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Lamda Functions Help Create DataFrames in Python


Python is the most popular language for developing machine learning applications. According to one survey, 69% of developers that create machine learning programs use Python.

There are many reasons machine learning developers use Python. One of the biggest benefits is that the language is platform independent. It is also simple, flexible, and has a large community that developers can call on for support.

However, there is still a learning curve for Python developers creating machine learning programs. Fortunately, they can find clever ways to make the programs more efficiently. They can use great artificial intelligence tools and learn different coding hacks to simplify the coding process and work with large datasets more easily.

One of the best ways to create more versatile machine learning applications in Python is to use lambda functions to create DataFrames. You will need to learn what lambda functions are and how to use them in the Pandas package to get the most benefit from them.

Using Lambda Functions to Create Machine Learning Applications

A growing number of developers are taking advantage of lambda functions to create powerful machine learning applications. Lloyd Hamilton of Towards Data Science talked about some of the benefits of using lambda functions to create more efficient machine learning applications. Some of these benefits include:

  • Training classifiers for K-Nearest neighbors
  • Initializing S3 Buckets for AWS
  • Organizing and calling important Docker functions

However, he didn’t discuss one of the biggest benefits, which is using them to create DataFrames by using the Pandas package.

Lambda functions are gamechangers for machine learning development. However, developers that haven’t worked with them before might need to spend some time familiarizing themselves with their syntax.

What Are Lambda Functions?

Lambda or anonymous functions are Python functions that are typically defined in one line. They can be executed with very little code.  Python lambdas are only a shorthand notation if you’re too lazy to define a function.

Here is an example of a Lambda function that adds two numbers:

def sum(a, b):

    return a+b

It could be expressed in the form of a lambda function as follows.

The first difference is that a lambda function does not have a name. Therefore, unless it is assigned to a variable, it is totally useless. You can assign it with the following syntax:

Once we have the function, it is possible to call it as if it were a normal function.

If it is a function that you only want to use once, it may not make sense to store it in a variable. It is possible to declare the function and call it on the same line.

(lambda a, b: a + b)(2, 4)

## Examples

A lambda function can be the input to a normal function.

def my_function(lambda_func):

    return lambda_func(2,4).

my_function(lambda a, b: a + b)

And a normal function can also be the input of a lambda function. Note that these are didactic examples without much practical use per se.

def my_other_function(a, b):

    return a + b

(lambda a, b: my_other_function(a, b))(2, 4)

Although lambda functions have many limitations compared to normal functions, they share a lot of functionality. It is possible to have arguments with a default value assigned.

(lambda a, b, c=3: a + b + c)(1, 2) # 6

It is also possible to pass parameters indicating their name.

(lambda a, b, c: a + b + c)(a=1, b=2, c=3) # 6

As in the functions, you can have a variable number of arguments by using *, known as tuple unpacking.

(lambda *args: sum(args))(1, 2, 3) # 6

If you have the input parameters stored in the form of key and value as if it were a dictionary, it is also possible to call the function.

(lambda **kwargs: sum(kwargs.values()))(a=1, b=2, c=3) # 6

Finally, it is possible to return more than one value.

x = lambda a, b: (b, a)

print(x(3, 9))

# Output (9,3)

Using Lambda Functions to Create DataFrames With the Pandas Package

Once you understand the basics of lambda functions, you can find creative ways to implement them while creating machine learning applications. Tonichi Edeza of Towards Data Science has emphasized a few of the benefits of using them for this purpose. One of the biggest benefits is that you can use lambda functions to create and add data to DataFrames. You can look at the code in the example in his blog post, which shows how you can create new columns in your DataFrame with only two lines of code.

DataFrames are very versatile elements in Python and are crucial for many machine learning applications. They are part of the Pandas package, which is a very important toolkit for machine learning development. However, as Pavan Kalyan points out in his article Must know Pandas Functions for Machine Learning Journey, they can be difficult to work with due to the challenges they face with storing large data sets. This underscores the benefits of using lambda functions to work with them much more easily.



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