Issue139/monetdbe append #167
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This is the precision that monetdbe offers. For us it is a shortcoming in pandas that it does not offer other units of precision outside of nanoseconds.
| work_objs = [] | ||
| # cffi_objects assists to keep all in-memory native data structure alive during the execution of this call | ||
| cffi_objects = [] | ||
| cffi_objects = list() |
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i think in an other PR or a previous commit i merged work_objs and cffi_objects, since afaik they do the same thing, or not?
| numpy_type: np.dtype | ||
| c_string_type: str | ||
| py_converter: Optional[Callable] | ||
| null_value: Optional[Union[int, np.floating]] |
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if you removed that to fix mypy complaining, numpy added new type annotations, and in master i fixed the type annotation for the null field (an optional np.floatable I believe)
| np_col: np.ndarray = np.array([extract(rcol, r) for r in range(result.nrows)]) | ||
| values = [extract(rcol, r) for r in range(result.nrows)] | ||
| np_col: np.ndarray = np.array(values) | ||
| np_mask = np.array([v is None for v in values]) |
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To me, this seems almost the same except for an extra list comprehension.
mask = np_col == type_info.null_value is evaluated in numpy land and should be faster
my guess is you did this to get rid of the monetdbType null value field, see comment above.
| work_column.name = ffi.new('char[]', column_name.encode()) | ||
| if type_info.numpy_type.kind == 'U': | ||
| if type_info.numpy_type.kind == 'M': | ||
| t = ffi.new('monetdbe_data_timestamp[]', work_column.count) |
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just a comment, no action required, but this function is getting a bit messy, we should split it up in the future.
| np_col: np.ndarray = np.array([extract(rcol, r) for r in range(result.nrows)]) | ||
| values = [extract(rcol, r) for r in range(result.nrows)] | ||
| np_col: np.ndarray = np.array(values) | ||
| np_mask = np.array([v is None for v in values]) |
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pull request for new numpy/type features