How to build a Python web scraping project from scratch?

Python web scraping

Extracting data from websites in bulk manually would be very much difficult. There is a possibility of missing important information from the website if you do it manually, like copy-pasting. Instead of using this technique, you can do web scraping.

Extracting data from websites through automation would take less time than extracting data manually. You will have to create a dataset for learning and research. 

Steps you need to follow to build a Python Web Scraping Project:

Here are a few steps that would help you to make a web scraper project work using Python and the ecosystem of libraries of Python:

1. Select a website and enter your selected object details

First, you need to select a website where you would like to extract information. You can visit and explore different websites to select the best one. Once you are done exploring and have selected a website, you can identify the information you would like to scrap from that URL. You need to decide the CSV file format where you will be uploading all the extracted data.

You need to summarize your web scraped data and outline the strategy you would be using and how you are going to use it for your benefit in a document. 

If you are confused about the web scraping ideas, you can check them in the “Project ideas” section.  

2. Request library would help you to download web pages

If you want to download web pages, you will first have to identify the right URLs and inspect the website’s HTML source. Once you know the URLs, you will be able to down the web pages easily locally. If you want to download several pages in one go, you can use the automation tool. 

You can automate different search queries and downloads, and it would be a more convenient option than looking for different topics manually and then downloading them.

3. Use parse and start extracting information

With the help of Beautiful soup, you can explore and parse the downloaded web pages structure. Once you have downloaded the web pages, you can extract the information you were looking for. You can create a customized function for arranging the extracted data into dictionaries and lists. 

This way, you won’t get confused because the data won’t get mixed up. There is another optional REST API if you want to acquire any additional information from the web pages.

4. Create files for saving extracted data in those files

If you want to get through the data scraping process without any hassle, then you know web scraping would help you in this task. Even in data scraping, you will have to create functions that would do the web scraping within no time, and you won’t have to do anything on your own other than entering the website URL and other minor details. 

You can create an end-to-end function that would help you to process downloading, parsing, and then saving the extracted data into CSVs. You can apply different functions to create a dataset of CSV files. 

You can use Pandas to verify the information in the CSV file whether everything is 100% fine or you need to do some filtering in it. 

5. Save the document and start sharing it

The last step of web scraping is to add a proper heading in the documentation, so whenever you come back and want to work on it, you won’t get confused. You need to do this in the Jupyter notebook. 

Once you are confident enough that you have done web scraping in the right way and now nothing is wrong with that saved document, then you can publish it on your profile. 

If you would like to share your file with the world, then you can write blogs about it. It is an optional thing to do only if you are interested that the world should know about your hard work. Once you are done writing the blog, simply share it online. 

What type of web scraping projects can you start working on?

If you are a beginner and you would like to learn about web scraping and how it would work for your business, then let’s talk about a few random web scraping project ideas:

  • Dataset of movies
  • Dataset of seasons (TV shows)
  • Dataset of songs
  • Scrape a well-known social media website
  • Collect brainy quotes

The Final Words:

We have talked about the step-by-step guide on how you can do web scraping while using Python. For beginners, it would be a great help to those who don’t know much about web scraping and how they need to do it in the right way. You can start with random web scraping project ideas if you are learning about them right now.

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