Python, one of the most sophisticated programming languages can drastically improve the quality of your SEO.
By abandoning Excel and the use of spreadsheets you can use it to automate the implementation process of machine learning algorithms and leveraging APIs.
Primarily intended to amplify coding readability, Python can come in handy when optimizing a website’s SEO. To show you exactly how this can be done, below is a detailed guide on learning Python for SEO and implementing the right tools along the way.
Python: The Basics
Python is a type of general-purpose programming language that is powerful yet pretty easy to read once you get the hang of it. Because of its specific nature, Python is a great first language choice. In essence, this programming language does it all – from data science and machine learning to web development and coding.
Why Learn Python for SEO?
Learning data science and programming is highly beneficial as is, especially for SEO automation purposes. You can harvest those fruits by putting in a decent effort to make it happen. Furthermore, when it comes to SEO, you will find a few reasons why mastering Python might be an excellent idea for you.
If you manage to achieve this, more traffic will come your way which equals more clicks. Also, you will reserve yourself a spot up the ladder in the field of data engineering, which turns out to be crucial when it comes to building your career.
How to Learn Python for SEO
Reading insights from JR Oakes or Hamlet Batista – two experts in the field – and applying them, is the best way to start learning Python. As many experts share their preferred Python Scripts and Tools, applying their insights can make it easier to learn and adopt various useful tips which will help your SEO.
Hamlet Batista advises beginners that the easiest way to get into Python is to start solving a small problem by finding code that will get you 80% closer to resolving it. After that, run the code one line at a time to understand how it really works and if it works at all. Using a well-crafted tutorial is a great step to achieving this, as it will provide you with the ultimate guidance in solving minor Python challenges.
Orit Mutznik, my dear bestie and a super SEO, said:
Before hearing it from the late Hamlet Batista (The Master), I had no idea that Python programming was a helpful tool for SEOs. I had no idea that Python existed. Seeing him in action, automating things like intent classification, meta tag optimization, and identifying crawler traps with Python has truly opened my eyes and sparked my curiosity. Though I was amazed by his knowledge, I didn’t see myself as someone who could actually “learn such power”. It’s just incredible that Hamlet, with his two decades of experience in SEO and Python, found the time to encourage me and convince me that I actually CAN do this, anyone can, and he’s the reason I onboarded on this journey and I dedicate my newfound knowledge and passion to Hamlet who believed in me.
I’m still learning, but I’ve already built a website from scratch with Python, and I’m working on building keyword research at scale. I’m also planning to challenge myself this year by trying out these amazing code resources for SEO by JC Chouinard, Greg Bernhardt, Charly Wargnier, Koray Tuğberk GÜBÜR, and “the professor” Elias Dabbas.
How to Get Started with SEO and Python
Different SEO experts will give different advice to this question, based on the expert’s personal experience and results. Either way, implementing a variety of expert strategies is the best way to scratch the surface and dive deeper into both SEO and Python.
When it comes to Python, JR Oakes, a Senior Director and Technical SEO Researcher at ‘Locomotive’, suggests the primary task one has to follow through is to master the Beautiful Soup and Pandas libraries. He primarily focuses on this aspect when scraping or manipulating data and executing collection tasks, as the two libraries have shown to be extremely useful over time. In addition to all this, JR Oakes also notes that nearly all coding he does begins with the line import pandas as .pd, simply because it helps save data in one form or another and makes it easier to access down the line. Oakes also indicates that Pandas make writing and reading to CSV extremely plain and simple, meaning it is very suitable for thirsty learners.
Considering these are an expert’s words, all beginners could benefit from implementing these tips as a way to master Python more efficiently.
Words of wisdom also come from the owner of ‘The Media Supermarket’, Elias Dabbas. Namely, he advises beginners to first start learning basic programming before moving on to data manipulation or visualization. Once these aspects are mastered, Python rookies should immerse themselves in statistics as well as detailed machine learning.
The person who created the Python SEO Analyzer, Seth Black, has a different approach to the subject. In terms of what to learn first, he believes that computer science and programming have a chicken-and-egg kind of nature. In other words, to be good at Python, one ought to have a solid foundation in the field of computer science, as well as a firm grip on advanced computer programming.
Seth’s advice for newbies is to automate a simple task or solve a basic problem first. This way, beginners will have a tangible goal ahead, and a learning curve as well. Although problem-solving in programming might be somewhat frustrating, inexperienced programmers will learn to manage essential programming tasks in real-time.
Which Tool to Use for Running the Python Code?
There is no right answer to this question as the Python running tools are abundant and applicable in various aspects. Choosing the best tool, thereafter, will largely depend on the programmer’s personal preference. For example, some experts recommend relying on the Jupyter Notebook when deciding on the best Python code tool. However, if your computer cannot handle the workload, the Google Colab platform is an excellent substitute tool to try, as it basically mimics the Jupyter Notebook concept – but for free and is monitored by Google.
Jupyter lab (notebook) should be perfectly suitable for those working in marketing. Once you master this tool and want to continue building applications, it is advisable to choose one of the recommended IDEs, like PyCharm for example. However, when it comes to general interactive work, the notebook makes a desirable and efficient fit.
Other tools that could help you build your Python knowledge are Spyder (Anaconda), PyCharm, Visual Studio Code, and, as mentioned, Google Colab.
