SNOW-1518378 - Provide a numpy compatibility mapping to np.full_like #2499
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sfc-gh-mvashishtha
approved these changes
Oct 30, 2024
…ew/numpy.full_like
sfc-gh-rdurrani
requested changes
Oct 31, 2024
| @@ -80,6 +80,9 @@ | |||
| - Added support for applying Snowpark Python function `snowflake_cortex_summarize`. | |||
| - Added support for `DataFrame.attrs` and `Series.attrs`. | |||
| - Added support for `DataFrame.style`. | |||
| - Added support for `Index.to_numpy`. | |||
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These are probably merge artifacts?
| return NotImplemented | ||
| if not order == "K": | ||
| return NotImplemented | ||
| if dtype is not None: |
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Why can't we support dtype here? It just overrides the datatype provided by a right?
| result_shape = (result_shape,) | ||
| if result_shape is None: | ||
| result_shape = a.shape | ||
| if len(result_shape) == 2: |
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Maybe check if result_shape is list-like? otherwise something like result_shape = 'ab' would enter this conditional?
| result_shape = a.shape | ||
| if len(result_shape) == 2: | ||
| height, width = result_shape # type: ignore | ||
| return pd.DataFrame(fill_value, index=range(height), columns=range(width)) |
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Don't we need to set dtype here? Also if a DataFrame contains multiple dtypes, are the returned object's columns supposed to be the same type as each of the columns mapped positionally?
| height, width = result_shape # type: ignore | ||
| return pd.DataFrame(fill_value, index=range(height), columns=range(width)) | ||
| if len(result_shape) == 1: | ||
| return pd.Series(fill_value, index=range(result_shape[0])) |
| with SqlCounter(query_count=2): | ||
| snow_result = np.full_like(snow_df, 1234) | ||
| pandas_result = np.full_like(pandas_df, 1234) | ||
| assert_array_equal(np.array(snow_result), np.array(pandas_result)) |
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Should we be checking if the dataframes are equal?
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SNOW-1518378 - Provide a numpy compatibility mapping to np.full_like
This provides a numpy compat mapping to np.full_like; which fills a dataframe/series with a fill value the same shape as the input.
checklist: