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.
# 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 |
Output:
Let’s find out all the non-missing values in the index
# 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
.
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