6 Most Popular Python Web Scraping & Crawling Libraries

In this Python article, let's learn about Web Scraping & Crawling: 6 Most Popular Python Web Scraping & Crawling Libraries

  1. Scrapy: Scrapy, a fast high-level web crawling & scraping framework for Python.
  2. You-Get: ⏬ Dumb downloader that scrapes the web
  3. feedparser: Parse feeds in Python
  4. dirsearch: Web path scanner
  5. pytrends: Pseudo API for Google Trends
  6. cloudscraper: A Python module to bypass Cloudflare's anti-bot page.

 

What is Python?

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. 

Web crawling is a component of web scraping, the crawler logic finds URLs to be processed by the scraper code. A web crawler starts with a list of URLs to visit, called the seed. For each URL, the crawler finds links in the HTML, filters those links based on some criteria and adds the new links to a queue

 

6 Most Popular Python Web Scraping & Crawling Libraries

1.Scrapy

Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.

Scrapy is maintained by Zyte (formerly Scrapinghub) and many other contributors.

Check the Scrapy homepage at https://scrapy.org for more information, including a list of features.

Requirements

  • Python 3.7+
  • Works on Linux, Windows, macOS, BSD

Install

The quick way:

pip install scrapy

See the install section in the documentation at https://docs.scrapy.org/en/latest/intro/install.html for more details.

Documentation

Documentation is available online at https://docs.scrapy.org/ and in the docs directory.

View on GitHub

2.    You-Get

You-Get is a tiny command-line utility to download media contents (videos, audios, images) from the Web, in case there is no other handy way to do it.

Installation

Prerequisites

The following dependencies are recommended:

Option 1: Install via pip

The official release of you-get is distributed on PyPI, and can be installed easily from a PyPI mirror via the pip package manager. Note that you must use the Python 3 version of pip:

$ pip3 install you-get

Option 2: Install via Antigen (for Zsh users)

Add the following line to your .zshrc:

antigen bundle soimort/you-get

Option 3: Download from GitHub

You may either download the stable (identical with the latest release on PyPI) or the develop (more hotfixes, unstable features) branch of you-get. Unzip it, and put the directory containing the you-get script into your PATH.

Alternatively, run

$ [sudo] python3 setup.py install

Or

$ python3 setup.py install --user

to install you-get to a permanent path.

You can also use the pipenv to install the you-get in the Python virtual environment.

$ pipenv install -e .
$ pipenv run you-get --version
you-get: version 0.4.1555, a tiny downloader that scrapes the web.

Option 4: Git clone

This is the recommended way for all developers, even if you don't often code in Python.

$ git clone git://github.com/soimort/you-get.git

Then put the cloned directory into your PATH, or run ./setup.py install to install you-get to a permanent path.

Option 5: Homebrew (Mac only)

You can install you-get easily via:

$ brew install you-get

Option 6: pkg (FreeBSD only)

You can install you-get easily via:

# pkg install you-get

View on GitHub

3.    feedparser 

feedparser - Parse Atom and RSS feeds in Python.


Installation

feedparser can be installed by running pip:

$ pip install feedparser

Documentation

The feedparser documentation is available on the web at:

https://feedparser.readthedocs.io/en/latest/

It is also included in its source format, ReST, in the docs/ directory. To build the documentation you'll need the Sphinx package, which is available at:

https://www.sphinx-doc.org/

You can then build HTML pages using a command similar to:

$ sphinx-build -b html docs/ fpdocs

This will produce HTML documentation in the fpdocs/ directory.

Testing

Feedparser has an extensive test suite, powered by tox. To run it, type this:

$ python -m venv venv
$ source venv/bin/activate  # or "venv\bin\activate.ps1" on Windows
(venv) $ python -m pip install --upgrade pip
(venv) $ python -m pip install poetry
(venv) $ poetry update
(venv) $ tox

This will spawn an HTTP server that will listen on port 8097. The tests will fail if that port is in use.

