A guide for automating your SEO with Python

A guide for automating your SEO with Python


Digital marketing has taken every industry by storm, and with the current technological revolution, only businesses with automated systems will survive in the future.

Search engine optimization (SEO) can be a daunting aspect of any digital marketing strategy, but with the help of programming languages such as Python, your work is cut out for you through automation.

This guide provides all there is to know about Python SEO automation.

What is Python?

Python is an interactive and object-oriented programming language that supports compound programming models such as functional and procedural programming. Additionally, it incorporates dynamic typing, exceptions, modules, classes, and active data types.

The language’s syntax (rules) are excellently clear with a remarkable power combination as it is extensible in both C and C++ with diverse system calls and library interfaces. Moreover, it can run on voluminous UNIX alternates like Windows, macOS, and Linux.

Python backs some of the world’s most popular applications from leading companies including Google, Microsoft and IBM.

What are the capabilities of Python?

Machine learning

With the increasing need for data synthesis, there is an appealing need for machine learning models to help find and execute data patterns. Programming languages such as Python are critical foundations of artificial intelligence.

Python can execute all sorts of data and data sets by easing a computer’s data processing mechanisms. This way, a computer program can calculate outcomes such as standard deviation, variance, mean, median, mode, percentiles, symbol interpretation, and data distribution.

Additionally, you can use Python string concatenation capabilities to create tables, databases, collect, insert into a table, select from a table, join tables, delete tables, insert a document, filter, limit and sort out the results, delete records, drop and update collections.

Furthermore, Python has a dictionary, tuple, built-in set methods, keywords developers, built-in exceptions, glossary, random requests, statistics, math, and math modules.  All these features are essential to programmers in machine learning.

Automating SEO

You can easily automate processes like SEO with Python while using crontab on Windows Task Scheduler or Mac. Crontab to run your Python scripts automatically. With this language, Code editor (vscode), and macOS, automating scripts has never been easier. Python automatically excerpts records from Google Search Console and Google Analytics on a specified pattern.

Additionally, you can create an auto-send program for your weekly email reports or research and collect daily keywords.

Python also allows setting up of an automated python scheduler script if you input the following instructions on the crontab.

  • Press I to switch to the insert mode so that you can edit the file.
  • Press ***** /usr/bin/pyth to execute the python script.
  • Press ESC and exit the editing mode and then save and quit the file by typing:wq
  • You will receive a message: crontab: installing new crontab, meaning that you have created a new crontab.

By now, you will have the test.txt file in the folder that continuously adds time every minute.

Web programming

This is one of Python’s strongholds whereby it offers a great collection of frameworks for programmers. Some of the most popular ones that rely on this programming language include Pyramid, Django, bottle.py, CherryPy, web2py, and Flask. These frameworks power popular sites such as Reddit, Mozilla, Yelp, and Spotify.

Additionally, programmers use Python to integrate back-end code with front-end libraries and framework, testing and mocking containerized web applications, design APIs, social authentication and caching to improve performance.

Data visualization

You can use Python to sort out vast chunks of data into visual output that can be used in statistics. Data is uploaded into a workspace and used to plot charts and graphs by comparing the lowest to the highest values. The most common visualizations used are histograms, heat maps, bar and stack-bar charts, scatter plots, correlograms, and area charts.

Top Python data visualization libraries are Plotly, matplotlib, ggplot, Altair, Leather, Bokeh, geoplotlib, and Seaborn. matplotlib is the most widely used library in Python for plotting.

Python and SEO automation

As earlier mentioned, Python is a real deal when it comes to SEO automation. Digital marketers use this programming language to carry out the following SEO practices.

Implementation

As an SEO marketer, you can never run away from SEO implementation. Fortunately, Python has software such as RankSense, an automation software that makes it easy to implement up to three strategies simultaneously. This can come in super handy in the overall SEO process.

Human developers can be prone to making mistakes that significantly affect a site’s SEO. This is where RankSense comes in to notify you when such errors occur so you can correct them quickly and avoid hitches that could affect traffic.

Response code analysis

Links are one of the standard signals used by most search engines. Having a tool like Pylinkvalidator which is founded on Python will be essential in checking URL status codes and making sure that all pages are connected directly to your site. Additionally, it identifies and fixes broken pages on the site.

You may need optional libraries when setting up Pylinkvalidator on a large site to increase the speed of site validation.

SEO analysis

Another Python-based software that will prove handy in your digital marketing process is SEO Analyser. This tool identifies multiple critical features of the content on your site. The program is coded to answer the following questions:

  • Is the data structure appealing?
  • Is the title tag suitable?
  • Is the meta description eye-catching?
  • Is there excess repetition of phrases?
  • What size of data does your page hold?

Python SEO analyzer speedily analyzes and answers all these questions, enhancing the organic performance of your site.

XML sitemaps

These are maps that guide search engine crawlers to all pages that you’d like Google to read and rank on your site. The tool is essential if you have a large website.

Python makes indexing and crawling processes easier by creating custom XML sitemaps that bring together pages considered more important in your site before transferring them to Google Search Console.

Visibility benchmarking

Visibility benchmarking compares the site’s present-day visibility against the competitors before concluding the differences in content analysis

Sources of data such as BrightEdge Data Cube and SEMrush pull your site’s data and arrange them into non-branded and branded keywords. The complexity of your information increases as you add more categories, competitors, and non-branded keywords.

Fortunately, you can use Python scripts to automate and analyze sites with similar keywords. This is important if you’re to dominate your competition and rank higher for your profitable keywords.

Are you using Python to automate any of your SEO processes? We’d like to hear your feedback in the comments below!