Python most powerful functions: map(), filter(), and reduce()

The map(), filter(), and reduce() functions are three of the most powerful functions built into Python. In this article, we’ll explain what they do and show you how to use them in Python scripts and programs. The Python programming language, created by Guido van Rossum, has many built-in functions that can make your code easier to read and execute. One common area where these functions are used is within data science tasks, such as data analysis or data visualization tasks. When you’re learning the basics of Python, you usually focus on the most used parts of the language, like functions and control flow statements. However, there are three other functions that can help you solve many common programming tasks, and they might be worth investing some time in learning how to use them: map(), filter(), and reduce(). In this article, I’ll tell you what they do and when you should use them. You’ll also see some examples of each function in action. Python is extremely powerful and flexible, allowing you to make use of numerous useful python filter function that you may not have heard of before. In this article, we’ll discuss three of the most useful python reduce function: map(), filter(), and reduce(). We’ll also discuss how these functions can be used to simplify your programs, make them more readable, and improve their performance by speeding up execution time when executing loops within loops.

List of All Functions

The map() python reduce function is used to apply a function to every element in an iterable. The filter() function is used to filter out elements from an iterable based on given criteria. The reduce() function is used to apply a function to each element in an iterable and reduce it to a single value. Other powerful functions include the zip() function, which can be used to zip two or more iterables together, and the sorted() function, which can be used to sort an iterable. There are many other powerful Python functions that you may want to explore for your own use cases. For example, there are the cmp() function (which returns -1 if one of its arguments is greater than the other), as well as tuple functions such as compress(), count(), and swap(). You might also find yourself using math-related python filter function such as sum(), round(), and hypot().

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The map() function:

The map() function is a higher-order function. As previously stated, this function accepts another function and a sequence of ‘iterables’ as parameters and provides output after applying the function to each iterable in the sequence. The syntax is SYNTAX: map(function, iterables).

The map() function is used to apply the result of an expression over a list (called iterable). For example, user-defined functions and lambda expressions can both be sent to the map(). User-defined functions can also be sent to the map() method. The key difference between these two functions is that only programmers/users are able to adjust/change the parameters for user-defined functions. x is the map object, as we can see. The map function is displayed next, which takes “function()” as the parameter and then applies “a * a” to all ‘iterables’. As the result, all iterables’ values are multiplied by themselves before being returned.

Let us look how lambda functions can be used within the map() method below

Functions without names are called lambdas, and they are often utilized as inputs for other functions. Let’s see how we can integrate lambdas into the map() function.

EXAMPLE

tup= (5, 7, 22, 97, 54, 62, 77, 23, 73, 61)
newtuple = tuple(map(lambda x: x+3 , tup)) 
print(newtuple)

OUTPUT

(8, 10, 25, 100, 57, 65, 80, 26, 76, 64)

The filter() function:

filter() function is used to generate an output list of values that return true when the function is called. It has  following syntax:

SYNTAX: filter (function, iterables)

This function like map(), can take user-defined functions and lambda functions as parameters.

Eg:

def func(x):
    if x>=3:
        return x
y = filter(func, (1,2,3,4))  
print(y)
print(list(y))

OUTPUT

[3, 4]
As we can see, y is the filter object, and the list is a collection of true values for the condition (x>=3).

reduce() function:

reduce() function applies a provided function to ‘iterables’ and returns a single value, as the name implies.

SYNTAX: reduce(function, iterables)

The functions.py file specifies which expression should be applied to the ‘iterables’ in this case. The function tools module must be used to import this function.

EXAMPLE

187

Python Function Syntax

All python reduce functions to start with the keyword def, followed by the function name, and a set of parentheses. The parentheses may include arguments, separated by commas. For example, let’s say we have a function that takes two arguments, x and y. The function might look like this: def my_function(x, y). 2 – map(): The map() function is one of the most powerful tools in Python. It allows you to map a function to an iterable object. That is, you can apply a function to every element in a list or tuple. For example, let’s say we have a list of numbers: [1, 2, 3, 4]. We could use the map() function to square each number in the list. So if we wanted to create a new list where every number in the original list is squared, it would be as simple as doing this: map(lambda x: x*x, originalList). When I did this on my computer, I got back another list where each element was squared (4 9 16 25), so that’s what I got back. Now when I print out the first five elements of both lists, I’ll see what changed from before to after mapping with lambda.

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List Comprehensions

If you’re coming from another programming language, you might not be familiar with the concept of list comprehension. In Python, a list comprehension is a way to create a new list by performing an operation on each element in a given iterable (such as a list). For example, if we wanted to take the squares of every number in our list, we could do this using list comprehension. List comprehensions are easy to read, but they can also be difficult to master. This guide will teach you how they work and how they can help make your code more readable while boosting its efficiency at the same time. By writing Python code that makes it easier for other programmers to understand what’s going on, you’ll add some extra flexibility to your own programs so that others don’t need to spend extra time trying to figure out what’s happening or why things were done a certain way. That helps them spend less time reading your code which means that they have more time for other tasks – like adding cool features! And let’s face it: who doesn’t want their software projects improved?

Loop through the list with for Loops

Python’s for loop syntax is easier to read and write than other languages, making it a great choice for beginners. But don’t be fooled—for loops are still a powerful tool. In this post, we’ll take a look at three of Python’s most powerful functions: map(), filter(), and reduce(). They’re powerful in different ways and each comes with its own benefits and drawbacks. You can think of the map function as running one operation on every item in the list so that the output is a new list where each item has been transformed by the operation you chose. The filter python filter function only returns items from the original list that meet certain criteria or pass certain tests – which can be anything from testing whether an item contains a certain letter to checking if an integer value is greater than 10 (think about how useful that would be!). Finally, reduce takes all those values returned by your operations on your list (think ‘map’ function) and combines them into one single value.

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Ranges – stepping across a sequence

We often need to iterate over a sequence of numbers. The built-in function range allows us to do this easily. It returns a sequence of numbers, starting from 0 (or the first number you specify) and going up to but not including the number you specify. So, range(5) will return the numbers 0, 1, 2, 3, 4. We can also specify a step size. For example, range(2, 10, 2) will return the numbers 2, 4, 6, 8. We can even use a negative step size to count down from a number. You can start at any number and then decrement by any amount. To see how it works, try typing the following into your interpreter: >>> print(numbers between 5 and 100)
numbers between 5 and 100
>>> for i in range(1,101):
… print( , i)

Built-in type conversion tools in Python

Python has a number of built-in type conversion tools that can be used to convert data from one type to another. The most common is the int() and float() functions, which convert strings to integers or floats, respectively. Python also has a bool() function, which converts strings to Boolean values (True or False). Finally, the chr() and ord() functions can be used to convert characters to and from their ASCII values. For example, O becomes ord(O) while ord(R) becomes 72. There is also a complex() function for converting between complex numbers and tuple representations of the form (real, imag). And finally, the hex() and oct() functions allow you to convert an integer into its hexadecimal or octal equivalent. The following are examples of some of these conversions:
Input Function Output
‘0x1234’ hex(0x1234) ‘0x2d2a’
‘567890.’ oct(567890.) ‘4561231056010240205560123456789012.5’, see it? It’s in decimal!

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