(df, *by, dropna=None, observed=None, **kwargs)
| 335 | |
| 336 | |
| 337 | def _apply_chunk(df, *by, dropna=None, observed=None, **kwargs): |
| 338 | func = kwargs.pop("chunk") |
| 339 | columns = kwargs.pop("columns") |
| 340 | dropna = {"dropna": dropna} if dropna is not None else {} |
| 341 | observed = {"observed": observed} if observed is not None else {} |
| 342 | |
| 343 | g = _groupby_raise_unaligned(df, by=by, **observed, **dropna) |
| 344 | if is_series_like(df) or columns is None: |
| 345 | return func(g, **kwargs) |
| 346 | else: |
| 347 | if isinstance(columns, (tuple, list, set, pd.Index)): |
| 348 | columns = list(columns) |
| 349 | return func(g[columns], **kwargs) |
| 350 | |
| 351 | |
| 352 | def _var_chunk(df, *by, numeric_only=no_default, observed=False, dropna=True): |
no test coverage detected