Python的標(biāo)準(zhǔn)庫(kù)包含多個(gè)常用模塊，用于處理數(shù)學(xué)運(yùn)算、日期時(shí)間、系統(tǒng)操作等。1. math模塊提供數(shù)學(xué)函數(shù)如sqrt、log及常量pi、e，適用于精確計(jì)算；2. datetime處理日期時(shí)間，支持獲取當(dāng)前時(shí)間、格式化及時(shí)間差計(jì)算；3. os和sys用于文件和系統(tǒng)操作，如創(chuàng)建目錄、訪問(wèn)命令行參數(shù)；4. re支持正則表達(dá)式，適用于文本模式匹配與驗(yàn)證；5. random生成隨機(jī)數(shù)或選擇隨機(jī)元素，適合游戲與模擬；6. json處理JSON數(shù)據(jù)轉(zhuǎn)換，便于API交互與配置讀寫(xiě)；7. csv用于讀寫(xiě)CSV文件，簡(jiǎn)化表格數(shù)據(jù)處理。

Python’s standard library is packed with useful modules that handle everything from math operations to file management. Here are some of the most commonly used ones, along with what they do and when you might want to use them.

1. math – For Mathematical Operations

The math module gives you access to common math functions like square roots, logarithms, trigonometry, and more.

• Use it when you need precise calculations beyond basic arithmetic.
• It doesn’t support complex numbers — for that, you’d use the cmath module instead.

Example:

import math
print(math.sqrt(16))  # Output: 4.0

Some handy functions:

• math.floor() – Rounds down
• math.ceil() – Rounds up
• math.pi, math.e – Constants

Note: If you're doing heavy number crunching or working with arrays, consider using numpy instead.

2. datetime – To Handle Dates and Times

This one is super helpful when you need to work with dates, times, time zones, or durations.

• You can get the current time, format dates, calculate differences between dates, etc.
• Key classes: datetime, date, time, timedelta

Example:

from datetime import datetime
print(datetime.now())  # Shows current date and time

Common use cases:

• Logging events with timestamps
• Scheduling tasks
• Calculating how many days between two dates

3. os and sys – For System and File Operations

These two modules often go hand-in-hand when dealing with your operating system or Python runtime environment.

os

• Interact with the file system: create directories, delete files, check if a path exists
• Useful for automation scripts that deal with files

import os
os.makedirs("new_folder", exist_ok=True)

sys

• Control the Python interpreter
• Access command-line arguments via sys.argv
• Exit the program with sys.exit()

They’re especially useful in CLI tools or scripts that run on different systems.

4. re – Regular Expressions

If you need to search, match, or replace patterns in text, re (regular expressions) is the way to go.

• Great for input validation (like checking email formats)
• Can be tricky at first but powerful once you get the hang of it

Example:

import re
if re.match(r"\d ", "123abc"):
    print("Starts with numbers")

A few tips:

• Start with simple patterns before jumping into complex regex
• Use online tools like regex101.com to test your expressions

5. random – Generate Random Data

Use this when you need random numbers, shuffle lists, or pick random elements.

• Not suitable for security-sensitive applications (use secrets module instead)

Examples:

import random
random.randint(1, 10)     # Random integer between 1 and 10
random.choice(["a", "b"]) # Picks a random item

Good for games, simulations, or generating sample data.

6. json – Work With JSON Data

JSON is everywhere these days — APIs, config files, etc. The json module makes it easy to convert between JSON strings and Python objects.

Example:

import json
data = {"name": "Alice"}
json_str = json.dumps(data)  # Convert dict to JSON string

Key functions:

• json.loads() – Parse JSON string
• json.load() – Read from a JSON file
• json.dump() – Write to a JSON file

7. csv – Read and Write CSV Files

Need to process spreadsheets or export data as CSV? This module has got you covered.

• Read rows from a CSV file as dictionaries or lists
• Write data back out in CSV format

Example:

import csv
with open('data.csv', 'r') as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row['Name'])

It’s much easier than parsing CSV manually.

There are plenty of other modules too — like collections, itertools, functools, and third-party packages like requests, pandas, or matplotlib. But the ones above are solid starting points and widely used across many types of Python projects.

Basically, these modules cover most day-to-day needs without needing to install anything extra.

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