Joins in insights

Connect datasets in the insight editor.

Combining or "joining" datasets connects information from two datasets based on a common column - called the join key. In Polyteia, this is typically a column like a name, ID, or location code.

If you're not familiar with the concept of joining tables, we recommend reading our introduction.

How Polyteia handles joins

By default, Polyteia uses natural joins. This means:

• It automatically recognizes columns that have the same name in both datasets.

• It uses these columns as join keys.

• If multiple matching column names are found, all are used together as keys.

You can choose between four join types:

• Inner join – keeps only the rows that have matching values in both tables

• Left join – keeps all rows from the left table and matching rows from the right table

• Right join – keeps all rows from the right table and matching rows from the left table

• Full join – keeps all rows from both tables

You can change the join type directly in the editor.

Best practices for preparing datasets

For best results:

• Make sure the columns you want to join have exactly the same name.

• Avoid typos or variations like Name vs. Full Name – natural joins won't match these.

If your column names don't match, you can use the manual key mapping option in Polyteia. This allows you to:

• Add a join condition manually

• Select which column from each dataset should be matched

Polyteia will then connect the datasets using the specified condition.

Currently, you need to re-upload the dataset to override system column names. Soon, you'll be able to use the edited column names.

Step by step: joining two datasets in Polyteia

Let's return to our wedding example.

You want to combine the Guest List and Seating Chart to create a complete table with names, RSVPs, meals, and table numbers.

You need at least editor permissions for all datasets you want to join.

1

Select your base dataset

Start by selecting the Guest List as your main dataset. In join logic, this dataset will be the "left" dataset.

2

Click "Join Dataset"

Use the + Join dataset button.

3

Select the second dataset

In this case, select the Seating Chart. This dataset will be the "right" dataset in join logic.

4

Set the join type

Choose your desired join type. The default join type is Inner join. Select Left join to keep all invited guests from the left dataset (Guest List) and only look for matches in the right dataset (Seating Chart).

5

Check the join key

If both datasets have a column named Guest Name, Polyteia will match them automatically.

If they're named differently (e.g., Guest Name and Name of Guest), click the ··· button next to the second dataset's name, then click Add condition and map them manually.

6

Preview the result

You'll see a joined table where each guest except Carla has their RSVP, meal, and table number. Since Carla wasn't in the Seating Chart dataset, the result only contains information from the Guest List dataset.

7

Continue your analysis

Now you can continue analyzing the joined datasets, e.g., adding numbers, counting individual guests, etc.

Tips and pitfalls

• Column names must match for natural joins to work.

• Cross joins can quickly explode in size – avoid them unless necessary.

• Null values in the result mean there was no match in the other dataset.

• You can join multiple datasets, not just two.