pdstools.utils.streamlit_utils¶

Functions¶

`cached_sample`()
`cached_datamart`(**kwargs)	Load ADMDatamart with caching.
`cached_sample_prediction`()
`cached_prediction_table`(**kwargs)	Load Prediction with caching.
`import_datamart`(extract_pyname_keys[, infer_schema_length])	Import ADMDatamart data from various sources.
`from_uploaded_file`(extract_pyname_keys, codespaces[, ...])
`from_file_path`(extract_pyname_keys, codespaces[, ...])
`model_selection_df`(df, context_keys)
`filter_dataframe`(→ polars.LazyFrame)	Adds a UI on top of a dataframe to let viewers filter columns
`model_and_row_counts`(df)	Returns unique model id count and row count from a dataframe
`configure_predictor_categorization`()
`convert_df`(df)
`st_get_latest_pdstools_version`()

cached_datamart(**kwargs)¶

Load ADMDatamart with caching.

cached_prediction_table(**kwargs)¶

Load Prediction with caching.

import_datamart(extract_pyname_keys: bool, infer_schema_length: int = 10000)¶

Import ADMDatamart data from various sources.

Parameters:

extract_pyname_keys (bool) – Whether to extract additional keys from pyName column
infer_schema_length (int, default 10000) – Number of rows to scan for schema inference when reading CSV/JSON files. For large production datasets, increase this value (e.g., 200000) if columns are not being detected correctly.

from_uploaded_file(extract_pyname_keys, codespaces, infer_schema_length=10000)¶

from_file_path(extract_pyname_keys, codespaces, infer_schema_length=10000)¶

model_selection_df(df: polars.LazyFrame, context_keys: list)¶

Parameters:

filter_dataframe(df: polars.LazyFrame, schema: dict | None = None, queries=[]) → polars.LazyFrame¶

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

Parameters:

Returns:

The filtered LazyFrame

Return type:

pl.LazyFrame

model_and_row_counts(df: pdstools.utils.types.ANY_FRAME)¶

Returns unique model id count and row count from a dataframe

Parameters:: df (Union[pl.DataFrame, pl.LazyFrame]) – The input dataframe
Returns:: unique model count row count
Return type:: Tuple[int, int]