8 Useful Python Libraries for SEO and How to Use Them
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8 Useful Python Libraries for SEO and How to Use Them
Presented By
Black Pearl Matrix
8 Python Libraries That Can Help You With Your Business’ SEO:
- Beautiful Soup
- Requests
- Selenium
- Google Analytics API
- Mozscape API
- Pandas
- Matplotlib
- Scikit-learn
Python is one of the languages of the moment in the world of programming. However, lately, we have seen that technology is invading other areas as well.
Also, the fact that one of the applications is, precisely, artificial intelligence means that companies are already learning to adopt the language to optimize decision-making and obtain results aligned with the company’s objectives.
This language is being heavily used in order to solve several problems for companies in the web scenario, whether in the development of systems that run on the Internet, or in strategies to optimize pages for search results.
In this sense, there is an extensive universe that specifically explores the use of Python for SEO.
Thus, it is possible to improve Marketing and improve the strategy for attracting and winning customers.
1- What is Python?
Python is a programming language that was created by mathematician Guido Van Rossum and officially released in 1991.
It is an open source technology, with a huge community of developers, which can be multiparadigm, that is, functional, structured or object-oriented. For this reason, it is considered a general purpose language.
Its versatility is a consequence of two main facts: the first is that it is cross-platform, that is, it works well in different ecosystems.
The second is that this programming pattern involves a huge library of functions, classes and patterns that can be used for different purposes. We will see, later on, what exactly are the applications of the language.
However, it is not only a technology for writing codes, but also carries a development philosophy — which, in a way, explains its success.
This set of ideas concentrates some principles, such as preference for simplicity, focus on code readability, clarity and practicality.
2- What is the Potential of Python Currently?
From the point of view of systems used for a specific purpose, Python stands out as one of the most interesting options.
If a professional from another area enters the world of programming precisely with this language, he will have less difficulty than he would with other standards.
Furthermore, the feedback is very interesting, as this programming pattern is applicable in different contexts and scenarios. Thus, it actively cooperates with different sectors of an organization.
Machine Learning and Deep Learning
In this sense, one of the great possibilities is the use of Python for artificial intelligence applications, as well as its subareas: Machine Learning and Deep Learning.
Crossing this subject, it is also worth mentioning the use of technology for processing Big Data and purposes related to decision-making based on data, in general.
This is possible due to its ease of use and support for processing large databases. There are a multitude of libraries and standards in the language aimed at numerical calculation, data structure and statistical analysis.
In this way, you have a wide range of possibilities with technology: regression, machine learning algorithms, decision trees, among other techniques.
All of them allow navigating between Big Data and extracting useful information by crossing this data and identifying patterns.
In practice, these strategies can become natural language processing, for example. Systems analyze texts and decompose this information in order to try to extract meaning from them.
Thus, it is possible to identify the feeling that was exposed in a text, in the same way that a human being would identify.
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Automation
In this sense, it is possible to notice that the main focus here is automation. The power of Python allows you to delegate some important and fundamental functions for machines to solve and learn from them.
With the evolution of algorithms, it is possible to obtain increasingly better results with these functions.
In this way, humans can focus on other tasks, such as more strategic issues that require a little more creativity.
Libraries and Data Visualization
The language can also be applied for data extraction, scientific computing, information analysis and preparation, among others.
For Machine Learning questions, some useful tools are: TensorFlow, Scikit-learn library, SpaCy and Panda. For scientific computing, we have SciPy and NumPy.
Also, it is a good tool for transforming data into easier visualization forms.
It is common to use this technology to create reports and plot graphs and diagrams that help to understand the information and extract value from it. This approach can be used to optimize campaigns, for example.
Web Programming
However, we also have Python application for web programming. Frameworks, such as Django, have become an interesting option to apply the principles of the language in this type of development, both in the front-end and in the back-end.
With its simplicity and versatility, you can drive results, facilitate testing, manage interactivity, as well as manage a greater amount of data.
On the web, Django helps with formatting and laying out information, authenticating data, as well as validating information in forms, for example.
Integration with Other Languages
Another advantage is the integration with other more common languages, such as Java. This makes Python powerful and used in different contexts, which further contributes to its versatility.
As we talk about the use of Python for automating tasks, we arrive, then, at the fundamentals of applying the language for SEO activities.
This is what we will explore in more detail in the next section.
3- What are the Possibilities of Python and SEO?
Initially, we can summarize the relationship between Python and SEO with two purposes: analyzing large amounts of data without susceptibility to errors, and automating tasks that leave professionals free for other issues, as we have already mentioned.
We will see, below, the main applications of this language for this universe.
URL Mapping
When a site is migrated to another address, it is common for the links from the old address to break and direct the user to an error.
However, the famous technology can be used for mapping URLs in these situations, correlating certain old links with new addresses.
Even if it is possible to remove these references from Google’s search pages, it is still important to carry out this mapping to avoid any constraints and business losses.
With Python, site administrators can do this in a practical and automated way, managing even large numbers of pages without major difficulties.
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Internal Link Analysis
If the objective is to conduct a survey of internal links, technology can also be useful.
