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From cookie, to beyond CRM and constant consent

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From cookie, to beyond CRM and constant consent

The demise of the cookie as we know it may have been given yet another stay of execution by Google, but let there be no doubt: its end is coming. Yet, people are still underprepared: one recent study of 500 CMOs in the UK and US suggests that nearly 50 percent are not well prepared for the days when cookies become a thing of the past.

They are not alone. Repeated delays and a lack of concrete roadmaps for credible scalable long-term alternatives for identification, targeting, reporting and evolving marketing strategies are muddying the waters. However, there are steps which can and should be taken by businesses of all kinds to prepare for the day the cookie is finally removed from the jar. Parking the issue and sleeping on the job could prove more problematic in the long run, as the cookie has been one of the more foundational aspects of performance marketing and digital infrastructure as a whole. Preparing for its absence is a marathon, not a sprint.

It may not be sexy, but a full data compliance, first-party data and activation strategy needs to be a crucial first step. The problem with cookies is their ubiquity. We’ve all become very used to dealing with them; still, they are far from the be all and end all of recognising customers online and especially in these increasingly privacy-conscious days, they have significant limitations. Google’s own VP and GM of ads, Gerry Dischler, put it best: “Cookies and other third party identifiers which some are advocating for within the industry do not meet rising expectations that consumers have when it comes to privacy. They will not stand up to rapidly evolving regulatory restrictions. They simply cannot be trusted in the long term.”

Luckily, businesses have been gifted more breathing space to prepare for this coming paradigm shift both organisationally and technically in how brands and platforms garner consent, remain relevant and foster full-funnel, and long-term, relationships. Make no bones about it, the impact of cookie depreciation will be wide ranging. It will restrict the potential for remarketing, long a staple of online acquisition in an attempt to recapture the attention of those who may have looked at a product or site and slipped through the net. It will also limit resolution with walled gardens, which have become so influential. Brands often cannot envisage a future without liaison with Facebook or LinkedIn platforms to broaden the perspective on customers. Apple are already ahead having taken a product first stance on ad privacy opt-ins – given this path is now beaten, it looks set to be a well-trodden one. This may also trigger a complete overhaul of consent and re-evaluation of remarketing as a strategy, and many should be acting now to overhaul their first party data consent if they re-imagine their propositions in a new, cookie-free future.

The reappraisal of data doesn’t stop there – to fill perceived gaps in knowledge we are looking at a rise again in use of second party data sources and partnerships, and profiling to build a more complete view of the customer. As ad networks’ audiences diminish, the size, scale and accuracy of cross-device tracking will make it harder and less valuable to sequence creative. CRM approaches will become much more valuable as a result, evolving into Experience Relationship Management (ERM) and providing a much richer view of customer behaviour. This will fold CRM-to-ERM strategies much more closely back into digital planning, but also drive yet further focus on consent. This in turn will raise the bar for value exchanges with consumers – basic offerings will no longer suffice, and bolder service exchanges will be needed to match the needs of audiences who are well aware of the value of their time, attention and data. When you need to reaffirm consent frequently, you open regular doors to people jumping ship. The value to stay needs to be significant.

The relationship between brand and publisher will also change – no longer as simple as starting with ‘dropping a cookie’, the onus will be on brands to pass express and clear first party consent on to any intended publisher for enrichment. Data clean rooms and an owned-ID graph will become much more widespread to manage this process alongside dynamically maintained consent practice. We also expect to see further IP masking develop, again following the path beaten by Apple with Mail’s ability to mask tracking pixels, and to mask IP addresses from email senders. All of this combines to make brand trust in data handling and stewardship a fundamental given within the post-cookie world.

All of this may seem like a lot – effectively some of the longstanding fabric of digital marketing practice and internet infrastructure is being unpicked, without clarity on what will replace it. But brands and marketers can take action to prepare for what comes next. Embrace changes of adtech partners, who are also better prepared for the newly cookieless landscape. Rethink consent and the reciprocal value exchanges to consumers. Amplify current data collection, and find an ID resolution partner who suits your purposes. Start to build second party data partnerships, and ultimately, recognise that tough conversations are coming and necessary. The cookie-free future might seem uncertain, scary and unfamiliar, but it is worth remembering it’s roots and the often missed potential. Cookies have always been given credibility without question which for technologists has always been a frustration. The cookieless future should remove the limits they have long set on the market, and instead open up a new, broader and richer future for well-rounded and valuable digital experiences with audiences as a whole.

