Pandas Series agg() Function:
Agg python: The agg() method of the Pandas Series is used to do aggregation using one or more operations over the given axis.
Syntax:
Series.agg(func=None, axis=0)
Parameters
func: This is required. It Indicates the function that will be used to aggregate the data. If a function, should either work when passed a Series or when passed to Series.apply. accepted combinations are as follows.
- function
- string function name
- list of functions and/or function names, e.g. [np.sum, ‘mean’]
- dictionary of axis labels -> functions, function names, or list of such.
axis: This is optional. This argument is required for DataFrame compatibility. It indicates a value of 0 or ‘index’. This is an axis on which the function is applied.
Return Value:
The following is returned:
- When calling Series.agg with a single function, the result is a Scalar.
- When DataFrame.agg is called with a single function, it returns a Series.
- When DataFrame.agg is called with multiple functions, it returns a DataFrame.
Pandas Series agg() Function in Python:
Example1
Approach:
- Import pandas module using the import keyword.
- Import numpy module using the import keyword.
- Pass some random list as an argument to the Series() function of the pandas module to create a series.
- Store it in a variable.
- Print the above-given series.
- Apply numpy.sum, agg() function on the given series to add all the elements of the given series and print the result.
-
The Exit of the Program.
Below is the implementation:
# Import pandas module using the import keyword. import pandas as pd # Import numpy module using the import keyword. import numpy as np # Pass some random list as an argument to the Series() function # of the pandas module to create a series. # Store it in a variable. gvn_series = pd.Series([10, 5, 2, 4]) # Print the above given series print("The given series is:") print(gvn_series) print() # Apply numpy.sum, agg() function on the given series to add # all the elements of the given series and print the result. print("Adding all the elements of the given series:") print(gvn_series.agg(np.sum))
Output:
The given series is: 0 10 1 5 2 2 3 4 dtype: int64 Adding all the elements of the given series: 21
Example2: Performing multiple operations on a Series
Python agg: Here sum, variance, mean and average operations are performed on a given series.
Approach:
- Import pandas module using the import keyword.
- Import numpy module using the import keyword.
- Pass some random list as an argument to the Series() function of the pandas module to create a series.
- Store it in a variable.
- Print the above-given series.
- Pass list of operations(sum, variance, mean, average) to the agg() function to do multiple operations on the given series and print the result.
-
The Exit of the Program.
Below is the implementation:
# Import pandas module using the import keyword. import pandas as pd # Import numpy module using the import keyword. import numpy as np # Pass some random list as an argument to the Series() function # of the pandas module to create a series. # Store it in a variable. gvn_series = pd.Series([10, 5, 2, 4]) # Print the above given series print("The given series is:") print(gvn_series) print() # Pass list of operations(sum, variance, mean, average) to the agg() function # to do multiple operations on the given series and print the result. print("Performing sum, variance, mean, average operations on the given series:") print(gvn_series.agg([np.sum, np.var, np.mean, 'average']))
Output:
The given series is: 0 10 1 5 2 2 3 4 dtype: int64 Performing sum, variance, mean, average operations on the given series: sum 21.000000 var 11.583333 mean 5.250000 average 5.250000 dtype: float64
Example3
Approach:
- Import pandas module using the import keyword.
- Import numpy module using the import keyword.
- Pass some random key-value pair(dictionary), index list as arguments to the DataFrame() function of the pandas module to create a dataframe.
- Store it in a variable.
- Print the given dataframe.
- Apply agg() function on the student_marks column of the dataframe to perform the addition operation of all the values of the student_marks column and print the result.
-
The Exit of the Program.
Below is the implementation:
# Import pandas module using the import keyword. import pandas as pd # Import numpy module using the import keyword. import numpy as np # Pass some random key-value pair(dictionary), index list as arguments to the # DataFrame() function of the pandas module to create a dataframe # Store it in a variable. data_frme = pd.DataFrame({ "student_rollno": [1, 2, 3, 4], "student_marks": [80, 35, 25, 90]}, index= ["virat", "nick" , "jessy", "sindhu"] ) # Print the given dataframe print("The given Dataframe:") print(data_frme) print() # Apply agg() function on the student_marks column of the dataframe to # perform the addition operation of all the values of the student_marks # column and print the result. print("The addition of all the values of the student_marks column of the dataframe:") print(data_frme['student_marks'].agg(np.sum))
Output:
The given Dataframe: student_rollno student_marks virat 1 80 nick 2 35 jessy 3 25 sindhu 4 90 The addition of all the values of the student_marks column of the dataframe: 230