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Python Seo Project

• November 16, 2021
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7 Example Projects to Get Started with Python for SEO

After starting to learn Python late last year, I’ve found myself putting into practice what I’ve been learning more and more for my daily tasks as an SEO ranges from fairly simple tasks such as comparing how things such as word count or status codes have changed over time, to analysis pieces including internal linking and log file addition, Python has been really helpful:For working with large data files that would usually crash Excel and require complex analysis to extract any meaningful Python Can Help With Technical SEOPython empowers SEO professionals in a number of ways due to its ability to automate repetitive, low-level tasks that typically take a lot of time to means we have more time (and energy) to spend on important strategic work and optimization efforts that cannot be also enables us to work more efficiently with large amounts of data in order to make more data-driven decisions, which can in turn provide valuable returns on our work, and our clients’ vertisementContinue Reading BelowIn fact, a study from McKinsey Global Institute found that data-driven organizations were 23 times more likely to acquire customers and six times as likely to retain those ’s also really helpful for backing up any ideas or strategies you have because you can quantify it with the data that you have and make decisions based on that, while also having more leverage power when trying to get things Python to Your SEO WorkflowThe best way to add Python into your workflow is to:Think about what can be automated, especially when performing tedious entify any gaps in the analysis work you are performing, or have completed. I have found that another useful way to get started learning is to use the data you already have access to, and extract valuable insights from it using is how I have learned most of the things I will be sharing in this vertisementContinue Reading BelowLearning Python isn’t necessary in order to become a good SEO pro, but if you’re interested in finding more about how it can help get ready to jump You Need to Get StartedIn order to get the best results from this article there are a few things you will need:Some data from a website (e. g., a crawl of your website, Google Analytics, or Google Search Console data) IDE (Integrated Development Environment) to run code on, for getting started I would recommend Google Colab or Jupyter open mind. This is perhaps the most important thing, don’t be afraid to break something or make mistakes, finding the cause of an issue and ways to fix it is a big part of what we do as SEO professionals, so applying this same mentality to learning Python is helpful to take any pressure off. 1. Trying Out LibrariesA great place to get started is to try out some of the many libraries which are available to use in are a lot of libraries to explore, but three that I find most useful for SEO related tasks are Pandas, Requests, and Beautiful ndasPandas is a Python library used for working with table data, it allows for high-level data manipulation where the key data structure is a Frames are essentially Pandas’ version of an Excel spreadsheet, however, it is not limited to Excel’s row and byte limits and also much faster and therefore efficient compared to best way to get started with Pandas is to take a simple CSV of data, for example, a crawl of your website, and save this within Python as a you have this store you’ll be able to perform a number of different analysis tasks, including aggregating, pivoting, and cleaning vertisementContinue Reading Belowimport pandas as pd
df = ad_csv(“/file_name/and_path”)
df. headRequestsThe next library is called Requests, which is used to make HTTP requests in uses different request methods such as GET and POST to make a request, with the results being stored in example of this in action is a simple GET request of URL, this will print out the status code of a page, which can then be used to create a simple decision-making requests
#Print HTTP response from page
response = (”)
print(response)
#Create decision making function
if atus_code == 200:
print(‘Success! ‘)
elif atus_code == 404:
print(‘Not Found. ‘)You can also use different requests, such as headers, which displays useful information about the page such as the content type and a time limit on how long it took to cache the response. #Print page header response
headers = response. headers
print(headers)
#Extract item from header response
response. headers[‘Content-Type’]There is also the ability to simulate a specific user agent, such as Googlebot, in order to extract the response this specific bot will see when crawling the vertisementContinue Reading Belowheaders = {‘User-Agent’: ‘Mozilla/5. 0 (compatible; Googlebot/2. 1; +)’}
ua_response = (”, headers=headers)
print(ua_response)
Beautiful SoupThe final library is called Beautiful Soup, which is used to extract data from HTML and XML ’s most often used for web scraping as it can transform an HTML document into different Python example, you can take a URL and using Beautiful Soup, together with the Requests library, extract the title of the page. #Beautiful Soup
from bs4 import BeautifulSoup
import requests
#Request URL to extract elements from
url= ”
req = (url)
soup = BeautifulSoup(, “”)
#Print title from webpage
title =
print(title)
Additionally, Beautiful Soup enables you to extract other elements from a page such as all a href links that are found on the link in nd_all(‘a’):
print((‘href’))
2. Segmenting PagesThe first task involves segmenting a website’s pages, which is essentially grouping pages together in categories dependent on their URL structure or page vertisementContinue Reading BelowStart by using simple regex to break the site up into different segments based on their URL:segment_definitions = [
[(r’\/blog\/’), ‘Blog’],
[(r’\/technical-seo-library\/’), ‘Technical SEO Library’],
[(r’\/hangout-library\/’), ‘Hangout Library’],
[(r’\/guides\/’), ‘Guides’], ]
Next, we add a small function that will loop through the list of URLs and assign each URL with a category, before adding these segments to a new column within the DataFrame which contains the original URL e_segment_definitions = True
def segment(url):
if use_segment_definitions == True:
for segment_definition in segment_definitions:
if ndall(segment_definition[0], url):
return segment_definition[1]
return ‘Other’
df[‘segment’] = df[‘url’](lambda x: get_segment(x))
