How To Do A Sitemap Audit For Higher Indexing & Crawling Through Python

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Sitemap auditing entails syntax, crawlability, and indexation checks for the URLs and tags in your sitemap recordsdata.

A sitemap file accommodates the URLs to index with additional data concerning the final modification date, precedence of the URL, photographs, movies on the URL, and different language alternates of the URL, together with the change frequency.

Sitemap index recordsdata can contain hundreds of thousands of URLs, even when a single sitemap can solely contain 50,000 URLs on the prime.

Auditing these URLs for higher indexation and crawling may take time.

However with the assistance of Python and website positioning automation, it’s doable to audit hundreds of thousands of URLs inside the sitemaps.

What Do You Want To Carry out A Sitemap Audit With Python?

To grasp the Python Sitemap Audit course of, you’ll want:

  • A basic understanding of technical website positioning and sitemap XML recordsdata.
  • Working data of Python and sitemap XML syntax.
  • The power to work with Python Libraries, Pandas, Advertools, LXML, Requests, and XPath Selectors.

Which URLs Ought to Be In The Sitemap?

A wholesome sitemap XML sitemap file ought to embody the next standards:

  • All URLs ought to have a 200 Standing Code.
  • All URLs ought to be self-canonical.
  • URLs ought to be open to being listed and crawled.
  • URLs shouldn’t be duplicated.
  • URLs shouldn’t be mushy 404s.
  • The sitemap ought to have a correct XML syntax.
  • The URLs within the sitemap ought to have an aligning canonical with Open Graph and Twitter Card URLs.
  • The sitemap ought to have lower than 50.000 URLs and a 50 MB dimension.

What Are The Advantages Of A Wholesome XML Sitemap File?

Smaller sitemaps are higher than bigger sitemaps for quicker indexation. That is notably vital in Information website positioning, as smaller sitemaps assist for growing the general legitimate listed URL rely.

Differentiate ceaselessly up to date and static content material URLs from one another to offer a greater crawling distribution among the many URLs.

Utilizing the “lastmod” date in an trustworthy approach that aligns with the precise publication or replace date helps a search engine to belief the date of the newest publication.

Whereas performing the Sitemap Audit for higher indexing, crawling, and search engine communication with Python, the factors above are adopted.

An Necessary Be aware…

Relating to a sitemap’s nature and audit, Google and Microsoft Bing don’t use “changefreq” for altering frequency of the URLs and “precedence” to know the prominence of a URL. The truth is, they name it a “bag of noise.”

Nonetheless, Yandex and Baidu use all these tags to know the web site’s traits.

A 16-Step Sitemap Audit For website positioning With Python

A sitemap audit can contain content material categorization, site-tree, or topicality and content material traits.

Nonetheless, a sitemap audit for higher indexing and crawlability primarily entails technical website positioning slightly than content material traits.

On this step-by-step sitemap audit course of, we’ll use Python to sort out the technical elements of sitemap auditing hundreds of thousands of URLs.

Picture created by the creator, February 2022

1. Import The Python Libraries For Your Sitemap Audit

The next code block is to import the required Python Libraries for the Sitemap XML File audit.

import advertools as adv

import pandas as pd

from lxml import etree

from IPython.core.show import show, HTML

show(HTML("<model>.container { width:100% !vital; }</model>"))

Right here’s what it is advisable to learn about this code block:

  • Advertools is critical for taking the URLs from the sitemap file and making a request for taking their content material or the response standing codes.
  • “Pandas” is critical for aggregating and manipulating the information.
  • Plotly is critical for the visualization of the sitemap audit output.
  • LXML is critical for the syntax audit of the sitemap XML file.
  • IPython is optionally available to develop the output cells of Jupyter Pocket book to 100% width.

2. Take All Of The URLs From The Sitemap

Tens of millions of URLs may be taken right into a Pandas knowledge body with Advertools, as proven beneath.

sitemap_url = "https://www.complaintsboard.com/sitemap.xml"
sitemap = adv.sitemap_to_df(sitemap_url)
sitemap.to_csv("sitemap.csv")
sitemap_df = pd.read_csv("sitemap.csv", index_col=False)
sitemap_df.drop(columns=["Unnamed: 0"], inplace=True)
sitemap_df

Above, the Complaintsboard.com sitemap has been taken right into a Pandas knowledge body, and you’ll see the output beneath.

