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.agg() is used to pass a function or list of function to be applied on a series or even each element of series separately. In case of list of function, multiple results are returned by agg() method.
Syntax: Series.agg(func, axis=0)
Parameters:
func: Function, list of function or string of function name to be called on Series.
axis:0 or ‘index’ for row wise operation and 1 or ‘columns’ for column wise operation.Return Type: The return type depends on return type of function passed as parameter.
Example #1:
In this example, a lambda function is passed which simply adds 2 to each value of series. Since the function will be applied to each value of series, the return type is also series. A random series of 10 elements is generated by passing array generated using Numpy random method.
# importing pandas module import pandas as pd # importing numpy module import numpy as np # creating random arr of 10 elements arr=np.random.randn(10) # creating series from array series=pd.Series(arr) # calling .agg() method result=series.agg(lambda num : num + 2) # display print('Array before operation: \n', series, '\n\nArray after operation: \n',result) |
Output:
As shown in output, the function was applied to each value and 2 was added to each value of series.
Example #2: Passing List of functions
In this example, a list of some Python’s default function is passed and multiple results are returned by agg() method into multiple variables.
# importing pandas module import pandas as pd # importing numpy module import numpy as np # creating random arr of 10 elements arr=np.random.randn(10) # creating series from array series=pd.Series(arr) # creating list of function names func_list=[min, max, sorted] # calling .agg() method # passing list of functions result1, result2, result3= series.agg(func_list) # display print('Series before operation: \n', series) print('\nMin = {}\n\nMax = {},\ \n\nSorted Series:\n{}'.format(result1,result2,result3)) |
Output:
As shown in output, multiple results were returned. Min, Max and Sorted array were returned into different variables result1, result2, result3 respectively.
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