Learn How to use the Transform Function in Pandas (with Python code)

7 min read

The Transform function in Pandas (Python) can be slightly difficult to understand, especially if you’re well-versed in Excel. Most data scientists don’t use it immediately in their learning journey.

But Pandas’ transform function is quite a handy tool for a data scientist! It is a powerful function that you can lean on for feature engineering in Python.

I personally started using this when I was looking to perform feature engineering in a hackathon – and I was pleasantly surprised by how quickly the Transform function worked. I strongly feel you will benefit from knowing and using the Transform function, so I decided to write about it in this article.

To learn the basics of Python and Pandas for data science, check out these popular courses:

• Python for Data Science
• Pandas for Data Analysis in Python

What is the Transform Function in Python?

Python’s Transform function returns a self-produced dataframe with transformed values after applying the function specified in its parameter. This dataframe has the same length as the passed dataframe. The argument supplied to the transform() method can be either a NumPy function, the name of a string function, or a user-defined function.

A Pandas series is a one-dimensional ndarray with axis labels. While the labels do not have to be unique, they must be of a hashable type.

That was a lot, so let me explain using an example.

Let’s say we want to multiply 10 by each element in a dataframe:

GitHub Link:

#import library
import pandas as pd
import numpy as np

#creating a dataframe
df=pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c']) 

The original dataframe looks like this:

Python Code:

Syntax

#import library
import pandas as pd
import numpy as np
#creating a dataframe
df=pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])
#applying the transform function
print(df.transform(func = lambda x : x * 10))

For better understanding, let’s dissect each line of the above-given code:

1. Import Libraries
   • pd is an alias for the Pandas library, which is used for data manipulation and analysis.
   • np is an alias for the NumPy library, used for numerical operations in Python.
2. Creating a DataFrame
   • Creates a Pandas DataFrame using a NumPy array. The array contains numerical data arranged in rows and columns, and column names are specified as ‘a, ‘b,’ and ‘c.’
3. Applying the Transform Function
   • Uses the transform function of the DataFrame to apply a specified function to each element of the DataFrame.
   • The function specified is a lambda function (lambda x: x * 10), which multiplies each element (x) by 10.
   • The transformed DataFrame is then printed.

This is the dataframe we get after applying Python’s Transform function:

Why is Python’s Transform Function Important?

Transform comes in handy during feature extraction. As the name suggests, we extract new features from existing ones. Let’s understand the importance of the transform function with the help of an example.

Here, we have a dataset about a department store:

We can see that each user has bought multiple products with different purchase amounts. We would like to know the mean purchase amount of each user. This helps us create a new feature for the model to understand the relationship better.

This is the desired output

There are multiple approaches to do this:

1. Using Groupby followed by merge()
2. Transform function approach

I’ll implement both of them in this article.

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Approach 1: Using Groupby followed by merge()

The first approach is using groupby to aggregate the data and then merge this data back into the original dataframe using the merge() function. Let’s do it!

Step1: Import the Libraries and Read the Dataset

import pandas as pd 
df=pd.read_csv(“purchase.csv”) #Can be any csv of your choice.

Step2: Use groupby to Calculate the Aggregate

df.groupby('User_ID')["Purchase"].mean()

Here is a pictorial representation of how groupby puts together the mean of each user:

Step3: Using merge() function to recombine

Now, the tough part. How do we combine this data back to the original dataframe? We’ll be using the merge() function for this task. You can read more about joins and merges in Python using Pandas here and here.

mean_purchase =df.groupby('User_ID')["Purchase"].mean().rename("User_mean").reset_index() 
df_1 = df.merge(mean_purchase)

Our original dataframe looks like this:

Creates a Pandas DataFrame using a NumPy array. The array contains numerical data arranged in rows and columns, and column names are specified as ‘a, ‘b,’ and ‘c.’

This certainly does our work. However, it is a multistep process and requires extra code to get the data in the form we require. This multistep process can be resource-consuming in hackathons where time is a major constraint.

We can solve this effectively using the transform function in Pandas.

Approach 2: Using Python’s Transform Function

This is an important function for creating features. Trust me, it can be a game-changer!

After performing the transformation, the transform function retains the same number of items as the original dataset. We’ll be leaning on a super-easy one-line step using groupby followed by a transform:

df["User_Mean"] = df.groupby('User_ID')["Purchase"].transform('mean')

The pictorial representation is as follows:

Couldn’t be simpler, right? The original dataframe looks similar to the above one in the last step.

The time taken by the transform function to perform the above operation is comparatively less over a large dataframe. That’s a sigificant advantage as comapred to the first approach we used.

Let me demonstrate the Transform function using Pandas in Python.

Suppose we create a random dataset of 1,000,000 rows and 3 columns. Now we calculate the mean of one column based on groupby (similar to mean of all purchases based on groupby user_id).

1 Step : Import the libraries

#importing libraries
import pandas as pd
import random

2 Step : Create the dataframe

data = pd.DataFrame({
 'C' : [random.choice(('a','b','c')) for i in range(1000000)],
 'A' : [random.randint(1,10) for i in range(1000000)],
 'B' : [random.randint(1,10) for i in range(1000000)]

})

3 Step : Use the merge procedure

%%timeit

data.groupby('C')["A"].mean()
mean =data.groupby('C')["A"].mean().rename("N").reset_index()
df_1 = data.merge(mean)

Output:
👁 Transform function

Step 4: Use the transform function

%%timeit
data['N3'] = data.groupby(['C'])['A'].transform('mean')

Output:
👁 Transform function

This clearly shows the transform function is much faster than the previous approach. Well done!

Also read Spark DataFrame: Complete Guide on DataFrame Operations in PySpark.

Difference Between Apply And Transform Function in Python

Now, let’s say we want to create a new column based on the values of another column. This is the dataframe we’re working with:

With the Apply Function

df['d'] = df.apply(lambda row: row.a + row.b + row.c, axis=1)

This is what the output looks like using the Apply function:

The apply function sends a whole copy of the dataframe to work upon so we can simultaneously manipulate all the rows or columns.

With the Transform Function

This feature is not possible in the Transform function. This manipulates a single row or column based on axis value and doesn’t manipulate a whole dataframe. So, we can use either the Apply or the Transform function, depending on the requirement.

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Conclusion

Python’s Transform function in Pandas, a valuable tool for efficient feature engineering, proves crucial in hackathons. It simplifies tasks like aggregations, making it a game-changer for data scientists dealing with data structures, data types, and dtype. Embracing this function enhances the toolkit, offering a faster feature extraction and manipulation approach.

The Transform function is super useful when I’m quickly looking to manipulate rows or columns. As I mentioned earlier, this is especially handy in hackathons when time is of the essence.

If you come across any more such Pandas functions, do comment, and I’ll be happy to learn and share!

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Also read Pandas series: The Ultimate Guide to Pandas For Data Science!

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