itholic opened a new pull request, #39786:
URL: https://github.com/apache/spark/pull/39786
### What changes were proposed in this pull request?
This PR proposes to allow `columns` parameter when creating `ps.DataFrame`
with `ps.Series` with limited condition.
### Why are the changes needed?
In pandas, they attach the new column consists with missing values when
`columns` contains more than 2 columns including valid column:
```python
>>> pser # pandas Series
0.427027 1
0.904592 2
0.599768 3
Name: x, dtype: int64
>>> pd.DataFrame(pser, columns=["x", "y", "z"])
x y z
0.427027 1 NaN NaN
0.904592 2 NaN NaN
0.599768 3 NaN NaN
```
But this method is potentially pretty expensive in pandas API on Spark, so I
guess that's why we currently don't support it.
However, I've seen examples of using the following:
```python
>>> ps.DataFrame(pser, columns=["x"])
x
0.427027 1
0.904592 2
0.599768 3
```
As shown in the example above, this just works the same as
`pd.DataFrame(pser)` (without `columns`).
But it fails with `ps.Series` as below:
```python
>>> ps.DataFrame(psser, columns=["x"]) # `psser` is pandas-on-Spark Series
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/pandas/frame.py", line 539, in __init__
assert columns is None
AssertionError
```
In this case, user might just want to clearly state column names in their
code, so I believe we can allow this rather than raising an `AssertionError`.
### Does this PR introduce _any_ user-facing change?
**Before**
```python
>>> ps.DataFrame(psser, columns=["x"]) # `psser` is pandas-on-Spark Series
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/pandas/frame.py", line 539, in __init__
assert columns is None
AssertionError
```
**After**
```python
>>> ps.DataFrame(psser, columns=["x"]) # `psser` is pandas-on-Spark Series
x
0.427027 1
0.904592 2
0.599768 3
```
### How was this patch tested?
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Added UTs.
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