Reaching the most potential clients is very important for most startups. In this way, they can generate better leads. One of the easiest ways to have a good clientage is to have as many business email addresses as possible and send them your service details time and again.

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3. Web Scraping By Python Example
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5. Python Web Scraping Pdf

They are many scraping tools present on the internet that provide these services for free, but they have withdrawal data limits. They also offer unlimited data extraction limits, but they are paid. Why pay them when you can build one with your own hands?

This article will demonstrate how easy it is to build a simple web crawler in Python. Although it will be a very simple example but for beginners, it will be a learning experience, especially for those who are new to web scraping. This will be a step-by-step tutorial that will help you get email addresses without any limits.

Scrape a Dynamic Website with Python; Web Scraping with Javascript (NodeJS) Turn Any Website Into An API with AutoScraper and FastAPI; 6 Puppeteer Tricks to Avoid Detection and Make Web Scraping Easier; How to use a proxy in Playwright. Manually Opening a Socket and Sending the HTTP Request. The most basic way to perform. BeautifulSoup is a Python library that is used to pull data of HTML and XML files. It is mainly designed for web scrapping. It works with the parser to provide a natural way of navigating, searching, and modifying the parse tree. The latest version of BeautifulSoup is 4.8.1.

Let’s start with the building process of our intelligent web scraper. I will divide the whole code into different pieces by commenting on what’s going on so that you can get a deeper insight into how the whole process works. I will also share the entire code at the end of the post to fully analyze the whole process.

The incredible amount of data on the Internet is a rich resource for any field of research or personal interest. To effectively harvest that data, you’ll need to become skilled at web scraping. The Python libraries requests and Beautiful Soup are powerful tools for the job. How to Setup the Scraping Project. Our setup is pretty simple. Just create a folder and install Beautiful Soup, pandas, and requests. To create a folder and install the libraries, enter the commands given below. I am assuming that you have already installed Python 3.x. Mkdir scraper pip install beautifulsoup4 pip install requests pip install pandas.

Step 1: Importing Modules

We will be using the following six modules for our project.

The details of the imported modules are given below:

1. re is for regular expression matching.
2. requests for sending HTTP requests.
3. urlsplit for dividing the URLs into component parts.
4. deque is a container that is in the form of a list used for appending and popping on either end.
5. BeautifulSoup for pulling data from HTML files of different web pages.
6. pandas for email formatting into DataFrame and for further operations.

Step 2: Initializing Variables

In this step, we will initialize a deque that will save scraped URLs, unscraped URLs, and a set of saving emails scraped successfully from the websites.

Duplicate elements are not allowed in a set, so they are all unique.

Step 3: Starting the Scraping Process

1. The first step is to distinguish between the scraped and unscraped URLs. The way to do this is to move a URL from unscraped to scraped.

2. The next step is to extract data from different parts of the URL. For this purpose, we will use urlsplit.

urlsplit() returns a 5-tuple: (addressing scheme, network location, path, query, fragment, identifier).

I can’t show sample inputs and outputs for urlsplit() due to confidential reasons, but once you try, the code will ask you to input some value (website address). The output will display the SplitResult(), and inside the SplitResult() there would be five attributes.

This will allow us to get the base and path part for the website URL.

3. This is the time to send the HTTP GET request to the website.

4. For extracting the email addresses we will use the regular experession and then add them to the email set.

Regular expressions are of massive help when you want to extract the information of your own choice. If you are not comfortable with them, you can have a look at Python RegEx for more details.

5. The next step is to find all linked URLs to the website.

The <a href=””> tag indicates a hyperlink that can be used to find all the linked URLs in the document.

Then we will find the new URLs and add them in the unscraped queue if they are not in the scraped nor in the unscraped.

When you try the code on your own, you will notice that not all the links are able to be scraped, so we also need to exclude them,

Step 4: Exporting Emails to a CSV file

To analyze the results in a better way, we will export the emails to the CSV file.

If you are using Google Colab,you can download the file to your local machine by

As already explained, I can’t show the scrapped email addresses due to confidentiality issues.

[Disclaimer! Some websites don’t allow to do web scraping and they have very intelligent bots that can permanently block your IP, so scrape at your own risk.]

Complete Code

Wrapping Up

In this article, we have explored one more wonder of web scraping by showing a practical example of scraping email addresses. We have tried the most intelligent approach by making our web crawler by using Python and its easiest and yet powerful library called BeautfulSoup. Web Scraping can be of massive help if done rightfully considering your requirements. Although we have written a very simple code for scraping email addresses, it is totally free of cost, and also, you don’t need to rely on other services for this. I tried my level best to simplify the code as much as possible and also added room for customization so you optimize it according to your own requirements.