There are some key actions that we’ve been taking with our savvy clients over the past 12-24 months which turn what can seem like a daunting negative into a consumer focused positive:

  1. Assess your vendor list to see which partners you already have, and may not be utilising their data clean room functionality e.g. Microsoft, AppsFlyer, Snowflake, AWS and GCP. Don’t be scared off by putting your eggs into one basket – the whole purpose of the clean room is to be a safe platform agnostic home for all your 1st part data to broker its integration between your external marketing ecosystem partners
  1. Get your technology, product marketing, data and experience design teams talking seriously about evolving your data-value exchanges. Start evolving now, and accelerate if you’ve already started. Move beyond newsletter sign-ups, voucher-codes and re-engagement well after purchase. Build true unique reasons to sign-up and keep connected with your brand e.g. exclusive bundles, loyalty only you can do, sustainability and community programmes that amplify reasons to share data beyond the core products. This can include recycling schemes, pop-up experiences, and partner events.
  2. Don’t forget that the 3rd party cookie-sunset doesn’t shut the door on partner data sharing. Use your clean room (AKA. CDP, DMP 2.0) to broker meaningful and transparent relationships with trusted partners whose proposition is complimentary or can extend new value-adds to your customer base.
  3. .. don’t forget addressing the measurement challenges that the cookie-sunset is already causing. Rethink or reconsider Multi-touch Attribution. It has fallen short of delivering on its promises. Multi-touch Attribution is developing a reputation for failure. It’s NOT about deploying an off the shelf CDP/DMP or attribution modeling solution and hey-presto!

It’s ABOUT combining all available data to interpret and contextualise performance drivers, to demystify contributors and influence confident optimisation – we call this Full-funnel Attribution outputs of which include:

  • Marketing spend with attributed view lens (e.g. Attributed vs Last Click)
  • Channel contribution to drive trusted budget reallocation
  • Explore conversion paths to easily act on conversion blockers
  • Act on segment impact to optimise linear spend and invest in specific cohorts
  • Content effectiveness attributes value to pages and contribution to conversion
  • Project and campaign incrementality drill-downs to map performance attributed to specific initiatives run across teams
  • Unify measurement of search (Paid + Organic) to align strategies and begin to eliminate cannibalisation – starting to confidently prove incrementality

 

funnel attribution modelling without the cookie


Anthony Magee is the Director of data and experience technology at SYZYGY.

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Essential Functions For SEO Data Analysis

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Essential Functions For SEO Data Analysis

Learning to code, whether with PythonJavaScript, or another programming language, has a whole host of benefits, including the ability to work with larger datasets and automate repetitive tasks.

But despite the benefits, many SEO professionals are yet to make the transition – and I completely understand why! It isn’t an essential skill for SEO, and we’re all busy people.

If you’re pressed for time, and you already know how to accomplish a task within Excel or Google Sheets, then changing tack can feel like reinventing the wheel.

When I first started coding, I initially only used Python for tasks that I couldn’t accomplish in Excel – and it’s taken several years to get to the point where it’s my defacto choice for data processing.

Looking back, I’m incredibly glad that I persisted, but at times it was a frustrating experience, with many an hour spent scanning threads on Stack Overflow.

This post is designed to spare other SEO pros the same fate.

Within it, we’ll cover the Python equivalents of the most commonly used Excel formulas and features for SEO data analysis – all of which are available within a Google Colab notebook linked in the summary.

Specifically, you’ll learn the equivalents of:

  • LEN.
  • Drop Duplicates.
  • Text to Columns.
  • SEARCH/FIND.
  • CONCATENATE.
  • Find and Replace.
  • LEFT/MID/RIGHT.
  • IF.
  • IFS.
  • VLOOKUP.
  • COUNTIF/SUMIF/AVERAGEIF.
  • Pivot Tables.

Amazingly, to accomplish all of this, we’ll primarily be using a singular library – Pandas – with a little help in places from its big brother, NumPy.

Prerequisites

For the sake of brevity, there are a few things we won’t be covering today, including:

  • Installing Python.
  • Basic Pandas, like importing CSVs, filtering, and previewing dataframes.

If you’re unsure about any of this, then Hamlet’s guide on Python data analysis for SEO is the perfect primer.

