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SEO

Should We Write Content for People or Search Engines?

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All high-quality content pieces in written form have two things in common: they offer real substance to the reader, and each is well-written (or at least decently written 😊).

But all too often, digital content writers worry too much about “writing for SEO,” mistakenly focusing on writing for search engines instead of the human beings who are actually reading the content.

They worry too much about content length, keyword density, using keyword variations, and adding local modifiers. And not because it helps the user — because it could potentially help SEO efforts.

This is problematic for a number of reasons, but most importantly because Google and other search engines don’t need us to write for them.

They need us, or the brand we represent in our writing, to understand a topic well enough to be able to offer a thorough, easy-to-understand answer for its audience (i.e., people) to be able to read, understand, and, yes, find on the web.

Google Is Built to Understand Good Content

Google has ever-evolving, highly sophisticated search algorithms that are getting better every day, and drastically better each year.

This isn’t limited to solely its traditional search results.

The growing number of search features that improve usability and accessibility are big additions to the overall experience Google offers its users.

All of this is helping parent company Alphabet generate billions of dollars in revenue every quarter.

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A large driver of that revenue is Google’s extensive advertising network, but it starts and ends with Google search and its pay-per-click advertising there.

Google is the most-visited website in the world, and its success to continue to be No. 1 hinges on the success and accuracy of its search platform in all other capacities. That drives the traffic, advertising, and 3.5 billion searches done daily in Google search.

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Google has become what it is today – essentially a synonym for search – because of the quality of its results.

And let’s not forget: Google wasn’t the first – there are plenty of alternative search engines.

But it is the best search engine.

While we wouldn’t go so far as to call it perfect, Google has been the most accurate and useful search engine we’ve ever had to date – and thus the longest-lasting – much due to its dedication to getting it right.

The proof is in the numbers (daily visits, users, revenues, etc.). That’s why Google serves more than 2 trillion searches annually.

And its role in the everyday lives of humans across the world becomes greater each passing minute, deeply rooted in Google’s dedication to ensuring its search engine is giving users the best-possible answers to specific search queries, anytime and anywhere.

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For these reasons, Google (or any other search engine) doesn’t need us to write content that is specifically designed for it. Google serves its users, and it wants content to serve them as well.

If you write good content for people, Google will reward. The same cannot be said for content that is strictly written for search engines.

What Exactly Is Good Content?

Google knows how to identify high-quality content.

We know its algorithm and ranking signals help find the right content to satisfy any given search query.

But what does Google deem is good content? What makes bad content bad? And how come well-written content doesn’t always rank well?

For information about “good content,” Google suggests reviewing its Search Quality Evaluator Guidelines.

And, while Google’s John Mueller made sure to point out that the quality-rater guidelines are not directly related to its ranking factors, he said the document, which was not released to the public until 2015, offers useful information for creating good content nonetheless.

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The 164-page document is made up of guidelines given to quality raters (people hired by Google to rate its search results) when manually evaluating the performance of Google’s algorithms.

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The document doesn’t just talk about what Google considers good content; it also points out the qualities of bad content.

Here are the most important factors to consider when selecting an overall Page Quality rating:

  • The purpose of the page: The first step in understanding a page is figuring out its purpose.
  • Expertise, authoritativeness, trustworthiness: This is an important quality characteristic. Learning the reputation and credentials of a certain piece of content should not be hard to achieve.
  • Main content quality and amount: The rating should be based on the landing page of the task URL.
  • Website information/information about who is responsible for the main content: Find information about the website as well as the creator of the main content.
  • Website reputation/reputation about who is responsible for the main content: Links to help with reputation research are provided to reviewers.

In addition to being well-written and researched content with a purpose, relevancy is incredibly important for visibility in search.

As simple as it sounds, one of the most important ranking factors for content on the web is relevancy to the query. No matter how good the content is, if it doesn’t answer the search query, it’s not the right result.

These aren’t signals triggered by keyword usage, exact-match phrase inclusion, or any other “gamey” search-marketing tactics. This is just good content being delivered for the right search queries. The only way to achieve that is to create good, wholesome content.

Basic Guidelines for High-Quality Written Content

Don’t fret. If it was easy, everyone would be doing it and doing it well.