Sitemap URL ExtractionA Common Sitemap URL Extraction with Sitemap Tags with Python is above.

In complete, now we have 245,691 URLs within the sitemap index file of Complaintsboard.com.

The web site makes use of “changefreq,” “lastmod,” and “precedence” with an inconsistency.

3. Verify Tag Utilization Inside The Sitemap XML File

To grasp which tags are used or not inside the Sitemap XML file, use the operate beneath.

def check_sitemap_tag_usage(sitemap):
     lastmod = sitemap["lastmod"].isna().value_counts()
     precedence = sitemap["priority"].isna().value_counts()
     changefreq = sitemap["changefreq"].isna().value_counts()
     lastmod_perc = sitemap["lastmod"].isna().value_counts(normalize = True) * 100
     priority_perc = sitemap["priority"].isna().value_counts(normalize = True) * 100
     changefreq_perc = sitemap["changefreq"].isna().value_counts(normalize = True) * 100
     sitemap_tag_usage_df = pd.DataFrame(knowledge={"lastmod":lastmod,
     "precedence":precedence,
     "changefreq":changefreq,
     "lastmod_perc": lastmod_perc,
     "priority_perc": priority_perc,
     "changefreq_perc": changefreq_perc})
     return sitemap_tag_usage_df.astype(int)

The operate check_sitemap_tag_usage is an information body constructor primarily based on the utilization of the sitemap tags.

It takes the “lastmod,” “precedence,” and “changefreq” columns by implementing “isna()” and “value_counts()” strategies through “pd.DataFrame”.

Under, you may see the output.

Sitemap Tag AuditSitemap Audit with Python for sitemap tags’ utilization.

The information body above reveals that 96,840 of the URLs don’t have the Lastmod tag, which is the same as 39% of the full URL rely of the sitemap file.

The identical utilization proportion is nineteen% for the “precedence” and the “changefreq” inside the sitemap XML file.

There are three important content material freshness indicators from a web site.

These are dates from an internet web page (seen to the person), structured knowledge (invisible to the person), “lastmod” within the sitemap.

If these dates usually are not in keeping with one another, search engines like google can ignore the dates on the web sites to see their freshness indicators.

4. Audit The Website-tree And URL Construction Of The Web site

Understanding an important or crowded URL Path is critical to weigh the web site’s website positioning efforts or technical website positioning Audits.

A single enchancment for Technical website positioning can profit 1000’s of URLs concurrently, which creates a cheap and budget-friendly website positioning technique.

URL Construction Understanding primarily focuses on the web site’s extra distinguished sections and content material community evaluation understanding.

To create a URL Tree Dataframe from a web site’s URLs from the sitemap, use the next code block.

sitemap_url_df = adv.url_to_df(sitemap_df["loc"])
sitemap_url_df

With the assistance of “urllib” or the “advertools” as above, you may simply parse the URLs inside the sitemap into an information body.

Python sitemap auditMaking a URL Tree with URLLib or Advertools is simple.
Checking the URL breakdowns helps to know the general data tree of a web site.

The information body above accommodates the “scheme,” “netloc,” “path,” and each “/” breakdown inside the URLs as a “dir” which represents the listing.

Auditing the URL construction of the web site is distinguished for 2 targets.

These are checking whether or not all URLs have “HTTPS” and understanding the content material community of the web site.

Content material evaluation with sitemap recordsdata just isn’t the subject of the “Indexing and Crawling” instantly, thus on the finish of the article, we are going to discuss it barely.

Verify the subsequent part to see the SSL Utilization on Sitemap URLs.

