#Python for SEO

7 sample projects to get started with Python for SEO:

Interested in learning Python then some ways to use it to automate technical SEO and data analysis work.

Python has been really useful for working with large datasets.

For files that typically crash Excel and require complex analysis to extract meaningful information.

Python can help with technical SEO.

Python empowers SEO professionals in several ways due to its ability to automate repetitive, low-level tasks that typically take a long time to accomplish.

Python makes it possible to work more efficiently with large amounts of data in order to make more data-driven decisions, which in turn can provide valuable feedback on the work and work of clients.

Adding Python to the SEO workflow is all about thinking about what can be automated, especially when performing tedious tasks.

Identify gaps in current or completed analytical work.

A useful way to start learning is to use the data that already has access to it and extract valuable information from it using Python.

In order to get the best results things are

Website data (for example, a crawl of your website, Google Analytics, or data from the Google Search Console).

An IDE (Integrated Development Environment) on which to run code, recommending Google Colab or Jupyter Notebook.

1. Libraries to explore that are useful for SEO tasks are Pandas, Requests, and Beautiful Soup.

* Pandas is a Python library used for working with table data.

Panda enables high level data manipulation where the key data structure is a DataFrame.

DataFrames are essentially a Pandas version of an Excel spreadsheet, however, they are not limited to Excel's row and byte limits and are also much faster and therefore efficient compared to Excel.

The best way to get started with Pandas is to take a simple data CSV, for example, a crawl of your website, and save it in Python as a DataFrame.

Perform a number of different analysis tasks, including data aggregation, pivoting, and cleansing.

* Requests, which is used to make HTTP requests in Python.

It uses different query methods such as GET and POST to perform a query with the results stored in Python.

An example of this in action is a simple URL GET request, which will print the status code of a page, which can then be used to create a simple decision-making function.

Using different queries, such as headers, which display useful information about the page, such as content type and a time limit on how long the response is cached.

Beautiful Soup, which is used to extract data from HTML and XML files.

It is most often used for web scraping because it can turn an HTML document into different Python objects.

2. Page segmentation

3. Redirect relevance

4. Analysis of internal links

Internal linking analysis is important for identifying linked sections of the site as well as for uncovering opportunities to improve internal linking on a site.

To perform this analysis, we only need certain columns of data from a web crawl, for example, any metric that shows inbound and outbound links between pages.

5. Analysis of the log file

Another important analysis relates to log files and the data we are able to collect for them in a number of different tools.

Some useful information that can be extracted includes identifying the areas of a site that Googlebot crawls the most and monitoring for any changes in the number of requests over time.

6. Data fusion

7. Google Trends

There is also a great library available called PyTrends, which basically allows large scale Google Trends data collection with Python.

There are several API methods available to extract different types of data.

One example is to track search interest over time for up to 5 keywords at a time.

Google Trends gets a score between 0 and 100, along with a percentage indicating interest that the keyword has increased over time.

This data can be easily added to a Google Sheet document for display in a Google Data Studio dashboard.