This is a minimalistic Instagram scraper written in Python. It can fetch media, accounts, videos, comments etc. `Comment` and `Like` actions are also supported. It is not easy to get Applications approved for Instagram's API therefore I created this tool inspired by instagram-php-scraper. Python social-media pinterest web-scraper web-scraping request instagram-scraper twitter-scraper facebook-scraper scrapping-python selenium-python reddit-scraper quora-scraper tiktok-scraper medium-scraper pinterest-scrapper.
- What this does: Scrapes pages to get alt tags and page titles, and saves as CSV
- Requires: Python Anaconda distribution, basic knowledge of Pandas and HTML structure
- Concepts covered: Basic scraper with BeautifulSoup, Scrape multiple pages, Loops, Export to CSV
- Download the entire Python file
Python has a lot of great uses for marketers, and one of the coolest and most practical tools is a web scraper.
There are many situations where you may need to collect data quickly from a website and save into a usable format. One example is getting image alt or title attributes, which have value for SEO purposes.
In this post, we’ll create a simple web scraper in Python that will collect the alt attributes of images and the title of the page on which they appear.
The scraper uses a library called BeautifulSoup. For a full tutorial on using BeautifulSoup, I’d recommend this tutorial, which provides a really great explanation of how it works.
Your web scraper will parse the HTML to pick out the relevant pieces of information and filter that content for specific words. You can scrape any site on the Internet that you can look at, but the difficulty of doing so depends on the site. This tutorial offers you an introduction to web scraping to help you understand the overall process. Thanks to Node.js, JavaScript is a great language to u se for a web scraper: not only is Node fast, but you’ll likely end up using a lot of the same methods you’re used to from querying the.
Getting started
First, we’ll import our libraries.
Next, we’ll generate the CSV file.
Next, we’ll define the URLs we want to scrape in a list.
Then, we’ll create a blank dataframe.
Conceptualizing data scraping
Our end goal for the data is to have two columns. The first column will have the page name and the second column will have the alt attribute. So, it should look a little something like this:
| pagename | alt |
| Blog Home | Computer screen |
| Blog Home | Pie chart |
| Portfolio | Mountains |
| Portfolio | Lake |
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Nfsu2 download free. full version. So, we can conceptualize the scraping process like this:
Scraping with BeautifulSoup
Because we’re going to be scraping multiple URLs, we’ll need to create a loop to repeat the steps for each page. Be sure to pay attention to the indents in the code (or download the .py file).
For the page title, we’ll want to scrape the H1 tag. We’ll use the find() function to find the H1 tag. We’ll print that information and also store it as a variable for a later step.
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Next, we’ll scrape the images and collect the alt attributes. Because some images like the logo are repeated on every page, I don’t want to scrape these. Instead, I’ll use .find_all() and only return images with the class “content-header”. Once it finds the images, we’ll print the alt attributes.
Because there may be multiple images on the page, we’ll have to create another loop within the larger loop.
Here comes the cool part. We’ll create a variable defined as the alt attribute. Using this and the variable for the H1 tag we created earlier, we’ll couple these and append them to the dataframe. This step will be repeated each time the loop runs, so for every image on the page with the content header class.
Finally, we’ll save our dataframe to a CSV file.
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