5. Verify The HTTPS Utilization On The URLs Inside Sitemap

Use the next code block to examine the HTTP Utilization ratio for the URLs inside the Sitemap.

sitemap_url_df["scheme"].value_counts().to_frame()

The code block above makes use of a easy knowledge filtration for the “scheme” column which accommodates the URLs’ HTTPS Protocol data.

utilizing the “value_counts” we see that every one URLs are on the HTTPS.

Python https scheme columnChecking the HTTP URLs from the Sitemaps may also help to search out larger URL Property consistency errors.

6. Verify The Robots.txt Disallow Instructions For Crawlability

The construction of URLs inside the sitemap is useful to see whether or not there’s a state of affairs for “submitted however disallowed”.

To see whether or not there’s a robots.txt file of the web site, use the code block beneath.

import requests
r = requests.get("https://www.complaintsboard.com/robots.txt")
R.status_code
200

Merely, we ship a “get request” to the robots.txt URL.

If the response standing code is 200, it means there’s a robots.txt file for the user-agent-based crawling management.

After checking the “robots.txt” existence, we will use the “adv.robotstxt_test” methodology for bulk robots.txt audit for crawlability of the URLs within the sitemap.

sitemap_df_robotstxt_check = adv.robotstxt_test("https://www.complaintsboard.com/robots.txt", urls=sitemap_df["loc"], user_agents=["*"])
sitemap_df_robotstxt_check["can_fetch"].value_counts()

We’ve created a brand new variable known as “sitemap_df_robotstxt_check”, and assigned the output of the “robotstxt_test” methodology.

We’ve used the URLs inside the sitemap with the “sitemap_df[“loc”]”.

We’ve carried out the audit for all the user-agents through the “user_agents = [“*”]” parameter and worth pair.

You may see the outcome beneath.

True     245690
False         1
Title: can_fetch, dtype: int64

It reveals that there’s one URL that’s disallowed however submitted.

We are able to filter the precise URL as beneath.

pd.set_option("show.max_colwidth",255)
sitemap_df_robotstxt_check[sitemap_df_robotstxt_check["can_fetch"] == False]

We’ve used “set_option” to develop all the values inside the “url_path” part.

Python Sitemap Audit Robots TXT CheckA URL seems as disallowed however submitted through a sitemap as in Google Search Console Protection Experiences.
We see {that a} “profile” web page has been disallowed and submitted.

Later, the identical management may be accomplished for additional examinations corresponding to “disallowed however internally linked”.

However, to do this, we have to crawl at the very least 3 million URLs from ComplaintsBoard.com, and it may be a wholly new information.

Some web site URLs don’t have a correct “listing hierarchy”, which might make the evaluation of the URLs, when it comes to content material community traits, more durable.

Complaintsboard.com doesn’t use a correct URL construction and taxonomy, so analyzing the web site construction just isn’t simple for an website positioning or Search Engine.

However essentially the most used phrases inside the URLs or the content material replace frequency can sign which subject the corporate truly weighs on.

Since we concentrate on “technical elements” on this tutorial, you may learn the Sitemap Content material Audit right here.

7. Verify The Standing Code Of The Sitemap URLs With Python

Each URL inside the sitemap has to have a 200 Standing Code.

A crawl must be carried out to examine the standing codes of the URLs inside the sitemap.

However, because it’s pricey when you might have hundreds of thousands of URLs to audit, we will merely use a brand new crawling methodology from Advertools.

With out taking the response physique, we will crawl simply the response headers of the URLs inside the sitemap.

It’s helpful to lower the crawl time for auditing doable robots, indexing, and canonical indicators from the response headers.

To carry out a response header crawl, use the “adv.crawl_headers” methodology.

adv.crawl_headers(sitemap_df["loc"], output_file="sitemap_df_header.jl")
df_headers = pd.read_json("sitemap_df_header.jl", traces=True)
df_headers["status"].value_counts()

The reason of the code block for checking the URLs’ standing codes inside the Sitemap XML Information for the Technical website positioning facet may be seen beneath.

200    207866
404        23
Title: standing, dtype: int64

It reveals that the 23 URL from the sitemap is definitely 404.

And, they need to be faraway from the sitemap.

