Implement missing Expanding and Rolling APIs in Series, Frame, SeriesGroupBy, FrameGroupBy #977
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HyukjinKwon
added
enhancement
|
I am starting with Expanding in Series and Frame. |
HyukjinKwon
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Oct 31, 2019
This PR implements `expanding().count()`:
```python
>>> import databricks.koalas as ks
>>> ks.Series([2, 3, float("nan"), 10]).expanding().count()
0 1.0
1 2.0
2 2.0
3 3.0
Name: 0, dtype: float64
>>> ks.DataFrame({'a': [1, float('nan'), 3], 'b': [1.0, 2.0, 3.0]}).expanding().count()
a b
0 1.0 1.0
1 1.0 2.0
2 2.0 3.0
```
Relates to #977
|
I am starting with Rolling in Series and Frame. |
|
Please go ahead! |
HyukjinKwon
pushed a commit
that referenced
this issue
Nov 6, 2019
This PR implements rolling().count():
```python
>>> import databricks.koalas as ks
>>> s = ks.Series([2, 3, float("nan"), 10])
>>> s.rolling(1).count()
0 1.0
1 1.0
2 0.0
3 1.0
Name: 0, dtype: float64
>>> s.to_frame().rolling(1).count()
0
0 1.0
1 1.0
2 0.0
3 1.0
```
Relates to #977
HyukjinKwon
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Nov 7, 2019
…996) This PR implement min, max, sum and mean of Rolling in Series and DataFrame For Series: ```python >>> kser = ks.Series([2, 3, float("nan"), 10]) >>> kser 0 2.0 1 3.0 2 NaN 3 10.0 Name: 0, dtype: float64 >>> ksr.rolling(2).sum() 0 NaN 1 5.0 2 3.0 3 NaN Name: 0, dtype: float64 >>> ks.rolling(2).min() 0 NaN 1 2.0 2 3.0 3 NaN Name: 0, dtype: float64 >>> ks.rolling(2).max() 0 NaN 1 3.0 2 3.0 3 NaN Name: 0, dtype: float64 >>> ks.rolling(2).mean() 0 NaN 1 2.5 2 3.0 3 NaN Name: 0, dtype: float64 ``` For DataFrame ```python >>> kdf = ks.DataFrame({'a': [1, float('nan'), 3], 'b': [1.0, 2.0, 3.0]}) >>> kdf a b 0 1.0 1.0 1 NaN 2.0 2 3.0 3.0 >>> kdf.rolling(2).sum() a b 0 NaN NaN 1 1.0 3.0 2 NaN 5.0 >>> kdf.rolling(2).min() a b 0 NaN NaN 1 1.0 1.0 2 NaN 2.0 >>> kdf.rolling(2).max() a b 0 NaN NaN 1 1.0 2.0 2 NaN 3.0 >>> kdf.rolling(2).mean() a b 0 NaN NaN 1 1.0 1.5 2 NaN 2.5 ``` relates to #977
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See https://github.com/databricks/koalas/blob/master/databricks/koalas/missing/window.py
It mimics the class hierarchy from pandas internally. Ideally, we will be able to share implementation across APIs (like
cumsum).The text was updated successfully, but these errors were encountered: