Python Lambda

Before learning about Python Lambda, it is important to talk about the Python functions.
Functions are commonly written in Python using the following format:

The list of parameters is added when we declare them using the “def” keyword, give them a name, and put the round parentheses around the list of arguments. There might be several lines of code, each containing as many statements and expressions as required. However, there are situations when we may want a function with just one internal expression such as when we need a function that doubles an argument.

We may utilize the double() method along with the map() function.

Given that a lambda function may be built precisely where it is needed, this would be an excellent spot to utilize one. This results in writing a fewer lines of code and preventing the creation of the named functions that are only ever called once (and then have to be stored in a memory).

Definition of Lambda Function

These are the methods without names. The terms anonymous and nameless functions are also used to describe them. In place of a name, the term “lambda” serves as a keyword. The anonymous nature of the function that comes next is indicated by this term.

Why Use the Python Lambda Functions

The primary use of anonymous functions is when a certain function is required only once. They can be produced anywhere that needs them. Python Lambda functions are therefore sometimes referred to as throw-away methods since they are utilized in conjunction with the other predefined functions like filter(), map(), and other functions. When compared to the standard Python functions, these functions assist in reducing the number of lines in the code.

How to Utilize the Lambda Methods in Python

When a short expression is required such as a parameter for a complex method, lambda functions are used.

The syntax of a lambda function is as follows:

The expression that makes up the function’s body is written after the term lambda, one space, a list of all the parameters separated by commas, and a colon.
The number of parameters that we use in a lambda function is up to us. But only one expression may be used in the body.

We note that although there can be an unlimited number of parameters, there can only ever be one expression per argument. The return statement, which is typically part of the syntax for a definition of the function, is absent here.

The syntax is explained by defining the argument and expressions. Let “y” be the argument and “y+y” is the expression.

Before moving on to the actual topic, let’s talk about some technical aspects of the lambda functions and what the Python community considers to be their advantages and drawbacks.


Good for clear-cut, straightforward logical procedures. This improves the code’s readability as well.
Effective when we need a one-time usage function.


They are limited to using one phrase. One lambda function cannot include many independent operations.

Unfavorable to actions that would need more than one statement in a standard definition of the function. Use a named function in its place if it takes us more than a minute to grasp the code.

Example 1:

We will create a lambda function that multiplies its attribute by 2.

Now, we execute the program by using the previous syntax.

We declare a list having 6 integers under the “my_list” variable. Then, we utilize the lambda function along with the map() function in a new variable -“new-list”. At last, we utilize the print() function and get the desired output.

Observe the dissimilarities between this method and the double function that we wrote about earlier. This one is more manageable and uses less memory because it doesn’t include an additional function. It takes only a single line coding which is easy to use.

Example 2:

Lambda function can also be used to find the required integers or set on numbers easily and alternatively. We may create a lambda function that determines if an integer is positive.

The succeeding example is run by the use of the previously mentioned syntax.

Begin with declaring the list of 6 numbers under the variable specified as “my-list”. After initializing the values of the list, we make use of the lambda function along with the filter() function under the “new-list” variable. To print the output in the last step, we apply the print() function.

This prevents the need for a named function to be stored in memory because the lambda function is declared when it is utilized. To prevent a clutter, it is appropriate to use a lambda function when a function is only called once and to get the desired output using a single line formula.

Example 3:

Multiple statements are not allowed in lambda functions. But we may build two lambda functions and then call one of them as a parameter to another lambda function. Using lambda, let’s attempt to locate the second greatest element:

Here, we initialize a set of integers in the “list” variable. After making the list and specifying the variable, we developed a lambda function that sorts each sub-list of the supplied list. The second lambda function which retrieves the n-2th element from the sorted list and returns n as the length of the sub list is then called using this list as an argument.

Example 4:

A function and a list are both accepted as parameters by the map() function. A new list that includes all the lambda-modified elements provided by that method for every element is returned when the method is invoked with a lambda method and a list.

We start with a list of 10 integers in the “list” variable. Then, we utilize the two functions (map and lambda) together in a “final_list”. In the end, we represent our output by utilizing the print() function.

Example 5:

We utilize the lambda() function with reduce(). A function and a list are the two arguments accepted by the reduce() Python function. A result is returned when the method is invoked with a lambda method and an iterable. Using the iterable’s pair values, this repeats an operation. The “functools” module is where the reduce() method resides.

After integrating the module “reduce” from the “functools”, we make a list of 6 integers and declare the variable as “list”. Then, make use of the lambda and reduce function in the “sum” variable to show the output. We utilized the print() function.


In this guide, we explained the Lambda functions. These are defined as functions if our function just has one brief expression. Though we demonstrated here that they are easy to use at any level, they are not commonly employed by programmers. For a better understanding on what are the lambda functions and how one can use these functions to reduce the size or length of a function, we also implemented some codes in the article along with some of the pros and cons so that one can know the advantages and disadvantages of using the lambda functions before using, get a better overview of the situation, and whether the lambda function is applicable in such a situation or not.

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