If you are looking for proxy services to use during your scraping projects, don’t forget to look at ProxyScraperesidential and premium proxies.

That was all for this article. See you in the next ones!

The internet has an amazingly wide variety of information for human consumption. But this data is often difficult to access programmatically if it doesn't come in the form of a dedicated REST API. With Python tools like Beautiful Soup, you can scrape and parse this data directly from web pages to use for your projects and applications.

Let's use the example of scraping MIDI data from the internet to train a neural network with Magenta that can generate classic Nintendo-sounding music. In order to do this, we'll need a set of MIDI music from old Nintendo games. Using Beautiful Soup we can get this data from the Video Game Music Archive.

Getting started and setting up dependencies

Before moving on, you will need to make sure you have an up to date version of Python 3 and pip installed. Make sure you create and activate a virtual environment before installing any dependencies.

You'll need to install the Requests library for making HTTP requests to get data from the web page, and Beautiful Soup for parsing through the HTML.

With your virtual environment activated, run the following command in your terminal:

Web Scraping Python Beautifulsoup

We're using Beautiful Soup 4 because it's the latest version and Beautiful Soup 3 is no longer being developed or supported.

Using Requests to scrape data for Beautiful Soup to parse

First let's write some code to grab the HTML from the web page, and look at how we can start parsing through it. The following code will send a GET request to the web page we want, and create a BeautifulSoup object with the HTML from that page:

With this soup object, you can navigate and search through the HTML for data that you want. For example, if you run soup.title after the previous code in a Python shell you'll get the title of the web page. If you run print(soup.get_text()), you will see all of the text on the page.

Getting familiar with Beautiful Soup

The find() and find_all() methods are among the most powerful weapons in your arsenal. soup.find() is great for cases where you know there is only one element you're looking for, such as the body tag. On this page, soup.find(id='banner_ad').text will get you the text from the HTML element for the banner advertisement.

soup.find_all() is the most common method you will be using in your web scraping adventures. Using this you can iterate through all of the hyperlinks on the page and print their URLs:

You can also provide different arguments to find_all, such as regular expressions or tag attributes to filter your search as specifically as you want. You can find lots of cool features in the documentation.

Parsing and navigating HTML with BeautifulSoup

Before writing more code to parse the content that we want, let’s first take a look at the HTML that’s rendered by the browser. Every web page is different, and sometimes getting the right data out of them requires a bit of creativity, pattern recognition, and experimentation.

Our goal is to download a bunch of MIDI files, but there are a lot of duplicate tracks on this webpage as well as remixes of songs. We only want one of each song, and because we ultimately want to use this data to train a neural network to generate accurate Nintendo music, we won't want to train it on user-created remixes.

When you're writing code to parse through a web page, it's usually helpful to use the developer tools available to you in most modern browsers. If you right-click on the element you're interested in, you can inspect the HTML behind that element to figure out how you can programmatically access the data you want.

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Let's use the find_all method to go through all of the links on the page, but use regular expressions to filter through them so we are only getting links that contain MIDI files whose text has no parentheses, which will allow us to exclude all of the duplicates and remixes.

Create a file called nes_midi_scraper.py and add the following code to it:

This will filter through all of the MIDI files that we want on the page, print out the link tag corresponding to them, and then print how many files we filtered.

Run the code in your terminal with the command python nes_midi_scraper.py.

Downloading the MIDI files we want from the webpage

Now that we have working code to iterate through every MIDI file that we want, we have to write code to download all of them.

Web Scraping By Python Example

In nes_midi_scraper.py, add a function to your code called download_track, and call that function for each track in the loop iterating through them:

In this download_track function, we're passing the Beautiful Soup object representing the HTML element of the link to the MIDI file, along with a unique number to use in the filename to avoid possible naming collisions.

Run this code from a directory where you want to save all of the MIDI files, and watch your terminal screen display all 2230 MIDIs that you downloaded (at the time of writing this). This is just one specific practical example of what you can do with Beautiful Soup.

The vast expanse of the World Wide Web

Now that you can programmatically grab things from web pages, you have access to a huge source of data for whatever your projects need. One thing to keep in mind is that changes to a web page’s HTML might break your code, so make sure to keep everything up to date if you're building applications on top of this.

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If you're looking for something to do with the data you just grabbed from the Video Game Music Archive, you can try using Python libraries like Mido to work with MIDI data to clean it up, or use Magenta to train a neural network with it or have fun building a phone number people can call to hear Nintendo music.

I’m looking forward to seeing what you build. Feel free to reach out and share your experiences or ask any questions.

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