Python Built-in Functions [With Syntax and Examples]

Python built-in functions are predefined functions available in Python that you can use without importing any module. They make programming faster and simpler by performing common operations like type conversion, mathematical computation, input/output handling, and more. These functions are always available, making Python more efficient and readable. 

In this guide, you’ll read more about what built-in functions in Python are, their syntax and usage, the most commonly used functions with examples, user-defined vs. built-in functions, and a detailed list of all Python built-in functions with syntax and outputs. 

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What Are Built-in Functions in Python 

In simple terms, built-in functions in Python are functions that are "built-in" to the Python interpreter itself. They are predefined and can be used directly in your code without the need to import any special libraries or modules. 

Think of them as the essential tools in a toolbox that comes with Python. Just as you'd expect a toolbox to have a hammer, screwdriver, and wrench, you can expect Python to always have print(), len(), and int(). 

These functions are always available in the global namespace. This means you can call them from any part of your script, at any time, without any setup. 

Key Points 

• Predefined: They are part of the core Python language. 
• No Import Needed: Unlike library functions (like math.sqrt()), you don't need an import statement. 
• Efficiency: They are highly optimized (often written in C) and are very fast. 
• Readability: Using a standard function like len(my_list) is much clearer to other developers than writing a custom loop to count items. 
• Core Functionality: They perform common, essential operations like data type conversion, mathematical calculations, sequence manipulation, file handling, and more. 

Also Read: Module and Package in Python 

Difference Between Built-in, User-defined, and Library Functions 

It's important to distinguish Python built-in functions from other types of functions you'll encounter. 

Type	Description	Example
Built-in	Predefined in the Python core and always available. No import required.	len([1, 2, 3])
User-defined	Functions you create yourself using the def keyword to perform custom tasks.	def greet(): print("Hi")
Library	Functions that exist inside modules (Python's "libraries") and must be imported before use.	import math; math.sqrt(16)

• User-defined functions give you the power to create your own reusable blocks of code. 
• Library functions (or "module functions") massively extend Python's capabilities for specific domains like data science (pandas), web development (Django), or numerical computing (numpy). 
• Built-in functions are the foundation that all other code (including libraries) is built upon. 

Understanding what are built-in functions in Python is the first step. Next, let's see how to use them. 

Also Read: Top 7 Python Data Types: Examples, Differences, and Best Practices (2025) 

Syntax of Built-in Functions in Python 

Every built-in function in Python follows a simple and consistent syntax: 

function_name(arguments) 

• function_name: The name of the function (e.g., print, len, abs). 
• (): Parentheses are required to call the function, even if it takes no arguments. 
• arguments: The values or variables you "pass" to the function for it to work on. These are also sometimes called "parameters." 

Some functions require arguments, while others do not. 

Examples of Syntax Variations 

1. No Arguments: 

Some functions don't need any input.  

print()  # Simply prints a blank line 

2. Single Positional Argument: 

Most common. You provide one piece of data.  

x = abs(-25) 
print(x)  # Output: 25 

Here, -25 is a positional argument. The abs() function knows to take the first (and only) item as the number to process. 

3. Multiple Positional Arguments: 

The order matters. 

result = pow(2, 3) 
print(result) # Output: 8 

pow() (power) expects the base first and the exponent second. pow(3, 2) would give 9. 

4. Keyword Arguments: 

Some functions let you name the arguments for clarity. This is common in more complex Python built-in functions. 

rounded_num = round(5.6789, ndigits=2) 
print(rounded_num) # Output: 5.68 

Here, we explicitly state that the "number of digits" (ndigits) should be 2. This is clearer than just round(5.6789, 2). 

Also Read: Python pow() Function Explained 

Understanding Return Values 

A critical part of the syntax is understanding what the function gives back. This is called the return value. 

• Functions that return a value: abs(-10) returns the value 10. You must assign this to a variable (like x = abs(-10)) or use it directly (like print(abs(-10))) to capture the result. 
• Functions that perform an action: print("Hello") performs an action (displays "Hello" on the screen) but technically returns a special value called None. If you try x = print("Hello"), x will hold the value None, not "Hello". 

Understanding this "input -> process -> output" pattern is key to using all Python built-in functions. 

Complete List of Python Built-in Functions 

There are over 60 built-in functions in Python (69 in Python 3.10). We can group them by their purpose to make them easier to learn. Below is a categorized list with syntax and detailed examples. 