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The one thing working in our favor is that if you write well currently, you know how to write well for people. Don’t change that position and think too much about creating content for search engines.

The brands and marketers that rush to publish content just to try and give their website some life with no real purpose aren’t hitting the mark, and they won’t win the click, either.

Here are the basics for writing quality content:

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  • Define a purpose: All content should have a purpose. In turn, it should have a topic of focus, an audience, and even an intent stage for that audience.
  • Research thoroughly: Get the whole story before you start tearing stuff apart. What’s the beginning, middle, and end? Personally, I like to outline my content first so that I always know where it’s heading and what I need.
  • Write well and make sure to edit (and edit again!): It doesn’t need to be Hemingway. But use punctuation, check grammar, and try to keep it to the point and on-topic. Give background when necessary.
  • Have a byline: Google cares about where content comes from. Who is the brand or person behind the content? It wants to know. Make sure it can find out. The more authority a person’s reputation has, the better.
  • Make it informative, thorough, educational: Make sure there is substance in the content. We have a purpose. Does it satisfy the purpose? And does it explain it thoroughly? Educate your readers and they will appreciate and depend on you.
  • Cite sources: Always cite your sources. Statistics and data mean nothing if we don’t know where they came from. Be sure to always cite the original source whenever possible.

SEO

Using Python + Streamlit To Find Striking Distance Keyword Opportunities

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Using Python + Streamlit To Find Striking Distance Keyword Opportunities

Python is an excellent tool to automate repetitive tasks as well as gain additional insights into data.

In this article, you’ll learn how to build a tool to check which keywords are close to ranking in positions one to three and advises whether there is an opportunity to naturally work those keywords into the page.

It’s perfect for Python beginners and pros alike and is a great introduction to using Python for SEO.

If you’d just like to get stuck in there’s a handy Streamlit app available for the code. This is simple to use and requires no coding experience.

There’s also a Google Colaboratory Sheet if you’d like to poke around with the code. If you can crawl a website, you can use this script!

Here’s an example of what we’ll be making today:

Screenshot from Microsoft Excel, October 2021An Excel sheet documenting onpage keywords opportunites generated with Python

These keywords are found in the page title and H1, but not in the copy. Adding these keywords naturally to the existing copy would be an easy way to increase relevancy for these keywords.

By taking the hint from search engines and naturally including any missing keywords a site already ranks for, we increase the confidence of search engines to rank those keywords higher in the SERPs.

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This report can be created manually, but it’s pretty time-consuming.

So, we’re going to automate the process using a Python SEO script.

Preview Of The Output

This is a sample of what the final output will look like after running the report:

Excel sheet showing and example of keywords that can be optimised by using the striking distance reportScreenshot from Microsoft Excel, October 2021Excel sheet showing and example of keywords that can be optimised by using the striking distance report

The final output takes the top five opportunities by search volume for each page and neatly lays each one horizontally along with the estimated search volume.

It also shows the total search volume of all keywords a page has within striking distance, as well as the total number of keywords within reach.

The top five keywords by search volume are then checked to see if they are found in the title, H1, or copy, then flagged TRUE or FALSE.

This is great for finding quick wins! Just add the missing keyword naturally into the page copy, title, or H1.

Getting Started

The setup is fairly straightforward. We just need a crawl of the site (ideally with a custom extraction for the copy you’d like to check), and an exported file of all keywords a site ranks for.

This post will walk you through the setup, the code, and will link to a Google Colaboratory sheet if you just want to get stuck in without coding it yourself.

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To get started you will need:

We’ve named this the Striking Distance Report as it flags keywords that are easily within striking distance.

(We have defined striking distance as keywords that rank in positions four to 20, but have made this a configurable option in case you would like to define your own parameters.)

Striking Distance SEO Report: Getting Started

1. Crawl The Target Website

  • Set a custom extractor for the page copy (optional, but recommended).
  • Filter out pagination pages from the crawl.

2. Export All Keywords The Site Ranks For Using Your Favorite Provider

  • Filter keywords that trigger as a site link.
  • Remove keywords that trigger as an image.
  • Filter branded keywords.
  • Use both exports to create an actionable Striking Distance report from the keyword and crawl data with Python.

Crawling The Site

I’ve opted to use Screaming Frog to get the initial crawl. Any crawler will work, so long as the CSV export uses the same column names or they’re renamed to match.

