returns a Boolean of the same size indicating that the values ​​are not NA. Values ​​not missing are displayed as True. Characters such as empty strings “or numpy.inf are not considered NA values ​​(unless you set pandas.options.mode.use_inf_as_na = True). NA values ​​such as None or numpy.NaN are mapped to false values.

Syntax: Index.notnull ()

Returns: Boolean array to indicate which entries are not NA.

Example # 1: Use Index.notnull () () to detect missing values ​​in the given index.

Output:

Let`s find out all the non-missing values ​​in the index

# import pandas as pd import pandas as pd    # Create index idx = pd.Index ([ ` Jan` , ` ` , ` Mar `, None, ` May `, ` Jun `, ` Jul`, `Aug` , `Sep` , `Oct` , ` Nov` , `Dec` ])    # Print index idx

# not found omitted values. idx.notnull ()

Output:

As we can see in the output, all non-missing values ​​were mapped to True and all missing values ​​were mapped to False . Note that an empty string was matched against True because an empty string is not considered a missing value.

Example # 2: Use Index.notnull () find all non-missing values ​​in the index.

# import pandas as pd import pandas as pd   # Create index idx = pd.Index ([ 22 , 14 , 8 , 56 , None , 21 , None , 23 ])   # Print index idx

Output:

Let`s find out all the non-missing values ​​in the index

# find non-missing values. idx.notnull ()

Output:

As we can see in the output, everything is not the missing values ​​were matched against True and all missing values ​​were matched against False .