Now, without further ado, let’s jump in.

LEN

LEN provides a count of the number of characters within a string of text.

For SEO specifically, a common use case is to measure the length of title tags or meta descriptions to determine whether they’ll be truncated in search results.

Within Excel, if we wanted to count the second cell of column A, we’d enter:

=LEN(A2)
Screenshot from Microsoft Excel, November 2022

Python isn’t too dissimilar, as we can rely on the inbuilt len function, which can be combined with Pandas’ loc[] to access a specific row of data within a column:

len(df['Title'].loc[0])

In this example, we’re getting the length of the first row in the “Title” column of our dataframe.

len function python
Screenshot of VS Code, November, 2022

Finding the length of a cell isn’t that useful for SEO, though. Normally, we’d want to apply a function to an entire column!

In Excel, this would be achieved by selecting the formula cell on the bottom right-hand corner and either dragging it down or double-clicking.

When working with a Pandas dataframe, we can use str.len to calculate the length of rows within a series, then store the results in a new column:

df['Length'] = df['Title'].str.len()

Str.len is a ‘vectorized’ operation, which is designed to be applied simultaneously to a series of values. We’ll use these operations extensively throughout this article, as they almost universally end up being faster than a loop.

Another common application of LEN is to combine it with SUBSTITUTE to count the number of words in a cell:

=LEN(TRIM(A2))-LEN(SUBSTITUTE(A2," ",""))+1

In Pandas, we can achieve this by combining the str.split and str.len functions together:

df['No. Words'] = df['Title'].str.split().str.len()

We’ll cover str.split in more detail later, but essentially, what we’re doing is splitting our data based upon whitespaces within the string, then counting the number of component parts.

word count PythonScreenshot from VS Code, November 2022

Dropping Duplicates

Excel’s ‘Remove Duplicates’ feature provides an easy way to remove duplicate values within a dataset, either by deleting entirely duplicate rows (when all columns are selected) or removing rows with the same values in specific columns.

Excel drop duplicatesScreenshot from Microsoft Excel, November 2022

In Pandas, this functionality is provided by drop_duplicates.

To drop duplicate rows within a dataframe type:

df.drop_duplicates(inplace=True)

To drop rows based on duplicates within a singular column, include the subset parameter:

df.drop_duplicates(subset="column", inplace=True)

Or specify multiple columns within a list:

df.drop_duplicates(subset=['column','column2'], inplace=True)

One addition above that’s worth calling out is the presence of the inplace parameter. Including inplace=True allows us to overwrite our existing dataframe without needing to create a new one.

There are, of course, times when we want to preserve our raw data. In this case, we can assign our deduped dataframe to a different variable:

df2 = df.drop_duplicates(subset="column")

Text To Columns

Another everyday essential, the ‘text to columns’ feature can be used to split a text string based on a delimiter, such as a slash, comma, or whitespace.

As an example, splitting a URL into its domain and individual subfolders.

Excel drop duplicatesScreenshot from Microsoft Excel, November 2022

When dealing with a dataframe, we can use the str.split function, which creates a list for each entry within a series. This can be converted into multiple columns by setting the expand parameter to True:

df['URL'].str.split(pat="/", expand=True)
str split PythonScreenshot from VS Code, November 2022

As is often the case, our URLs in the image above have been broken up into inconsistent columns, because they don’t feature the same number of folders.

This can make things tricky when we want to save our data within an existing dataframe.

Specifying the n parameter limits the number of splits, allowing us to create a specific number of columns:

df[['Domain', 'Folder1', 'Folder2', 'Folder3']] = df['URL'].str.split(pat="/", expand=True, n=3)

Another option is to use pop to remove your column from the dataframe, perform the split, and then re-add it with the join function:

df = df.join(df.pop('Split').str.split(pat="/", expand=True))

Duplicating the URL to a new column before the split allows us to preserve the full URL. We can then rename the new columns:🐆

df['Split'] = df['URL']

df = df.join(df.pop('Split').str.split(pat="/", expand=True))

df.rename(columns = {0:'Domain', 1:'Folder1', 2:'Folder2', 3:'Folder3', 4:'Parameter'}, inplace=True)
Split pop join functions PythonScreenshot from VS Code, November 2022

CONCATENATE

The CONCAT function allows users to combine multiple strings of text, such as when generating a list of keywords by adding different modifiers.