The script expects to find the following columns in the crawl CSV export:

"Address", "Title 1", "H1-1", "Copy 1", "Indexability"

Crawl Settings

The first thing to do is to head over to the main configuration settings within Screaming Frog:

Configuration > Spider > Crawl

The main settings to use are:

Crawl Internal Links, Canonicals, and the Pagination (Rel Next/Prev) setting.

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(The script will work with everything else selected, but the crawl will take longer to complete!)

Recommended Screaming Frog Crawl SettingsScreenshot from Screaming Frog, October 2021Recommended Screaming Frog Crawl Settings

Next, it’s on to the Extraction tab.

Configuration > Spider > Extraction

Recommended Screaming Frog Extraction Crawl SettingsScreenshot from Screaming Frog, October 2021Recommended Screaming Frog Extraction Crawl Settings

At a bare minimum, we need to extract the page title, H1, and calculate whether the page is indexable as shown below.

Indexability is useful because it’s an easy way for the script to identify which URLs to drop in one go, leaving only keywords that are eligible to rank in the SERPs.

If the script cannot find the indexability column, it’ll still work as normal but won’t differentiate between pages that can and cannot rank.

Setting A Custom Extractor For Page Copy

In order to check whether a keyword is found within the page copy, we need to set a custom extractor in Screaming Frog.

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Configuration > Custom > Extraction

Name the extractor “Copy” as seen below.

Screaming Frog Custom Extraction Showing Default Options for Extracting the Page CopyScreenshot from Screaming Frog, October 2021Screaming Frog Custom Extraction Showing Default Options for Extracting the Page Copy

Important: The script expects the extractor to be named “Copy” as above, so please double check!

Lastly, make sure Extract Text is selected to export the copy as text, rather than HTML.

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There are many guides on using custom extractors online if you need help setting one up, so I won’t go over it again here.

Once the extraction has been set it’s time to crawl the site and export the HTML file in CSV format.

Exporting The CSV File

Exporting the CSV file is as easy as changing the drop-down menu displayed underneath Internal to HTML and pressing the Export button.

Internal > HTML > Export

Screaming Frog - Export Internal HTML SettingsScreenshot from Screaming Frog, October 2021Screaming Frog - Export Internal HTML Settings

After clicking Export, It’s important to make sure the type is set to CSV format.

The export screen should look like the below:

Screaming Frog Internal HTML CSV Export SettingsScreenshot from Screaming Frog, October 2021Screaming Frog Internal HTML CSV Export Settings

Tip 1: Filtering Out Pagination Pages

I recommend filtering out pagination pages from your crawl either by selecting Respect Next/Prev under the Advanced settings (or just deleting them from the CSV file, if you prefer).

Screaming Frog Settings to Respect Rel / PrevScreenshot from Screaming Frog, October 2021Screaming Frog Settings to Respect Rel / Prev

Tip 2: Saving The Crawl Settings

Once you have set the crawl up, it’s worth just saving the crawl settings (which will also remember the custom extraction).

This will save a lot of time if you want to use the script again in the future.

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File > Configuration > Save As

How to save a configuration file in screaming frogScreenshot from Screaming Frog, October 2021How to save a configuration file in screaming frog

Exporting Keywords

Once we have the crawl file, the next step is to load your favorite keyword research tool and export all of the keywords a site ranks for.

The goal here is to export all the keywords a site ranks for, filtering out branded keywords and any which triggered as a sitelink or image.

For this example, I’m using the Organic Keyword Report in Ahrefs, but it will work just as well with Semrush if that’s your preferred tool.

In Ahrefs, enter the domain you’d like to check in Site Explorer and choose Organic Keywords.

Ahrefs Site Explorer SettingsScreenshot from Ahrefs.com, October 2021Ahrefs Site Explorer Settings

Site Explorer > Organic Keywords

Ahrefs - How Setting to Export Organic Keywords a Site Ranks ForScreenshot from Ahrefs.com, October 2021Ahrefs - How Setting to Export Organic Keywords a Site Ranks For

This will bring up all keywords the site is ranking for.

Filtering Out Sitelinks And Image links

The next step is to filter out any keywords triggered as a sitelink or an image pack.

