milenkovicm commented on issue #1142:
URL:
https://github.com/apache/datafusion-ballista/issues/1142#issuecomment-2516839714
>
> I don’t have my computer this weekend so I can’t test to verify but you
may get unblocked if you do `ctx = SessionContext(ctx)`
>
I finally got some time to try this, but unfortunately no luck, no such
function.
I tried variation of the proposal wrapping DataFrame, but same error
```python
from ballista import BallistaBuilder
# from datafusion.context import SessionContext
from datafusion import functions as f
from datafusion.dataframe import DataFrame
ctx = BallistaBuilder()\
.standalone()
df = ctx.sql("SELECT 1 as r")
df0 = DataFrame(df)
df0.aggregate(
[f.col("r")], [f.count_star()]
)
df0.show()
```
```
TypeError Traceback (most recent call last)
Cell In[4], [line 14](vscode-notebook-cell:?execution_count=4&line=14)
[11](vscode-notebook-cell:?execution_count=4&line=11) df =
ctx.sql("SELECT 1 as r")
[13](vscode-notebook-cell:?execution_count=4&line=13) df0 =
DataFrame(df)
---> [14](vscode-notebook-cell:?execution_count=4&line=14) df0.aggregate(
[15](vscode-notebook-cell:?execution_count=4&line=15) [f.col("r")],
[f.count_star()]
[16](vscode-notebook-cell:?execution_count=4&line=16) )
[17](vscode-notebook-cell:?execution_count=4&line=17) df0.show()
File
~/git/arrow-ballista/python/venv/lib/python3.12/site-packages/datafusion/dataframe.py:197,
in DataFrame.aggregate(self, group_by, aggs)
[195](https://file+.vscode-resource.vscode-cdn.net/Users/marko/git/arrow-ballista/python/~/git/arrow-ballista/python/venv/lib/python3.12/site-packages/datafusion/dataframe.py:195)
group_by = [e.expr for e in group_by]
[196](https://file+.vscode-resource.vscode-cdn.net/Users/marko/git/arrow-ballista/python/~/git/arrow-ballista/python/venv/lib/python3.12/site-packages/datafusion/dataframe.py:196)
aggs = [e.expr for e in aggs]
-->
[197](https://file+.vscode-resource.vscode-cdn.net/Users/marko/git/arrow-ballista/python/~/git/arrow-ballista/python/venv/lib/python3.12/site-packages/datafusion/dataframe.py:197)
return DataFrame(self.df.aggregate(group_by, aggs))
```
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