Those interesting and powerful Python libraries
The python language has always been famous for its rich third-party libraries. Today I will introduce some very nice libraries, which are fun, fun and powerful!
Data Collection
In today’s Internet era, data is really important. First, let’s introduce several excellent data collection projects
AKShare
AKShare is a Python-based financial data interface library that aims to collect fundamental data, real-time and historical market data, and derivative data from financial products such as stocks, futures, options, funds, foreign exchange, bonds, indices, and cryptocurrencies. , a set of tools from data cleaning to data landing, mainly used for academic research purposes.
import akshare as ak stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol="000001", period="daily", start_date="20170301", end_date='20210907', adjust="") print(stock_zh_a_hist_df)
Output:
日期开盘 收盘最高...振幅 涨跌幅 涨跌额 换手率 0 2017-03-01 9.49 9.49 9.55...0.840.110.010.21 1 2017-03-02 9.51 9.43 9.54...1.26 -0.63 -0.060.24 2 2017-03-03 9.41 9.40 9.43...0.74 -0.32 -0.030.20 3 2017-03-06 9.40 9.45 9.46...0.740.530.050.24 4 2017-03-07 9.44 9.45 9.46...0.630.000.000.17 ............... ... ... ... ... 11002021-09-0117.4817.8817.92...5.110.450.081.19 11012021-09-0218.0018.4018.78...5.482.910.521.25 11022021-09-0318.5018.0418.50...4.35 -1.96 -0.360.72 11032021-09-0617.9318.4518.60...4.552.270.410.78 11042021-09-0718.6019.2419.56...6.564.280.790.84 [1105 rows x 11 columns]
https://github.com/akfamily/akshare
TuShare
TuShare is the implementation of A tool for data collection, cleaning, processing and data storage of financial data such as stocks/futures, which meets the data acquisition needs of financial quantitative analysts and people who study data analysis. It is characterized by wide data coverage and simple interface calls. Respond quickly.
However, some functions of this project are chargeable, please choose to use them
import tushare as ts
ts.get_hist_data('600848') #一次性获取全部数据Output:
openhigh close low volumep_changema5 date 2012-01-11 6.880 7.380 7.060 6.880 14129.96 2.62 7.060 2012-01-12 7.050 7.100 6.980 6.9007895.19-1.13 7.020 2012-01-13 6.950 7.000 6.700 6.6906611.87-4.01 6.913 2012-01-16 6.680 6.750 6.510 6.4802941.63-2.84 6.813 2012-01-17 6.660 6.880 6.860 6.4608642.57 5.38 6.822 2012-01-18 7.000 7.300 6.890 6.880 13075.40 0.44 6.788 2012-01-19 6.690 6.950 6.890 6.6806117.32 0.00 6.770 2012-01-20 6.870 7.080 7.010 6.8706813.09 1.74 6.832 ma10ma20v_ma5 v_ma10 v_ma20 turnover date 2012-01-11 7.060 7.060 14129.96 14129.96 14129.96 0.48 2012-01-12 7.020 7.020 11012.58 11012.58 11012.58 0.27 2012-01-13 6.913 6.9139545.679545.679545.67 0.23 2012-01-16 6.813 6.8137894.667894.667894.66 0.10 2012-01-17 6.822 6.8228044.248044.248044.24 0.30 2012-01-18 6.833 6.8337833.338882.778882.77 0.45 2012-01-19 6.841 6.8417477.768487.718487.71 0.21 2012-01-20 6.863 6.8637518.008278.388278.38 0.23
https://github.com/waditu/tushare
GoPUP
The data collected by the GoPUP project comes from public data sources and does not involve any personal privacy data or non-public data. But similarly, some interfaces require TOKEN registration before they can be used.
import gopup as gp df = gp.weibo_index(word="疫情", time_type="1hour") print(df)
Output:
疫情 index 2022-12-17 18:15:0018544 2022-12-17 18:20:0014927 2022-12-17 18:25:0013004 2022-12-17 18:30:0013145 2022-12-17 18:35:0013485 2022-12-17 18:40:0014091 2022-12-17 18:45:0014265 2022-12-17 18:50:0014115 2022-12-17 18:55:0015313 2022-12-17 19:00:0014346 2022-12-17 19:05:0014457 2022-12-17 19:10:0013495 2022-12-17 19:15:0014133
https://github.com/justinzm/gopup
GeneralNewsExtractor
This project is based on The paper "Webpage Text Extraction Method Based on Text and Symbol Density" uses a text extractor implemented in Python, which can be used to extract the content, author, and title of the text in HTML.