In this case, we’re adding “mens” and whitespace to column A’s list of product types:

=CONCAT($F$1," ",A2)
concat Excel
Screenshot from Microsoft Excel, November 2022

Assuming we’re dealing with strings, the same can be achieved in Python using the arithmetic operator:

df['Combined] = 'mens' + ' ' + df['Keyword']

Or specify multiple columns of data:

df['Combined'] = df['Subdomain'] + df['URL']
concat PythonScreenshot from VS Code, November 2022

Pandas has a dedicated concat function, but this is more useful when trying to combine multiple dataframes with the same columns.

For instance, if we had multiple exports from our favorite link analysis tool:

df = pd.read_csv('data.csv')
df2 = pd.read_csv('data2.csv')
df3 = pd.read_csv('data3.csv')

dflist = [df, df2, df3]

df = pd.concat(dflist, ignore_index=True)

SEARCH/FIND

The SEARCH and FIND formulas provide a way of locating a substring within a text string.

These commands are commonly combined with ISNUMBER to create a Boolean column that helps filter down a dataset, which can be extremely helpful when performing tasks like log file analysis, as explained in this guide. E.g.:

=ISNUMBER(SEARCH("searchthis",A2)
isnumber search ExcelScreenshot from Microsoft Excel, November 2022

The difference between SEARCH and FIND is that find is case-sensitive.

The equivalent Pandas function, str.contains, is case-sensitive by default:

df['Journal'] = df['URL'].str.contains('engine', na=False)

Case insensitivity can be enabled by setting the case parameter to False:

df['Journal'] = df['URL'].str.contains('engine', case=False, na=False)

In either scenario, including na=False will prevent null values from being returned within the Boolean column.

One massive advantage of using Pandas here is that, unlike Excel, regex is natively supported by this function – as it is in Google sheets via REGEXMATCH.

Chain together multiple substrings by using the pipe character, also known as the OR operator:

df['Journal'] = df['URL'].str.contains('engine|search', na=False)

Find And Replace

Excel’s “Find and Replace” feature provides an easy way to individually or bulk replace one substring with another.

find replace ExcelScreenshot from Microsoft Excel, November 2022

When processing data for SEO, we’re most likely to select an entire column and “Replace All.”

The SUBSTITUTE formula provides another option here and is useful if you don’t want to overwrite the existing column.

As an example, we can change the protocol of a URL from HTTP to HTTPS, or remove it by replacing it with nothing.

When working with dataframes in Python, we can use str.replace:

df['URL'] = df['URL'].str.replace('http://', 'https://')

Or:

df['URL'] = df['URL'].str.replace('http://', '') # replace with nothing

Again, unlike Excel, regex can be used – like with Google Sheets’ REGEXREPLACE:

df['URL'] = df['URL'].str.replace('http://|https://', '')

Alternatively, if you want to replace multiple substrings with different values, you can use Python’s replace method and provide a list.

This prevents you from having to chain multiple str.replace functions:

df['URL'] = df['URL'].replace(['http://', ' https://'], ['https://www.', 'https://www.’], regex=True)

LEFT/MID/RIGHT

Extracting a substring within Excel requires the usage of the LEFT, MID, or RIGHT functions, depending on where the substring is located within a cell.

Let’s say we want to extract the root domain and subdomain from a URL:

=MID(A2,FIND(":",A2,4)+3,FIND("/",A2,9)-FIND(":",A2,4)-3)
left mid right ExcelScreenshot from Microsoft Excel, November 2022

Using a combination of MID and multiple FIND functions, this formula is ugly, to say the least – and things get a lot worse for more complex extractions.

Again, Google Sheets does this better than Excel, because it has REGEXEXTRACT.

What a shame that when you feed it larger datasets, it melts faster than a Babybel on a hot radiator.

Thankfully, Pandas offers str.extract, which works in a similar way:

df['Domain'] = df['URL'].str.extract('.*://?([^/]+)')
str extract PythonScreenshot from VS Code, November 2022

Combine with fillna to prevent null values, as you would in Excel with IFERROR:

df['Domain'] = df['URL'].str.extract('.*://?([^/]+)').fillna('-')

If

IF statements allow you to return different values, depending on whether or not a condition is met.

To illustrate, suppose that we want to create a label for keywords that are ranking within the top three positions.

