Python Built-in Functions: A Comprehensive Guide

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Ever wondered about the power of Python’s built-in functions? Like a Swiss army knife, Python’s built-in functions offer a wide range of tools that can simplify your coding experience and make your programming journey more efficient and enjoyable.

In this guide, we will delve into the world of Python’s built-in functions, from basic usage to advanced techniques. We’ll explore the core functionality of these built-in functions, understand their advantages, and even discuss common issues and their solutions.

So, let’s dive in and start mastering Python’s built-in functions!

TL;DR: What are Python’s Built-in Functions?

Python’s built-in functions are pre-defined functions that are always available for use in Python. They provide basic functionality that is commonly used in Python programming. For instance, the print() function is a built-in function that outputs the specified message to the screen.

Here’s a simple example:

print('Hello, World!')

# Output:
# 'Hello, World!'

In this example, we’ve used the print() function to output the string ‘Hello, World!’ to the console. This is a basic usage of a Python built-in function, but there’s a whole world of built-in functions to explore in Python.

Continue reading for a more detailed understanding and advanced usage scenarios of Python’s built-in functions.

Exploring Python’s Built-in Functions: A Beginner’s Guide

Python’s built-in functions are the bread and butter of Python programming. They are pre-defined, always available, and designed to provide basic functionality that is commonly used in Python programming. Let’s take a look at some of the most common Python built-in functions like print(), len(), and type().

The Print Function

The print() function is probably the most used Python built-in function. It outputs the specified message to the screen.

print('Hello, Python!')

# Output:
# 'Hello, Python!'

In this example, the print() function is used to output the string ‘Hello, Python!’ to the console.

The Len Function

The len() function returns the number of items in an object. When the object is a string, the len() function returns the number of characters in the string.

print(len('Hello, Python!'))

# Output:
# 14

In the example above, the len() function returns the number of characters in the string ‘Hello, Python!’, which is 14.

The Type Function

The type() function returns the type of the specified object.

print(type('Hello, Python!'))

# Output:
# <class 'str'>

In this example, the type() function returns <class 'str'>, indicating that ‘Hello, Python!’ is a string.

These are just a few examples of Python’s built-in functions. They are powerful tools that can simplify your coding experience, but it’s important to understand how they work and their potential pitfalls to use them effectively.

Advanced Python Built-in Functions: An Intermediate Guide

Once you’ve mastered the basic built-in functions, Python offers more complex built-in functions that provide powerful ways to manipulate data and solve problems. Let’s delve into the map(), filter(), and reduce() functions.

The Map Function

The map() function applies a given function to each item of an iterable (e.g., list) and returns a list of the results.

numbers = [1, 2, 3, 4, 5]
doubled = map(lambda x: x * 2, numbers)


# Output:
# [2, 4, 6, 8, 10]

In this example, we’ve used the map() function to double each number in the numbers list. The map() function makes it easy to apply a function to each item in a list without the need for a loop.

The Filter Function

The filter() function constructs an iterator from elements of an iterable for which a function returns true.

numbers = [1, 2, 3, 4, 5]
evens = filter(lambda x: x % 2 == 0, numbers)


# Output:
# [2, 4]

In this example, we’ve used the filter() function to filter out the even numbers from the numbers list. The filter() function is a powerful tool for filtering data based on a condition.

The Reduce Function

The reduce() function applies a rolling computation to sequential pairs of values in a list.

from functools import reduce

numbers = [1, 2, 3, 4, 5]
total = reduce(lambda x, y: x + y, numbers)


# Output:
# 15

In this example, we’ve used the reduce() function to calculate the sum of all numbers in the numbers list. The reduce() function is a powerful tool for performing computations on lists.

These are just a few examples of the more complex built-in functions available in Python. Understanding and effectively using these functions can significantly enhance your Python programming skills.

List Of Functions

For your reference, we’ll provide you with a list of Python’s built in functions. First we’ll show you many of the more common functions, and then we’ll show you a list of many of the more advanced functions.

Common Python Built in functions:

