


What are some popular Python modules and packages (e.g., math, datetime, os, sys, re, random, json, csv)?
Jun 25, 2025 am 01:01 AMPython's standard library contains multiple commonly used modules for processing mathematical operations, date and time, system operations, etc. 1. The math module provides mathematical functions such as sqrt, log and constants pi and e, suitable for precise calculations; 2. Datetime processes date and time, supports obtaining the current time, formatting and time difference calculations; 3. Os and sys are used for file and system operations, such as creating directories and accessing command line parameters; 4. Re supports regular expressions, suitable for text pattern matching and verification; 5. random generates random numbers or selects random elements, suitable for games and simulations; 6. json processes JSON data conversion, facilitates API interaction and configuration reading and writing; 7. csv is used to read and write CSV files, simplifying table data processing.
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 precision 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.
The above is the detailed content of What are some popular Python modules and packages (e.g., math, datetime, os, sys, re, random, json, csv)?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

To test the API, you need to use Python's Requests library. The steps are to install the library, send requests, verify responses, set timeouts and retry. First, install the library through pipinstallrequests; then use requests.get() or requests.post() and other methods to send GET or POST requests; then check response.status_code and response.json() to ensure that the return result is in compliance with expectations; finally, add timeout parameters to set the timeout time, and combine the retrying library to achieve automatic retry to enhance stability.

In Python, variables defined inside a function are local variables and are only valid within the function; externally defined are global variables that can be read anywhere. 1. Local variables are destroyed as the function is executed; 2. The function can access global variables but cannot be modified directly, so the global keyword is required; 3. If you want to modify outer function variables in nested functions, you need to use the nonlocal keyword; 4. Variables with the same name do not affect each other in different scopes; 5. Global must be declared when modifying global variables, otherwise UnboundLocalError error will be raised. Understanding these rules helps avoid bugs and write more reliable functions.

To create modern and efficient APIs using Python, FastAPI is recommended; it is based on standard Python type prompts and can automatically generate documents, with excellent performance. After installing FastAPI and ASGI server uvicorn, you can write interface code. By defining routes, writing processing functions, and returning data, APIs can be quickly built. FastAPI supports a variety of HTTP methods and provides automatically generated SwaggerUI and ReDoc documentation systems. URL parameters can be captured through path definition, while query parameters can be implemented by setting default values ??for function parameters. The rational use of Pydantic models can help improve development efficiency and accuracy.

How to efficiently handle large JSON files in Python? 1. Use the ijson library to stream and avoid memory overflow through item-by-item parsing; 2. If it is in JSONLines format, you can read it line by line and process it with json.loads(); 3. Or split the large file into small pieces and then process it separately. These methods effectively solve the memory limitation problem and are suitable for different scenarios.

In Python, the method of traversing tuples with for loops includes directly iterating over elements, getting indexes and elements at the same time, and processing nested tuples. 1. Use the for loop directly to access each element in sequence without managing the index; 2. Use enumerate() to get the index and value at the same time. The default index is 0, and the start parameter can also be specified; 3. Nested tuples can be unpacked in the loop, but it is necessary to ensure that the subtuple structure is consistent, otherwise an unpacking error will be raised; in addition, the tuple is immutable and the content cannot be modified in the loop. Unwanted values can be ignored by \_. It is recommended to check whether the tuple is empty before traversing to avoid errors.

Pure functions in Python refer to functions that always return the same output with no side effects given the same input. Its characteristics include: 1. Determinism, that is, the same input always produces the same output; 2. No side effects, that is, no external variables, no input data, and no interaction with the outside world. For example, defadd(a,b):returna b is a pure function because no matter how many times add(2,3) is called, it always returns 5 without changing other content in the program. In contrast, functions that modify global variables or change input parameters are non-pure functions. The advantages of pure functions are: easier to test, more suitable for concurrent execution, cache results to improve performance, and can be well matched with functional programming tools such as map() and filter().

Python implements asynchronous API calls with async/await with aiohttp. Use async to define coroutine functions and execute them through asyncio.run driver, for example: asyncdeffetch_data(): awaitasyncio.sleep(1); initiate asynchronous HTTP requests through aiohttp, and use asyncwith to create ClientSession and await response result; use asyncio.gather to package the task list; precautions include: avoiding blocking operations, not mixing synchronization code, and Jupyter needs to handle event loops specially. Master eventl
