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Filters

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Last updated 1 year ago

Filters are one of the core features within the data tables, providing you with the ability to refine and segment large datasets to focus on specific aspects. Essentially, a filter is a set of criteria applied to data to isolate particular elements based on defined parameters. This tool is crucial for managing and interpreting data more effectively, as it helps in extracting meaningful information from potentially overwhelming datasets.

Filters are applied individually to each or (organized in ). By applying filters to these individual elements, you can tailor analysis to specific areas of interest. For example, you can apply a filter to view sales data for a particular portfolio or analyze click data originating from a specific campaign.

This individual application of filters to each dimension or metric allows for a high degree of customization in your data analysis. You are free to combine multiple filters across different dimensions and metrics to create complex, layered data views. This approach enables deep dives into specific data segments, facilitating more detailed and targeted analysis, which is essential for informed decision-making and strategic planning.

Filtering data

Adding/removing sets of parameters

Defining each set of parameters

Each individual set of parameters includes input fields for:

  1. Values: An input field where you specify the value relevant to the chosen condition, which can either be a string (text) or a number.

Implementing/discarding changes

To filter a column quickly without opening the filters modal and configuring multiple filters, you can use the column filtering controller instead.


Filters for each column are defined by sets of parameters, allowing you to tailor the data view according to your specific needs. You have the ability to incorporate more than one set of parameters and use to combine them in various ways. By stacking multiple sets of parameters, you can refine your views to target very specific datasets. This multi-layered approach to filtering is essential for deep data analysis, enabling users to drill down into the most relevant details.

To filter the current data table, click the "filters" button in the .

: Here, you select from a dropdown menu the specific condition (like 'equals', 'contains', etc.) that applies to the data.

: Another dropdown menu where you select how the conditions will interact (such as 'any', 'all').

Stacked sets of parameters will trigger the primary operators' function, which combines these sets in different ways, as detailed in the . This layered approach is key for creating precise and tailored data views.

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Conditions
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Conditions
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Nested operators