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Title:
GroupBy nth with Categorical, NA Data and dropna · Issue #26454 · pandas-dev/pandas
Description:
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], ca...
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Keywords {🔍}
categorical, ser, data, willayd, cat, groupby, dropna, npnan, elements, values, dropnaall, length, sign, nth, type, projects, issue, dfgroupbycatsernth, valueerror, mismatch, expected, axis, jorisvandenbossche, commented, member, object, bug, navigation, issues, pull, requests, actions, security, closed, method, doesnt, pdcategoricala, categoriesa, pdseries, pddataframecat, dtype, int, added, missingdata, pdnat, pdna, isnull, interpolate, mentioned, handling,
Topics {✒️}
categorical data type projects nth method interpolate type comment metadata assignees object data na data df['cat'] = df['cat'] handling na values projects milestone groupby objects bug categorical observed=true np separate issues groupby expected axis nonsensical result categories matches makes sense worth calling milestone relationships length mismatch na values categories=['a' github dropna keyword cat = pd df = pd ser cat ser = pd dtype int64 observed nth 3 elements 2 elements object seriesgroupby na {'cat' cat 'cat' df values length pd 'ser' ser}
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DiscussionForumPosting:
context:https://schema.org
headline:GroupBy nth with Categorical, NA Data and dropna
articleBody:The `nth` method of GroupBy objects can accept a `dropna` keyword, but it doesn't handle Categorical data appropriately. For instance:
```python-traceback
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:
```python-traceback
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
@TomAugspurger
author:
url:https://github.com/WillAyd
type:Person
name:WillAyd
datePublished:2019-05-19T02:30:51.000Z
interactionStatistic:
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interactionType:https://schema.org/CommentAction
userInteractionCount:3
url:https://github.com/26454/pandas/issues/26454
context:https://schema.org
headline:GroupBy nth with Categorical, NA Data and dropna
articleBody:The `nth` method of GroupBy objects can accept a `dropna` keyword, but it doesn't handle Categorical data appropriately. For instance:
```python-traceback
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:
```python-traceback
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
@TomAugspurger
author:
url:https://github.com/WillAyd
type:Person
name:WillAyd
datePublished:2019-05-19T02:30:51.000Z
interactionStatistic:
type:InteractionCounter
interactionType:https://schema.org/CommentAction
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url:https://github.com/26454/pandas/issues/26454
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name:WillAyd
url:https://github.com/WillAyd
name:WillAyd
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