Aggregate using one or more operations The purpose of this class is to expose an API similar to Pandas' `Resampler` for dask-expr
| 276 | |
| 277 | |
| 278 | class Resampler: |
| 279 | """Aggregate using one or more operations |
| 280 | |
| 281 | The purpose of this class is to expose an API similar |
| 282 | to Pandas' `Resampler` for dask-expr |
| 283 | """ |
| 284 | |
| 285 | def __init__(self, obj, rule, **kwargs): |
| 286 | if obj.divisions[0] is None: |
| 287 | msg = ( |
| 288 | "Can only resample dataframes with known divisions\n" |
| 289 | "See https://docs.dask.org/en/latest/dataframe-design.html#partitions\n" |
| 290 | "for more information." |
| 291 | ) |
| 292 | raise ValueError(msg) |
| 293 | self.obj = obj |
| 294 | self.rule = rule |
| 295 | self.kwargs = kwargs |
| 296 | |
| 297 | def _single_agg(self, expr_cls, how_args=(), how_kwargs=None): |
| 298 | return new_collection( |
| 299 | expr_cls( |
| 300 | self.obj, |
| 301 | self.rule, |
| 302 | self.kwargs, |
| 303 | how_args=how_args, |
| 304 | how_kwargs=how_kwargs, |
| 305 | ) |
| 306 | ) |
| 307 | |
| 308 | @derived_from(pd_Resampler) |
| 309 | def count(self): |
| 310 | return self._single_agg(ResampleCount) |
| 311 | |
| 312 | @derived_from(pd_Resampler) |
| 313 | def sum(self): |
| 314 | return self._single_agg(ResampleSum) |
| 315 | |
| 316 | @derived_from(pd_Resampler) |
| 317 | def prod(self): |
| 318 | return self._single_agg(ResampleProd) |
| 319 | |
| 320 | @derived_from(pd_Resampler) |
| 321 | def mean(self): |
| 322 | return self._single_agg(ResampleMean) |
| 323 | |
| 324 | @derived_from(pd_Resampler) |
| 325 | def min(self): |
| 326 | return self._single_agg(ResampleMin) |
| 327 | |
| 328 | @derived_from(pd_Resampler) |
| 329 | def max(self): |
| 330 | return self._single_agg(ResampleMax) |
| 331 | |
| 332 | @derived_from(pd_Resampler) |
| 333 | def first(self): |
| 334 | return self._single_agg(ResampleFirst) |
| 335 |