View on GitHub

4.    dirsearch

An advanced command-line tool designed to brute force directories and files in webservers, AKA web path scanner

dirsearch is being actively developed by @maurosoria and @shelld3v

Reach to our Discord server to communicate with the team at best

Installation & Usage

Requirement: python 3.7 or higher

Choose one of these installation options:

  • Install with git: git clone https://github.com/maurosoria/dirsearch.git --depth 1 (RECOMMENDED)
  • Install with ZIP file: Download here
  • Install with Docker: docker build -t "dirsearch:v0.4.2" . (more information can be found here)
  • Install with PyPi: pip3 install dirsearch
  • Install with Kali Linux: sudo apt-get install dirsearch (deprecated)

Wordlists (IMPORTANT)

Summary:

  • Wordlist is a text file, each line is a path.
  • About extensions, unlike other tools, dirsearch only replaces the %EXT% keyword with extensions from -e flag.
  • For wordlists without %EXT% (like SecLists), -f | --force-extensions switch is required to append extensions to every word in wordlist, as well as the /.
  • To apply your extensions to wordlist entries that have extensions already, use -O | --overwrite-extensions (Note: some extensions are excluded from being overwritted such as .log, .json, .xml, ... or media extensions like .jpg, .png)
  • To use multiple wordlists, you can separate your wordlists with commas. Example: wordlist1.txt,wordlist2.txt.

Examples:

  • Normal extensions:
index.%EXT%

Passing asp and aspx as extensions will generate the following dictionary:

index
index.asp
index.aspx
  • Force extensions:
admin

Passing php and html as extensions with -f/--force-extensions flag will generate the following dictionary:

admin
admin.php
admin.html
admin/
  • Overwrite extensions:
login.html

Passing jsp and jspa as extensions with -O/--overwrite-extensions flag will generate the following dictionary:

login.html
login.jsp
login.jspa

View on GitHub

5.    pytrends

Unofficial API for Google Trends

Allows simple interface for automating downloading of reports from Google Trends. Only good until Google changes their backend again :-P. When that happens feel free to contribute!

Installation

pip install pytrends

Requirements

  • Written for Python 3.3+
  • Requires Requests, lxml, Pandas

back to top

API

Connect to Google

from pytrends.request import TrendReq

pytrends = TrendReq(hl='en-US', tz=360)

or if you want to use proxies as you are blocked due to Google rate limit:

from pytrends.request import TrendReq

pytrends = TrendReq(hl='en-US', tz=360, timeout=(10,25), proxies=['https://34.203.233.13:80',], retries=2, backoff_factor=0.1, requests_args={'verify':False})

timeout(connect, read)

tz

  • Timezone Offset
  • For example US CST is '360' (note NOT -360, Google uses timezone this way...)

proxies

  • https proxies Google passed ONLY
  • list ['https://34.203.233.13:80','https://35.201.123.31:880', ..., ...]

retries

  • number of retries total/connect/read all represented by one scalar

backoff_factor

  • A backoff factor to apply between attempts after the second try (most errors are resolved immediately by a second try without a delay). urllib3 will sleep for: {backoff factor} * (2 ^ ({number of total retries} - 1)) seconds. If the backoff_factor is 0.1, then sleep() will sleep for [0.0s, 0.2s, 0.4s, …] between retries. It will never be longer than Retry.BACKOFF_MAX. By default, backoff is disabled (set to 0).

requests_args

  • A dict with additional parameters to pass along to the underlying requests library, for example verify=False to ignore SSL errors

Note: the parameter hl specifies host language for accessing Google Trends. Note: only https proxies will work, and you need to add the port number after the proxy ip address

View on GitHub

6.    cloudscraper

A simple Python module to bypass Cloudflare's anti-bot page (also known as "I'm Under Attack Mode", or IUAM), implemented with Requests. Cloudflare changes their techniques periodically, so I will update this repo frequently.

This can be useful if you wish to scrape or crawl a website protected with Cloudflare. Cloudflare's anti-bot page currently just checks if the client supports Javascript, though they may add additional techniques in the future.

Due to Cloudflare continually changing and hardening their protection page, cloudscraper requires a JavaScript Engine/interpreter to solve Javascript challenges. This allows the script to easily impersonate a regular web browser without explicitly deobfuscating and parsing Cloudflare's Javascript.

For reference, this is the default message Cloudflare uses for these sorts of pages:

Checking your browser before accessing website.com.

This process is automatic. Your browser will redirect to your requested content shortly.

Please allow up to 5 seconds...

Any script using cloudscraper will sleep for ~5 seconds for the first visit to any site with Cloudflare anti-bots enabled, though no delay will occur after the first request.

Usage

The simplest way to use cloudscraper is by calling create_scraper().

import cloudscraper

scraper = cloudscraper.create_scraper()  # returns a CloudScraper instance
# Or: scraper = cloudscraper.CloudScraper()  # CloudScraper inherits from requests.Session
print(scraper.get("http://somesite.com").text)  # => "<!DOCTYPE html><html><head>..."