To audit which URLs from the sitemap are 404, use the filtration methodology beneath from Pandas.

df_headers[df_headers["status"] == 404]

The outcome may be seen beneath.

Python Sitemap Audit for URL Status CodeDiscovering the 404 URLs from Sitemaps is useful in opposition to Hyperlink Rot.

8. Verify The Canonicalization From Response Headers

On occasion, utilizing canonicalization hints on the response headers is useful for crawling and indexing sign consolidation.

On this context, the canonical tag on the HTML and the response header must be the identical.

If there are two completely different canonicalization indicators on an internet web page, the various search engines can ignore each assignments.

For ComplaintsBoard.com, we don’t have a canonical response header.

  • Step one is auditing whether or not the response header for canonical utilization exists.
  • The second step is evaluating the response header canonical worth to the HTML canonical worth if it exists.
  • The third step is checking whether or not the canonical values are self-referential.

Verify the columns of the output of the header crawl to examine the Canonicalization from Response Headers.

df_headers.columns

Under, you may see the columns.

Python Sitemap URL Response Header AuditPython website positioning Crawl Output Information Body columns. “dataframe.columns” methodology is at all times helpful to examine.

If you’re not conversant in the response headers, it’s possible you’ll not know learn how to use canonical hints inside response headers.

A response header can embody the canonical trace with the “Hyperlink” worth.

It’s registered as “resp_headers_link” by the Advertools instantly.

One other drawback is that the extracted strings seem inside the “<URL>;” string sample.

It means we are going to use regex to extract it.

df_headers["resp_headers_link"]

You may see the outcome beneath.

Sitemap URL Response HeaderScreenshot from Pandas, February 2022

The regex sample “[^<>][a-z:/0-9-.]*” is nice sufficient to extract the precise canonical worth.

A self-canonicalization examine with the response headers is beneath.

df_headers["response_header_canonical"] = df_headers["resp_headers_link"].str.extract(r"([^<>][a-z:/0-9-.]*)")
(df_headers["response_header_canonical"] == df_headers["url"]).value_counts()

We’ve used two completely different boolean checks.

One to examine whether or not the response header canonical trace is the same as the URL itself.

One other to see whether or not the standing code is 200.

Since now we have 404 URLs inside the sitemap, their canonical worth shall be “NaN”.

Non-canonical URL in Sitemap Audit with PythonIt reveals there are particular URLs with canonicalization inconsistencies.
We’ve 29 outliers for Technical website positioning. Each flawed sign given to the search engine for indexation or rating will trigger the dilution of the rating indicators.

To see these URLs, use the code block beneath.

Response Header Python SEO AuditScreenshot from Pandas, February 2022.

The Canonical Values from the Response Headers may be seen above.

df_headers[(df_headers["response_header_canonical"] != df_headers["url"]) & (df_headers["status"] == 200)]

Even a single “/” within the URL may cause canonicalization battle as seems right here for the homepage.

Canonical Response Header CheckComplaintsBoard.com Screenshot for checking the Response Header Canonical Worth and the Precise URL of the net web page.
You may examine the canonical battle right here.

In the event you examine log recordsdata, you will notice that the search engine crawls the URLs from the “Hyperlink” response headers.

Thus in technical website positioning, this ought to be weighted.

9. Verify The Indexing And Crawling Instructions From Response Headers

There are 14 completely different X-Robots-Tag specs for the Google search engine crawler.

The most recent one is “indexifembedded” to find out the indexation quantity on an internet web page.

The Indexing and Crawling directives may be within the type of a response header or the HTML meta tag.

This part focuses on the response header model of indexing and crawling directives.

  • Step one is checking whether or not the X-Robots-Tag property and values exist inside the HTTP Header or not.
  • The second step is auditing whether or not it aligns itself with the HTML Meta Tag properties and values in the event that they exist.

Use the command beneath yo examine the X-Robots-Tag” from the response headers.

def robots_tag_checker(dataframe:pd.DataFrame):
     for i in df_headers:
          if i.__contains__("robots"):
               return i
          else:
               return "There isn't any robots tag"
robots_tag_checker(df_headers)
OUTPUT>>>
'There isn't any robots tag'

We’ve created a customized operate to examine the “X-Robots-tag” response headers from the net pages’ supply code.