Built-in FunctionDescriptionSyntax Example
abs(x)Returns the absolute value of x.abs(-7)
bool(exp)Returns the boolean value of exp.bool(0)
chr(i)Returns a string representing a character at Unicode code point i.chr(97)
complex(real[,imag])Creates a complex number.complex(1,2)
dict(**kwarg)Creates a dictionary.dict(x=5, y=0)
filter(func, iterable)Constructs an iterator from elements of an iterable.filter(lambda x: x < 5, [3, 4, 5, 6, 7])
float([x])Converts a number or string x to a floating point number.float(7)
hex(x)Converts an integer to a hexadecimal string.hex(255)
int(x[ , base])Converts a number or string to an integer, or return 0 if no arguments are'7')
list([iterable])Creates a new list.list('hello')
map(func, *iterable)Applies a function to all items in an input, ['hello', 'world'])
max(iterable)Returns the largest item in an iterable.max([3, 4, 5, 6, 7])
min(iterable)Returns the smallest item in an iterable.min([3, 4, 5, 6, 7])
pow(x, y[, z])Returns x to the power of y.pow(2, 3)
print(*objects, sep=’ ‘, end=’\n’, file=sys.stdout, flush=False)Prints the specified message(s) to the screen.print('Hello, World!')
range(start, stop[, step])Creates a sequence of numbers.range(3, 7)
reduce(func, iterable[ ,initializer])Applies a function of two arguments cumulatively to the items of an iterable.reduce(lambda a, b: a*b, [1, 2, 3, 4, 5])
round(x [, n])Rounds a floating point number to n digits.round(5.76543, 2)
set([iter])Creates a new set.set('hello')
str(object =”)Converts object to a string.str(100)
sum(iterable[, start])Sums the items of the iterable from left to right and returns the total.sum([1, 2, 3])
type(object)Returns the type of an object.type('hello')

Each of these built-in functions provides a unique solution to a common coding problem in Python. From manipulating numerical values to handling data structures and categorical values, usage of Python’s built-in functions can contribute significantly to coding speed as well as code performance and readability.

Advanced Python Built in functions:

There are several more built-in functions provided by Python. Here are some of them:

Built-in FunctionDescriptionSyntax Example
ascii(object)Returns a string containing a printable representation of an objectascii('Pythön is interesting')
bin(x)Converts an integer to a binary stringbin(3)
bytearray([source[, encoding[, errors]]])Returns a bytearray objectbytearray('Python', 'utf-8')
bytes([source[, encoding[, errors]]])Returns a bytes objectbytes('Python', 'utf-8')
callable(object)Checks if the object appears callablecallable(0)
divmod(x, y)Returns a pair of numbers (a tuple) consisting of their quotient and remainderdivmod(9, 2)
eval(expression[, globals[, locals]])Parses the expression, executes python expression/code created by a stringeval('print(5)')
format(value[, format_spec])Converts a value to a “formatted” representationformat(123, "d")
frozenset([iterable])Returns an immutable frozenset objectfrozenset([1, 2, 3, 4, 5])
globals()Returns the dictionary of the current global symbol tableglobals()
hasattr(object, name)Returns whether the object has an attribute with the given namehasattr(list, 'append')
hash(object)Returns the hash value of the objecthash('Python')
help([object])Invokes the built-in help systemhelp(str)
id(object)Returns identity of an objectid('Python')
input([prompt])Reads a line from input and returns itinput('Enter something:')
isinstance(object, classinfo)Checks if the object is an instance or subclass of classinfoisinstance('Hello', str)
len(s)Returns the number of items in a containerlen('Python')
locals()Updates and returns a dictionary representing the current local symbol tablelocals()
open(file, mode=’r’, buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None)Opens a file, returning an object of the file typeopen('file.txt', 'r')
ord(c)Returns an integer representing the Unicode characterord('a')
repr(object)Returns a string containing a printable representation of an objectrepr('Python')
sorted(iterable, key=None, reverse=False)Returns a new sorted list from elements in iterablesorted([3,2,1])
vars([object])Returns the dict attribute of an objectvars()
zip(*iterables)Returns an iterator of tupleszip(['a', 'b'], [1, 2])

Above built-in functions can cater to a wide range of utility requirements and are an inherent part of the Python language. Being familiar with them is beneficial in developing efficient and concise Python codes.

Alternative Approaches to Built-in Functions

Python is a versatile language that offers multiple ways to accomplish the same task. While built-in functions like map() and filter() are powerful, Python also provides alternative approaches such as list comprehensions that can often be more efficient and easier to read.

List Comprehensions

List comprehension is a concise way to create lists based on existing lists. Let’s see how we can use list comprehensions as alternatives to map() and filter() functions.

List Comprehensions as an Alternative to Map

Let’s recreate the earlier map() example using a list comprehension:

numbers = [1, 2, 3, 4, 5]
doubled = [x * 2 for x in numbers]


# Output:
# [2, 4, 6, 8, 10]

This example achieves the same result as the map() function but in a more readable way. List comprehensions are often faster than map() because they don’t require the creation of a lambda function.

List Comprehensions as an Alternative to Filter

Now let’s use a list comprehension to recreate the filter() example:

numbers = [1, 2, 3, 4, 5]
evens = [x for x in numbers if x % 2 == 0]


# Output:
# [2, 4]

Like the map() example, this list comprehension provides a more readable and efficient alternative to the filter() function.

