Last Updated on July 14, 2022 by Jay

Python lambda function, aka anonymous function. Unlike how we create a function with ** def ...** statement, where we can name a function, the lambda function doesn’t require a name. It’s useful when we need a quick and (generally a small) function which doesn’t need to be re-used often. Using Lambda function on its own probably doesn’t make too much sense. The value of lambda function is where it’s used together with another function such as

**or**

`map()`

`filter()`

.## Intro to lambda function

A lambda function:

- Doesn’t require a name
- Can take any number of arguments
**returns**only 1 expression

Let’s look at an example of a normal def function vs a lambda function. We are going to create a function to calculate the square of a value.

```
>>> def square(x):
return x ** 2
>>> square(3)
9
>>> lambda_sq = lambda x: x**2
>>> lambda_sq(3)
9
```

I know, I know that I said lambda is anonymous function. In the above example, I assigned a name lambda_sq to it. But the lambda function syntax actually doesn’t require a name.

`lambda arguments: expression`

## Intro to `map()`

A map() function basically runs a specific function on each item in an iterable (such as a list or tuple). For example, let’s calculate the square of numbers from 1-10. We start off by creating a function square, which returns the square of a give number. Then we can created a list from 1-10 using list comprehension. Note the below code output –

is a map object, which is an iterable, and we can convert it into a list by **a**** list(a)**.

```
>>> def square(x):
return x ** 2
>>> li = [i for i in range(1,11)]
>>> li
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> a = map(square, li)
>>> a
<map object at 0x000001A366423B50>
>>> list(a)
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
```

In this example, we had to pre-define a function that calculates the square of a number. Let’s assume that this

function is only used once by the map function, then no longer used. In this situation, it’s better to use a lambda function to calculate the square. Below is the same example using a lambda function.**square()**

```
>>> a_2 = map(lambda x: x**2, li)
>>> a_2
<map object at 0x000001A3663B7670>
>>> list(a_2)
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
```

## Intro to `filter()`

`filter()`

A filter() function is kind of similar to map(). However, map() execute a specific function on an iterable and returns every element from that iterable. Whereas filter() returns just the elements for which a function returns True. Let’s see this in an example where we have a list of number 1-20, and we want to return only the odd numbers. We’ll start off by creating a list of 1-20 using list comprehension.

```
>>> li = [i for i in range(1,21)]
>>> li
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
```

Then let’s define a function that checks whether an input is an odd number, the function returns True if the given number is odd.

```
>>> def is_odd(x):
return x % 2 != 0
>>> is_odd(2)
False
>>> is_odd(1)
True
```

Now let’s try it with the ** map()** function first, see what do we get.

```
>>> a = map(is_odd, li)
>>> list(a)
[True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False]
```

As expected, the ** map()** function took

**, and apply to every single item (1-20). The returned value is an iterable containing either True or False, which is the values returned by**

`is_odd()`

**.**

`is_odd()`

On the other hand, when we replace** map()** with

**, this is what we get:**

`filter()`

```
>>> a_2 = filter(is_odd,li)
>>> a_2
<filter object at 0x000001A366423B50>
>>> list(a_2)
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
```

Again, this is expected as the ** filter()** function “filtered” on the list and returned the elements where

**returned True.**

`is_odd()`

## Now I know lambda, map and filter. What’s next?

You probably already guessed it! Any column (i.e. pandas series) in a pandas dataframe is an iterable, so we can use the same technique on pandas dataframe! Next chapter we’ll talk about how to create some complex calculated columns.

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