Best Setup for Python Installation
After analyzing SEO expert opinions, there’s one thing all of them have in common – they all recommend Anaconda. The mutual agreement of Anaconda being the best set-up when it comes to initializing Python speaks volumes of this tool’s capabilities. Since this tool could practically help any newcomer master the art of Python, let’s present a brief introduction to Anaconda and the steps required to install it.
What is Anaconda?
To put it in a better perspective, Anaconda is basically free and an open-source distributor of both R and Python programming languages for machine learning and data science.
This amazing tool will help you simplify both the overall deployment of Python and your package management process as well.
Anaconda comes with more than 1500 packages which include the Conda package management system, as well as a GUI, called Anaconda Navigator. This GUI will also install some applications by default. These include Rstudio (for R), Spyder IDE, and Jupyter Notebook.
Without further ado, here are the 10 steps on how to install this helpful tool.
- Go to anaconda.com and download the Windows version
- Download the Windows/Mac version for Python 3
- Double-click on the executable file
- Click ‘next’
- Choose ‘I agree’ to the set terms and conditions
- Select who you want to give Anaconda to
- Select the location for the installation
- Determine the environment variables (If you install Python for the very first time then you should check the add Anaconda to ‘my PATH’ environment. By doing so, you can use Anaconda in the command prompt)
- Click on ‘Next’ and then click ‘Finish’
- Check to see if Python is done installing
SEO Tasks You Can Do with Python
If you are wondering what are the most common SEO tasks one can do with Python, you’d be surprised to know the answer is multi-layered. According to JR Oakes, the most common tasks are forecasting or predicting time series, topic analysis, as well as internal linking. Among many other applications, here are the most typical SEO tasks you can accomplish with Python:
- Learn how to use Github/Git to share your code. Keep in mind that you will most likely feel rewarded if some other people find your code to be of any help.
- Learn how to parse and access JSON from APIs. There are many different types of tool vendors in the space of SEO that includes APIs. Often data delivered from APIs is much better than what you get in the UX product.
- All types of AWS Lambda functions are both very approachable and overall incredible. With them, you can create scheduling processes, as well as develop APIs, which will turn out to be fairly easy and very cost-effective.
- The CRO and SEO Manager at Rise at Seven, Mathew Jones, says that the most common SEO task he takes care of when using Python is most definitely keyword research. Even though this may sound like an extremely simple task type to execute with Python, the ease and speed of accomplishing this task make it a good use of time.
- The second most common task Mathew Jones performs with Python is running technical audits on a variety of websites that he carries out periodically. Mathew has several various scripts which allow him to do so.
If you ask Realtor.com’s Head of SEO, Konrad Burchardt, he will tell you that cleaning data is the most common task he performs using Python. This comes as a result of his realization that the process of cleaning data is quite time-efficient and learning a few tricks here and there can be astoundingly beneficial. The most difficult part is the process of putting the script together. However, once everything is assembled, a programmer will be able to recycle the data and then use it on all kinds of different projects in the future, which comes as a huge plus.
Four Python Tools for Every Developer
It is common knowledge that Python has taken its place among the most popular languages for the Data Science and Developers community. Although many different reasons make Python super-popular, two main reasons predominate:
- Python possesses an extremely simple syntax, which is almost equivalent to mathematical syntax. This is why Python can be easily learned and well understood.
- The language offers a large coverage, featuring both tools and libraries suited for Data Science and scientific computing.
Here are some tools that I believe every programmer in the field of Data Science needs to know to learn Python. After one gains sufficient knowledge and skills, using Python for SEO will be a piece of cake.
Here are the four main Python tools every programmer can benefit from.
Scikit-Learn is a tool primarily designed for Machine Learning and Data Science. It is mostly used by ML Data Scientists, Engineers, and Developers for data analysis and data mining. This tool’s main characteristics include regression, classification, dimensionality reduction, clustering, preprocessing, and model selection.
Keras is a high-level, open-source neural network library that has been written in Python. Keras is based on few core principles such as easy extensibility, modularity, user-friendliness, and working with Python. Since it is written in Python, Keras can run on top of other neural network frameworks such as TensorFlow, Theano, and CNTK.
This is a Python library-designed tool that is mostly used for expressing multi-dimensional arrays. Theano will allow users to optimize, evaluate and define mathematical computations comprising multi-dimensional arrays. This amazing tool possesses some unique features such as transparent use of GPU, tight integration with NumPy, stability and speed optimizations, efficient symbolic differentiation, and extensive unit-testing and dynamic C code generation among many others.
SciPy is a Python-based open-source ecosystem library used for technical and scientific computing. It is extremely used in fields like Engineering, Science, and Mathematics. SciPy can leverage some other Python packages, including Pandas, IPython, or NumPy, to create libraries for science-oriented and common math programming tasks. SciPy is also an excellent tool when it comes to manipulation and management of numbers on a computer as well as generating visualized results.
Learn Python From the Best
The best way of learning something is to learn it from the best. Furthermore, the best ones become such by tons of experience. This way you will reduce your erring process and maximize your efficiency in the language.
Aside from the aforementioned, some of the key experts whose resources you want to keep an eye on, include Ruth Everett, Britney Muller, Charly Wargnier, Kristin Tynski, Natzir Turrado, Hülya Coban, Nacho Mascort, Nick Duddy, Tyler Reardon, Paul Shapiro, Rory Truesdale, Andrea Volpini, and David Sottimano.