Excel IFScreenshot from Microsoft Excel, November 2022

Rather than using Pandas in this instance, we can lean on NumPy and the where function (remember to import NumPy, if you haven’t already):

df['Top 3'] = np.where(df['Position'] <= 3, 'Top 3', 'Not Top 3')

Multiple conditions can be used for the same evaluation by using the AND/OR operators, and enclosing the individual criteria within round brackets:

df['Top 3'] = np.where((df['Position'] <= 3) & (df['Position'] != 0), 'Top 3', 'Not Top 3')

In the above, we’re returning “Top 3” for any keywords with a ranking less than or equal to three, excluding any keywords ranking in position zero.

IFS

Sometimes, rather than specifying multiple conditions for the same evaluation, you may want multiple conditions that return different values.

In this case, the best solution is using IFS:

=IFS(B2<=3,"Top 3",B2<=10,"Top 10",B2<=20,"Top 20")
IFS ExcelScreenshot from Microsoft Excel, November 2022

Again, NumPy provides us with the best solution when working with dataframes, via its select function.

With select, we can create a list of conditions, choices, and an optional value for when all of the conditions are false:

conditions = [df['Position'] <= 3, df['Position'] <= 10, df['Position'] <=20]

choices = ['Top 3', 'Top 10', 'Top 20']

df['Rank'] = np.select(conditions, choices, 'Not Top 20')

It’s also possible to have multiple conditions for each of the evaluations.

Let’s say we’re working with an ecommerce retailer with product listing pages (PLPs) and product display pages (PDPs), and we want to label the type of branded pages ranking within the top 10 results.

The easiest solution here is to look for specific URL patterns, such as a subfolder or extension, but what if competitors have similar patterns?

In this scenario, we could do something like this:

conditions = [(df['URL'].str.contains('/category/')) & (df['Brand Rank'] > 0),
(df['URL'].str.contains('/product/')) & (df['Brand Rank'] > 0),
(~df['URL'].str.contains('/product/')) & (~df['URL'].str.contains('/category/')) & (df['Brand Rank'] > 0)]

choices = ['PLP', 'PDP', 'Other']

df['Brand Page Type'] = np.select(conditions, choices, None)

Above, we’re using str.contains to evaluate whether or not a URL in the top 10 matches our brand’s pattern, then using the “Brand Rank” column to exclude any competitors.

In this example, the tilde sign (~) indicates a negative match. In other words, we’re saying we want every brand URL that doesn’t match the pattern for a “PDP” or “PLP” to match the criteria for ‘Other.’

Lastly, None is included because we want non-brand results to return a null value.

np select PythonScreenshot from VS Code, November 2022

VLOOKUP

VLOOKUP is an essential tool for joining together two distinct datasets on a common column.

In this case, adding the URLs within column N to the keyword, position, and search volume data in columns A-C, using the shared “Keyword” column:

=VLOOKUP(A2,M:N,2,FALSE)
vlookup ExcelScreenshot from Microsoft Excel, November 2022

To do something similar with Pandas, we can use merge.

Replicating the functionality of an SQL join, merge is an incredibly powerful function that supports a variety of different join types.

For our purposes, we want to use a left join, which will maintain our first dataframe and only merge in matching values from our second dataframe:

mergeddf = df.merge(df2, how='left', on='Keyword')

One added advantage of performing a merge over a VLOOKUP, is that you don’t have to have the shared data in the first column of the second dataset, as with the newer XLOOKUP.

It will also pull in multiple rows of data rather than the first match in finds.

One common issue when using the function is for unwanted columns to be duplicated. This occurs when multiple shared columns exist, but you attempt to match using one.

To prevent this – and improve the accuracy of your matches – you can specify a list of columns:

mergeddf = df.merge(df2, how='left', on=['Keyword', 'Search Volume'])

In certain scenarios, you may actively want these columns to be included. For instance, when attempting to merge multiple monthly ranking reports:

mergeddf = df.merge(df2, on='Keyword', how='left', suffixes=('', '_october'))
    .merge(df3, on='Keyword', how='left', suffixes=('', '_september'))

The above code snippet executes two merges to join together three dataframes with the same columns – which are our rankings for November, October, and September.

