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.isna() function detect missing values in the given series object. It return a boolean same-sized object indicating if the values are NA. Missing values gets mapped to True and non-missing value gets mapped to False.
Syntax: Series.isna()
Parameter : None
Returns : boolean
Example #1: Use Series.isna() function to detect missing values in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([10, 25, 3, 25, 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.isna() function to detect all the missing values in the given series object.
# detect missing values result = sr.isna() # Print the result print(result) |
Output :
As we can see in the output, the Series.isna() function has returned an object containing boolean values. All values have been mapped to False because there is no missing value in the given series object.
Example #2 : Use Series.isna() function to detect missing values in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([11, 21, 8, 18, 65, None, 32, 10, 5, 24, None]) # Create the Index index_ = pd.date_range('2010-10-09', periods = 11, freq ='M') # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.isna() function to detect all the missing values in the given series object.
# detect missing values result = sr.isna() # Print the result print(result) |
Output :
As we can see in the output, the Series.isna() function has returned an object containing boolean values. All missing values have been mapped to True.
