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How To Balance SEO And B2B Marketing Goals



How To Balance SEO And B2B Marketing Goals

Despite the benefits of aligning strategies, marketing and SEO managers don’t always have the same goals when it comes to promoting content, from what I have observed with clients and partners.

Often, SEO professionals are striving to meet their key performance indicators (KPIs) on time, yet depend on the output of the marketing team for success.

Meanwhile, the marketing department strives to deliver a long-term content strategy guided by the broader goals of the CEO and subsequent priorities of the Chief Marketing Officer (CMO) to advance brand awareness and demand generation strategies, to name but a few.

It is often the macro view of these goals which can lead to SEO not being leveraged to its full potential due to the time taken to yield results or attribute value.

While SEO strategies are often long-term, they are fundamental to maximizing the potential of content marketing and delivering the demand performance to boost exponential growth.

In this article, I present a four-step strategy to align both teams and make sure their goals are met, as well as four best practices to establish harmony between your SEO and marketing efforts.

4-Step Strategy To Balance SEO And B2B Marketing Goals

Avoid ambiguity and establish clear protocols for your creatives to make sure both marketing and SEO goals are met.

1. Create A Brand And Style Guide With SEO In Mind

To ensure SEO and marketing strategies are completely aligned, it is important for brand and style guides to consider SEO.

In other words, rather than SEO being an afterthought, it should be a key component of the marketing process – particularly for content marketing.

Dedicating a chapter to SEO in the brand and content style guides will solidify this relationship and set tasks for SEO pros to advance brand awareness.

This shifts the common “adjust content to rank for SEO” mindset toward the more effective “optimize SEO for marketing” approach, which is especially important for businesses that rely on writers and freelancers to be in charge of their own SEO efforts.

To maintain an amicable tug-of-war between CMO and SEO goals, it is also important for the marketing strategy to allow keywords that rank well but may bend grammatical rules (or not use the C-Suite’s preferred language).

An example is “top of funnel” as an adjective, which ranks better for SEO than the grammatically correct “top-of-funnel.”

Additional tips on what to include in the brand and style guides:

  • Suggested and prohibited SEO keyword lists, so managers, new hires, and freelancers can consult the guides easily to avoid ranking for keywords deemed irrelevant.
  • List of branded terms that can’t be adjusted for SEO, so the CMO and the marketing team don’t see their strategy impacted by keywords and branded terms that have been “tweaked” to rank better for SEO.
  • Key content topics: Define key topics in the guide to advance brand awareness and rank for SEO. Implementing this in the guides (rather than only in a content calendar) makes the content strategy definitive and provides expectations for SEO managers to plan their long-term strategy.

2. Optimize Each Content Asset For SEO And Marketing Goals

Ideally, all content should rank for SEO.

However, the goal of each content piece will likely differ based on the topic covered, its search intent, as well as its role in brand awareness and shaping audience opinion.

Thought leadership, for instance, may present a challenge for implementing SEO, particularly if the author is pitching an innovative, original idea for their audience that has no search intent yet.

In this case, it is better to prioritize “marketing goals” and optimize to boost ranking where possible rather than guide content creation with SEO. This ensures content meets its purpose.

Consider amplifying reach via content syndication and paid media to boost the impact of this content.

On the other hand, content that is highly influenced by search intent, such as FAQs or guides, should focus on SEO first and foremost to inform content creation and rank better for highly-searched queries.

While branding may take a backseat here, it’s important that this content remains aligned.

Thus, to meet both outcomes, planning each piece of content beforehand with a marketing or SEO focus helps to determine KPIs for each asset – as well as guide the production and promotion of content to meet these goals effectively.

However, it is important to strive for harmony between CMO and SEO goals by establishing shared KPIs whenever possible and creating content that advances brand awareness while also ranking for popular queries.

3. Survey Your Audience To Measure The Impact Of SEO On Marketing

Survey your audience to assess if marketing goals are being met with content, as well as the impact of SEO on marketing strategies.

By asking questions about the values your audience associates with your brand as well as the top keywords that come to mind (to evaluate SEO priorities), you can gauge if the overall impression your audience currently forms of your brand is on par with the CMO and marketing team’s goals.

It is important to mention (particularly in this step) that SEO should be seen as a promotion tool for driving brand awareness and long-lasting demand.

Therefore, if the survey results point to values or keywords that are SEO-driven, yet don’t meet marketing expectations, then it is important to adjust the SEO-focused content to deliver the intended brand messaging.

If you wish to test certain assets or topics, then A/B test a “marketing-driven” and “SEO-driven” version to see which engages readers better, as well as survey their brand impressions.

This will provide plenty of intel to guide future content creation for your writers.

4. Create A Content Calendar And Hold Regular Meetings Between Marketing And SEO Managers

After assessing how your audience views your brand, it is time to create a content calendar to address possible unintended brand associations the public has made – all while meeting SEO goals.

