# Pivoting/Group By

## What is a pivot table?

A pivot table is a method of aggregating a dataframe within one or more discrete categories you create. This aggregation might include sums, averages, or other statistics, which the pivot table groups together using a chosen aggregation function applied to the grouped values.

If you're used to doing "group bys" in Pandas, or using Excel Pivot Tables, then you're looking for Pivoting in Mito!

## Creating a pivot table

* Click on the `Pivot` icon in the `Home` tab of the Mito toolbar.
* Add a column to the `Rows` section to construct a key to group the dataframe by.
  * Optionally, to stratify the groups into individual cells, add a column to the `Columns` section.
* Add a column to the the `Values` section to aggregate data within the buckets defined by `Rows` and `Columns` above.
  * Optionally, switch the aggregation method of the column in the `Values` section.
* Optionally, add and configure `Filters` on the pivot table. Notably, these filters are applied to the source dataset before it is pivoted. 

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### Group Dates within in Pivot Tables

If you're aggregating based on a `datetime` column (aka: you have a `datetime` column in either the `Rows` or `Columns` section), you can select how to group the date.&#x20;

By default, Mito will group dates by exact time. This means that two rows will be put in the same bucket within the pivot table if the `datetime` column matches to the exact second.&#x20;

However, if you wish to aggregate based on the `year`, then you can change the `group date by` to `year`. This will ignore months, days, minutes, and seconds, and combine all rows in the same year into the same bucket. Create a pivot table to explore all the ways to group dates.

{% hint style="info" %}
If the `group date by` option does not appear after adding a column to the Rows or Columns section of a pivot table, ensure that you have changed the dtype of that column to a `datetime` first.
{% endhint %}