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.aggregate() function aggregate using one or more operations over the specified axis in the given series object.
Syntax: Series.aggregate(func, axis=0, *args, **kwargs)
Parameter :
func : Function to use for aggregating the data.
axis : Parameter needed for compatibility with DataFrame.
*args : Positional arguments to pass to func.
**kwargs : Keyword arguments to pass to func.Returns : DataFrame, Series or scalar
Example #1: Use Series.aggregate() function to perform aggregation on the underlying data of the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([34, 5, 13, 32, 4, 15]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Coca Cola 34 Sprite 5 Coke 13 Fanta 32 Dew 4 ThumbsUp 15 dtype: int64
Now we will use Series.aggregate() function to find the sum of all the values in the given series object.
# Find the sum of all values result = sr.aggregate(func = sum) # Print the result print(result) |
Output :
103
As we can see in the output, the Series.aggregate() function has successfully returned the sum of the underlying data of the given series object.
Example #2 : Use Series.aggregate() function to perform aggregation on the underlying data of the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([51, 10, 24, 18, 1, 84, 12, 10, 5, 24, 0]) # Create the Index # apply yearly frequency index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y') # set the index sr.index = index_ # Print the series print(sr) |
Output :
2010-12-31 08:45:00 51 2011-12-31 08:45:00 10 2012-12-31 08:45:00 24 2013-12-31 08:45:00 18 2014-12-31 08:45:00 1 2015-12-31 08:45:00 84 2016-12-31 08:45:00 12 2017-12-31 08:45:00 10 2018-12-31 08:45:00 5 2019-12-31 08:45:00 24 2020-12-31 08:45:00 0 Freq: A-DEC, dtype: int64
Now we will use Series.aggregate() function to find the maximum of all the values in the given series object.
# Find the max of all values result = sr.aggregate(func = max) # Print the result print(result) |
Output :
84
As we can see in the output, the Series.aggregate() function has successfully returned the maximum of all the values in the given series object.
