Understanding Common Python File Extensions and Their Uses

The article explains various Python-related file extensions, describing their purpose and typical usage scenarios.

.py is the standard source file for Python code.

.ipynb denotes Jupyter Notebook files, which store interactive notebooks.

.pyi files provide type hint information for static analysis; an example:

<code>hellp.pyi
def hello(name: str) -> None:
    print(f"hello {name}")</code>

.pyc files are compiled byte‑code representations of Python modules, enabling faster loading.

.pyd files are binary extension modules written in C or C++ that can be imported like regular Python modules.

.pyw files are Windows‑specific scripts that run without opening a console window; an example GUI script:

<code># click_button.pyw
import tkinter as tk

def button_click():
    label.config(text="Button Clicked!")

window = tk.Tk()
button = tk.Button(window, text="Click Me", command=button_click)
button.pack()

label = tk.Label(window, text="Hello, World!")
label.pack()
window.mainloop()</code>

.pyx files are Cython source files that allow mixing Python with C for performance gains. The article provides a Fibonacci implementation in Cython and compares its speed to pure Python:

<code>cdef int a, b, i
def fibonacci(n):
    if n <= 0:
        raise ValueError("n必须是正整数")
    if n == 1:
        return 0
    elif n == 2:
        return 1
    else:
        a = 0
        b = 1
        for i in range(3, n + 1):
            a, b = b, a + b
        return b</code>

<code>import fb
import timeit

def fibonacci(n):
    if n <= 0:
        raise ValueError("n必须是正整数")
    if n == 1:
        return 0
    elif n == 2:
        return 1
    else:
        a, b = 0, 1
        for _ in range(3, n + 1):
            a, b = b, a + b
        return b

python_time = timeit.timeit("fibonacci(300)", setup="from __main__ import fibonacci", number=1000000)
cython_time = timeit.timeit("fb.fibonacci(300)", setup="import fb", number=1000000)
print("纯Python版本执行时间:", python_time)
print("Cython版本执行时间:", cython_time)</code>

<code>纯Python版本执行时间: 12.391942400000516
Cython版本执行时间: 6.574918199999956</code>

The benchmark shows that Cython can nearly double the execution speed for this compute‑intensive Fibonacci calculation.