>>> from gne import GeneralNewsExtractor >>> html = '''经过渲染的网页 HTML 代码''' >>> extractor = GeneralNewsExtractor() >>> result = extractor.extract(html, noise_node_list=['//div[@]']) >>> print(result)
Output:
{"title": "xxxx", "publish_time": "2019-09-10 11:12:13", "author": "yyy", "content": "zzzz", "images": ["/xxx.jpg", "/yyy.png"]}News page extraction example
https://github.com/GeneralNewsExtractor/GeneralNewsExtractor
Crawler
Crawler is also a major application direction of Python language. Many friends also start with crawler. Let’s take a look at some excellent crawler projects
playwright-python
Microsoft's open source browser automation tool can operate the browser using Python language. Supports Chromium, Firefox and WebKit browsers under Linux, macOS, and Windows systems.
from playwright.sync_api import sync_playwright
with sync_playwright() as p:
for browser_type in [p.chromium, p.firefox, p.webkit]:
browser = browser_type.launch()
page = browser.new_page()
page.goto('http://whatsmyuseragent.org/')
page.screenshot(path=f'example-{browser_type.name}.png')
browser.close()https://github.com/microsoft/playwright-python
awesome-python-login-model
This project collects various Login methods for large websites and crawler programs for some websites. Login methods include selenium login, direct simulated login through packet capture, etc. Helps novices research and write crawlers.
However, as we all know, crawlers are very demanding for post-maintenance. The project has not been updated for a long time, so there are still doubts whether the various login interfaces can still be used normally. Everyone chooses to use them, or develop them themselves.
https://github.com/Kr1s77/awesome-python-login-model
DecryptLogin
Compared with the previous one, this project is still being updated. It also simulates logging into major websites, which is still very valuable for novices.
from DecryptLogin import login # the instanced Login class object lg = login.Login() # use the provided api function to login in the target website (e.g., twitter) infos_return, session = lg.twitter(username='Your Username', password='Your Password')
https://github.com/CharlesPikachu/DecryptLogin
Scylla
Scylla is a high-quality free proxy IP pool tool. Currently only Python 3.6 is supported.
http://localhost:8899/api/v1/stats
Output:
{
"median": 181.2566407083,
"valid_count": 1780,
"total_count": 9528,
"mean": 174.3290085201
}https://github.com/scylladb/scylladb
ProxyPool
Crawler proxy IP pool The main function of the project is to regularly collect free proxies published online for verification and put them into the database. The proxies that are regularly verified and put into the database ensure the availability of the agents. It provides two usage methods: API and CLI. At the same time, the proxy source can also be expanded to increase the quality and quantity of proxy pool IPs. The project design document is detailed and the module structure is concise and easy to understand. It is also suitable for novice crawlers to better learn crawler technology.
import requests
def get_proxy():
return requests.get("http://127.0.0.1:5010/get/").json()
def delete_proxy(proxy):
requests.get("http://127.0.0.1:5010/delete/?proxy={}".format(proxy))
# your spider code
def getHtml():
# ....
retry_count = 5
proxy = get_proxy().get("proxy")
while retry_count > 0:
try:
html = requests.get('http://www.example.com', proxies={"http": "http://{}".format(proxy)})
# 使用代理访问
return html
except Exception:
retry_count -= 1
# 删除代理池中代理
delete_proxy(proxy)
return Nonehttps://github.com/Python3WebSpider/ProxyPool
getproxy
getproxy is a crawling and distribution proxy website that obtains http/https The agent's program updates data every 15 minutes.
(test2.7) ➜~ getproxy INFO:getproxy.getproxy:[*] Init INFO:getproxy.getproxy:[*] Current Ip Address: 1.1.1.1 INFO:getproxy.getproxy:[*] Load input proxies INFO:getproxy.getproxy:[*] Validate input proxies INFO:getproxy.getproxy:[*] Load plugins INFO:getproxy.getproxy:[*] Grab proxies INFO:getproxy.getproxy:[*] Validate web proxies INFO:getproxy.getproxy:[*] Check 6666 proxies, Got 666 valid proxies ...
https://github.com/fate0/getproxy
freeproxy
is also a project to capture free proxies. This project supports There are many proxy websites to crawl and it is easy to use.
from freeproxy import freeproxy
proxy_sources = ['proxylistplus', 'kuaidaili']
fp_client = freeproxy.FreeProxy(proxy_sources=proxy_sources)
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36'
}
response = fp_client.get('https://space.bilibili.com/406756145', headers=headers)
print(response.text)https://github.com/CharlesPikachu/freeproxy
fake-useragent
Disguise browser identity, often used for crawlers. The code of this project is very small, you can read it to see how ua.random returns a random browser identity.