It seems that our check topic web site doesn’t use the X-Robots-Tag.

If there could be an X-Robots-tag, the code block beneath ought to be used.

df_headers["response_header_x_robots_tag"].value_counts()
df_headers[df_headers["response_header_x_robots_tag"] == "noindex"]

Verify whether or not there’s a “noindex” directive from the response headers, and filter the URLs with this indexation battle.

Within the Google Search Console Protection Report, these seem as “Submitted marked as noindex”.

Contradicting indexing and canonicalization hints and indicators may make a search engine ignore all the indicators whereas making the search algorithms belief much less to the user-declared indicators.

10. Verify The Self Canonicalization Of Sitemap URLs

Each URL within the sitemap XML recordsdata ought to give a self-canonicalization trace.

Sitemaps ought to solely embody the canonical variations of the URLs.

The Python code block on this part is to know whether or not the sitemap URLs have self-canonicalization values or not.

To examine the canonicalization from the HTML Paperwork’ “<head>” part, crawl the web sites by taking their response physique.

Use the code block beneath.

user_agent = "Mozilla/5.0 (Linux; Android 6.0.1; Nexus 5X Construct/MMB29P) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/W.X.Y.Z Cell Safari/537.36 (appropriate; Googlebot/2.1; +http://www.google.com/bot.html)"

The distinction between “crawl_headers” and the “crawl” is that “crawl” takes the whole response physique whereas the “crawl_headers” is just for response headers.

adv.crawl(sitemap_df["loc"],

output_file="sitemap_crawl_complaintsboard.jl",

follow_links=False,

custom_settings={"LOG_FILE":"sitemap_crawl_complaintsboard.log", “USER_AGENT”:user_agent})

You may examine the file dimension variations from crawl logs to response header crawl and full response physique crawl.

SEO Crawl PythonPython Crawl Output Dimension Comparability.

From 6GB output to the 387 MB output is sort of economical.

If a search engine simply desires to see sure response headers and the standing code, creating data on the headers would make their crawl hits extra economical.

How To Deal With Massive DataFrames For Studying And Aggregating Information?

This part requires coping with the big knowledge frames.

A pc can’t learn a Pandas DataFrame from a CSV or JL file if the file dimension is bigger than the pc’s RAM.

Thus, the “chunking” methodology is used.

When a web site sitemap XML File accommodates hundreds of thousands of URLs, the full crawl output shall be bigger than tens of gigabytes.

An iteration throughout sitemap crawl output knowledge body rows is critical.

For chunking, use the code block beneath.

df_iterator = pd.read_json(

    'sitemap_crawl_complaintsboard.jl',

    chunksize=10000,

     traces=True)
for i, df_chunk in enumerate(df_iterator):

    output_df = pd.DataFrame(knowledge={"url":df_chunk["url"],"canonical":df_chunk["canonical"], "self_canonicalised":df_chunk["url"] == df_chunk["canonical"]})
    mode="w" if i == 0 else 'a'

    header = i == 0

    output_df.to_csv(

        "canonical_check.csv",

        index=False,

        header=header,

        mode=mode

       )

df[((df["url"] != df["canonical"]) == True) & (df["self_canonicalised"] == False) & (df["canonical"].isna() != True)]

You may see the outcome beneath.

Python SEO AuditPython website positioning Canonicalization Audit.

We see that the paginated URLs from the “e book” subfolder give canonical hints to the primary web page, which is a non-correct observe in response to the Google tips.

11. Verify The Sitemap Sizes Inside Sitemap Index Information

Each Sitemap File ought to be lower than 50 MB. Use the Python code block beneath within the Technical website positioning with Python context to examine the sitemap file dimension.

pd.pivot_table(sitemap_df[sitemap_df["loc"].duplicated()==True], index="sitemap")

You may see the outcome beneath.

Python SEO sitemap sizingPython website positioning Sitemap Dimension Audit.

