How to Use Python Lambda Functions? 6 Comprehensive Aspects

Developers today, both new and experienced, rely on the Python Programming Language to undertake tasks involving Software Development, Machine Learning, Data Visualization, Automation and many more. This robust language wth its numerous libraries provides users with a complete package of rich programming features. Moreover, businesses leverage the open-source availability of Python Programming Language to perform Data Analytics and extract insights from their huge datasets. Its easy integration with other tools also motivates developers to choose Python as their primary tool for developing Application Software. 

The Python Programming Language contains a special feature, called Lambda functions. The Python Lambda functions are special category functions that intake multiple arguments and operate them over a single expression. Furthermore, a Lambda function in Python does not need a name and therefore is ideal for use in a nested function.

This article will introduce you to the Python Programming Language along with its key features. It will also explain the concept, syntax and importance of Python Lambda functions and will list some of the most popular use cases of such functions. Read along to understand Python Lambda functions in detail and learn about their limitations!

Table of Contents

• What is Python?
  • Key Features of Python
• What are Anonymous or Lambda Functions in Python?
• Importance of Python Lambda Functions
• Syntax & Uses of Python Lambda Functions
• Popular Lambda Functions in Python
  • Using Lambda Functions as Argument with filter()
  • Using Lambda Functions as Argument with map()
  • Using Lambda Functions as Argument with reduce()
• Limitations of Python Lambda Functions
• Conclusion

What is Python?

Python is a widely-used GPP(General-purpose Programming) Language that simplifies your coding experience. It requires a small learning curve and provides you with numerous libraries that allow you to deploy high-level functions directly in your program. Python’s robust programming model makes it the first choice of developers today. It finds widespread applications in the fields of Website & Software Development, Data Analysis, Data Visualization, Automation and much more. Moreover, Python operates on a straightforward syntax that allows professionals from any vertical such as Commerce, Science, etc to leverage it for automating daily tasks. The Python Programming Language also acts as an integral tool in Data Science and is ideal for implementing vast Statistical Calculations, generating Machine Learning models & algorithms, etc. 

Businesses can also use the Python Programming Language for generating rich Data Visualization & Reports in the form of Bar Charts, Histograms, Pie Charts, etc. Furthermore, it supports libraries that empower you to execute advanced ML (Machine Learning) programs at faster rates and in a simpler manner. 

Key Features of Python

The following features make the Python Programming Language the first choice for developers today:

• Small Learning Curve: The Python Programming Language facilitates a hassle-free environment for the purpose of development & automation. The straightforward workflow coupled with its open-source availability has made this language, the first choice of millions of entry-level coders.
• Robust Applications: Its simple syntax, based on human-readable language, makes it the primary choice for the project. Moreover, the constraints and exceptions in Python Programming Language, are managed better compared to its peer Programming Languages. This robust nature and versatile application possibilities make Python codes an ideal fit for Web Development & ML Projects.
• Large Communities: Python’s immense popularity has led to a huge online community of active programmers that contributes regularly to the updation of this language’s modules and libraries. Moreover, this worldwide community readily offers help and suggestions to new coders. You can simply post your problems on the community page and multiple experienced developers will suggest you the best possible answers in no time.

You can learn more about the Python Programming Language by visiting here.

What are Anonymous or Lambda Functions in Python?

The Python Lambda function is also known as the Anonymous function is a type of function that can be defined without a name or reputation. Moreover, to define a normal function in the Python Programming Language, you require the def keyword. However, a Python Lambda function needs the keyword lambda in its definition. The basic difference between a normal function and a lambda function is shown in the following example: 

#Normal python function
def a_name(x):
   return x+x

 The same work can be done using the Lambda function as follows:

#Lambda function
lambda x: x+x

The above 2 functions perform the same task and operate in exactly the same way internally. However, the Python Lambda function provides you with higher flexibility and is easy to use. You can leverage Python Lambda functions to perform simple logical operations and make your code more readable. Furthermore, these functions are ideal for situations where you need to use a function just once.

Importance of Python Lambda Functions

You learned in the previous section that at the level of the interpreter, Python Lambda functions are treated the same way as any normal function of this programming language. This implies, that a Lambda function offers you the same functionality as any other function but in a compact syntax. Therefore, you can achieve the same task but in a seamless manner using the Python Lambda functions.

Using Lambda functions in the Python Programming Language, you can easily define a function and call it immediately, something which is not possible with the normal functions. Moreover, Lambda functions enable you to pass a function as a parameter to another function. This way you can utilize the Lambda functionality to build a one-time function and pass it as a parameter to any function.

