Web Scraping Python Modules

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In this article, we’re going to talk about how to perform web scraping with python, using Selenium in the Python programming language. Web scraping, also called web data extraction, refers to the technique of harvesting data from a web page through leveraging the patterns in the page’s underlying code. The Ultimate Guide For Using Proxies For web Scraping With Python. Python is a high-level programming language that is used for web development, mobile application development, and also for scraping the web. Python is considered as the finest programming language for web scraping because it can handle all the crawling processes smoothly. If you are just getting started in Python and would like to learn more, take DataCamp's Introduction to Data Science in Python course. In the time when the internet is rich with so much data, and apparently, data has become the new oil, web scraping has become even more important and practical to use in various applications.

  • Among all these languages, Python is considered as one of the best for Web Scraping because of features like – a rich library, easy to use, dynamically typed, etc. Here are some most commonly used python3 web Scraping libraries.
  • That's where the concept of web scraping comes in handy! What We are Going to Build. We will learn all about Web Scraping using Python and BeautifulSoup4 by building a real-world project. I don't want to give you a headache by teaching you how to scrape an ever-changing dynamic website.

Scrapy is a well-established framework for scraping, but it is also a very heavy framework. For smaller jobs, it may be overkill and for extremely large jobs it is very slow. If you would like to roll up your sleeves and perform web scraping in Python. continue reading.

If you need publicly available data from scraping the Internet, before creating a web scraper, it is best to check if this data is already available from public data sources or APIs. Check the site’s FAQ section or Google for their API endpoints and public data. Even if their API endpoints are available you have to create some parser for fetching and structuring the data according to your needs.

Here are some basic steps performed by most web spiders:

  1. Start with a URL and use an HTTP GET or PUT request to access the URL

  2. Fetch all the contents in it and parse the data

  3. Store the data in any database or put it into any data warehouse

  4. Enqueue all the URLs in a page

  5. Use the URLs in the queue and repeat from process 1

Read More – Best Web Scraping Tools

Here are the 3 major modules in every web crawler:

  1. Request/Response handler.

  2. Data parsing/data cleansing/data munging process.

  3. Data serialization/data pipelines.

Let’s look at each of these modules and see what they do and how to use them.

Request/Response Handler

Request/response handlers are managers who make HTTP requests to a url or a group of urls, and fetch the response objects as HTML contents and pass this data to the next module. If you use Python for performing request/response url-opening process libraries such as the following are most commonly used

  1. urllib (20.5. urllib – Open arbitrary resources by URL – Python v2.7.8 documentation) -Basic python library yet high-level interface for fetching data across the World Wide Web.

  2. urllib2 (20.6. urllib2 – extensible library for opening URLs – Python v2.7.8 documentation) – an extensible library of urllib, which would handle basic HTTP requests, digest authentication, redirections, cookies and more.

  3. requests (Requests: HTTP for Humans) – Much advanced request library

which is built on top of basic request handling libraries.

Data parsing/data cleansing/data munging process

This is the module where the fetched data is processed and cleaned. Unstructured data is transformed into structured during this processing. Usually a set of Regular Expressions (regexes) which perform pattern matching and text processing tasks on the html data are used for this processing.

In addition to regexes, basic string manipulation and search methods are also used to perform this cleaning and transformation. You must have a thorough knowledge of regular expressions and so that you could design the regex patterns.

Data serialization/data pipelines

Once you get the cleaned data from the parsing and cleaning module, the data serialization module will be used to serialize the data according to the data models that you require. This is the final module that will output data in a standard format that can be stored in databases, JSON/CSV files or passed to any data warehouses for storage. These tasks are usually performed by libraries listed below for web scraping in python

  1. pickle (pickle – Python object serialization) – This module implements a fundamental, but powerful algorithm for serializing and de-serializing a Python object structure

  2. JSON (JSON encoder and decoder)

  3. CSV (https://docs.python.org/2/library/csv.html)

  4. Basic database interface libraries like pymongo (Tutorial – PyMongo), mysqldb ( on python.org), sqlite3(sqlite3 – DB-API interface for SQLite databases)

And many more such libraries based on the format and database/data storage.

Basic spider rules

The rules to follow while building a spider are to be nice to the sites you are scraping and follow the rules in the site’s spider policies outlined in the site’s robots.txt.

Limit the number of requests in a second and build enough delays in the spiders so that you don’t adversely affect the site.

It just makes sense to be nice.