Plan your content calendar per quarter, establishing “marketing” and “SEO” goals per topic/asset.

For SEO content, such as pure search intent content, outline the keywords beforehand to avoid unintended off-brand impressions after the content is launched.

As for the marketing content, establish goals for yielding engagement and the purpose of the content (to drive awareness or lead generation, for example), as well as branding goals and promotion methods – since SEO will not be the primary option for driving traffic.

Hold regular meetings between the marketing and SEO managers to discuss the metrics and impressions of the campaign as it is ongoing.

Social listening tools can assess the impact of the content and guide adjustments for writers before the next assets are promoted.

By gauging audience impressions while the campaign is ongoing, it becomes easier to adjust live content on a case-by-case basis, as well as change topics/assets to meet goals in the case of shared KPIs suffering.

4 Best Practices To Maintain Common Ground Between CMO And SEO Needs

Employ these four best practices to maintain harmony between marketing and SEO:

1. Onboard Writers With Marketing And SEO Dos And Don’ts

Onboarding writers, freelance or otherwise, is a crucial task for maintaining brand voice and content goals.

Considering that, it is also important to create an onboarding guide for writers that covers SEO dos and don’ts alongside additional training resources.

Typically, new writers will not be familiar with the minutia of your branding and style guides, so clarifying how to promote brand awareness correctly (such as boilerplate language, product descriptions, recommended adjectives, and allowed/forbidden keywords) will provide guidance for them to meet both marketing and SEO goals.

2. Utilize Social Listening

As mentioned previously, leveraging social listening tools can help to define your audience’s sentiment toward your brand and evaluate the results of your overall messaging.

This, combined with survey feedback, will help you make critical adjustments.

As a result, social listening tools are perhaps the most valuable weapon in your arsenal to balance CMO and SEO needs, so utilize them frequently to collect insights and guide future content creation.

Another approach is to search for your brand name and products on forums and social media channels manually, gaining insights from the way your audience comments on your solutions to evaluate if the discourse is aligned with your intended messaging and current SEO priorities.

3. Develop A Detailed Content Strategy

To meet your overarching content marketing and SEO goals, establish a strategy based on your content calendar to ensure that any content produced has a key role in driving your desired outcomes.

This should include how content pieces interlink and support one another, regardless of whether they are marketing or SEO-driven.

For example, start by defining an innovative thought leadership piece and link this out to supplementary videos, short blog posts, and podcast episodes.

As you analyze the performance of this content piece, you will be able to expand the topic to match the ICP’s buyer journey and search intent.

This could take the shape of a foundational SEO-focused piece for the topic that matches search intent and acts as a hub linking out to all the supplementary content that also ranks for keywords to drive brand traffic.

This strategy, combined with a consistent publishing cadence for your editorial calendar, will ensure that campaigns meet intended marketing and SEO outcomes.

While having content that speaks to marketing and SEO focuses independently, interlinking and guiding content with a long-term strategy is essential.

The best-performing content pieces are those that blend both priorities, establishing harmony between ranking for SEO keywords and leading the way in your industry with innovative thought leadership.

If done successfully, this will build long-term demand generation for your business.

4. Establish Joint Paid Media And Social Media Strategies And Goals

Bridge the gap between CMO and SEO by building upon both focuses with complimentary paid media and social media goals/strategies.

By viewing both as promotion methods for marketing and SEO goals, it is possible to fine-tune when to utilize paid media and social media to boost a variety of content pieces across a campaign that supports both SEO and marketing initiatives.

The more developed your content strategy and calendar, the better interlinked your content will be, facilitating your ability to craft omnichannel campaigns that deliver on all KPIs.


Meeting the demands of both the CMO and SEO manager requires a mindful approach that balances ranking in search engine results pages with promoting positive brand awareness.

This article presents best practices and a four-step strategy to achieve this balance, however, there are additional elements that you can incorporate into your content calendar to better meet CMO and SEO goals.

Developing a content strategy for the top-of-the-funnel (TOFU) stage, where search intent is less niche, is a good example of how to advance brand awareness while ranking for highly searched keywords.

You can then utilize this foundation to incentivize the lead to go through the buyer’s journey and consume thought-provoking, innovative content optimized with more specific keywords that further your marketing efforts.

By considering content marketing and SEO as two sides of the same coin, you can better align content creation to feed into each other, build an overall positive brand experience for your audience, and therefore leverage the full potential of your marketing efforts to drive demand.

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Essential Functions For SEO Data Analysis



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.
  • Find and Replace.
  • IF.
  • IFS.
  • 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.


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 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:

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:


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:


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:


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


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)


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 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://')


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)


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:

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 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.


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'] =, 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'] =, 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 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 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


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:

Excel countifScreenshot from Microsoft Excel, November 2022

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

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:


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'),

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,
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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