The reason we need to filter out sitelinks is that they have no influence on the parent URL ranking. This is because only the parent page technically ranks for the keyword, not the sitelink URLs displayed under it.

Filtering out sitelinks will ensure that we are optimizing the correct page.

Ahrefs Screenshot Demonstrating Pages Ranking for Sitelink KeywordsScreenshot from Ahrefs.com, October 2021Ahrefs Screenshot Demonstrating Pages Ranking for Sitelink Keywords

Here’s how to do it in Ahrefs.

Image showing how to exclude images and sitelinks from a keyword exportScreenshot from Ahrefs.com, October 2021Image showing how to exclude images and sitelinks from a keyword export

Lastly, I recommend filtering out any branded keywords. You can do this by filtering the CSV output directly, or by pre-filtering in the keyword tool of your choice before the export.

Finally, when exporting make sure to choose Full Export and the UTF-8 format as shown below.

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Image showing how to export keywords in UTF-8 format as a csv fileScreenshot from Ahrefs.com, October 2021Image showing how to export keywords in UTF-8 format as a csv file

By default, the script works with Ahrefs (v1/v2) and Semrush keyword exports. It can work with any keyword CSV file as long as the column names the script expects are present.

Processing

The following instructions pertain to running a Google Colaboratory sheet to execute the code.

There is now a simpler option for those that prefer it in the form of a Streamlit app. Simply follow the instructions provided to upload your crawl and keyword file.

Now that we have our exported files, all that’s left to be done is to upload them to the Google Colaboratory sheet for processing.

Select Runtime > Run all from the top navigation to run all cells in the sheet.

Image showing how to run the stirking distance Python script from Google CollaboratoryScreenshot from Colab.research.google.com, October 2021Image showing how to run the stirking distance Python script from Google Collaboratory

The script will prompt you to upload the keyword CSV from Ahrefs or Semrush first and the crawl file afterward.

Image showing how to upload the csv files to Google CollaboratoryScreenshot from Colab.research.google.com, October 2021Image showing how to upload the csv files to Google Collaboratory

That’s it! The script will automatically download an actionable CSV file you can use to optimize your site.

Image showing the Striking Distance final outputScreenshot from Microsoft Excel, October 2021Image showing the Striking Distance final output

Once you’re familiar with the whole process, using the script is really straightforward.

Code Breakdown And Explanation

If you’re learning Python for SEO and interested in what the code is doing to produce the report, stick around for the code walkthrough!

Install The Libraries

Let’s install pandas to get the ball rolling.

!pip install pandas

Import The Modules

Next, we need to import the required modules.

import pandas as pd
from pandas import DataFrame, Series
from typing import Union
from google.colab import files

Set The Variables

Now it’s time to set the variables.

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The script considers any keywords between positions four and 20 as within striking distance.

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Changing the variables here will let you define your own range if desired. It’s worth experimenting with the settings to get the best possible output for your needs.

# set all variables here
min_volume = 10  # set the minimum search volume
min_position = 4  # set the minimum position  / default = 4
max_position = 20 # set the maximum position  / default = 20
drop_all_true = True  # If all checks (h1/title/copy) are true, remove the recommendation (Nothing to do)
pagination_filters = "filterby|page|p="  # filter patterns used to detect and drop paginated pages

Upload The Keyword Export CSV File

The next step is to read in the list of keywords from the CSV file.

It is set up to accept an Ahrefs report (V1 and V2) as well as a Semrush export.

This code reads in the CSV file into a Pandas DataFrame.

upload = files.upload()
upload = list(upload.keys())[0]
df_keywords = pd.read_csv(
    (upload),
    error_bad_lines=False,
    low_memory=False,
    encoding="utf8",
    dtype={
        "URL": "str",
        "Keyword": "str",
        "Volume": "str",
        "Position": int,
        "Current URL": "str",
        "Search Volume": int,
    },
)
print("Uploaded Keyword CSV File Successfully!")

If everything went to plan, you’ll see a preview of the DataFrame created from the keyword CSV export. 

Dataframe showing sucessful upload of the keyword export fileScreenshot from Colab.research.google.com, October 2021Dataframe showing sucessful upload of the keyword export file

Upload The Crawl Export CSV File

Once the keywords have been imported, it’s time to upload the crawl file.