There is also a way to segment pages without having to manually create the segments, using the URL structure. This will grab the folder that is contained after the main domain in order to categorize each vertisementContinue Reading BelowAgain, this will add a new column to our DataFrame with the segment that was get_segment(url):
slug = (r’? :\/\/. *? \//? ([^\/]*)\/’, url)
if slug:
return (1)
else:
return ‘None’
# Add a segment column, and make into a category
3. Redirect RelevancyThis task is something I would have never thought about doing if I wasn’t aware of what was possible using llowing a migration, when redirects were put in place, we wanted to find out if the redirect mapping was accurate by reviewing if the category and depth of each page had changed or remained the vertisementContinue Reading BelowThis involved taking a pre and post-migration crawl of the site and segmenting each page based on their URL structure, as mentioned llowing this I used some simple comparison operators, which are built into Python, to determine if the category and depth for each URL had [‘category_match’] = df[‘old_category’] == (df[‘redirected_category’])
df[‘segment_match’] = df[‘old_segment’] == (df[‘redirected_segment’])
df[‘depth_match’] = df[‘old_count’] == (df[‘redirected_count’])
df[‘depth_difference’] = df[‘old_count’] – (df[‘redirected_count’])
As this is essentially an automated script, it will run through each URL to determine if the category or depth has changed and output the results as a new new DataFrame will include additional columns displaying True if they match, or False if they don’ just like in Excel, the Pandas library enables you to pivot data based on an index from the original vertisementContinue Reading BelowFor example, to get a count of how many URLs had matching categories following the analysis will enable you to review the redirect rules that have been set and identify if there are any categories with a big difference pre and post-migration which might need further investigation. 4. Internal Link AnalysisAnalyzing internal links is important to identify which sections of the site are linked to the most, as well as discover opportunities to improve internal linking across a vertisementContinue Reading BelowIn order to perform this analysis, we only need some columns of data from a web crawl, for example, any metric displaying links in and links out between, we want to segment this data in order to determine the different categories of a website and analyze the linking between ternal_linking_pivot[‘followed_links_in_count’] = (internal_linking_pivot[‘followed_links_in_count’])(‘{:. 1f}’)
internal_linking_pivot[‘links_in_count’] = (internal_linking_pivot2[‘links_in_count’])(‘{:. 1f}’)
internal_linking_pivot[‘links_out_count’] = (internal_linking_pivot[‘links_out_count’])(‘{:. 1f}’)
internal_linking_pivot
Pivot tables are really useful for this analysis, as we can pivot on the category in order to calculate the total number of internal links for vertisementContinue Reading BelowPython also allows us to perform mathematical functions in order to get a count, sum, or mean of any numerical data we have. 5. Log File AnalysisAnother important analysis piece is related to log files, and the data we are able to collect for these in a number of different useful insights you can extract include identifying which areas of a site are crawled the most by Googlebot and monitoring any changes to the number of requests over addition, they can also be used to see how many non-indexable or broken pages are still receiving bot hits in order to address any potential issues with crawl, the easiest way to perform this analysis is to segment the URLs based on the category they sit under and use pivot tables to generate a count, or average, for each vertisementContinue Reading BelowIf you are able to access historic log file data, there is also the possibility to monitor how Google’s visits to your website have changed over are also great visualization libraries available within Python, such as Matplotlib and Seaborn, which allow you to create bar charts or line graphs to plot the raw data into easy to follow charts displaying comparisons or trends over time. 6. Merging DataWith the Pandas library, there is also the ability to combine DataFrames based on a shared column, for example, vertisementContinue Reading BelowSome examples of useful merges for SEO purposes include combining data from a web crawl with conversion data that is collected within Google will take each URL to match upon and display the data from both sources within one rging data in this way helps to provide more insights into top-performing pages, while also identifying pages that are not performing as well as you are vertisementContinue Reading BelowMerge TypesThere are a couple of different ways to merge data in Python, the default is an inner merge where the merge will occur on values that exist in both the left and right ever, you can also perform an outer merge which will return all the rows from the left DataFrame, and all rows from the right DataFrame and match them where vertisementContinue Reading BelowAs well as a right merge, or left merge which will merge all matching rows and keep those that don’t match if they are present in either the right or left merge respectively. 7. Google TrendsThere is also a great library available called PyTrends, which essentially allows you to collect Google Trends data at scale with are several API methods available to extract different types of example is to track search interest over-time for up to 5 keywords at once. Another useful method is to return related queries for a certain topic, this will display a Google Trends score between 0-100, as well as a percentage showing how much interest the keyword has increased over vertisementContinue Reading BelowThis data can be easily added to a Google Sheet document in order to display within a Google Data Studio ConclusionThese projects have helped me to save a lot of time on manual analysis work, while also allowing me to discover even more insights from all of the data that I have access to. I hope this has given you some inspiration for SEO projects you can get started with to kickstart your Python vertisementContinue Reading BelowI’d love to hear how you get on if you decide to try any of these and I’ve included all of the above projects within this Github Resources:How to Predict Content Success with PythonAn Introduction to Natural Language Processing with Python for SEOsAdvanced Technical SEO: A Complete GuideImage CreditsAll screenshots taken by author, December 2020