Numeric and Conversion Functions 

These Python built-in functions are used for handling numbers and converting data from one type to another. This is one of the most common tasks in programming. 

Function	Description	Example
abs()	Returns the absolute value of a number.	abs(-9) → 9
round()	Rounds a number to the nearest integer or a specified number of decimals.	round(7.567, 2) → 7.57
pow()	Returns base to the power of exponent. Can also take a third mod argument.	pow(2, 3) → 8
int()	Converts a value to an integer. Can truncate floats or parse strings.	int(5.7) → 5
float()	Converts a value to a floating-point number.	float(3) → 3.0
complex()	Creates a complex number.	complex(2, 3) → (2+3j)

Detailed Usage and Examples of Numeric and Conversion Functions: 

# --- abs() --- 
# Works on integers, floats, and even complex numbers (returns magnitude) 
print(f"abs(-7): {abs(-7)}")          # Output: 7 
print(f"abs(4.5): {abs(4.5)}")          # Output: 4.5 
print(f"abs(complex(3, 4)): {abs(complex(3, 4))}") # Output: 5.0 (sqrt(3^2 + 4^2)) 
 
# --- round() --- 
# Note: Python 3 rounds to the *nearest even* number for .5 cases 
print(f"round(4.5): {round(4.5)}")      # Output: 4 
print(f"round(5.5): {round(5.5)}")      # Output: 6 
print(f"round(5.678, 2): {round(5.678, 2)}") # Output: 5.68 
 
# --- pow() --- 
# pow(base, exp) is like base ** exp 
print(f"pow(3, 4): {pow(3, 4)}")         # Output: 81 
# pow(base, exp, mod) is (base ** exp) % mod 
print(f"pow(3, 4, 5): {pow(3, 4, 5)}")     # Output: 1 (81 % 5) 
 
# --- int(), float() --- 
# Essential for converting user input 
user_input = "25" 
age = int(user_input) 
print(f"Next year, you will be: {age + 1}") 
 
price_str = "199.99" 
price_float = float(price_str) 
print(f"Price with tax: {price_float * 1.18}")

Sequence and Iterable Functions 

These are some of the most powerful Python built-in functions. They operate on iterables—any object you can loop over, like lists, tuples, strings, sets, and dictionaries. 

Function	Description	Example
len()	Returns the number of items in an iterable.	len([10, 20, 30]) → 3
max()	Returns the largest item. Can use a key for complex sorting.	max([4, 7, 2]) → 7
min()	Returns the smallest item. Can use a key for complex sorting.	min([4, 7, 2]) → 2
sum()	Returns the sum of all items in an iterable (must be numbers).	sum([1, 2, 3]) → 6
sorted()	Returns a new sorted list from an iterable.	sorted([3, 1, 2]) → [1, 2, 3]
any()	Returns True if any element in the iterable is true.	any([False, True]) → True
all()	Returns True if all elements in the iterable are true.	all([True, True]) → True
zip()	Combines multiple iterables element-wise into tuples.	zip([1,2],['a','b'])
map()	Applies a function to all items in an iterable.	map(str, [1,2,3])
filter()	Filters items from an iterable, keeping only those where a function returns True.	filter(lambda x: x>5, [1,7,3])

Detailed Usage and Examples of Sequence and Iterable Functions: 

map(), filter(), and zip() return iterators. You often need to wrap them in list() to see the results. 

# --- len(), max(), min(), sum() --- 
numbers = [23, 4, 56, 1, 9] 
print(f"Length: {len(numbers)}")        # Output: 5 
print(f"Max: {max(numbers)}")          # Output: 56 
print(f"Min: {min(numbers)}")          # Output: 1 
print(f"Sum: {sum(numbers)}")          # Output: 93 

# --- sorted() --- 
# Does NOT change the original list 
unsorted_list = [5, 1, 4] 
new_sorted_list = sorted(unsorted_list) 
print(f"Original: {unsorted_list}")  # Output: [5, 1, 4] 
print(f"New List: {new_sorted_list}")  # Output: [1, 4, 5] 

# Using 'key' and 'reverse' 
words = ["apple", "Banana", "cherry"] 
print(f"Sorted (reverse): {sorted(words, reverse=True)}") 
# Sort by length 
print(f"Sorted by length: {sorted(words, key=len)}") 

