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 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.hasnans attribute returns a boolean value. It return True if the given Series object has missing values in it else it return False.
Syntax:Series.hasnans
Parameter : None
Returns : boolean
Example #1: Use Series.hasnans attribute to check if the given Series object has any missing values in it.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon'])
# Creating the row axis labels sr.index = ['City 1', 'City 2', 'City 3', 'City 4']
# Print the series print(sr)
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Output :
Now we will use Series.hasnans attribute to check for the missing values in sr object.
# check for missing values. sr.hasnans |
Output :
As we can see in the output, the Series.hasnans attribute has returned False indicating that there is no missing values in the given series object.
Example #2 : Use Series.hasnans attribute to check if the given Series object has any missing values in it.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series([1000, 'Calgarry', 5000, None])
# Print the series print(sr)
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Output :
Now we will use Series.hasnans attribute to check for the missing values in sr object.
# check for missing values. sr.hasnans |
Output :
As we can see in the output, the Series.hasnans attribute has returned True indicating that there is at least one missing value in the given series object.