Syntax & Uses of Python Lambda Functions

You learned in the previous sections that defining a normal function requires the def keyword in Python while defining anonymous functions is possible by using the lambda keyword. Moreover, a Python Lambda function has a subsequent syntax. This implies, that a Lambda function can use any number of arguments, but can operate on only one expression. Keeping this in mind, the general syntax of any Python Lambda function is as follows:

lambda arguments: expression

The single expression is evaluated against the multiple arguments and its value after the evaluation is returned. You will further understand the Lambda function syntax with the following examples:

Example 1: The below Lambda function adds 1 to any given argument

lambda x: x + 1

You can apply the above Lambda function to the argument of your choice by adding parentheses to the function and writing the required argument next to it as sown below.:

(lambda x: x + 1)(2)

The above Lambda function intakes the argument “2”, adds 1 to it and provides “3” as the output.

Since Lambda functions are expressions, you can assign names to them. Therefore you could rename the previous Lambda function as follows:

AddOne = lambda x: x + 1
AddOne(2)

The code gives the following output:

3

The above Python Lambda function works the same as the following normal Python function:

def add_one(x):
    return x + 1

Keep in mind that a Lambda function can not contain statements. This implies, that using statements like pass, return, assert, etc. will cause a SyntaxError exception in your Python code.

Using a Lambda function requires adequate knowledge of its optimal use cases. This implies you need to be clear on the situations that are ideal for a Lambda function’s implementation in place of a normal function. One of the most commonly occurring situations for using the Lambda function is during Functional Programming. This type of code requires a developer to use a nameless function and that also for only a short time frame. In such a situation a Python Lambda function is your best option.

Popular Lambda Functions in Python

In Python, Lambda functions are generally used as an argument for a higher-order function. A higher-order function is one that can pass other functions as arguments. The following 3 higher-order built-in functions are frequently used by developers with Python Lambda functions as parameters:

• Using Lambda Functions as Argument with filter()
• Using Lambda Functions as Argument with map()
• Using Lambda Functions as Argument with reduce()

Using Lambda Functions as Argument with filter()

The Python Programming Language offers a filter() function that readily inputs a function & a list as arguments. The function returns a new list containing which the function’s expression evaluates to true. 

The following example depends on the filter() function and screens out the even numbers from its input list:

MyList = [5, 1, 6, 8, 11, 3, 4, 12]
NewList = list(filter(lambda x: (x%2 == 0) , MyList))
print(NewList)

The above code leverage the Python Lambda function as an argument to perform the required filtering and outputs the following result:

Output

[6, 8, 4, 12]

Using Lambda Functions as Argument with map()

The map() function also operates on a list and takes in a function as an argument alongside the list. Developers use the map() function to get a new list as output containing items returned by that input function for each item in the original list. The following example showcases the use of the Python Lambda function as an argument for the map() function:

The below code uses the map() function to input a list and double all its items in the output.

MyList = [2, 4, 5, 6, 8, 3, 11, 12]
NewList = list(map(lambda x: x * 2 , MyList))
print(NewList)

The Lambda function given as an argument performs the required mapping and returns the following output:

Output

[4, 8, 10, 12, 16, 6,22, 24]

Using Lambda Functions as Argument with reduce()

Python’s reduce() function takes in a Lambda function and a list as arguments. The Lambda function’s role is to perform iterations on pair values from the input list and return a reduced list as a result.

The following example illustrates the use of the Python Lambda function in reduce() to get the sum of a list:

from functools import reduce

lis = [8, 5, 20, 10, 100, 60]
sum = reduce((lambda x, y: x + y), lis)
print (sum)

The code will provide you with the following output:

203

Limitations of Pyhton Lambda Functions

The Python Lambda function has great importance in certain use cases as explained in the previous sections. However, it comes along with the following limitations:

• Lambda functions can operate on only one expression. This implies that regardless of the number of arguments you want to pass as input, the Lambda function will not support more than 1 independent operation.
• Using a Python Lambda function for operations that may consume more than 1 line in a normal function is problematic. For instance, if you are using a function with nested conditional operations, don’t implement Lambda functions as it will not be feasible for other readers to understand your code. Instead, use normal named functions and create a better and more understandable code.
• You can assign a name to the Lambda function, however, it is not encouraged. This is because Lambda is an anonymous function that was not designed to be stored. This implies Lambda functions are good for only those situations where implementing them without any name will be beneficial.
• Lambda functions can not include doc-strings to explain every input, operation, and output. This is why using a normal definition function is more appropriate if you wish to generate a structured code.

Conclusion

This article introduced you to the Python Programming Language and discussed its key features. It also explained the concept of Python Lambda functions and why they are important. The article further discussed the syntax and uses of Lambda functions and listed the examples of some popular use cases of python Lambda functions in real life. Furthermore, the article explained certain limitations associated with the Lambda function set.

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