Pandas Series transform() Function:
The transform() function of Pandas Series invokes func on self to generate a Series with transformed data.
Syntax:
Series.transform(func, axis=0)
Parameters
func: This is required. It Indicates the function that will be used to transform the data. If a function, must either work when passed a Series or when passed to Series.apply. When func has both listlike and dictlike behavior, the dictlike behavior takes precedence. The following are acceptable combinations.
 function
 string function name
 list of functions and/or function names, e.g. [np.exp, ‘sqrt’]
 dictionary of axis labels > functions, function names, or list of such.
axis: This is optional. It indicates a value of 0 or ‘index’. This is an axis on which the function is applied.
Return Value:
A Series with transformed values of the same length as self is returned by the transform() function of Pandas Series.
Note: If the returned Series is not the same length as self, ValueError is returned.
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Pandas Series transform() Function in Python
Example1
Approach:
 Import pandas 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 some random lambda function to the transform() function, apply it on the given series to get the Series with transformed data and print the result.
 Here the lambda function adds 5 to each element of the series.
 The Exit of the Program.
Below is the implementation:
# Import pandas module using the import keyword. import pandas as pd # 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 some random lambda function to the transform() function, # apply it on the given series to get the Series with transformed data # and print the result. # Here the lambda function adds 5 to each element of the series. print("The given Series with transformed data:") print(gvn_series.transform(lambda gvn_series: gvn_series+5))
Output:
The given series is: 0 10 1 5 2 2 3 4 dtype: int64 The given Series with transformed data: 0 15 1 10 2 7 3 9 dtype: int64
Example2
A Series can be subjected to multiple operations at the same time. Here, two operations: ‘sqrt’ (square root) and ‘cbrt’ (cube root) are applied at the same time, and each produces a Series of the same length.
Approach:
 Import pandas 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 abovegiven series.
 Pass some random list of operations to the transform() function, apply it on the given series to get the Series with transformed data, and print the result.
 Here the squareroot, cuberoot operations are performed on each element of the series.
 The Exit of the Program.
Below is the implementation:
# Import pandas module using the import keyword. import pandas as pd # 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, 125, 16, 4]) # Print the above given series print("The given series is:") print(gvn_series) print() # Pass some random list of operations to the transform() function, # apply it on the given series to get the Series with transformed data # and print the result. # Here the squareroot, cuberoot operations are performed on each element of the series. print("The squareroot, cuberoot values of each element of the series:") print(gvn_series.transform(['sqrt', 'cbrt']))
Output:
The given series is: 0 10 1 125 2 16 3 4 dtype: int64 The squareroot, cuberoot values of each element of the series: sqrt cbrt 0 3.162278 2.154435 1 11.180340 5.000000 2 4.000000 2.519842 3 2.000000 1.587401
Example3
Approach:
 Import pandas module using the import keyword.
 Pass some random keyvalue 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 transform() function on the student_marks column of the dataframe by passing ‘sqrt’ as an argument to it to get the squareroot 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 # Pass some random keyvalue 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": [64, 45, 25, 90]}, index= ["virat", "nick" , "jessy", "sindhu"] ) # Print the given dataframe print("The given Dataframe:") print(data_frme) print() # Apply transform() function on the student_marks column of the dataframe # by passing 'sqrt' as an argument to it to get the squareroot of all the values # of the student_marks column and print the result. print("The squareroot of all the values of the student_marks column of the dataframe:") print(data_frme['student_marks'].transform('sqrt'))
Output:
The given Dataframe: student_rollno student_marks virat 1 64 nick 2 45 jessy 3 25 sindhu 4 90 The squareroot of all the values of the student_marks column of the dataframe: virat 8.000000 nick 6.708204 jessy 5.000000 sindhu 9.486833 Name: student_marks, dtype: float64