To learn more on web scraping in Python check out our tutorial page below:

We can help with your data or automation needs

Turn the Internet into meaningful, structured and usable data



This document assumes you have already installed Python 3, and you have used both pip and venv. If not, refer to these instructions.

Sweigart briefly covers scraping in chapter 12 of Automate the Boring Stuff with Python (second edition).

This chapter here and the two following chapters provide additional context and examples for beginners.

BeautifulSoup documentation:

Setup for BeautifulSoup¶

BeautifulSoup is a scraping library for Python. We want to run all our scraping projects in a virtual environment, so we will set that up first. (Students have already installed Python 3.)

Create a directory and change into it¶

The first step is to create a new folder (directory) for all your scraping projects. Mine is:

Do not use any spaces in your folder names. If you must use punctuation, do not use anything other than an underscore _. It’s best if you use only lowercase letters.

Python best web scraper

Change into that directory. For me, the command would be:

Create a new virtualenv in that directory and activate it¶

Create a new virtual environment there (this is done only once).

MacOS:

Web Scraping Modules In Python

Windows:

Activate the virtual environment:

MacOS:

Windows:

Important: You should now see (env) at the far left side of your prompt. This indicates that the virtual environment is active. For example (MacOS):

When you are finished working in a virtual environment, you should deactivate it. The command is the same in MacOS or Windows (DO NOT DO THIS NOW):

You’ll know it worked because (env) will no longer be at the far left side of your prompt.

Install the BeautifulSoup library¶

In MacOS or Windows, at the command prompt, type:

This is how you install any Python library that exists in the Python Package Index. Pretty handy. pip is a tool for installing Python packages, which is what you just did.

Note

You have installed BeautifulSoup in the Python virtual environment that is currently active. When that virtual environment is not active, BeautifulSoup will not be available to you. This is ideal, because you will create different virtual environments for different Python projects, and you won’t need to worry about updated libraries in the future breaking your (past) code.

Test BeautifulSoup¶

Start Python. Because you are already in a Python 3 virtual environment, Mac users need only type python (NOT python3). Windows users also type python as usual.

You should now be at the >>> prompt — the Python interactive shell prompt.

In MacOS or Windows, type (or copy/paste) one line at a time:

  1. You imported two Python modules, urlopen and BeautifulSoup (the first two lines).

  2. You used urlopen to copy the entire contents of the URL given into a new Python variable, page (line 3).

  3. You used the BeautifulSoupfunction to process the value of that variable (the plain-text contents of the file at that URL) through a built-in HTML parser called html.parser.

  4. The result: All the HTML from the file is now in a BeautifulSoup object with the new Python variable name soup. (It is just a variable name.)

  5. Last line: Using the syntax of the BeautifulSoup library, you printed the first h1 element (including its tags) from that parsed value.

If it works, you’ll see:

Check out the page on the web to see what you scraped.

Attention

If you got an error about SSL, quit Python (quit() or Command-D) and COPY/PASTE this at the command prompt (MacOS only):

Then return to the Python prompt and retry the five lines above.

The command soup.h1 would work the same way for any HTML tag (if it exists in the file). Instead of printing it, you might stash it in a variable:

Python web scraping table

Then, to see the text in the element without the tags:

Understanding BeautifulSoup¶

BeautifulSoup is a Python library that enables us to extract information from web pages and even entire websites.

We use BeautifulSoup commands to create a well-structured data object (more about objects below) from which we can extract, for example, everything with an <li> tag, or everything with class='book-title'.

After extracting the desired information, we can use other Python commands (and libraries) to write the data into a database, CSV file, or other usable format — and then we can search it, sort it, etc.

What is the BeautifulSoup object?¶

It’s important to understand that many of the BeautifulSoup commands work on an object, which is not the same as a simple string.

Many programming languages include objects as a data type. Python does, JavaScript does, etc. An object is an even more powerful and complex data type than an array (JavaScript) or a list (Python) and can contain many other data types in a structured format.

When you extract information from an object with a BeautifulSoup command, sometimes you get a single Tag object, and sometimes you get a Python list (similar to an array in JavaScript) of Tag objects. The way you treat that extracted information will be different depending on whether it is one item or a list (usually, but not always, containing more than one item).

That last paragraph is REALLY IMPORTANT, so read it again. For example, you cannot call .text on a list. You’ll see an error if you try it.

How BeautifulSoup handles the object¶

In the previous code, when this line ran:

… you copied the entire contents of a file into a new Python variable named page. The contents were stored as an HTTPResponse object. We can read the contents of that object like this:

… but that’s not going to be very usable, or useful — especially for a file with a lot more content in it.

