Pandas DataFrame DataFrame.transform() Function

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  3. Python Pandas Functions
  4. Pandas DataFrame DataFrame.transform() …
Minahil Noor Jan 30, 2023
Pandas Pandas DataFrame
  1. Syntax of pandas.DataFrame.transform()
  2. Example Codes: DataFrame.transform()
  3. Example Codes: DataFrame.transform() to Use sqrt String as Function
  4. Example Codes: DataFrame.transform() to Pass the List of Functions
  5. DataFrame.apply() vs DataFrame.transform() Function
Pandas DataFrame DataFrame.transform() Function

Python Pandas DataFrame.transform() applies a function on a DataFrame and transforms the DataFrame. The function to be applied is passed as a parameter to the transform() function. The transformed DataFrame should have the same length of the axis as the original DataFrame.

Syntax of pandas.DataFrame.transform()

DataFrame.transform(func, axis, *args, **kwargs)

Parameters
func It is the function to be applied to the DataFrame. It brings a change in the values of the DataFrame. It can be a function, function name string, list of functions or function names, or dictionary of axis labels.
axis It is an integer or a string. It specifies the target axis either rows or columns. It can be 0 or index for rows and 1 or columns for columns.
*args These are the positional arguments to pass to the function.
**kwargs These are the additional keyword arguments to pass to the function.

Return

It returns a transformed DataFrame that has the same length as the original DataFrame. If the returned DataFrame has an unequal length then the function raises a ValueError.

Example Codes: DataFrame.transform()

Let’s first try this function by adding a number to each value of the DataFrame.

import pandas as pd

dataframe = pd.DataFrame({
                            'A': 
                                {0: 6, 
                                1: 20, 
                                2: 80,
                                3: 78,
                                4: 95}, 
                            'B': 
                                {0: 60, 
                                1: 50, 
                                2: 7,
                                3: 67,
                                4: 54}
                        })

print(dataframe)

The example DataFrame is,

    A   B
0   6  60
1  20  50
2  80   7
3  78  67
4  95  54
5  98  34

This function has only one mandatory parameter i.e func. Now we will use this function to add 20 to each value of the DataFrame.

import pandas as pd

dataframe = pd.DataFrame(
    {"A": {0: 6, 1: 20, 2: 80, 3: 78, 4: 95}, "B": {0: 60, 1: 50, 2: 7, 3: 67, 4: 54}}
)

dataframe1 = dataframe.transform(func=lambda x: x + 20)
print(dataframe1)

Output:

     A   B
0   26  80
1   40  70
2  100  27
3   98  87
4  115  74
5  118  54

The lambda keyword is used to declare an anonymous function of addition here.

Example Codes: DataFrame.transform() to Use sqrt String as Function

import pandas as pd

dataframe = pd.DataFrame(
    {"A": {0: 6, 1: 20, 2: 80, 3: 78, 4: 95}, "B": {0: 60, 1: 50, 2: 7, 3: 67, 4: 54}}
)

dataframe1 = dataframe.transform(func="sqrt")
print(dataframe1)

Output:

          A         B
0  2.449490  7.745967
1  4.472136  7.071068
2  8.944272  2.645751
3  8.831761  8.185353
4  9.746794  7.348469
5  9.899495  5.830952

Here, instead of passing a lambda function, we have passed the function name as a string.

Example Codes: DataFrame.transform() to Pass the List of Functions

import pandas as pd

dataframe = pd.DataFrame(
    {"A": {0: 6, 1: 20, 2: 80, 3: 78, 4: 95}, "B": {0: 60, 1: 50, 2: 7, 3: 67, 4: 54}}
)

dataframe1 = dataframe.transform(func=["sqrt", "exp"])
print(dataframe1)

Output:

          A                       B              
       sqrt           exp      sqrt           exp
0  2.449490  4.034288e+02  7.745967  1.142007e+26
1  4.472136  4.851652e+08  7.071068  5.184706e+21
2  8.944272  5.540622e+34  2.645751  1.096633e+03
3  8.831761  7.498417e+33  8.185353  1.252363e+29
4  9.746794  1.811239e+41  7.348469  2.830753e+23

We have passed a list of two function names i.e ['sqrt', 'exp'] as the func. The returned DataFrame contains two extra columns due to an extra function.

DataFrame.apply() vs DataFrame.transform() Function

We can also achieve the above results using DataFrame.apply() function. But if we compare these two functions, we will say that the DataFrame.transform() function is more efficient to handle the complex manipulations.

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