(
self,
data: Mapping[Hashable, np.ndarray],
)
| 362 | return self.read(rows=size) |
| 363 | |
| 364 | def _convert_data( |
| 365 | self, |
| 366 | data: Mapping[Hashable, np.ndarray], |
| 367 | ) -> Mapping[Hashable, ArrayLike]: |
| 368 | # apply converters |
| 369 | clean_conv = self._clean_mapping(self.converters) |
| 370 | clean_dtypes = self._clean_mapping(self.dtype) |
| 371 | |
| 372 | # Apply NA values. |
| 373 | clean_na_values = {} |
| 374 | clean_na_fvalues = {} |
| 375 | |
| 376 | if isinstance(self.na_values, dict): |
| 377 | for col in self.na_values: |
| 378 | if col is not None: |
| 379 | na_value = self.na_values[col] |
| 380 | na_fvalue = self.na_fvalues[col] |
| 381 | |
| 382 | if isinstance(col, int) and col not in self.orig_names: |
| 383 | col = self.orig_names[col] |
| 384 | |
| 385 | clean_na_values[col] = na_value |
| 386 | clean_na_fvalues[col] = na_fvalue |
| 387 | else: |
| 388 | clean_na_values = self.na_values |
| 389 | clean_na_fvalues = self.na_fvalues |
| 390 | |
| 391 | return self._convert_to_ndarrays( |
| 392 | data, |
| 393 | clean_na_values, |
| 394 | clean_na_fvalues, |
| 395 | clean_conv, |
| 396 | clean_dtypes, |
| 397 | ) |
| 398 | |
| 399 | @final |
| 400 | def _convert_to_ndarrays( |
no test coverage detected