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Popping item from categorical series returns index instead of value #2198

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rwedge opened this issue Sep 10, 2021 · 3 comments
Open

Popping item from categorical series returns index instead of value #2198

rwedge opened this issue Sep 10, 2021 · 3 comments

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@rwedge
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rwedge commented Sep 10, 2021

This was using koalas 1.8.1

>>> import databricks.koalas as ks
>>> series = ks.Series(['a', 'b', 'c', 'a'])
>>> cat_series = series.astype('category')
>>> series.pop(0)
'a'
>>> cat_series.pop(0)
0
>>> cat_series
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']

I would expect pop to return 'a' in this case, which is what a pandas categorical series would do
@HyukjinKwon
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cc @ueshin @xinrong-databricks FYI

@HyukjinKwon
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Would you mind filing a JIRA in https://issues.apache.org/jira/projects/SPARK?

@xinrong-meng
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Thanks for letting us know!

https://issues.apache.org/jira/browse/SPARK-36771 is created for this issue.

@xinrong-meng xinrong-meng mentioned this issue Sep 20, 2021
Closed
ueshin pushed a commit to apache/spark that referenced this issue Sep 21, 2021
@xinrong-meng @ueshin
[SPARK-36771][PYTHON] Fix pop of Categorical Series
079a9c5 
### What changes were proposed in this pull request?
Fix `pop` of Categorical Series to be consistent with the latest pandas (1.3.2) behavior.

### Why are the changes needed?
As databricks/koalas#2198, pandas API on Spark behaves differently from pandas on `pop` of Categorical Series.

### Does this PR introduce _any_ user-facing change?
Yes, results of `pop` of Categorical Series change.

#### From
```py
>>> psser = ps.Series(["a", "b", "c", "a"], dtype="category")
>>> psser
0    a
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> psser.pop(0)
0
>>> psser
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> psser.pop(3)
0
>>> psser
1    b
2    c
dtype: category
Categories (3, object): ['a', 'b', 'c']
```

#### To
```py
>>> psser = ps.Series(["a", "b", "c", "a"], dtype="category")
>>> psser
0    a
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> psser.pop(0)
'a'
>>> psser
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> psser.pop(3)
'a'
>>> psser
1    b
2    c
dtype: category
Categories (3, object): ['a', 'b', 'c']

```

### How was this patch tested?
Unit tests.

Closes #34052 from xinrong-databricks/cat_pop.

Authored-by: Xinrong Meng <[email protected]>
Signed-off-by: Takuya UESHIN <[email protected]>
@xinrong-meng xinrong-meng mentioned this issue Sep 21, 2021
Closed
ueshin pushed a commit to apache/spark that referenced this issue Sep 22, 2021
@xinrong-meng @ueshin
[SPARK-36771][PYTHON][3.2] Fix pop of Categorical Series
4543ac6 
### What changes were proposed in this pull request?
Fix `pop` of Categorical Series to be consistent with the latest pandas (1.3.2) behavior.

This is a backport of #34052.

### Why are the changes needed?
As databricks/koalas#2198, pandas API on Spark behaves differently from pandas on `pop` of Categorical Series.

### Does this PR introduce _any_ user-facing change?
Yes, results of `pop` of Categorical Series change.

#### From
```py
>>> psser = ps.Series(["a", "b", "c", "a"], dtype="category")
>>> psser
0    a
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> psser.pop(0)
0
>>> psser
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> psser.pop(3)
0
>>> psser
1    b
2    c
dtype: category
Categories (3, object): ['a', 'b', 'c']
```

#### To
```py
>>> psser = ps.Series(["a", "b", "c", "a"], dtype="category")
>>> psser
0    a
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> psser.pop(0)
'a'
>>> psser
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> psser.pop(3)
'a'
>>> psser
1    b
2    c
dtype: category
Categories (3, object): ['a', 'b', 'c']

```

### How was this patch tested?
Unit tests.

Closes #34063 from xinrong-databricks/backport_cat_pop.

Authored-by: Xinrong Meng <[email protected]>
Signed-off-by: Takuya UESHIN <[email protected]>
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3 participants
@rwedge @HyukjinKwon @xinrong-meng