SEO

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

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

A sitemap file comprises the URLs to index with additional info relating to 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 tens of millions 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 search engine marketing automation, it’s potential to audit tens of millions 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 search engine marketing 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 embrace the next standards:

  • All URLs ought to have a 200 Standing Code.
  • All URLs needs to be self-canonical.
  • URLs needs to be open to being listed and crawled.
  • URLs shouldn’t be duplicated.
  • URLs shouldn’t be comfortable 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 measurement.

What Are The Advantages Of A Wholesome XML Sitemap File?

Smaller sitemaps are higher than bigger sitemaps for sooner indexation. That is significantly necessary in Information search engine marketing, as smaller sitemaps assist for rising the general legitimate listed URL depend.

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

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

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

An Vital Word…

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

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

A 16-Step Sitemap Audit For search engine marketing 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 includes technical search engine marketing fairly than content material traits.

On this step-by-step sitemap audit course of, we’ll use Python to sort out the technical features of sitemap auditing tens of millions of URLs.

Python Sitemap Audit InfographicPicture created by the writer, 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("<fashion>.container  width:100% !necessary; </fashion>"))

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

  • Advertools is important for taking the URLs from the sitemap file and making a request for taking their content material or the response standing codes.
  • “Pandas” is important for aggregating and manipulating the information.
  • Plotly is important for the visualization of the sitemap audit output.
  • LXML is important 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

Thousands and thousands of URLs might be taken right into a Pandas knowledge body with Advertools, as proven under.

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 under.

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

In whole, we now 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 perform under.

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 perform check_sitemap_tag_usage is a knowledge 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 possibly can see the output.

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

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

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

There are three essential content material freshness indicators from an internet site.

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

If these dates should not in line with one another, 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 crucial or crowded URL Path is important to weigh the web site’s search engine marketing efforts or technical search engine marketing Audits.

A single enchancment for Technical search engine marketing can profit hundreds of URLs concurrently, which creates a cheap and budget-friendly search engine marketing technique.

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

To create a URL Tree Dataframe from an internet 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 possibly can simply parse the URLs inside the sitemap into a knowledge body.

Python sitemap auditMaking a URL Tree with URLLib or Advertools is straightforward.
Checking the URL breakdowns helps to grasp the general info tree of an internet site.

The information body above comprises 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 outstanding for 2 aims.

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 shouldn’t be the subject of the “Indexing and Crawling” instantly, thus on the finish of the article, we’ll speak about 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 test 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 comprises the URLs’ HTTPS Protocol info.

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

Python https scheme columnChecking the HTTP URLs from the Sitemaps will help to search out greater 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 under.

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” technique 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” technique.

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

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

You’ll be able to see the outcome under.

True     245690
False         1
Identify: can_fetch, dtype: int64

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

We are able to filter the particular URL as under.

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 the entire 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 Stories.
We see {that a} “profile” web page has been disallowed and submitted.

Later, the identical management might be completed for additional examinations reminiscent of “disallowed however internally linked”.

However, to do this, we have to crawl a minimum of 3 million URLs from ComplaintsBoard.com, and it may be a completely new information.

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

Complaintsboard.com doesn’t use a correct URL construction and taxonomy, so analyzing the web site construction shouldn’t be straightforward for an search engine marketing or Search Engine.

However probably the most used phrases inside the URLs or the content material replace frequency can sign which matter the corporate really weighs on.

Since we deal with “technical features” on this tutorial, you possibly can 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 needs to be carried out to test the standing codes of the URLs inside the sitemap.

However, because it’s expensive when you’ve gotten tens of millions of URLs to audit, we will merely use a brand new crawling technique 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 potential robots, indexing, and canonical indicators from the response headers.

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

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 search engine marketing side might be seen under.

200    207866
404        23
Identify: 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 technique under from Pandas.

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

The outcome might be seen under.

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

8. Verify The Canonicalization From Response Headers

Occasionally, 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 needs to be the identical.

If there are two totally different canonicalization indicators on an online 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 test the Canonicalization from Response Headers.

df_headers.columns

Under, you possibly can see the columns.

Python Sitemap URL Response Header AuditPython search engine marketing Crawl Output Knowledge Body columns. “dataframe.columns” technique is at all times helpful to test.

In case you are not conversant in the response headers, chances are you’ll not know the right way to use canonical hints inside response headers.

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

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

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

It means we’ll use regex to extract it.

df_headers["resp_headers_link"]

You’ll be able to see the outcome under.

Sitemap URL Response HeaderScreenshot from Pandas, February 2022

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

A self-canonicalization test with the response headers is under.

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 totally different boolean checks.

One to test 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 we now have 404 URLs inside the sitemap, their canonical worth can be “NaN”.

Non-canonical URL in Sitemap Audit with PythonIt reveals there are particular URLs with canonicalization inconsistencies.
We’ve 29 outliers for Technical search engine marketing. Each fallacious 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 under.