By labeling the months within the suffix parameters, we end up with a much cleaner dataframe that clearly displays the month, as opposed to the defaults of _x and _y seen in the earlier example.

multi merge PythonScreenshot from VS Code, November 2022

COUNTIF/SUMIF/AVERAGEIF

In Excel, if you want to perform a statistical function based on a condition, you’re likely to use either COUNTIF, SUMIF, or AVERAGEIF.

Commonly, COUNTIF is used to determine how many times a specific string appears within a dataset, such as a URL.

We can accomplish this by declaring the ‘URL’ column as our range, then the URL within an individual cell as our criteria:

=COUNTIF(D:D,D2)
Excel countifScreenshot from Microsoft Excel, November 2022

In Pandas, we can achieve the same outcome by using the groupby function:

df.groupby('URL')['URL'].count()
Python groupbyScreenshot from VS Code, November 2022

Here, the column declared within the round brackets indicates the individual groups, and the column listed in the square brackets is where the aggregation (i.e., the count) is performed.

The output we’re receiving isn’t perfect for this use case, though, because it’s consolidated the data.

Typically, when using Excel, we’d have the URL count inline within our dataset. Then we can use it to filter to the most frequently listed URLs.

To do this, use transform and store the output in a column:

df['URL Count'] = df.groupby('URL')['URL'].transform('count')
Python groupby transformScreenshot from VS Code, November 2022

You can also apply custom functions to groups of data by using a lambda (anonymous) function:

df['Google Count'] = df.groupby(['URL'])['URL'].transform(lambda x: x[x.str.contains('google')].count())

In our examples so far, we’ve been using the same column for our grouping and aggregations, but we don’t have to. Similarly to COUNTIFS/SUMIFS/AVERAGEIFS in Excel, it’s possible to group using one column, then apply our statistical function to another.

Going back to the earlier search engine results page (SERP) example, we may want to count all ranking PDPs on a per-keyword basis and return this number alongside our existing data:

df['PDP Count'] = df.groupby(['Keyword'])['URL'].transform(lambda x: x[x.str.contains('/product/|/prd/|/pd/')].count())
Python groupby countifsScreenshot from VS Code, November 2022

Which in Excel parlance, would look something like this:

=SUM(COUNTIFS(A:A,[@Keyword],D:D,{"*/product/*","*/prd/*","*/pd/*"}))

Pivot Tables

Last, but by no means least, it’s time to talk pivot tables.

In Excel, a pivot table is likely to be our first port of call if we want to summarise a large dataset.

For instance, when working with ranking data, we may want to identify which URLs appear most frequently, and their average ranking position.

pivot table ExcelScreenshot from Microsoft Excel, November 2022

Again, Pandas has its own pivot tables equivalent – but if all you want is a count of unique values within a column, this can be accomplished using the value_counts function:

count = df['URL'].value_counts()

Using groupby is also an option.

Earlier in the article, performing a groupby that aggregated our data wasn’t what we wanted – but it’s precisely what’s required here:

grouped = df.groupby('URL').agg(
     url_frequency=('Keyword', 'count'),
     avg_position=('Position', 'mean'),
     )

grouped.reset_index(inplace=True)
groupby-pivot PythonScreenshot from VS Code, November 2022

Two aggregate functions have been applied in the example above, but this could easily be expanded upon, and 13 different types are available.

There are, of course, times when we do want to use pivot_table, such as when performing multi-dimensional operations.

To illustrate what this means, let’s reuse the ranking groupings we made using conditional statements and attempt to display the number of times a URL ranks within each group.

ranking_groupings = df.groupby(['URL', 'Grouping']).agg(
     url_frequency=('Keyword', 'count'),
     )
python groupby groupingScreenshot from VS Code, November 2022

This isn’t the best format to use, as multiple rows have been created for each URL.

Instead, we can use pivot_table, which will display the data in different columns:

pivot = pd.pivot_table(df,
index=['URL'],
columns=['Grouping'],
aggfunc="size",
fill_value=0,
)
pivot table PythonScreenshot from VS Code, November 2022

Final Thoughts

Whether you’re looking for inspiration to start learning Python, or are already leveraging it in your SEO workflows, I hope that the above examples help you along on your journey.

As promised, you can find a Google Colab notebook with all of the code snippets here.

In truth, we’ve barely scratched the surface of what’s possible, but understanding the basics of Python data analysis will give you a solid base upon which to build.

More resources:


Featured Image: mapo_japan/Shutterstock



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