That's it...

Any requests made from this session object to websites protected by Cloudflare anti-bot will be handled automatically. Websites not using Cloudflare will be treated normally. You don't need to configure or call anything further, and you can effectively treat all websites as if they're not protected with anything.

You use cloudscraper exactly the same way you use Requests. cloudScraper works identically to a Requests Session object, just instead of calling requests.get() or requests.post(), you call scraper.get() or scraper.post().

Consult Requests' documentation for more information.

View on GitHub


Web Scraping & Crawling with Python FAQ

  • What is web crawling and scraping in Python?

Web crawling is a component of web scraping, the crawler logic finds URLs to be processed by the scraper code. A web crawler starts with a list of URLs to visit, called the seed. For each URL, the crawler finds links in the HTML, filters those links based on some criteria and adds the new links to a queue.

  • What is the difference between web crawler and scraper?

A Web Crawler will generally go through every single page on a website, rather than a subset of pages. On the other hand, Web Scraping focuses on a specific set of data on a website. These could be product details, stock prices, sports data or any other data sets.

  • Why Python is used for web scraping?

Python is one of the easiest ways to get started as it is an object-oriented language. Python's classes and objects are significantly easier to use than in any other language. Additionally, many libraries exist that make building a tool for web scraping in Python an absolute breeze.


Related videos:

Python Scrapy Tutorial | Web Scraping and Crawling Using Scrapy

Related posts:

#python 

What is GEEK

Buddha Community

6 Most Popular Python Web Scraping & Crawling Libraries
Sival Alethea

Sival Alethea

1624402800

Beautiful Soup Tutorial - Web Scraping in Python

The Beautiful Soup module is used for web scraping in Python. Learn how to use the Beautiful Soup and Requests modules in this tutorial. After watching, you will be able to start scraping the web on your own.
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=87Gx3U0BDlo&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=12
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#web scraping #python #beautiful soup #beautiful soup tutorial #web scraping in python #beautiful soup tutorial - web scraping in python

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

How POST Requests with Python Make Web Scraping Easier

When scraping a website with Python, it’s common to use the

urllibor theRequestslibraries to sendGETrequests to the server in order to receive its information.

However, you’ll eventually need to send some information to the website yourself before receiving the data you want, maybe because it’s necessary to perform a log-in or to interact somehow with the page.

To execute such interactions, Selenium is a frequently used tool. However, it also comes with some downsides as it’s a bit slow and can also be quite unstable sometimes. The alternative is to send a

POSTrequest containing the information the website needs using the request library.

In fact, when compared to Requests, Selenium becomes a very slow approach since it does the entire work of actually opening your browser to navigate through the websites you’ll collect data from. Of course, depending on the problem, you’ll eventually need to use it, but for some other situations, a

POSTrequest may be your best option, which makes it an important tool for your web scraping toolbox.

In this article, we’ll see a brief introduction to the

POSTmethod and how it can be implemented to improve your web scraping routines.

#python #web-scraping #requests #web-scraping-with-python #data-science #data-collection #python-tutorials #data-scraping

AutoScraper Introduction: Fast and Light Automatic Web Scraper for Python

In the last few years, web scraping has been one of my day to day and frequently needed tasks. I was wondering if I can make it smart and automatic to save lots of time. So I made AutoScraper!

The project code is available on Github.

This project is made for automatic web scraping to make scraping easy. It gets a url or the html content of a web page and a list of sample data which we want to scrape from that page. This data can be text, url or any html tag value of that page. It learns the scraping rules and returns the similar elements. Then you can use this learned object with new urls to get similar content or the exact same element of those new pages!

Installation

Install latest version from git repository using pip:

$ pip install git+https://github.com/alirezamika/autoscraper.git

How to use

Getting similar results

Say we want to fetch all related post titles in a stackoverflow page:

from autoscraper import AutoScraper

url = 'https://stackoverflow.com/questions/2081586/web-scraping-with-python'

## We can add one or multiple candidates here.
## You can also put urls here to retrieve urls.
wanted_list = ["How to call an external command?"]

scraper = AutoScraper()
result = scraper.build(url, wanted_list)
print(result)

#python #web-scraping #web-crawling #data-scraping #website-scraping #open-source #repositories-on-github #web-development

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development