Thread by @GiordMarco96

A list of the most useful #Python libraries you can use for #SEO right now. 🐍

This updated thread will tell you the main libraries for #DataScience and #NLP that you should consider. Use them in your workflow! 🧵

Numpy & Pandas: the foundations for data analysis, just learn them.

Without these 2 libraries, you cannot do Data Science at all. Good knowledge of Pandas can get you quite far.

Advertools: the best SEM library out there and for SEO too. It’s very useful for crawling, log file analysis, analyzing SERPs and querying the Knowledge Graph.

The ideal Swiss-knife you need in your arsenal.

Ecommercetools: The ideal package for analyzing eCommerce data and getting access to some useful NLP functions.

It’s a rare jewel in your collection that is very handy for technical SEO and e-commerce as well.

Requests: Make HTTPS requests via Python, essential for web scraping.

Sure, there are alternatives but you should learn them. It's very important and a lot of your initial work will require this library.

urllibb: for working with URLs. It should be part of your arsenal.

Take some time to study all the options and possible use cases.

BeautifulSoup: a library to extract data from HTML/XML files, used in combination with scraping libraries to convert data into Python objects.

One of the first ones you’ll probably learn in your Python journey. You can also try Scrapy as an alternative.

Matplotlib/Seaborn/Plotly: you need some sort of visualization and these libs are here to help you.

You can start with Seaborn which is easier to use. DataViz is an important topic and you should value it.

NLTK/spaCy: work with human language toanalyzee text data and get insightsintot the nuances of our language.

This is necessary to get your hands dirty with text data.

Querycat: an awesome library with few functions but with extreme use thanks to association rule mining and BERT/umap.

It's one of my favorite libraries in general, I love it.

It's useful for visualizing losses in impressions over time.

Sklearn: Another staple for Machine Learning, some models are pretty useful for SEO so understanding the basics is highly beneficial.

I don't think you really need it, it can be extremely beneficial though.

Transformers: Pretrained models to handle a wide range of tasks. Essential for NLP!

This library is essential for the most advanced tasks and quite reliable too. I highly suggest you check my other thread:

https://twitter.com/GiordMarco96/status/1504910522608619521

sentence_transformers: Python framework for state-of-the-art sentence, text and image embeddings.

Use it for keyword clustering and other text-related tasks. It's one of my most used libraries right now.

Trafilatura: download, parse and scrape web page, definitely useful and I have used it quite a lot now!

This is a good library when you don't want to spend too much time on cleaning the HTML elements of a page.

It can be extremely solid for some projects.

Streamlit/Dash: interactive web applications are very useful, needless to say they can boost your communication by a lot.

Learning something like Streamlit is useful in the SEO community because we consider it the standard for web apps.

PyCaret: if you already are on the ML side you already know this, but for those who don't, it's an awesome library that goes from data manipulation to model deployment.

I don't use it quite often but it's extremely powerful and definitely an important library.

searchconsole: Use this library to import your data from the GSC API. It's easy to set up and once you do it there should be no problems.

This is part of an ideal workflow where you query data and then perform some data manipulation. Extremely recommended.

BERTopic: one of my most used NLP libraries and for good reasons. I dedicated an entire thread on the topic:

https://twitter.com/GiordMarco96/status/1492289867908366336

scattertext: library for finding distinguishing terms in corpora and displaying them in an interactive HTML scatter plot.

Short example from the official docs (github.com/JasonKessler/s…).

openpyxl: if you have to work with Excel data and create spreadsheets.

There are other libraries but I prefer to use this one. It's quite nice and it works well for most of the tasks.

In my opinion, you should start with scraping and data analysis. Then, you can move to NLP libraries and check some popular use cases.

Clustering would be one of the most common because it's super common and useful.

Sticking to the mainstream libraries is necessary to get access to "better" documentation. My suggestion is to try alternatives and always look for new opportunities across the web.

Be sure to always do your research, you could find that perfect library for your needs.

Shorter thread today because I was sooooo busy. Like and retweet if you like it, more to come in the next few days!

openai: Technically another super versatile library for NLP tasks.

I am not using it quite often, although it's extremely powerful for content outlines and much more!