# --- map() --- 
# Applies a function to every item 
nums = [1, 2, 3, 4] 
# We use list() to convert the map iterator into a list 
squared_nums = list(map(lambda x: x**2, nums)) 
print(f"Squared: {squared_nums}") # Output: [1, 4, 9, 16] 

# --- filter() --- 
# Keeps only items that pass a test 
even_nums = list(filter(lambda x: x % 2 == 0, nums)) 
print(f"Evens: {even_nums}")   # Output: [2, 4] 

# --- zip() --- 
# Combines two lists 
names = ["Alice", "Bob", "Charlie"] 
ages = [30, 25, 40] 
combined = list(zip(names, ages)) 
print(f"Zipped: {combined}") 
# Output: [('Alice', 30), ('Bob', 25), ('Charlie', 40)] 

Also Read: How to Find Square Root in Python: Techniques Explained 

Type Checking and Object Functions 

These Python built-in functions help you inspect and manipulate objects, check their types, and interact with their attributes. They are essential for object-oriented and dynamic programming. 

Function	Description	Example
isinstance()	Checks if an object is an instance of a specified class or type.	isinstance(5, int) → True
type()	Returns the type of an object.	type("Hello") → <class 'str'>
hasattr()	Checks if an object has a given attribute (variable or method).	hasattr([], 'append') → True
getattr()	Gets the value of an attribute from an object.	getattr(str, 'upper')
setattr()	Sets the value of an attribute on an object.	setattr(obj, 'x', 5)
dir()	Returns a list of valid attributes and methods for an object.	dir(str)
id()	Returns the unique memory address (identity) of an object.	id(a)

Detailed Usage and Examples of Type Checking and Objects Functions: 

isinstance() is generally preferred over type() because isinstance() correctly handles inheritance, while type() does not. 

# --- isinstance() vs type() --- 
my_num = 5 
print(f"type(my_num) == int: {type(my_num) == int}")         # Output: True 
print(f"isinstance(my_num, int): {isinstance(my_num, int)}") # Output: True 
# In inheritance, type() fails 
class MyInt(int): pass 
my_new_num = MyInt(5) 
print(f"type(my_new_num) == int: {type(my_new_num) == int}")         # Output: False 
print(f"isinstance(my_new_num, int): {isinstance(my_new_num, int)}") # Output: True (Correct!) 

# --- dir() --- 
# Great for debugging and exploring 
print(dir("a string")) # Shows all string methods like 'upper', 'lower', 'split' 

# --- hasattr(), getattr(), setattr() --- 
# Used for dynamic programming 
class Person: 
    name = "John" 

p = Person() 
print(f"Does p have 'name'? {hasattr(p, 'name')}") # Output: True 
print(f"Get p's 'name': {getattr(p, 'name')}")   # Output: John 

# Set a new attribute 
setattr(p, 'age', 30) 
print(f"Get p's new 'age': {p.age}")        # Output: 30 

Input and Output Functions 

These Python built-in functions manage the flow of data into and out of your program. They handle user interaction, file operations, and debugging. 

Function	Description	Example
print()	Displays output to the console.	print("Hello")
input()	Takes user input from the console (always as a string).	x = input("Enter name: ")
open()	Opens a file and returns a file object.	open('file.txt', 'r')
help()	Displays interactive help documentation for an object.	help(len)
eval()	(Use with caution) Executes a string as Python code and returns the result.	eval('5+7') → 12
exec()	(Use with caution) Executes a block of Python statements from a string.	exec("a=5;print(a)")

Also Read: Type Conversion & Type Casting in Python Explained with Examples 

Detailed Usage and Examples of Input and Output Functions: 

SECURITY WARNING: Never use eval() or exec() on input from a user you don't trust. It allows them to run any code on your computer, which is a massive security risk. 

# --- input() --- 
# Always returns a string! 
name = input("What is your name? ") 
age_str = input("What is your age? ") 
age_int = int(age_str) # Must convert 
print(f"Hello, {name}. You are {age_int} years old.") 

# --- print() with 'sep' and 'end' --- 
print("apple", "banana", "cherry", sep=", ") 
print("This is one line...", end="") 
print("...and this is the same line.") 
# Output: 
# apple, banana, cherry 
# This is one line......and this is the same line. 

