5 min read

Making pivot tables 100x easier & better with Polymer (5 minute guide)

Start making pivot tables in 2 clicks with Polymer!

Updated interactive tutorial here.

Pivot tables are a common tool for analyzing data, but most people find them annoying to set up in Excel. The interface can be counter-intuitive to beginners and the data quickly becomes messy when trying to analyze multiple variables at once.

It just takes 2 clicks to create a pivot table in Polymer. Here's how to do it:

Let's say this is our dataset:

City Category Product Quantity UnitPrice TotalPrice
Boston Bars Carrot 33 1.77 58.41
Boston Crackers Whole   Wheat 87 3.49 303.63
Los Angeles Cookies Chocolate   Chip 58 1.87 108.46
New York Cookies Chocolate   Chip 82 1.87 153.34
Boston Cookies Arrowroot 38 2.18 82.84
Boston Bars Carrot 54 1.77 95.58
Boston Crackers Whole   Wheat 149 3.49 520.01
Los Angeles Bars Carrot 51 1.77 90.27

And you wanna know:

Which city produces the most sales?

First, you'll need to sign up to Polymer and upload your dataset (instructions are here).

Once that is done, Polymer will use an AI to analyze your data and present you with a better-looking table:

Note: The data is interactive & shareable! This is a great way to show off to clients or stakeholders during presentations!

Next, input your variables into the 'smart pivot' section (located above the data):

Pivot Tables for Beginners

Pivot Tables (Dummies Guide)

As a rule of thumb, non-numeric values should go into the 'columns' section whilst numeric (number) values should go into 'metrics.' Sometimes this isn't the case though, for instance, 'age' could go into columns as well.


(You can ignore the middle column for now).

The data says:

  • Boston made 5,650 sales (which is 46.35% above average)
  • New York made 4,006 sales (3.77% above average)
  • Los Angeles made 3,769 sales (2.37% below average)
  • San Diego made 2,017 sales (47.75% below average)

The green/red number gives us context to the information which is important for larger datasets. Imagine if there were 50 cities instead of just 4!  

Best part was we did all this within 2 clicks!

Understanding the Calculations (SUM, AVERAGE, COUNT)

In the 'metrics' column, there are several calculations: SUM, AVERAGE, COUNT, MAX and MIN

What do those mean?

First of, notice how there are multiple entries for the city 'Boston:'

SUM Pivot Tables
  • Quantity (SUM) will add up the total quantity for that city, giving us the total sales.
  • Quantity (AVERAGE) will find the average of these numbers giving us the average sales across products
  • COUNT will tell you how many 'Bostons' there are (in this case 5). It'll do the same calculations for the other cities too.
  • MAX and MIN will tell you the highest and lowest result for each city e.g. 149 is the MAX for Boston and 33 is the MIN for Boston

MOST calculations involve either SUM or AVERAGE.

Polymer Simplifies Complex Data Analysis

With Polymer's Smart Pivot, you can perform ultra-complex analyses in a matter of clicks whilst it's near impossible to do the same in Excel (and extremely time-consuming).


Let's say we ran some Facebook ads and we wanna know: What are the highest converting ads across several demographics (gender, age, country and device type)?

This is an absolute nightmare to do in Excel, but for the Smart Pivot tool, you just need to input these variables into the 'columns' and 'values' section and we get our results:

The table bascially tells us the 'Europe' ad group has the highest conversions for people aged 18-25 who are female, living in Indonesia and using Desktop. This value is 4133.71% above average. However...

The #COUNT# Column

Take a look at the #COUNT# Column. This tells us our sample size. Only 5 people are from this demographic, so we can say this sample size is too small.

We should either increase it (by spending more on this ad group for these categories) OR by removing filters to get a larger sample size (e.g. we can remove the 'device' category).

Advantages of Polymer over Excel

There are three main advantages of using Polymer over Excel:

  1. Data analysis that involves looking at how multiple variables interact with each other (like the above example) is insanely difficult to do in Excel and much more time-consuming. Polymer enables you to do it in a few clicks.
  2. Much cleaner and visually appealing interface. Excel's interface can easily confuse a beginner: the data is very messy and you have to do a bunch of digging to find where all the functions are. Most people resort to looking up guides which take a lot more time.
  3. Everything in Polymer is shareable and embeddable. This allows you to show off these interactive dashboards to clients/stakeholders during presentations or reports. Many agencies use Polymer just to share reports to their clients.

Disadvantages of Polymer

Polymer is a powerful tool for analyzing data and presenting it, however, it is NOT a tool for storing and editing data. There is no way to make edits to your data in Polymer - that has to be done in Excel.

Polymer is NOT a replacement for Excel. Instead, it acts as a super-easy, intelligent layer on top of your existing data and can save you hundreds of hours of analysis.

Other Features of Polymer

Pivot tables are just a very basic feature of Polymer. Where the real magic happens is in:

  • The auto-insights feature allows you to deep-dive into your data to find trends/correlations without having a starting hypothesis or question
  • The visualization features allow you to quickly create beautiful graphs/charts for extremely complicated data. Creating complicated charts with multiple variables + slicing is a huge hassle to do in Excel!
  • The sharing/embedding feature allows you to present data in an interactive and aesthetically pleasing way that'll impress your clients/stakeholders. Many marketing agencies use Polymer to create reports for clients.

Where to go from here?

Now that you understand pivot tables, here are other things you might find useful:

  1. How to analyze any dataset
  2. Learn about data visualization techniques
  3. Creating dashboards and search engines with Polymer
Posted on
November 3, 2021
under Blog
November 3, 2021
Written by
Ash Gupta
Former Tech Lead for Machine Learning at Google AdWords (6 years) and a quant developer on Wall Street. Co-Founder & CEO of Polymer Search.

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