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# Python 3: Fibonacci series up to n >>> def fib(n): >>> a, b = 0, 1 >>> while a < n: >>> print(a, end=' ') >>> a, b = b, a+b >>> print() >>> fib(1000) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987Functions Defined
The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3
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# Python 3: List comprehensions >>> fruits = ['Banana', 'Apple', 'Lime'] >>> loud_fruits = [fruit.upper() for fruit in fruits] >>> print(loud_fruits) ['BANANA', 'APPLE', 'LIME'] # List and the enumerate function >>> list(enumerate(fruits)) [(0, 'Banana'), (1, 'Apple'), (2, 'Lime')]Compound Data Types
Lists (known as arrays in other languages) are one of the compound data types that Python understands. Lists can be indexed, sliced and manipulated with other built-in functions. More about lists in Python 3
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# Python 3: Simple arithmetic >>> 1 / 2 0.5 >>> 2 ** 3 8 >>> 17 / 3 # classic division returns a float 5.666666666666667 >>> 17 // 3 # floor division 5Intuitive Interpretation
Calculations are simple with Python, and expression syntax is straightforward: the operators
+,-,*and/work as expected; parentheses()can be used for grouping. More about simple math functions in Python 3. -
# For loop on a list >>> numbers = [2, 4, 6, 8] >>> product = 1 >>> for number in numbers: ... product = product * number ... >>> print('The product is:', product) The product is: 384All the Flow You’d Expect
Python knows the usual control flow statements that other languages speak —
if,for,whileandrange— with some of its own twists, of course. More control flow tools in Python 3 -
# Simple output (with Unicode) >>> print("Hello, I'm Python!") Hello, I'm Python! # Input, assignment >>> name = input('What is your name?\n') What is your name? Python >>> print(f'Hi, {name}.') Hi, Python.Quick & Easy to Learn
Experienced programmers in any other language can pick up Python very quickly, and beginners find the clean syntax and indentation structure easy to learn. Whet your appetite with our Python 3 overview.
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Latest: Python 3.13.5
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Maintaining our ever-evolving Python codebase poses an intricate challenge: how do we make updates to reflect the changing rules and regulations of 200+ global markets without compromising access to the systems that our engineers and traders use on a daily basis? While an inner layer of shared business logic enables coherency in our codebase performance, it also means small regulatory changes can impact many systems. In this article, Python Engineer John Lekberg details how we use Python type annotations to minimize the time and risk involved in manual verification.
Building Robust Codebases with Python's Type Annotations by John Lekberg |
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The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers. Learn more