This fairly simple piece of code reads in the crawl with some error handling option and creates a Pandas DataFrame named df_crawl.

upload = files.upload()
upload = list(upload.keys())[0]
df_crawl = pd.read_csv(
    (upload),
        error_bad_lines=False,
        low_memory=False,
        encoding="utf8",
        dtype="str",
    )
print("Uploaded Crawl Dataframe Successfully!")

Once the CSV file has finished uploading, you’ll see a preview of the DataFrame.

Image showing a dataframe of the crawl file being uploaded successfullyScreenshot from Colab.research.google.com, October 2021Image showing a dataframe of the crawl file being uploaded successfully

Clean And Standardize The Keyword Data

The next step is to rename the column names to ensure standardization between the most common types of file exports.

Essentially, we’re getting the keyword DataFrame into a good state and filtering using cutoffs defined by the variables.

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df_keywords.rename(
    columns={
        "Current position": "Position",
        "Current URL": "URL",
        "Search Volume": "Volume",
    },
    inplace=True,
)

# keep only the following columns from the keyword dataframe
cols = "URL", "Keyword", "Volume", "Position"
df_keywords = df_keywords.reindex(columns=cols)

try:
    # clean the data. (v1 of the ahrefs keyword export combines strings and ints in the volume column)
    df_keywords["Volume"] = df_keywords["Volume"].str.replace("0-10", "0")
except AttributeError:
    pass

# clean the keyword data
df_keywords = df_keywords[df_keywords["URL"].notna()]  # remove any missing values
df_keywords = df_keywords[df_keywords["Volume"].notna()]  # remove any missing values
df_keywords = df_keywords.astype({"Volume": int})  # change data type to int
df_keywords = df_keywords.sort_values(by="Volume", ascending=False)  # sort by highest vol to keep the top opportunity

# make new dataframe to merge search volume back in later
df_keyword_vol = df_keywords[["Keyword", "Volume"]]

# drop rows if minimum search volume doesn't match specified criteria
df_keywords.loc[df_keywords["Volume"] < min_volume, "Volume_Too_Low"] = "drop"
df_keywords = df_keywords[~df_keywords["Volume_Too_Low"].isin(["drop"])]

# drop rows if minimum search position doesn't match specified criteria
df_keywords.loc[df_keywords["Position"] <= min_position, "Position_Too_High"] = "drop"
df_keywords = df_keywords[~df_keywords["Position_Too_High"].isin(["drop"])]
# drop rows if maximum search position doesn't match specified criteria
df_keywords.loc[df_keywords["Position"] >= max_position, "Position_Too_Low"] = "drop"
df_keywords = df_keywords[~df_keywords["Position_Too_Low"].isin(["drop"])]

Clean And Standardize The Crawl Data

Next, we need to clean and standardize the crawl data.

Essentially, we use reindex to only keep the “Address,” “Indexability,” “Page Title,” “H1-1,” and “Copy 1” columns, discarding the rest.

We use the handy “Indexability” column to only keep rows that are indexable. This will drop canonicalized URLs, redirects, and so on. I recommend enabling this option in the crawl.

Lastly, we standardize the column names so they’re a little nicer to work with.

# keep only the following columns from the crawl dataframe
cols = "Address", "Indexability", "Title 1", "H1-1", "Copy 1"
df_crawl = df_crawl.reindex(columns=cols)
# drop non-indexable rows
df_crawl = df_crawl[~df_crawl["Indexability"].isin(["Non-Indexable"])]
# standardise the column names
df_crawl.rename(columns={"Address": "URL", "Title 1": "Title", "H1-1": "H1", "Copy 1": "Copy"}, inplace=True)
df_crawl.head()

Group The Keywords

As we approach the final output, it’s necessary to group our keywords together to calculate the total opportunity for each page.

Here, we’re calculating how many keywords are within striking distance for each page, along with the combined search volume.

# groups the URLs (remove the dupes and combines stats)
# make a copy of the keywords dataframe for grouping - this ensures stats can be merged back in later from the OG df
df_keywords_group = df_keywords.copy()
df_keywords_group["KWs in Striking Dist."] = 1  # used to count the number of keywords in striking distance
df_keywords_group = (
    df_keywords_group.groupby("URL")
    .agg({"Volume": "sum", "KWs in Striking Dist.": "count"})
    .reset_index()
)
df_keywords_group.head()
DataFrame showing how many keywords were found within striking distanceScreenshot from Colab.research.google.com, October 2021DataFrame showing how many keywords were found within striking distance

Once complete, you’ll see a preview of the DataFrame.