We see that every one sitemap XML recordsdata are beneath 50MB.

For higher and quicker indexation, conserving the sitemap URLs helpful and distinctive whereas lowering the dimensions of the sitemap recordsdata is useful.

12. Verify The URL Depend Per Sitemap With Python

Each URL inside the sitemaps ought to have fewer than 50.000 URLs.

Use the Python code block beneath to examine the URL Counts inside the sitemap XML recordsdata.

(pd.pivot_table(sitemap_df,

values=["loc"],

index="sitemap",

aggfunc="rely")

.sort_values(by="loc", ascending=False))

You may see the outcome beneath.

Sitemap URL Count CheckPython website positioning Sitemap URL Depend Audit.
All sitemaps have lower than 50.000 URLs. Some sitemaps have just one URL, which wastes the search engine’s consideration.

Maintaining sitemap URLs which are ceaselessly up to date completely different from the static and off content material URLs is useful.

URL Depend and URL Content material character variations assist a search engine to regulate crawl demand successfully for various web site sections.

13. Verify The Indexing And Crawling Meta Tags From URLs’ Content material With Python

Even when an internet web page just isn’t disallowed from robots.txt, it may possibly nonetheless be disallowed from the HTML Meta Tags.

Thus, checking the HTML Meta Tags for higher indexation and crawling is critical.

Utilizing the “customized selectors” is critical to carry out the HTML Meta Tag audit for the sitemap URLs.

sitemap = adv.sitemap_to_df("https://www.holisticseo.digital/sitemap.xml")

adv.crawl(url_list=sitemap["loc"][:1000], output_file="meta_command_audit.jl",

follow_links=False,

xpath_selectors= {"meta_command": "//meta[@name="robots"]/@content material"},

custom_settings={"CLOSESPIDER_PAGECOUNT":1000})

df_meta_check = pd.read_json("meta_command_audit.jl", traces=True)

df_meta_check["meta_command"].str.accommodates("nofollow|noindex", regex=True).value_counts()

The “//meta[@name=”robots”]/@content material” XPATH selector is to extract all of the robots instructions from the URLs from the sitemap.

We’ve used solely the primary 1000 URLs within the sitemap.

And, I cease crawling after the preliminary 1000 responses.

I’ve used one other web site to examine the Crawling Meta Tags since ComplaintsBoard.com doesn’t have it on the supply code.

You may see the outcome beneath.

URL Indexing Audit from Sitemap PythonPython website positioning Meta Robots Audit.
Not one of the URLs from the sitemap have “nofollow” or “noindex” inside the “Robots” instructions.

To examine their values, use the code beneath.

df_meta_check[df_meta_check["meta_command"].str.accommodates("nofollow|noindex", regex=True) == False][["url", "meta_command"]]

You may see the outcome beneath.

Meta Tag Audit from the WebsitesMeta Tag Audit from the Web sites.

14. Validate The Sitemap XML File Syntax With Python

Sitemap XML File Syntax validation is critical to validate the combination of the sitemap file with the search engine’s notion.

Even when there are particular syntax errors, a search engine can acknowledge the sitemap file in the course of the XML Normalization.

However, each syntax error can lower the effectivity for sure ranges.

Use the code block beneath to validate the Sitemap XML File Syntax.

def validate_sitemap_syntax(xml_path: str, xsd_path: str)
    xmlschema_doc = etree.parse(xsd_path)
    xmlschema = etree.XMLSchema(xmlschema_doc)
    xml_doc = etree.parse(xml_path)
    outcome = xmlschema.validate(xml_doc)
    return outcome
validate_sitemap_syntax("sej_sitemap.xml", "sitemap.xsd")

For this instance, I’ve used “https://www.searchenginejournal.com/sitemap_index.xml”. The XSD file entails the XML file’s context and tree construction.

It’s acknowledged within the first line of the Sitemap file as beneath.

For additional data, it’s also possible to examine DTD documentation.

15. Verify The Open Graph URL And Canonical URL Matching

It isn’t a secret that search engines like google additionally use the Open Graph and RSS Feed URLs from the supply code for additional canonicalization and exploration.

