BUG: Series(category).replace to maintain order of categories #51057
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mroeschke
reviewed
Jan 30, 2023
| # GH51016 | ||
| dtype = pd.CategoricalDtype([0, 1, 2], ordered=True) | ||
| ser = pd.Series([0, 1, 2], dtype=dtype) | ||
| result = ser.replace(0, 2) |
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Do we have a test where a missing value is replaced?
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I don't see any. Series(category).replace doesn't actually work for replacing missing values - see below. I checked 1.5 and it doesn't replace in 1.5 either.
In [13]: import pandas as pd
In [14]: import numpy as np
In [15]: dtype = pd.CategoricalDtype(["A", "B"])
In [16]: ser = pd.Series(["A", None], dtype=dtype)
In [17]: ser
Out[17]:
0 A
1 NaN
dtype: category
Categories (2, object): ['A', 'B']
In [18]: ser.replace(np.nan, "C") # <- this doesn't actually replace the missing value
Out[18]:
0 A
1 NaN
dtype: category
Categories (2, object): ['A', 'B']
mroeschke
reviewed
Feb 1, 2023
| new_categories = Index(ser.drop_duplicates(keep="first")) | ||
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| # GH51016: maintain order of existing categories | ||
| idxr = cat.categories.get_indexer_for(all_values) |
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Do we need to do all this if ordered=False?
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1.5x maintains the existing order of categories regardless of ordered so I'd lean towards keeping that behavior. The new ordering logic is barely measurable perf-wise relative to the whole replace operation.
mroeschke
approved these changes
Feb 1, 2023
|
Thanks @lukemanley |
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replaceis changing the order of ordered categories #51016No whatsnew as this is a regression on main only.