mixle.data.sources.pandas_source module

Pandas DataFrame connector + adapters – DataFrame columns into the sequence-encoded stats API.

The adapter never imports pandas (it duck-types df.columns/.loc/.itertuples); the pandas extra is only needed to construct the DataFrame you pass in. read_dataframe wraps a DataFrame as a DataSource so it funnels into the same encoder contract as every other source.

dataframe_records(df, fields=None, as_dict=False)[source]

Convert DataFrame columns into observation records for seq_encode.

A single selected field becomes scalar observations. Multiple selected fields become tuple observations in the requested field order, matching the data shape expected by composite distributions. When as_dict=True, each row is returned as a mapping keyed by the selected source field names.

Parameters:
Return type:

list[Any]

seq_encode_dataframe(df, fields=None, encoder=None, estimator=None, model=None, num_chunks=1, chunk_size=None)[source]

Sequence-encode selected DataFrame columns with the ordinary stats API.

Parameters:

  • df (Any)

  • fields (str | Sequence[Any] | None)

  • encoder (DataSequenceEncoder | None)

  • estimator (ParameterEstimator | None)

  • model (SequenceEncodableProbabilityDistribution | None)

  • num_chunks (int)

  • chunk_size (int | None)

read_dataframe(df, fields=None, *, as_dict=False, structure=EXCHANGEABLE, schema=None)[source]

Wrap a pandas DataFrame’s selected columns as a DataSource (scalar/tuple/dict records).

Parameters:

  • df (Any)

  • fields (str | Sequence[Any] | None)

  • as_dict (bool)

  • structure (SampleStructure)

  • schema (Schema | None)

Return type:

MaterializedSource