Using Python scripts to analyse SEO and broken links on your …

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Python is all about automating repetitive tasks, leaving more time for your other Search Engine Optimization (SEO) efforts. Not many SEOs use Python for their problem-solving, even though it could save you a lot of time and effort. Python, for example, can be used for the following tasks:
Data extraction
Preparation
Analysis & visualization
Machine learning
Deep learning
We’ll be focussing mostly on data extraction and analysis in this article. The required modules will be indicated for each script.
Python SEO analyzer
A really useful script for analyzing your website is called ‘SEO analyzer’. It’s an all round website crawler that analyses the following information:
Word count
Page Title
Meta Description
Keywords on-page
Warnings
Missing title
Missing description
Missing image alt-text
This is great for a quick analysis of your basic SEO problems. As page title, meta descriptions and on-page keywords are important ranking factors, this script is perfect for gaining a clear picture of any problems that might be in play.
Using the SEO analyzer
After having installed the necessary modules (BeautifulSoup 4 + urllib2) for this script and having updated your Python to version 3. 4+, you are technically ready to use this script. Json or working variants, however, can be useful for exporting the data you gain from the SEO analyser. After having installed the script, these are the commands you can use:
seoanalyze seoanalyze –sitemap As seen in the examples above, for both
internetvergelijk
and
telefoonvergelijk, it’s possible to either crawl the website, or the XML sitemap of a website in order to do an SEO analysis. Another option is to generate HTML output from the analysis instead of using json. This can be done through the following command:
seoanalyze –output-format-html
If you have installed json and want to export the data, use the following command:
from seoanalyzer import analyse
output = analyse(site, sitemap)
print(output)
You can also choose for an alternative path, running the analysis as a script, as seen in the example below:
This will export the file into a html after having run the –output-format html script.
This seoanalyze script is great for optimizing your page titles, meta descriptions, images and on-page keywords. It’s also a lot faster than Screaming Frog, so if you’re only looking for this information, running the seoanalyze script is more efficient.
Link status analyser
Another way to use Python for Search Engine Optimization is by using a script that crawls your website and analyses your URL status codes. This script is called Pylinkvalidator and can be found here). All it requires is BeautifulSoup if you’re running it with Python 3. x. If you’re running a 2. x version like 2. 6 or 2. 7, you should not need BeautifulSoup.
In order to speed up the crawling, however, it might be useful to install the following libraries:
1) lxml – Speeds up the crawling of HTML pages (requires C libraries)
1) gevent – enables pylinkvalidator to use green threads
1) cchardet – Speeds up document encoding detection
Do keep this in mind, they could be very useful for crawling larger websites, and just to enhance the link status analyser.
What this script essentially does, is crawl the entire URL structure of a website in order to analyse the status codes of each and every URL. This makes it a very long process for bigger websites, hence the recommendation of using the optional libraries to speed this up.
Using the link status analyser
Pylinkvalidator has a ton of different usage options for usage. For example, you can:
Show progress
Crawl the website and pages belonging to another host
Only crawl a single page and the pages it links to
Only crawl links, ignore others (images, stylesheets, etc. )
Crawl a website with more threads or processes than default
Change your user agent
Crawl multiple websites
Check
Crawling body tags and paragraph tags
Showing progress through -P or –progress is recommended, as without it, you will find yourself wondering when your crawl will be done without any visual signs. The command for crawling more threads (– workers=’number of workers’) and processes (– mode=process –workers=’number of workers’) can be very useful as well.
Of course, the script has many more options to explore. The examples below show some of the possible uses:
-p The function above crawls the website and shows progress.
-p workers=4 This function crawls a website with multiple threads and shows progress.
-p –parser=lxml This function uses the lxml library in order to speed up the crawl while showing progress.
-P –types=a The function above only crawls links () on your website, ignoring images, scripts, stylesheets and any other non-link attribute on your website. This is also a useful function when crawling the URLs of large websites.
After the script has run its course, you’ll get a list of URLs with status codes 4xx and 5xx that have been found by crawling your website. Along with that, you’ll gain a list of URLs that link to that page, so you’ll have an easier time fixing the broken links. The regular crawl does not show any 3xx status codes. For more detailed information about what URLs your pages can be reached from, try the following function:
–report-type=all This give information about the status code of a page, and all the other pages that link to a page.
An incredibly useful SEO tool you can use to crawl your website for broken links (404) and server errors. Both of these errors can be bad for your SEO efforts, so be sure to regularly crawl your own website in order to fix these errors ASAP.
Conclusion
While these scripts are incredibly useful, there are a lot of various uses for Python in the world of SEO. Challenge yourself to create scripts that make your SEO efforts more efficient. There are plenty of Python scripts to make your life easier. There’s scripts for checking your hreflang tags, canonicals, and much more. Because who, in today’s day and age, still does stuff manually when it can be automated?

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