Display Keywords In Adjacent Rows

We use the grouped data as the basis for the final output. We use Pandas.unstack to reshape the DataFrame to display the keywords in the style of a GrepWords export.

DataFrame showing a grepwords type-view of keywords laid out horizontallyScreenshot from Colab.research.google.com, October 2021DataFrame showing a grepwords type-view of keywords laid out horizontally
# create a new df, combine the merged data with the original data. display in adjacent rows ala grepwords
df_merged_all_kws = df_keywords_group.merge(
    df_keywords.groupby("URL")["Keyword"]
    .apply(lambda x: x.reset_index(drop=True))
    .unstack()
    .reset_index()
)

# sort by biggest opportunity
df_merged_all_kws = df_merged_all_kws.sort_values(
    by="KWs in Striking Dist.", ascending=False
)

# reindex the columns to keep just the top five keywords
cols = "URL", "Volume", "KWs in Striking Dist.", 0, 1, 2, 3, 4
df_merged_all_kws = df_merged_all_kws.reindex(columns=cols)

# create union and rename the columns
df_striking: Union[Series, DataFrame, None] = df_merged_all_kws.rename(
    columns={
        "Volume": "Striking Dist. Vol",
        0: "KW1",
        1: "KW2",
        2: "KW3",
        3: "KW4",
        4: "KW5",
    }
)

# merges striking distance df with crawl df to merge in the title, h1 and category description
df_striking = pd.merge(df_striking, df_crawl, on="URL", how="inner")

Set The Final Column Order And Insert Placeholder Columns

Lastly, we set the final column order and merge in the original keyword data.

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There are a lot of columns to sort and create!

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# set the final column order and merge the keyword data in

cols = [
    "URL",
    "Title",
    "H1",
    "Copy",
    "Striking Dist. Vol",
    "KWs in Striking Dist.",
    "KW1",
    "KW1 Vol",
    "KW1 in Title",
    "KW1 in H1",
    "KW1 in Copy",
    "KW2",
    "KW2 Vol",
    "KW2 in Title",
    "KW2 in H1",
    "KW2 in Copy",
    "KW3",
    "KW3 Vol",
    "KW3 in Title",
    "KW3 in H1",
    "KW3 in Copy",
    "KW4",
    "KW4 Vol",
    "KW4 in Title",
    "KW4 in H1",
    "KW4 in Copy",
    "KW5",
    "KW5 Vol",
    "KW5 in Title",
    "KW5 in H1",
    "KW5 in Copy",
]

# re-index the columns to place them in a logical order + inserts new blank columns for kw checks.
df_striking = df_striking.reindex(columns=cols)

Merge In The Keyword Data For Each Column

This code merges the keyword volume data back into the DataFrame. It’s more or less the equivalent of an Excel VLOOKUP function.

# merge in keyword data for each keyword column (KW1 - KW5)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW1", right_on="Keyword", how="left")
df_striking['KW1 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW2", right_on="Keyword", how="left")
df_striking['KW2 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW3", right_on="Keyword", how="left")
df_striking['KW3 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW4", right_on="Keyword", how="left")
df_striking['KW4 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW5", right_on="Keyword", how="left")
df_striking['KW5 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)

Clean The Data Some More

The data requires additional cleaning to populate empty values, (NaNs), as empty strings. This improves the readability of the final output by creating blank cells, instead of cells populated with NaN string values.

Next, we convert the columns to lowercase so that they match when checking whether a target keyword is featured in a specific column.

# replace nan values with empty strings
df_striking = df_striking.fillna("")
# drop the title, h1 and category description to lower case so kws can be matched to them
df_striking["Title"] = df_striking["Title"].str.lower()
df_striking["H1"] = df_striking["H1"].str.lower()
df_striking["Copy"] = df_striking["Copy"].str.lower()

Check Whether The Keyword Appears In The Title/H1/Copy and Return True Or False

This code checks if the target keyword is found in the page title/H1 or copy.