When you transform that HTTPResponse object into a BeautifulSoup object — with the following line — you create a well-structured object from which you can extract any HTML element and the text and/or attributes within any HTML element.

Some basic BeautifulSoup commands¶

Let’s look at a few examples of what BeautifulSoup can do.

Finding elements that have a particular class¶

Deciding the best way to extract what you want from a large HTML file requires you to dig around in the source, using Developer Tools, before you write the Python/BeautifulSoup commands. In many cases, you’ll see that everything you want has the same CSS class on it. After creating a BeautifulSoup object (here, as before, it is soup), this line will create a Python list containing all the <td> elements that have the class city.

Attention

The word class is a reserved word in Python. Using class (alone) in the code above would give you a syntax error. So when we search by CSS class with BeautifulSoup, we use the keyword argument class_ — note the added underscore. Other HTML attributes DO NOT need the underscore.

Maybe there were 10 cities in <td> tags in that HTML file. Maybe there were 10,000. No matter how many, they are now in a list (assigned to the variable city_list), and you can search them, print them, write them out to a database or a JSON file — whatever you like. Often you will want to perform the same actions on each item in the list, so you will use a normal Python for-loop:

.get_text() is a handy BeautifulSoup method that will extract the text — and only the text — from the Tag object. If instead you wrote just print(city), you’d get the complete <td> — and any other tags inside that as well.

Note

Scraping

The BeautifulSoup methods .get_text() and .getText() are the same. The BeautifulSoup property .text is a shortcut to .get_text() and is acceptable unless you need to pass arguments to .get_text().

Finding all vs. finding one¶

The BeautifulSoup find_all() method you just saw always produces a list. (Note: findAll() will also work.) If you know there will be only one item of the kind you want in a file, you should use the find() method instead.

For example, maybe you are scraping the address and phone number from every page in a large website. In this case, there is only one phone number on the page, and it is enclosed in a pair of tags with the attribute id='call'. One line of your code gets the phone number from the current page:

You don’t need to loop through that result — the variable phone_number will contain only one Tag object, for whichever HTML tag had that ID. To test what the text alone will look like, just print it using get_text() to strip out the tags.

Notice that you’re often using soup. Review above if you’ve forgotten where that came from. (You may use another variable name instead, but soup is the usual choice.)

Finding the contents of a particular attribute¶

One last example from the example page we have been using.

Say you’ve made a BeautifulSoup object from a page that has dozens of images on it. You want to capture the path to each image file on that page (perhaps so that you can download all the images). I would do this in two steps:

  1. First, you make a Python list containing all the img elements that exist in the soup object.

  2. Second, you loop through that list and print the contents of the src attribute from each img tag in the list.

It is possible to condense that code and do the task in two lines, or even one line, but for beginners it is clearer to get the list of elements and name it, then use the named list and get what is wanted from it.

Web Scraping Python Tutorials

Important

We do not need get_text() in this case, because the contents of the src attribute (or any HTML attribute) are nothing but text. There are never tags inside the src attribute. So think about exactly what you’re trying to get, and what is it like inside the HTML of the page.

You can see the code from above all in one file.

There’s a lot more to learn about BeautifulSoup, and we’ll be working with various examples. You can always read the docs. Most of what we do with BeautifulSoup, though, involves these tasks:

  • Find everything with a particular class

  • Find everything with a particular attribute

  • Find everything with a particular HTML tag

  • Find one thing on a page, often using its id attribute

  • Find one thing that’s inside another thing

A BeautifulSoup scraping example¶

To demonstrate the process of thinking through a small scraping project, I made a Jupyter Notebook that shows how I broke down the problem step by step, and tested one thing at a time, to reach the solution I wanted. Open the notebook here on GitHub to follow along and see all the steps. (If that link doesn’t work, try this instead.)

The code in the final cell of the notebook produces this 51-line CSV file by scraping 10 separate web pages.

Python Crawler Library

To run the notebook, you will need to have installed the Requests module and also Jupyter Notebook.

See these instructions for information about how to run Jupyter Notebooks.

Attention

After this introduction, you should NOT use fromurllib.requestimporturlopen or the urlopen() function. Instead, you will use requests as demonstrated in the notebook linked above.

Web Scraping Python Modules Examples

Next steps¶

In the next chapter, we’ll look at how to handle common web scraping projects with BeautifulSoup and Requests.

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