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

Python includes a number of predefined built-in functions that can be utilized by the end-user by simply calling them. These functions not only make programmers’ jobs easier, but they help establish a common coding environment. In this tutorial, you’ll learn about three of Python’s most powerful functions: map(), filter(), and reduce().

Functional programming’s three pillars are map, filter, and reduce functions. While Python isn’t exactly a functional programming language, it does have a lot of features that make it so. The python map function, filter, and reduce functions in Python are discussed in this article, as well as how they correspond to functional programming principles.

This article was published as a part of the Data Science Blogathon

Functional Programming

A programming paradigm that uses functions to define computation is known as functional programming. The concept of an unchangeable state is one of functional programming’s key defining traits.

Computation is done through statements in imperative programming, which is arguably the most prevalent programming paradigm you’re already familiar with. These are commands that affect the value of a variable, and thus the state of the computation after they are executed. A for loop, for example, can execute a statement repeatedly, altering the value of a variable each time, as shown below:

counter = 0
for i in range(10):
    counter += 1

Each time the value of the counter is increased by one in each iteration of the loop, the state of the calculation changes, bringing it closer to the end state.

Before we go into examples of ‘map()’, ‘filter()’, and ‘reduce()’ functions in Python, we’ll need to go over another concept: higher-order functions.

Also Read: 90+ Python Interview Questions to Ace Your Next Job Interview

Higher-Order Functions

In functional programming, higher-order functions are our primary tool for defining computation. These are functions that take a function as a parameter and return a function as the result. Reduce(), map(), and filter() are three of Python’s most useful higher-order functions. They can be used to perform complex operations when paired with simpler functions.

Implementing Operations

With higher-order functions like map(), filter(), and reduce(), you can implement a wide range of operations on data structures such as lists, sets, and dictionaries. These functions allow you to apply transformations, filters, and aggregations efficiently, making it easier to manipulate integers, hash values, and strings within your programs.

Performing Data Transformations

By combining higher-order functions with user-define or built-in functions, you can perform sophisticated data transformations with ease. For example, map() can be used to apply a function to each element of a list, filter() can selectively remove elements based on a condition, and reduce() can aggregate values into a single result. This flexibility enables you to process and manipulate data in diverse ways, enhancing the capabilities of your programs.

Handling Collections

Higher-order functions are particularly useful when working with collections of data. Whether you’re dealing with lists, sets, or dictionaries, map(), filter(), and reduce() provide powerful tools for iterating over and processing elements efficiently. By applying these functions strategically, you can achieve complex data manipulations while maintaining concise and readable code.

By leveraging higher-order functions such as map(), filter(), and reduce(), Python developers can streamline their code, enhance code readability, and perform sophisticated data manipulations with ease. These functions empower programmers to express complex computation in a concise and declarative manner, facilitating the development of robust and efficient applications.A higher-order function is demonstrated in the code sample below. print greeting() takes two arguments: a function f and a name n, and returns the result of calling f. (n).

def greet(name):
    return "Hello, {}!".format(name)

def print_greeting(f, n):
    print(f(n))

Default Sequence Mapping in Python

Slice sequences are automatically connected to Python lists with exceptions if a sequence of bytes has an underlying bytes object or string. By using native type, the Python mapping doesn’t generate a separate name type for slice sequences. This means gaining all the native Python capabilities. The following definitions have been used to define this fruit plate: a fruit plate; a tin = a fruit apple, fruit pear. assert(len(platter) – = 2).

Mapping Types

A mapping object combines values from an object of one type with another. Mapping is a changeable object whose underlying data can be modified. There exists at present only a common mapping type: Dictionary. Dictionary keys can easily be used as random values. Only the types of value which are not acceptable as key values are values involving lists or dictionary or other modable types and are considered to exist in the contexts in which the values are not identified. Numeral type is used to index keys in the same way that the basic metric is used for comparing a number of values. Dictionaries can be arranged by comma-separated keys.

Anonymous Functions

We saw an example of a function that takes another function as an argument in the previous section. The functions map(), filter(), and reduce() all do the same thing: They each take a function and a list of elements, and then return the result of applying the function to each element in the list.

As previously stated, Python has built-in functions like map(), filter(), and reduce(). Python’s functional programming feature is enabled through these functions. The only factors that determine the output in functional programming are the inputs passed in. These functions can accept any other function as a parameter and can also be passed as parameters to other functions. Let’s look at each of these functions in more detail now.

we have three main functions:

• map()
• filter()
• reduce()

The map() function

The map() function or map and filter in Python (also called as map filter in Python) 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. It has the following syntax:

SYNTAX: map(function, iterables)

The function is used to define an expression which is then applied to the ‘iterables’. User-define functions and lambda functions can both be sent to the map function.

User-defined functions can be sent to the map() method. The user or programmer is the only one who can change the parameters of these functions.

EXAMPLE

def function(a):
    return a*a
x = map(function, (1,2,3,4))  #x is the map object
print(set(x))

x is a map object, as you can see. The map function is displayed next, which takes “function()” as a parameter and then applies “a * a” to all ‘iterables’. As a result, all iterables’ values are multiplied by themselves before being returned.

Let’s look at how lambda functions can be used within the map() method to reduce map filter python.

Lambda within map() functions:

Functions with no name are known as lambda functions. These functions are frequently used as input to other functions. Let’s try to integrate Lambda functions 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

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

SYNTAX: filter (function, iterables)

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

EXAMPLE

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

OUTPUT

[3, 4]

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

Lambda within filter() functions:

The condition to be checked is defined by the lambda function that is provided as an argument.

EXAMPLE

y = filter(lambda x: (x>=3), (1,2,3,4))
print(list(y))

OUTPUT

[3, 4]

The reduce() function

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

SYNTAX: reduce(function, iterables)

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

EXAMPLE

from functools import reduce

reduce(lambda a,b: a+b,[23,21,45,98])

OUTPUT

187

The reduce function in the preceding example adds each iterable in the list one by one and returns a single result.

The Python functions map(), filter(), and reduce() can all be used together.

So, This article on map(), filter(), and reduce functions in Python. I hope you have understood everything clearly. Make sure you practice on map(), filter(), and reduce() because this function is very useful when we are doing data analysis using python.

Allowable Sequence Values in Python

Although all sequence types have a default mapping, Ice’s running times allow for other types for sending. A TUPLE can also be given to any byte or sequence that map into a list in order to send an alternative to string. A bytes object in a bytes sequence bypasses validation and avoids a copying step, which results in more readable throughput. For larger sequence bytes it is strongly recommended to use bytes objects.

Conclusion

To sum up, Python’s map(), filter(), and reduce() methods are strong instruments that facilitate clear and effective data processing. Map reduces aggregate values into a single result, filters elements according to a criteria, and applies a function to each member of a sequence. Python programmers can simplify their code, improve readability, and easily carry out complex data transformations by utilizing these higher-order functions. These ideas in functional programming enable programmers to express intricate calculations declaratively, which makes it easier to create reliable and effective programs.

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Hardikkumar

Data Analyst | Digital Data Analysis Specialist | Data Science Learner Currently working in Data Analytics field. I have done my post-graduation. My main focus is growing in the fields of Data Science and Analytics.