from fake_useragent import UserAgent ua = UserAgent() ua.ie # Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US); ua.msie # Mozilla/5.0 (compatible; MSIE 10.0; Macintosh; Intel Mac OS X 10_7_3; Trident/6.0)' ua['Internet Explorer'] # Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; GTB7.4; InfoPath.2; SV1; .NET CLR 3.3.69573; WOW64; en-US) ua.opera # Opera/9.80 (X11; Linux i686; U; ru) Presto/2.8.131 Version/11.11 ua.chrome # Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.2 (KHTML, like Gecko) Chrome/22.0.1216.0 Safari/537.2' ua.google # Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_4) AppleWebKit/537.13 (KHTML, like Gecko) Chrome/24.0.1290.1 Safari/537.13 ua['google chrome'] # Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11 ua.firefox # Mozilla/5.0 (Windows NT 6.2; Win64; x64; rv:16.0.1) Gecko/20121011 Firefox/16.0.1 ua.ff # Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:15.0) Gecko/20100101 Firefox/15.0.1 ua.safari # Mozilla/5.0 (iPad; CPU OS 6_0 like Mac OS X) AppleWebKit/536.26 (KHTML, like Gecko) Version/6.0 Mobile/10A5355d Safari/8536.25 # and the best one, get a random browser user-agent string ua.random
https://github.com/fake-useragent/fake-useragent
Web related
Python Web has too many excellent and veteran There are many libraries, such as Django and Flask. I won’t talk about them as everyone knows them. We will introduce a few niche but easy-to-use ones.
streamlit
streamlit is a Python framework that can quickly turn data into visual and interactive pages. Turn our data into graphs in minutes.
import streamlit as st
x = st.slider('Select a value')
st.write(x, 'squared is', x * x)Output:
https://github.com/streamlit/streamlit
wagtail
是一个强大的开源 Django CMS（内容管理系统）。首先该项目更新、迭代活跃，其次项目首页提到的功能都是免费的，没有付费解锁的骚操作。专注于内容管理，不束缚前端实现。
https://github.com/wagtail/wagtail
fastapi
基于 Python 3.6+ 的高性能 Web 框架。“人如其名”用 FastAPI 写接口那叫一个快、调试方便，Python 在进步而它基于这些进步，让 Web 开发变得更快、更强。
from typing import Union
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():
return {"Hello": "World"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
return {"item_id": item_id, "q": q}https://github.com/tiangolo/fastapi
django-blog-tutorial
这是一个 Django 使用教程，该项目一步步带我们使用 Django 从零开发一个个人博客系统，在实践的同时掌握 Django 的开发技巧。
https://github.com/jukanntenn/django-blog-tutorial
dash
dash 是一个专门为机器学习而来的 Web 框架，通过该框架可以快速搭建一个机器学习 APP。
https://github.com/plotly/dash
PyWebIO
同样是一个非常优秀的 Python Web 框架，在不需要编写前端代码的情况下就可以完成整个 Web 页面的搭建，实在是方便。
https://github.com/pywebio/PyWebIO
Python 教程
practical-python
一个人气超高的 Python 学习资源项目，是 MarkDown 格式的教程，非常友好。
https://github.com/dabeaz-course/practical-python
learn-python3
一个 Python3 的教程，该教程采用 Jupyter notebooks 形式，便于运行和阅读。并且还包含了练习题，对新手友好。
https://github.com/jerry-git/learn-python3
python-guide
Requests 库的作者——kennethreitz，写的 Python 入门教程。不单单是语法层面的，涵盖项目结构、代码风格，进阶、工具等方方面面。一起在教程中领略大神的风采吧~
https://github.com/realpython/python-guide
其他
pytools
这是一位大神编写的类似工具集的项目，里面包含了众多有趣的小工具。
截图只是冰山一角，全貌需要大家自行探索了
import random from pytools import pytools tool_client = pytools.pytools() all_supports = tool_client.getallsupported() tool_client.execute(random.choice(list(all_supports.values())))
https://github.com/CharlesPikachu/pytools
amazing-qr
可以生成动态、彩色、各式各样的二维码，真是个有趣的库。
#3 -n, -d amzqr https://github.com -n github_qr.jpg -d .../paths/
https://github.com/x-hw/amazing-qr
sh
sh 是一个成熟的，用于替代 subprocess 的库，它允许我们调用任何程序，看起来它就是一个函数一样。
$> ./run.sh FunctionalTests.test_unicode_arg
https://github.com/amoffat/sh
tqdm
强大、快速、易扩展的 Python 进度条库。
from tqdm import tqdm for i in tqdm(range(10000)): ...
https://github.com/tqdm/tqdm
loguru
一个让 Python 记录日志变得简单的库。
from loguru import logger
logger.debug("That's it, beautiful and simple logging!")https://github.com/Delgan/loguru
click
Python 的第三方库，用于快速创建命令行。支持装饰器方式调用、多种参数类型、自动生成帮助信息等。
import click
@click.command()
@click.option("--count", default=1, help="Number of greetings.")
@click.option("--name", prompt="Your name", help="The person to greet.")
def hello(count, name):
"""Simple program that greets NAME for a total of COUNT times."""
for _ in range(count):
click.echo(f"Hello, {name}!")
if __name__ == '__main__':
hello()Output:
$ python hello.py --count=3 Your name: Click Hello, Click! Hello, Click! Hello, Click!
KeymouseGo
Python 实现的精简绿色版按键精灵，记录用户的鼠标、键盘操作，自动执行之前记录的操作，可设定执行的次数。在进行某些简单、单调重复的操作时，使用该软件可以十分省事儿。只需要录制一遍，剩下的交给 KeymouseGo 来做就可以了。
https://github.com/taojy123/KeymouseGo
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