Response Header Python SEO AuditScreenshot from Pandas, February 2022.

The Canonical Values from the Response Headers might 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’ll be able to test the canonical battle right here.

For those who test log recordsdata, you will note that the search engine crawls the URLs from the “Hyperlink” response headers.

Thus in technical search engine marketing, this needs to be weighted.

9. Verify The Indexing And Crawling Instructions From Response Headers

There are 14 totally 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 online web page.

The Indexing and Crawling directives might 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 under yo test 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 a robots tag"
robots_tag_checker(df_headers)
OUTPUT>>>
'There isn't a robots tag'

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

It seems that our take a look at topic web site doesn’t use the X-Robots-Tag.

If there could be an X-Robots-tag, the code block under needs 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 the entire 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 embrace the canonical variations of the URLs.

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

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

Use the code block under.

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 Cellular 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’ll be able to test the file measurement variations from crawl logs to response header crawl and whole response physique crawl.

SEO Crawl PythonPython Crawl Output Measurement Comparability.

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

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

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

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 measurement is bigger than the pc’s RAM.

Thus, the “chunking” technique is used.

When an internet site sitemap XML File comprises tens of millions of URLs, the whole crawl output can be bigger than tens of gigabytes.

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

For chunking, use the code block under.

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’ll be able to see the outcome under.

Python SEO AuditPython search engine marketing 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 apply in accordance with the Google pointers.

11. Verify The Sitemap Sizes Inside Sitemap Index Information

Each Sitemap File needs to be lower than 50 MB. Use the Python code block under within the Technical search engine marketing with Python context to test the sitemap file measurement.

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

You’ll be able to see the outcome under.

Python SEO sitemap sizingPython search engine marketing Sitemap Measurement Audit.

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

For higher and sooner indexation, preserving the sitemap URLs invaluable and distinctive whereas lowering the dimensions of the sitemap recordsdata is useful.

12. Verify The URL Rely Per Sitemap With Python

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

Use the Python code block under to test the URL Counts inside the sitemap XML recordsdata.

(pd.pivot_table(sitemap_df,

values=["loc"],

index="sitemap",

aggfunc="depend")

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

You’ll be able to see the outcome under.

Sitemap URL Count CheckPython search engine marketing Sitemap URL Rely 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 can be incessantly up to date totally different from the static and rancid content material URLs is useful.

URL Rely 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 online web page shouldn’t be disallowed from robots.txt, it will possibly nonetheless be disallowed from the HTML Meta Tags.

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

Utilizing the “customized selectors” is important 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.comprises("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 test the Crawling Meta Tags since ComplaintsBoard.com doesn’t have it on the supply code.

You’ll be able to see the outcome under.

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

To test their values, use the code under.

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

You’ll be able to see the outcome under.

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 important 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 under 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 includes the XML file’s context and tree construction.

It’s said within the first line of the Sitemap file as under.

For additional info, you can too test DTD documentation.

15. Verify The Open Graph URL And Canonical URL Matching

It isn’t a secret that 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 needs to be the identical because the canonical URL submission.

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

To test the Open Graph URL and Canonical URL consistency, use the code block under.

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 a Open Graph URL Property")
There isn't a Open Graph URL Property

If there may be an Open Graph URL Property on the web site, it is going to give a CSV file to test 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 a Open Graph URL Property")

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

df

You’ll be able to see the outcome under.

Sitemap Open Graph Audit with PythonPython search engine marketing Open Graph URL Audit.

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

Python Audit with CanonicalizationPython search engine marketing Canonicalization Audit.

16. Verify The Duplicate URLs Inside Sitemap Submissions

A sitemap index file shouldn’t have duplicated URLs throughout totally 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 will possibly seem as a spamming try to regulate the crawling schemes of the search engine crawlers.

use the code block under to test the duplicate URLs inside the sitemap submissions.

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

You’ll be able to see that the 49574 URLs from the sitemap are duplicated.

Python SEO Duplicated URL in SitemapPython search engine marketing Duplicated URL Audit from the Sitemap XML Information

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

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

You’ll be able to see the outcome.

Python SEO Sitemap AuditPython search engine marketing Sitemap Audit for duplicated URLs.

Chunking the sitemaps will help with site-tree and technical search engine marketing evaluation.

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

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

You’ll be able to see the outcome under.

Duplicated Sitemap URLDuplicated Sitemap URL Audit Output.

Conclusion

I needed to indicate the right way to validate a sitemap file for higher and more healthy indexation and crawling for Technical search engine marketing.

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

However, you can too use it for Technical search engine marketing Audits to help the opposite search engine marketing Verticals with a Holistic search engine marketing Method.

In a future article, we will develop these Technical search engine marketing Audits additional with totally different particulars and strategies.

However, usually, this is without doubt one of the most complete Technical search engine marketing guides for Sitemaps and Sitemap Audit Tutorial with Python.

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