Filters

Limit the data you want to analyze.

Use filters to refine your data, focus on specific cases, or highlight key insights. To apply a filter on summaries, choose a column, select an aggregation (optional) (e.g., count or average), and set a condition. You can hide the column used for filtering, and the filter will remain effective. Filters help you narrow your data to what you need precisely. You can filter individual rows (like "city equals Berlin") or apply filters based on summaries (like "show only countries with more than 450 cities").

Applying filters

Filters are essential tools for refining datasets to the precise information required. The insight editor features a dynamic filtering system that adjusts according to the data type of each column, offering context-sensitive options for data splitting

How Filters Work

A filter consists of three parts:

1. Column – the field you want to filter (like "City" or "Age").

2. Filter type – how the data should be checked (like "equals" or "greater than").

3. Value – what to look for (like "Berlin" or "30").

You can add multiple filters at once. All filters work together — this means a row must match all filters to be displayed.

Filter Options

Filter options for text columns.

Text columns are for things like names, cities, or departments.

Filter type	What it does
equals	Finds rows that exactly match the text.
not equals	Hides rows that match the text.
contains	Finds rows that contain the text somewhere in the field.
does not contain	Hides rows that contain the text.
starts with	Finds rows where the text starts with your input.
ends with	Finds rows where the text ends with your input.
is empty	Shows rows where the field is completely empty.
is not empty	Shows rows where something is entered.

Examples:

• Only show rows where City is "Berlin" → equals Berlin

• Show rows where Department is not empty → is not null or empty

Filter options for number columns.

These apply to fields like "Age", "Score", or "Amount".

Filter type	What it does
equals	Finds rows with exactly this number.
not equals	Hides rows with this number.
greater than	Shows rows with numbers higher than your input.
greater than or equal	Shows rows that are equal to or higher than your number.
less than	Shows rows with numbers lower than your input.
less than or equal	Shows rows that are equal to or lower.
is empty	Finds rows where the number is missing.
is not empty	Finds rows that have a number.

Examples:

• Only show people older than 65 → Age greater than 65

• Exclude rows where the score is missing → Score is not empty

Filter options for date columns.

Date columns include fields like "Submission date" or "Created at".

Filter type	What it does
equals	Finds rows with exactly the same date.
not equals	Hides rows with exactly this date.
after	Shows rows with dates after your input.
after or on	Shows rows on or after your chosen date.
before	Shows rows with dates before your input.
before or on	Shows rows on or before your chosen date.
is empty	Finds rows where the date is missing.
is not empty	Shows rows that have a filled date.

Examples:

• See all entries after January 1, 2024 → after 01/01/2024

• Find missing submission dates → is null

Filtering on aggregated values

Sometimes you don't want to filter individual data rows — instead, you want to filter based on a summary like a sum or an average.

For example:

"I only want to see countries if they have more than 450 cities."

To do this in the insight editor, use aggregation filters. Here's how it works.

How it Works Step by Step

Let's say you're working with customer data. You want to see:

• Each country

• How many customers they have

But only if that country has more than 450 cities in the data.

Here's how:

1

Select the columns you want to use.

In the Columns section, add:

• Country (no aggregation)

• City → Aggregation: Count of unique values

• Customer ID → Aggregation: Count of unique values

You'll now see a table like this:

Country	Number of Cities	Number of Customers
Korea	812	9,320
Congo	829	8,750
Saint Martin	451	5,320
France	300	6,700

2

Add a filter for the aggregated column.

In the Filters section:

• Select City

• Set the aggregation: Count of unique values

• Condition: greater than 450

This keeps only the countries that have more than 450 unique cities in the data.

Your table now looks like this:

Country	Number of Cities	Number of Customers
Korea	812	9,320
Congo	829	8,750
Saint Martin	451	5,320

3

Remove the column used for filtering. (optional)

If you don't want to show the "Number of Cities" column in the table anymore, simply remove it in the Columns section. Don't worry — the filter will still work!

Now the table will look like this:

Country	Number of Customers
Korea	9,320
Congo	8,750
Saint Martin	5,320

You still only see countries with more than 450 cities — you're just hiding the column with the city count.

Use this when you want to:

• Show grouped data (like sums per country)

• Include rows after aggregation

• Filter on things like "number of cities", "average age", or "total revenue"

How to remove filters

To remove a filter, click the small X next to it.

Tips for Working with Filters

• You can add more than one filter. For example: only show people from Berlin and over 60 years old.

• If you only want to show rows with filled values, use is not empty.

• Use contains when you're not sure of the exact value (like searching for "heim" to find "Mannheim", "Heimfeld", etc.).

• Empty values and "null" mean there's no data in that field.