It’ll flag true or false depending on whether a keyword was found within the on-page elements.

df_striking["KW1 in Title"] = df_striking.apply(lambda row: row["KW1"] in row["Title"], axis=1)
df_striking["KW1 in H1"] = df_striking.apply(lambda row: row["KW1"] in row["H1"], axis=1)
df_striking["KW1 in Copy"] = df_striking.apply(lambda row: row["KW1"] in row["Copy"], axis=1)
df_striking["KW2 in Title"] = df_striking.apply(lambda row: row["KW2"] in row["Title"], axis=1)
df_striking["KW2 in H1"] = df_striking.apply(lambda row: row["KW2"] in row["H1"], axis=1)
df_striking["KW2 in Copy"] = df_striking.apply(lambda row: row["KW2"] in row["Copy"], axis=1)
df_striking["KW3 in Title"] = df_striking.apply(lambda row: row["KW3"] in row["Title"], axis=1)
df_striking["KW3 in H1"] = df_striking.apply(lambda row: row["KW3"] in row["H1"], axis=1)
df_striking["KW3 in Copy"] = df_striking.apply(lambda row: row["KW3"] in row["Copy"], axis=1)
df_striking["KW4 in Title"] = df_striking.apply(lambda row: row["KW4"] in row["Title"], axis=1)
df_striking["KW4 in H1"] = df_striking.apply(lambda row: row["KW4"] in row["H1"], axis=1)
df_striking["KW4 in Copy"] = df_striking.apply(lambda row: row["KW4"] in row["Copy"], axis=1)
df_striking["KW5 in Title"] = df_striking.apply(lambda row: row["KW5"] in row["Title"], axis=1)
df_striking["KW5 in H1"] = df_striking.apply(lambda row: row["KW5"] in row["H1"], axis=1)
df_striking["KW5 in Copy"] = df_striking.apply(lambda row: row["KW5"] in row["Copy"], axis=1)

Delete True/False Values If There Is No Keyword

This will delete true/false values when there is no keyword adjacent.

# delete true / false values if there is no keyword
df_striking.loc[df_striking["KW1"] == "", ["KW1 in Title", "KW1 in H1", "KW1 in Copy"]] = ""
df_striking.loc[df_striking["KW2"] == "", ["KW2 in Title", "KW2 in H1", "KW2 in Copy"]] = ""
df_striking.loc[df_striking["KW3"] == "", ["KW3 in Title", "KW3 in H1", "KW3 in Copy"]] = ""
df_striking.loc[df_striking["KW4"] == "", ["KW4 in Title", "KW4 in H1", "KW4 in Copy"]] = ""
df_striking.loc[df_striking["KW5"] == "", ["KW5 in Title", "KW5 in H1", "KW5 in Copy"]] = ""
df_striking.head()

Drop Rows If All Values == True

This configurable option is really useful for reducing the amount of QA time required for the final output by dropping the keyword opportunity from the final output if it is found in all three columns.

def true_dropper(col1, col2, col3):
    drop = df_striking.drop(
        df_striking[
            (df_striking[col1] == True)
            & (df_striking[col2] == True)
            & (df_striking[col3] == True)
        ].index
    )
    return drop

if drop_all_true == True:
    df_striking = true_dropper("KW1 in Title", "KW1 in H1", "KW1 in Copy")
    df_striking = true_dropper("KW2 in Title", "KW2 in H1", "KW2 in Copy")
    df_striking = true_dropper("KW3 in Title", "KW3 in H1", "KW3 in Copy")
    df_striking = true_dropper("KW4 in Title", "KW4 in H1", "KW4 in Copy")
    df_striking = true_dropper("KW5 in Title", "KW5 in H1", "KW5 in Copy")

Download The CSV File

The last step is to download the CSV file and start the optimization process.

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df_striking.to_csv('Keywords in Striking Distance.csv', index=False)
files.download("Keywords in Striking Distance.csv")

Conclusion

If you are looking for quick wins for any website, the striking distance report is a really easy way to find them.

Don’t let the number of steps fool you. It’s not as complex as it seems. It’s as simple as uploading a crawl and keyword export to the supplied Google Colab sheet or using the Streamlit app.

The results are definitely worth it!

More Resources:


Featured Image: aurielaki/Shutterstock

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