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.dropna() function return a new Series with missing values in the given series object removed.
Syntax: Series.dropna(axis=0, inplace=False, **kwargs)
Parameter :
axis : There is only one axis to drop values from.
inplace : If True, do operation inplace and return None.Returns : Series
Example #1: Use Series.dropna() function to drop the missing values in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', None, 'Rio']) # Create the Index index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.dropna() function to drop all the missing values in the given series object.
# drop the missing values result = sr.dropna() # Print the result print(result) |
Output :
As we can see in the output, the Series.dropna() function has successfully dropped all the missing values in the given series object.
Example #2 : Use Series.dropna() function to drop the missing values in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([100, None, None, 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.dropna() function to drop all the missing values in the given series object.
# drop the missing values result = sr.dropna() # Print the result print(result) |
Output :
As we can see in the output, the Series.dropna() function has successfully dropped all the missing values in the given series object.
