The nth method of GroupBy objects can accept a dropna keyword, but it doesn't handle Categorical data appropriately. For instance:
In [9]: cat = pd.Categorical(['a', np.nan, np.nan], categories=['a', 'b'])
...: ser = pd.Series([1, 2, 3])
...: df = pd.DataFrame({'cat': cat, 'ser': ser})
In [10]: df.groupby('cat')['ser'].nth(0, dropna='all')
ValueError: Length mismatch: Expected axis has 3 elements, new values have 2 elements
In [11]: df.groupby('cat', observed=True)['ser'].nth(0, dropna='all')
ValueError: Length mismatch: Expected axis has 3 elements, new values have 1 elements
Note that you can get a nonsensical result if the length of the categories matches the length of the Categorical:
In [9]: cat = pd.Categorical(['a', np.nan, np.nan], categories=['a', 'b', 'c'])
...: ser = pd.Series([1, 2, 3])
...: df = pd.DataFrame({'cat': cat, 'ser': ser})
In [10]: df.groupby('cat')['ser'].nth(0, dropna='all')
Out[12]:
cat
a 1
b 2
c 3
Name: ser, dtype: int64
I'm not sure it makes sense to specify both observed and dropna anyway, so I think we should be raising if a categorical with NA values is detected within that method
You can’t perform that action at this time.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4