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This article introduces the relevant knowledge of "what are the efficiency tools of Python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Pandas- is used for data analysis.

Pandas is a powerful toolset for analyzing structured data; it is based on Numpy (providing high-performance matrix operations); it is used for data mining and data analysis, as well as data cleaning.

# 1. Install package $pip install pandas # 2, enter python interactive interface $python-I # 3, use Pandas > import pandas as pd > df = pd.DataFrame () > print (df) # 4, output result Empty DataFrame Columns: [] Index: [] 2, Selenium- automated test

Selenium is a tool for testing Web applications that can be tested from an end-user perspective. By running tests in different browsers, it is easier to find browser incompatibility. And it works with many browsers.

You can do a simple test by opening a browser and visiting Google's home page:

From selenium import webdriver import time browser = webdriver.Chrome (executable_path = "C:\ Program Files (x86)\ Google\ Chrome\ chromedriver.exe") website_URL = "https://www.google.co.in/" brower.get (website_URL) refreshrate = int (3) # refreshes the Google home page every 3 seconds. # it will run until you stop the compiler. While True: time.sleep (refreshrate) browser.refresh () 3, Flask-- mini Web framework

Flask is a lightweight customizable framework written in Python language, which is more flexible, lightweight, secure and easy to use than other similar frameworks. Flask is a very popular web framework at present. Developers can quickly implement a website or Web service in Python.

From flask import Flask app = Flask (_ _ name__) @ app.route ('/') def hello_world (): return 'Hello, wordcrawling 4, Scrapy-- page crawling

Scrapy can provide you with strong support so that you can accurately crawl information from the site. It's very practical.

Now basically most developers will use crawler tools to automate crawling work. So you can use this Scrapy when writing crawler code.

Starting Scrapy Shell is also very simple:

Scrapy shell

We can try to extract the value of the search button on Baidu's home page. The first thing to do is to find the class that the button uses. An inspect element shows that the class is "bt1".

Do the following:

Response = fetch ("https://baidu.com") response.css (" .bt1:: text ") .extract_first () = >" Search "5, Requests-- do API call

Requests is a powerful HTTP library. With it, you can easily send requests. There is no need to manually add a query string to the URL. In addition, there are many functions, such as authorization processing, JSON / XML parsing, session processing and so on.

Official examples:

> r = requests.get ('https://api.github.com/user', auth= (' user', 'pass')) > r.status_code 200 > r.headers [' content-type'] 'application/json; charset=utf8' > r.encoding' utf-8' > r.text'{"type": "User"... > > r.json () {'private_gists': 419,' total_private_repos': 77,...} 6, Faker- is used to create fake data

Faker is a Python package that generates fake data for you. Whether you need to bootstrap the database, create a good-looking XML document, fill in your persistence to emphasize testing it, or get data of the same name from a production service, Faker is for you

With it, you can generate fake names, addresses, descriptions, etc. Very quickly! In the following script, for example, I create a contact entry that contains the name, address, and some description text:

Installation:

Pip install Faker from faker import Faker fake = Faker () fake.name () fake.address () fake.text () 7, Pillow- for image processing

Pillow, a Python image processing tool, has quite powerful image processing functions. It can be used when you need to do image processing. After all, as a developer, you should choose a more powerful image processing tool.

Simple example:

From PIL import Image, ImageFilter try: original = Image.open ("Lenna.png") blurred = original.filter (ImageFilter.BLUR) original.show () blurred.show () blurred.save ("blurred.png") except: print "Unable to load image" what are the Python efficiency tools? thank you for reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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