pdstools.utils.streamlit_utils
==============================

.. py:module:: pdstools.utils.streamlit_utils


Functions
---------

.. autoapisummary::

   pdstools.utils.streamlit_utils.cached_sample
   pdstools.utils.streamlit_utils.cached_datamart
   pdstools.utils.streamlit_utils.import_datamart
   pdstools.utils.streamlit_utils.from_uploaded_file
   pdstools.utils.streamlit_utils.from_file_path
   pdstools.utils.streamlit_utils.model_selection_df
   pdstools.utils.streamlit_utils.filter_dataframe
   pdstools.utils.streamlit_utils.model_and_row_counts
   pdstools.utils.streamlit_utils.configure_predictor_categorization
   pdstools.utils.streamlit_utils.convert_df
   pdstools.utils.streamlit_utils.st_get_latest_pdstools_version


Module Contents
---------------

.. py:function:: cached_sample()

.. py:function:: cached_datamart(**kwargs)

.. py:function:: import_datamart(extract_pyname_keys: bool)

.. py:function:: from_uploaded_file(extract_pyname_keys, codespaces)

.. py:function:: from_file_path(extract_pyname_keys, codespaces)

.. py:function:: model_selection_df(df: polars.LazyFrame, context_keys: list)

.. py:function:: filter_dataframe(df: polars.LazyFrame, schema: Optional[dict] = None, queries=[]) -> polars.LazyFrame

   Adds a UI on top of a dataframe to let viewers filter columns

   :param df: Original dataframe
   :type df: pl.DataFrame

   :returns: The filtered LazyFrame
   :rtype: pl.LazyFrame


.. py:function:: model_and_row_counts(df: pdstools.utils.types.ANY_FRAME)

   Returns unique model id count and row count from a dataframe

   :param df: The input dataframe
   :type df: Union[pl.DataFrame, pl.LazyFrame]

   :returns: unique model count
             row count
   :rtype: Tuple[int, int]


.. py:function:: configure_predictor_categorization()

.. py:function:: convert_df(df)

.. py:function:: st_get_latest_pdstools_version()