ENH(string dtype): Implement cumsum for Python-backed strings #60938
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pandas/core/arrays/string_.py
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| # We can retain the running min/max by forward/backward filling. | ||
| ndarray = ndarray.copy() | ||
| missing.pad_or_backfill_inplace( | ||
| ndarray.T, |
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Is the .T needed? (I would think that ndarray is 1D)
pandas/core/arrays/string_.py
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| # the first NA value onward. | ||
| idx = np.argmax(na_mask) | ||
| tail = np.empty(len(ndarray) - idx, dtype="object") | ||
| tail[:] = np.nan |
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| tail[:] = np.nan | |
| tail[:] = self.dtype.na_value |
So we directly fill it with the appropriate NA value (although I assume the constructor would fix it up anyway)
pandas/core/arrays/string_.py
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| if tail is not None: | ||
| np_result = np.hstack((np_result, tail)) | ||
| elif na_mask is not None: | ||
| np_result = np.where(na_mask, np.nan, np_result) |
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| np_result = np.where(na_mask, np.nan, np_result) | |
| np_result = np.where(na_mask, self.dtype.na_value, np_result) |
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Thanks @rhshadrach |
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Owee, I'm MrMeeseeks, Look at me. There seem to be a conflict, please backport manually. Here are approximate instructions:
And apply the correct labels and milestones. Congratulations — you did some good work! Hopefully your backport PR will be tested by the continuous integration and merged soon! Remember to remove the If these instructions are inaccurate, feel free to suggest an improvement. |
Follow-up on #60633