# --- open() --- 
# The 'with' statement is the recommended way to handle files 
# It ensures the file is closed automatically 
try: 
    with open("my_file.txt", "w") as f: 
        f.write("Hello, file!") 

    with open("my_file.txt", "r") as f: 
        content = f.read() 
        print(f"File content: {content}") 
except FileNotFoundError: 
    print("The file was not found.") 

Object Creation Functions 

These Python built-in functions are actually the constructors for Python's main data types. You can use them to create new objects or, more commonly, to convert between types. 

Function	Description	Example
dict()	Creates a dictionary.	dict(a=1, b=2)
list()	Creates a list (or converts an iterable to a list).	list((1,2,3))
tuple()	Creates a tuple (or converts an iterable to a tuple).	tuple([4,5,6])
set()	Creates a set (or converts an iterable to a set).	set([1,2,3,3]) → {1, 2, 3}
frozenset()	Creates an immutable (unchangeable) set.	frozenset([1,2,3])
bytearray()	Creates a mutable array of bytes.	bytearray(4)
bytes()	Creates an immutable array of bytes.	bytes("abc", "utf-8")
classmethod()	Decorator to convert a method into a class method.	Used inside class
staticmethod()	Decorator to convert a method into a static method.	Used inside class

Detailed Usage and Examples of Object Creation Functions:  

# --- Creating new objects --- 
my_dict = dict(name="John", age=25) 
print(my_dict)  # Output: {'name': 'John', 'age': 25} 

# --- Converting between types --- 
my_tuple = (1, 2, 3) 
my_list = list(my_tuple) # Convert tuple to list 
print(my_list)  # Output: [1, 2, 3] 

my_list_with_dupes = [1, 1, 2, 3, 3, 3] 
my_set = set(my_list_with_dupes) # Remove duplicates 
print(my_set)   # Output: {1, 2, 3} 

# --- frozenset() --- 
# A set cannot be a key in a dictionary (it's mutable) 
# But a frozenset can! 
my_frozenset = frozenset([1, 2, 3]) 
my_dict_with_set_key = {my_frozenset: "value"} 
print(my_dict_with_set_key) 

Also Read: 4 Built-in Data Structures in Python: Dictionaries, Lists, Sets, Tuples 

Miscellaneous Built-in Functions 

This final group of Python built-in functions provides a variety of useful tools for iteration, introspection, and data formatting. 

Function	Description	Example
enumerate()	Adds a counter (index) to an iterable.	list(enumerate(['a','b'])) → [(0, 'a'), (1, 'b')]
iter()	Returns an iterator object from an iterable.	iter([1,2,3])
next()	Returns the next item from an iterator.	next(iterator)
globals()	Returns a dictionary of the current global variables.	globals()
locals()	Returns a dictionary of the current local variables.	locals()
compile()	Compiles a string source into a code or AST object.	compile('5+5','<string>','eval')
format()	Formats a value using a specified format.	"{} {}".format("Hi","User")
reversed()	Returns a reverse iterator for a sequence.	list(reversed([1,2,3])) → [3, 2, 1]
slice()	Creates a slice object, used for [] notation.	slice(1,5)
hash()	Returns the hash value of an object (for dictionary lookups).	hash('python')

Detailed Usage and Examples of Miscellaneous Built-in Functions: 

# --- enumerate() --- 
# The modern way to loop with an index 
fruits = ['apple', 'banana', 'cherry'] 
for i, fruit in enumerate(fruits, start=1): 
    print(f"Fruit {i}: {fruit}") 
# Output: 
# Fruit 1: apple 
# Fruit 2: banana 
# Fruit 3: cherry 

# --- iter() and next() --- 
# This is how 'for' loops work under the hood 
my_iter = iter([1, 2, 3]) 
print(next(my_iter))  # Output: 1 
print(next(my_iter))  # Output: 2 
print(next(my_iter))  # Output: 3 
# print(next(my_iter)) # Raises StopIteration error 

# --- reversed() --- 
# Returns an iterator, so wrap in list() 
my_list = [10, 20, 30] 
print(list(reversed(my_list))) # Output: [30, 20, 10] 

Also Read: Python Slicing - Techniques & Examples 

Practical Examples of Built-in Functions 

Let's combine some of these Python built-in functions to solve common problems. 

