Python | Pandas Series.aggregate()

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.

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# 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)

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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.

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# Find the sum of all values
result = sr.aggregate(func = sum)
  
# Print the result
print(result)

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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.

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# 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)

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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.

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# Find the max of all values
result = sr.aggregate(func = max)
  
# Print the result
print(result)

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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.



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