Python Pandas Series transform() Function

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 list-like and dict-like behavior, the dict-like 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.

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 above-given 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 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 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 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": [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

Pandas Series transform() Function in Python

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