With it, it is possible to analyze the number of links, check if there are broken links, which are the links to the image without an alt tag, as well as a relationship with the types of links — whether they are internal, external or to some subdomain.
That is, it is possible to obtain a general mapping of the site’s link building work, in order to outline improvement strategies.
On a large website, this is practically impossible to do manually. Therefore, the tool helps a lot and offers support, quickly and dynamically.
Keyword Search
Digital Marketing relies heavily on keywords. So why not use programming in that function as well?
It is possible to do a search for keywords relevant to your persona, as well as the most valuable ones on the market, in order to create specific content for ranking on Google.
Furthermore, the language allows you to categorize the thousands of existing keywords by intent, in order to improve the strategy and offer a complete view.
This categorization, made with Deep Learning, allows you to understand the purpose of each keyword and how the site can best match it.
As we know, there are four types of search: informational, transactional, navigational and for commercial investigation.
Understanding how keywords align within these groups helps companies better understand customer preferences. In addition, it is also interesting to analyze the current SERPs and which companies are already managing to rank better for each group.
Image Optimization
In digital strategies, performance is an important indicator.
When working with websites and Digital Marketing, it is essential to stipulate metrics to monitor the website loading speed and understand how this affects the user experience.
For this, administrators can use Python to optimize images and reduce their size without sacrificing quality.
Keyword Tracking
If the focus is on tracking the performance of keywords, technology can also be useful. It is possible to monitor their performance and obtain data to work on content improvements and improve positioning on search pages.
Performance Improvement
Overall, the responsible team is also able to optimize the site’s performance using Python tools.
With them, there is the possibility of analyzing each page and identifying factors that are hampering the loading speed and interfering with the experience.
You can track each one and get actionable insights for improvements.
Data extraction
The practice of web scraping is another factor that can be automated. It is the extraction and mining of data from websites for various purposes, such as understanding the user, gaining competitive advantage and others.
Libraries and frameworks favor working with a large amount of data, following the already mentioned principles of analyzing them in search of useful information.
4- Why Are Python Libraries Useful For SEO?
Each Python library contains functions and variables of all kinds (arrays, dictionaries, objects, etc.) that can be used to perform different tasks.
For SEO, for example, they can be used to automate certain things, predict results and provide intelligent insights.
It’s possible to just work with vanilla Python, but libraries can be used to make tasks much easier and faster to write and complete.
5 – Python Libraries For SEO
1. BeautifulSoup
Is a library to extract data from HTML and XML pages. It can be useful for scraping website data for SEO analysis, such as collecting external links or keywords on a page.
2. Requests
Is a library for making HTTP requests in Python. You can use it to make requests to websites and collect data for SEO analysis, such as a page’s status code or page content.
3. Selenium
Is a library for automating web browsers. You can use it to simulate user actions like clicking links or filling out forms, or to collect data from dynamic pages that can be difficult to scrape with other tools.
4. Google Analytics API
Is a library for programmatically accessing Google Analytics data. You can use it to collect traffic and user behavior data from your website for SEO analysis.
5. Mozscape API
Is a library for accessing data from Moz’s SEO tool. You can use it to collect data on links, keywords and domain authority for SEO analysis.
6. Pandas
Is a library for data analysis in Python. You can use it to manipulate and analyze large sets of SEO data, such as keyword ranking history or website traffic.
7. Matplotlib
Is a library for creating graphs in Python. You can use it to create graphs and visualizations of SEO data, such as line graphs of website traffic or keyword ranking bar graphs.
8. Scikit-learn
Is a library for machine learning in Python. You can use it to build machine learning models to predict SEO performance like keyword ranking or site traffic.
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FAQ - Python Libraries FAQ for SEO
1. What are Python libraries for SEO?
These are tools written in Python that can be used to collect, analyze and visualize SEO data programmatically. Some examples include Beautiful Soup for data scraping, requests for making HTTP requests, and Pandas for data manipulation.
2. What is the advantage of using Python libraries for SEO compared to other tools?
One of the main advantages is flexibility. With Python, you have full control over the data collection and analysis process, and you can create custom scripts to meet your specific needs. Plus, many of these libraries are free and open source, which means you can use them without spending money or worrying about licenses.
3. How can I learn to use Python libraries for SEO?
There are many resources available for learning how to use Python libraries for SEO. Some places to start include: Library documentation; Online tutorials and guides; Online courses; Books.
4. What are some examples of SEO tasks I can do with Python libraries?
Some examples of SEO tasks you can perform with Python libraries include: Scraping site data to gather information about links, keywords, and other aspects of SEO; Analyzing Google Analytics data to understand your website traffic and user behavior; Monitoring keyword rankings using APIs from SEO tools like Mozscape.
Conclusion
As we have seen, there is great potential for using Python for SEO. Language is one of the most prominent technologies of the moment, precisely because of its speed, versatility, clarity and ease of learning.
In addition, it can be used for Machine Learning and Deep Learning applications, as well as automating SEO analysis, checking for broken links, among others.
Thus, it is an important technology to use in Marketing, especially in the digital world. Companies can identify and mitigate common mistakes and ensure better results.
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