Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas Index.notna() function Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to 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, get mapped to False values.
Syntax: Index.notna()
Parameters : Doesn’t take any parameter.
Returns : numpy.ndarray: Boolean array to indicate which entries are not NA.
Example #1: Use Index.notna() function to find all non-missing values in Index.
Python3
# importing pandas as pdimport pandas as pd# Creating the Indexidx = pd.Index(['Labrador', None, 'Beagle', 'Mastiff', 'Lhasa', None, 'Husky', 'Beagle'])# Print the Indexidx |
Output :
Now we check for the non-missing values in the Index.
Python3
# checks for non-missing values.idx.notna() |
Output :
The function returned an array object having the same size as that of the index. True value means the index label is not missing and False value means the index label are missing.
Example #2: Use Index.notna() function to check for the non-missing label in the Datetime Index.
Python3
# importing pandas as pdimport pandas as pd# Creating the Datetime Indexidx = pd.DatetimeIndex([pd.Timestamp('2015-02-11'), None, pd.Timestamp(''), pd.NaT])# Print the Datetime Indexidx |
Output :
Now we will check if the labels in the Datetime Index are present or missing.
Python3
# test whether the passed Datetime # Index labels are missing or not.idx.notna() |
Output :
As we can see in the output, the function has returned an array object having the same size as that of the Datetime Index. True value means the index label are not missing and False value means the index label are missing.