While built-in functions like map() and filter() are powerful tools, list comprehensions offer a more Pythonic approach to manipulating lists. However, the choice between using built-in functions or list comprehensions often comes down to personal preference and the specific requirements of your code.

Troubleshooting Python’s Built-in Functions

While Python’s built-in functions are designed to make programming easier, like all code, they can sometimes result in errors. These errors can occur for various reasons, such as providing the wrong type of argument, or using a function incorrectly. Let’s discuss some common issues you may encounter when using Python’s built-in functions, and how to resolve them.


A TypeError is usually thrown when an operation or function is applied to an object of an inappropriate type.


# Output:
# TypeError: object of type 'int' has no len()

In this example, we’ve tried to use the len() function on an integer, which results in a TypeError because len() is meant to be used on a sequence such as a string or list. A solution to this issue would be to ensure that you’re using the len() function on an appropriate object.


A ValueError is thrown when a function’s argument is of an appropriate type but of an invalid value.


# Output:
# ValueError: invalid literal for int() with base 10: 'Python'

In this example, we’ve tried to convert the string ‘Python’ to an integer using the int() function, which results in a ValueError because ‘Python’ is not a valid literal for an integer. A solution to this problem would be to ensure that the argument you’re providing to the int() function can be correctly converted to an integer.

Understanding these common issues and their solutions can help you use Python’s built-in functions more effectively and debug your code more efficiently.

Unveiling Python’s Built-in Functions

Python’s built-in functions are a core part of the Python language. They are pre-defined functions that are always available for use in your Python code, providing a wide range of functionalities that are fundamental to programming in Python.

What are Built-in Functions in Python?

Built-in functions in Python are globally available functions that you can use without needing to import any modules. They are part of the Python language itself. Python provides a variety of built-in functions, each with its own unique functionality. From performing mathematical operations to manipulating strings, Python’s built-in functions cover a wide range of tasks.

# Using the built-in function abs() to get the absolute value of a number
absolute_value = abs(-7)

# Output:
# 7

In the above example, we’ve used the abs() function, which is a built-in function in Python. This function returns the absolute value of the number it is given.

How are Built-in Functions Implemented in Python?

Python’s built-in functions are implemented in C and are part of the Python core. They are designed to be fast and efficient, as they are the building blocks of Python programming. They are always available and do not need to be imported, which makes them convenient to use.

Understanding the Concept of Functions in Python

In Python, a function is a block of organized, reusable code that is used to perform a single, related action. Functions provide better modularity for your application and a high degree of code reusing. Python’s built-in functions are a set of such functions that are always available for Python programmers to use.

Python’s built-in functions are a powerful tool for any Python programmer. Understanding how they work and how to use them can significantly enhance your Python programming skills.

The Relevance of Built-in Functions in Larger Projects

Python’s built-in functions are not just for small scripts or individual tasks. They play a crucial role in larger scripts and projects as well. Their efficiency and ease of use make them a go-to tool for many common tasks in Python programming.

Exploring Related Concepts

While built-in functions cover a wide range of tasks, Python offers even more tools for you to explore. For example, Python’s standard library is a vast treasure trove of modules that can help you accomplish almost anything in Python. From handling CSV files to creating web servers, Python’s standard library has it all.

In addition to using built-in functions and the standard library, you can also create your own functions in Python. Custom functions allow you to encapsulate a piece of code that performs a specific task into a reusable function. This can significantly enhance the readability and maintainability of your code.

Further Resources for Mastering Python’s Built-in Functions

To deepen your understanding of Python’s built-in functions, here are some additional resources:

By exploring these resources and practicing using built-in functions in your own projects, you can become a master of Python’s built-in functions.

Wrapping Up: Mastering Python’s Built-in Functions

In this comprehensive guide, we’ve delved into the world of Python’s built-in functions, exploring their capabilities and how they can streamline your Python programming journey.

We began with the basics, discussing how to use common built-in functions like print(), len(), and type(). We then ventured into more advanced territory, diving into complex built-in functions like map(), filter(), and reduce(). Along the way, we tackled common issues you might encounter when using these functions and provided solutions and workarounds for each issue.

We then provided you with two long lists of built in python functions: common and more advanced functions, each with a syntax example.

We also explored alternative approaches to built-in functions, showing how list comprehensions can often be a more Pythonic and efficient way to manipulate lists. Here’s a quick comparison of these methods:

Built-in FunctionsPowerful, VersatileCan be complex for beginners
List ComprehensionsEfficient, PythonicNot as comprehensive as built-in functions

Whether you’re just starting out with Python or looking to level up your Python skills, we hope this guide has given you a deeper understanding of Python’s built-in functions and their capabilities.

With their wide range of capabilities and their central role in Python programming, mastering Python’s built-in functions is a significant step towards becoming a proficient Python programmer. Happy coding!