In this post, I\u2019ll share how to use Python for SEO and how to implement the right tools.<\/p>\n
What is Python?<\/h2>\n
Python is a versatile programming language you can use to automate your SEO. While Python itself is not directly related to SEO, you can use it to build tools and scripts that help you in your SEO tasks, including data extraction, analysis & visualization, machine learning, and more.<\/p>\n
Why You Should Learn Python as an SEO<\/h2>\n
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As an SEO, learning Python to understand your data will help you immensely. The reason is, in our digital economy, data is your greatest asset.<\/p>\n
Data is a raw measure of how real people interact with your business. Python can help you find insights from your data. Insights that will help you optimize your site and gain more traffic.<\/p>\n
Strategy starts with insight.<\/p>\n
What\u2019s more, you can use Python to automate repetitive tasks that take you away from your most impactful SEO work. This way as a business owner you have more time to work on new projects or dive deeper into current projects.<\/p>\n
Whatever your goals are, automating repetitive tasks will make you more productive and give you more time to provide value to your clients and prospects.<\/p>\n
The only thing standing in your way is investing the time to learn.<\/p>\n
How to Get Started with Python for SEO: The Big Picture<\/h2>\n
There is more than one approach to getting started with Python as an SEO.<\/p>\n
One option is to look for a course that will give you the basics you need. And believe me, there is no shortage of free and paid resources online that could help you master the basics and take you to an advanced level.<\/p>\n
Another approach that was suggested by the late Hamlet Batista is to study existing code and learn to adapt it to what you need.<\/p>\n
You might take this approach because your goal is to improve your SEO. You don\u2019t need professional programmer levels skills. You just need enough to find insights in your data or to automate repetitive tasks.<\/p>\n
Now, whichever approach you take, your first step is to set up a Python environment.<\/p>\n
Setting Up a Python Environment<\/h2>\n
You must set up the right environment before you can start coding. Choosing the right setup is a personal choice.<\/p>\n
You can install and run Python on your PC or you can run it in the cloud using \u00a0Google Colab<\/a> or Jupyter<\/a> notebooks. Notebooks are environments where you can write and run code.<\/p>\n If you just want to get started without installing anything, your best option is to try out Google Colab. Think of Google Colab as a way to share code that is stored in your Google Drive.<\/p>\n Getting started is pretty simple.<\/p>\n Just go to Google Colab, click File > New notebook<\/b>, and you\u2019re ready to start coding.<\/p>\n This setup is not only fast, but Google Colab mimics Jupyter Notebooks with the advantage of existing on your Google Drive. Also, by using Google Colab, you\u2019ll have access to a host of pre-installed libraries for data science and machine learning.<\/p>\n This means you don\u2019t have to download anything to your machine.<\/p>\n This is possibly the best place to start if you are just getting your feet wet, but know that running all of your code in the cloud has its limitations.<\/p>\n When you encounter those limitations it might be time to run your Python code on your PC.<\/p>\n If you are ready to jump in and you want to run Python on your PC or Mac, the best place to start is to download Anaconda<\/a>.<\/p>\n Anaconda is a free and open-source distributor of both R and Python programming languages for machine learning and data science.<\/p>\n The reason Anaconda is so useful is, when you use Python, you have to use packages that are designed for your specific needs. For instance, you might want to use Python for:<\/p>\n There are specific packages designed for each specific need.<\/p>\n Anaconda allows you to set up separate environments for each of these purposes.<\/p>\n Anaconda will install Conda onto your computer which is both a package manager and also allows you to manage these virtual environments.<\/p>\n This will simplify your overall setup<\/p>\n 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.<\/p>\n Here is how you install Anaconda on your machine:<\/p>\n Once you\u2019ve downloaded it, run the program. This will show you a list of apps you can run. Click Jupyter Notebook and you are ready to start coding.<\/p>\n Whether you\u2019ve chosen to work in the cloud or you\u2019ve downloaded Anaconda to your machine, you are now ready to get started.<\/p>\n To get you started, here are some common Python scripts you can try now.<\/p>\n If you are wondering what are the most common SEO tasks one can do with Python, you\u2019d 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<\/a>. Among many other applications, here are the most typical SEO tasks you can accomplish with Python:<\/p>\n If you ask Realtor.com’s Head of SEO, Konrad Burchardt<\/a>, 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.<\/p>\n 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.<\/p>\n Here are the four main Python tools every programmer can benefit from.<\/p>\n 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\u2019s main characteristics include regression, classification, dimensionality reduction, clustering, preprocessing, and model selection.<\/p>\n Keras is a high-level, open-source neural network library that has been written in Python. Keras is based on a 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n When it comes to learning Python, SEO experts will give varying advice on learning Python for SEO. Implementing a variety of these strategies will help you dive deeper into both SEO and Python.<\/p>\n When it comes to Python, JR Oakes, a Senior Director and Technical SEO<\/a> Researcher at \u2018Locomotive\u2019, 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.<\/p>\n Words of wisdom also come from the owner of \u2018The Media Supermarket\u2019, 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.<\/p>\n 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.<\/p>\n Seth\u2019s 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.<\/p>\n As you\u2019ve seen from this post, there are many ways to get started with Python but the investment is well worth your time.<\/p>\nRunning Python in the Cloud<\/h3>\n
Running Python on Your Local Computer<\/h3>\n
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<\/p>\nSEO Tasks You Can Do with Python<\/h2>\n
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Four Python Tools for Every Developer<\/h2>\n
Scikit-Learn<\/h3>\n
Keras<\/h3>\n
Theano<\/h3>\n
SciPy<\/h3>\n
Learn Python from the Best<\/h2>\n
JR Oakes<\/h3>\n
Elias Dabbas<\/h3>\n
Seth Black<\/h3>\n
The Bottom Line<\/h2>\n