Example 1: Using len() and sum() for Data Analysis 

A common task is finding the average of a list of numbers. 

grades = [88, 92, 100, 75, 83, 95] 
count = len(grades) 
total = sum(grades) 
average = total / count 
print(f"Total students: {count}") 
print(f"Average grade: {round(average, 2)}") 

Output: 

Total students: 6 

Average grade: 88.83 

Also Read: Data Analysis Using Python [Everything You Need to Know] 

Example 2: Using map() and filter() for Data Cleaning 

Imagine you have a list of numbers as strings, and you want to get the squares of only the even numbers. 

nums_str = ["1", "2", "3", "4", "5", "6"] 
# 1. Convert all strings to integers 
nums_int = list(map(int, nums_str)) 
# -> [1, 2, 3, 4, 5, 6] 

# 2. Filter out the odd numbers 
even_nums = list(filter(lambda x: x % 2 == 0, nums_int)) 
# -> [2, 4, 6] 

# 3. Square the remaining even numbers 
squared_evens = list(map(lambda x: x*x, even_nums)) 
# -> [4, 16, 36] 

print(f"Squared evens: {squared_evens}") 

 

This shows how you can chain Python built-in functions to create a data-processing pipeline. 

Example 3: Using zip() to Create a Dictionary 

This is the most common use case for zip(): merging two lists into key-value pairs. 

names = ['Alice', 'Bob', 'Charlie'] 
scores = [85, 90, 98] 

# zip() creates [('Alice', 85), ('Bob', 90), ('Charlie', 98)] 
# dict() converts that list of tuples into a dictionary 
score_report = dict(zip(names, scores)) 

print(score_report) 
# Output: {'Alice': 85, 'Bob': 90, 'Charlie': 98} 
print(f"Bob's score: {score_report['Bob']}") 

 

This is a clean and highly readable example of built in function usage. 

Also Read: Step-by-Step Guide to Learning Python for Data Science 

Example 4: Using sorted() with a key 

How do you sort a list of dictionaries? sorted() handles this easily with its key argument. 

students = [ 
    {'name': 'Ravi', 'grade': 80}, 
    {'name': 'Neha', 'grade': 95}, 
    {'name': 'Amit', 'grade': 70} 
] 

# Sort by name (alphabetical) 
by_name = sorted(students, key=lambda s: s['name']) 
print(f"Sorted by name: {by_name}") 

# Sort by grade (highest first) 
by_grade = sorted(students, key=lambda s: s['grade'], reverse=True) 
print(f"Sorted by grade: {by_grade}") 

Also Read: Understanding List Methods in Python with Examples 

When to Use Built-in Functions in Python

You should always prefer using Python built-in functions over writing your own logic for a task they can accomplish. 

• Use them for standard operations: Don't write a for loop to find the sum of a list; use sum(). 
• Use them for performance: Built-in functions are optimized in C and will almost always be faster than equivalent code you write in Python. 
• Use them for readability: len(my_list) is instantly recognizable to any Python developer. A custom function get_list_item_count(my_list) is not. 

Criteria	Built-in Function	Custom Function (Loop)
Speed	Faster (Optimized C code)	Slower (Interpreted Python)
Code length	Short (e.g., sum(nums))	Long (e.g., total=0; for n in nums: total+=n)
Errors	Fewer chances (battle-tested)	More chances for off-by-one errors, etc.
Readability	High (Standard)	Variable (Depends on programmer)

Limitations of Built-in Functions 

Python built-in functions are general-purpose tools. They are not a magic solution for every problem. 

• Not for Complex Logic: They can't perform complex, multi-step business logic. That's what user-defined functions are for. 
• Limited to General Tasks: You won't find a built-in function for "calculate_invoice_tax" or "analyze_stock_market_data." For that, you need to write your own code or use external libraries. 
• Explicit Conversion is Required: They don't guess. input() always gives you a string, even if the user types 123. You must explicitly convert it using int(input()). This is a feature (explicitness), but can feel like a limitation. 

Conclusion 

Python built-in functions simplify coding by providing ready-to-use operations for data handling, computation, and input/output. They save time, reduce errors, and make code more readable. You can combine them with custom functions to build powerful programs efficiently. Mastering these functions helps you write cleaner, faster, and more effective Python code. Keep exploring their syntax and examples to strengthen your programming foundation and enhance your overall Python development skills.