What’s new in 3.2.0 (2023-11-13)
================================

New Features
------------

Eventstream
~~~~~~~~~~~

- Improved working with ``RawDataSchema``. All columns of a raw DataFrame except ``user_id``, ``event``, ``timestamp`` are considered as custom now and added to the eventstream automatically. New argument ``custom_cols`` shapes a white list for the columns to be added only. See :ref:`eventstream user guide<eventstream_custom_cols>` for details.

- ``EventstreamSchema`` can be defined as a dictionary. See :ref:`eventstream user guide<eventstream_field_names>`.

- Synthetic events ``path_start`` and ``path_end`` are added automatically to an eventstream as if :py:class:`.AddStartEndEvents` was applied.

Data processors
~~~~~~~~~~~~~~~

- Added :ref:`GroupEventsBulk<group_events_bulk>` data processor. Now you can apply multiple grouping operations simultaneously.

.. code-block:: python

    stream.group_events_bulk(
        {
            'product': lambda _df: _df['event'].str.startswith('product'),
            'delivery': lambda _df: _df['event'].str.startswith('delivery')
        }
    )

- Added :ref:`Pipe<pipe>` data processor. It allows you to modify an eventstream as if you worked with pandas DataFrame.

.. code-block:: python

    stream.pipe(lambda _df: _df.assign(new_column=100))

- ``schema`` argument is not mandatory for custom functions of :py:class:`.FilterEvents` and :py:class:`.GroupEvents` data processors.

.. code-block:: python

    stream.filter_events(lambda _df: _df['user_id'] == 'user_12345')

- The architecture of the data processors was improved and simplified. Some legacy features were removed.

Transition graph
~~~~~~~~~~~~~~~~

- The default ``edges_weight_col`` and ``nodes_weight_col`` is set to ``user_id``. It means that the default weights are associated with the number of unique users who had given transition or experienced given event.