zhengruifeng commented on PR #37369:
URL: https://github.com/apache/spark/pull/37369#issuecomment-1204668069
maybe we still need type checking in this PR:
```python
In [1]: import pandas as pd
In [2]: import pyspark.pandas as ps
In [3]: df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]},
columns=['A', 'B'])
In [4]: df.groupby(['A'])['B'].nsmallest(5.5)
...
TypeError: cannot do positional indexing on Int64Index with these indexers
[5.5] of type float
In [5]: df = ps.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]},
columns=['A', 'B'])
In [6]: df.groupby(['A'])['B'].nsmallest(5.5)
A
1 0 3
2 1 4
3 2 5
4 3 6
Name: B, dtype: int64
In [7]: df.groupby(['A'])['B'].nsmallest("5.5")
Out[7]:
A
1 0 3
2 1 4
3 2 5
4 3 6
Name: B, dtype: int64
```
`ps` runs successfully with `5.5` and `"5.5"`, while`pd` throws a `TypeError`
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