The Open Graph URLs ought to be the identical because the canonical URL submission.

On occasion, even in Google Uncover, Google chooses to make use of the picture from the Open Graph.

To examine the Open Graph URL and Canonical URL consistency, use the code block beneath.

for i, df_chunk in enumerate(df_iterator):

    if "og:url" in df_chunk.columns:

        output_df = pd.DataFrame(knowledge={

        "canonical":df_chunk["canonical"],

        "og:url":df_chunk["og:url"],

        "open_graph_canonical_consistency":df_chunk["canonical"] == df_chunk["og:url"]})

        mode="w" if i == 0 else 'a'

        header = i == 0

        output_df.to_csv(

            "open_graph_canonical_consistency.csv",

            index=False,

            header=header,

            mode=mode

        )
    else:

        print("There isn't any Open Graph URL Property")
There isn't any Open Graph URL Property

If there may be an Open Graph URL Property on the web site, it should give a CSV file to examine whether or not the canonical URL and the Open Graph URL are the identical or not.

However for this web site, we don’t have an Open Graph URL.

Thus, I’ve used one other web site for the audit.

if "og:url" in df_meta_check.columns:

     output_df = pd.DataFrame(knowledge={

     "canonical":df_meta_check["canonical"],

     "og:url":df_meta_check["og:url"],

     "open_graph_canonical_consistency":df_meta_check["canonical"] == df_meta_check["og:url"]})

     mode="w" if i == 0 else 'a'

     #header = i == 0

     output_df.to_csv(

            "df_og_url_canonical_audit.csv",

            index=False,

            #header=header,

            mode=mode
     )

else:

     print("There isn't any Open Graph URL Property")

df = pd.read_csv("df_og_url_canonical_audit.csv")

df

You may see the outcome beneath.

Sitemap Open Graph Audit with PythonPython website positioning Open Graph URL Audit.

We see that every one canonical URLs and the Open Graph URLs are the identical.

Python Audit with CanonicalizationPython website positioning Canonicalization Audit.

16. Verify The Duplicate URLs Inside Sitemap Submissions

A sitemap index file shouldn’t have duplicated URLs throughout completely different sitemap recordsdata or inside the identical sitemap XML file.

The duplication of the URLs inside the sitemap recordsdata could make a search engine obtain the sitemap recordsdata much less since a sure proportion of the sitemap file is bloated with pointless submissions.

For sure conditions, it may possibly seem as a spamming try to manage the crawling schemes of the search engine crawlers.

use the code block beneath to examine the duplicate URLs inside the sitemap submissions.

sitemap_df["loc"].duplicated().value_counts()

You may see that the 49574 URLs from the sitemap are duplicated.

Python SEO Duplicated URL in SitemapPython website positioning Duplicated URL Audit from the Sitemap XML Information

To see which sitemaps have extra duplicated URLs, use the code block beneath.

pd.pivot_table(sitemap_df[sitemap_df["loc"].duplicated()==True], index="sitemap", values="loc", aggfunc="rely").sort_values(by="loc", ascending=False)

You may see the outcome.

Python SEO Sitemap AuditPython website positioning Sitemap Audit for duplicated URLs.

Chunking the sitemaps may also help with site-tree and technical website positioning evaluation.

To see the duplicated URLs inside the Sitemap, use the code block beneath.

sitemap_df[sitemap_df["loc"].duplicated() == True]

You may see the outcome beneath.

Duplicated Sitemap URLDuplicated Sitemap URL Audit Output.

Conclusion

I wished to indicate learn how to validate a sitemap file for higher and more healthy indexation and crawling for Technical website positioning.

Python is vastly used for knowledge science, machine studying, and pure language processing.

However, it’s also possible to use it for Technical website positioning Audits to help the opposite website positioning Verticals with a Holistic website positioning Method.

In a future article, we will develop these Technical website positioning Audits additional with completely different particulars and strategies.

However, on the whole, this is likely one of the most complete Technical website positioning guides for Sitemaps and Sitemap Audit Tutorial with Python.

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