internlm/SWE-Fixer-Train-110K huggingface dataset:
Repository: modin-project/modin

Issue:
REFACTOR: make series __getattr__ and __getitem__ (string)  work better with non-pandas index
This is upstreaming some relevant portions of https://github.com/ponder-org/modin-public/commit/4c4dd74d95e1dc50f3940128e95fd39731078a9b

context: 
modin-on-ponder dataframes have an index that's not stored entirely in memory, and `key in self.index` [here](https://github.com/modin-project/modin/blob/f4e96758402d757eedfb515425464eca5af25878/modin/pandas/series.py#L317) requires an extra query, while this code inside of base `__getitem__` constructs a pandas series out of the non-pandas index, which is slow because it iterates through the entire index: https://github.com/modin-project/modin/blob/f4e96758402d757eedfb515425464eca5af25878/modin/pandas/base.py#L3667-L3668

We should
1) try to getitem instead of checking the index first
1) pass a struct-like object instead of a pandas dataframe to `convert_to_index_sliceable`



Discussion:
i realized that without key in self.index here, getattr(series, partial_datetime_index_key) will work, though it shouldn’t: https://github.com/modin-project/modin/blob/f4e96758402d757eedfb515425464eca5af25878/modin/pandas/series.py#L317
i don’t see a neat way to solve this we'll leave the `in index` check, but i'll get rid of the pandas constructor call still.

Resolving patch:
diff --git a/modin/pandas/base.py b/modin/pandas/base.py
--- a/modin/pandas/base.py
+++ b/modin/pandas/base.py
@@ -54,6 +54,7 @@
 )
 import pickle as pkl
 import re
+from types import SimpleNamespace
 from typing import Optional, Union, Sequence, Hashable
 import warnings
 
@@ -3743,8 +3744,10 @@ def __getitem__(self, key):
         if isinstance(key, slice) or (
             isinstance(key, str) and (not self._is_dataframe or key not in self.columns)
         ):
+            # use convert_to_index_sliceable without creating a pandas.DataFrame because
+            # of https://github.com/modin-project/modin/issues/6151
             indexer = convert_to_index_sliceable(
-                pandas.DataFrame(index=self.index), key
+                SimpleNamespace(index=self.index, columns=pandas.Index([])), key
             )
         if indexer is not None:
             return self._getitem_slice(indexer)
@@ -3850,7 +3853,11 @@ def _setitem_slice(self, key: slice, value):
         value : object
             Value to assing to the rows.
         """
-        indexer = convert_to_index_sliceable(pandas.DataFrame(index=self.index), key)
+        # use convert_to_index_sliceable without creating a pandas.DataFrame because of
+        # https://github.com/modin-project/modin/issues/6151
+        indexer = convert_to_index_sliceable(
+            SimpleNamespace(index=self.index, columns=pandas.Index([])), key
+        )
         self.iloc[indexer] = value
 
     def _getitem_slice(self, key: slice):


Test patch:
Repository: modin-project/modin

Issue:
BUG: groupby quantile has default of numeric_only=False
have to upstream https://github.com/ponder-org/modin-public/commit/26ff9b0871d9ce57bba2b8835f20f728fb4a9833

this does not match pandas 1.5: https://pandas.pydata.org/pandas-docs/version/1.5/reference/api/pandas.core.groupby.DataFrameGroupBy.quantile.html


Discussion:

Resolving patch:
diff --git a/modin/pandas/groupby.py b/modin/pandas/groupby.py
--- a/modin/pandas/groupby.py
+++ b/modin/pandas/groupby.py
@@ -1114,9 +1114,20 @@ def rolling(self, *args, **kwargs):
     def hist(self):
         return self._default_to_pandas(lambda df: df.hist())
 
-    def quantile(self, q=0.5, interpolation="linear"):
-        # TODO: pandas 1.5 now supports numeric_only as an argument
+    def quantile(self, q=0.5, interpolation="linear", numeric_only=no_default):
         # TODO: handle list-like cases properly
+        if numeric_only is no_default:
+            numeric_only = True
+        # We normally handle `numeric_only` by masking non-numeric columns; however
+        # pandas errors out if there are only non-numeric columns and `numeric_only=True`
+        # for groupby.quantile.
+        if numeric_only:
+            if all(
+                [not is_numeric_dtype(dtype) for dtype in self._query_compiler.dtypes]
+            ):
+                raise TypeError(
+                    f"'quantile' cannot be performed against '{self._query_compiler.dtypes[0]}' dtypes!"
+                )
         if is_list_like(q):
             return self._default_to_pandas(
                 lambda df: df.quantile(q=q, interpolation=interpolation)
@@ -1125,7 +1136,7 @@ def quantile(self, q=0.5, interpolation="linear"):
         return self._check_index(
             self._wrap_aggregation(
                 type(self._query_compiler).groupby_quantile,
-                numeric_only=False,
+                numeric_only=numeric_only,
                 agg_kwargs=dict(q=q, interpolation=interpolation),
             )
         )


Test patch:
Repository: modin-project/modin

Issue:
BUG: groupby getitem gets columns in wrong order
have to port fix from https://github.com/ponder-org/modin-public/commit/a4f7eb5ee4cfc61fb1dd72be7ae0e75d26f11d2a


Discussion:

Resolving patch:
diff --git a/modin/pandas/groupby.py b/modin/pandas/groupby.py
--- a/modin/pandas/groupby.py
+++ b/modin/pandas/groupby.py
@@ -651,8 +651,14 @@ def __getitem__(self, key):
                         + "df.groupby(df['by_column'].copy())['by_column']"
                     ),
                 )
-            cols_to_grab = internal_by.union(key)
-            key = [col for col in self._df.columns if col in cols_to_grab]
+            # We need to maintain order of the columns in key, using a set doesn't
+            # maintain order.
+            # We use dictionaries since they maintain insertion order as of 3.7,
+            # and its faster to call dict.update than it is to loop through `key`
+            # and select only the elements which aren't in `cols_to_grab`.
+            cols_to_grab = dict.fromkeys(self._internal_by)
+            cols_to_grab.update(dict.fromkeys(key))
+            key = [col for col in cols_to_grab.keys() if col in self._df.columns]
             return DataFrameGroupBy(
                 self._df[key],
                 drop=self._drop,


Test patch:
diff --git a/modin/pandas/test/test_groupby.py b/modin/pandas/test/test_groupby.py
--- a/modin/pandas/test/test_groupby.py
+++ b/modin/pandas/test/test_groupby.py
@@ -1453,6 +1453,18 @@ def test_groupby_on_index_values_with_loop():
         df_equals(modin_dict[k], pandas_dict[k])
 
 
+def test_groupby_getitem_preserves_key_order_issue_6154():
+    a = np.tile(["a", "b", "c", "d", "e"], (1, 10))
+    np.random.shuffle(a[0])
+    df = pd.DataFrame(
+        np.hstack((a.T, np.arange(100).reshape((50, 2)))),
+        columns=["col 1", "col 2", "col 3"],
+    )
+    eval_general(
+        df, df._to_pandas(), lambda df: df.groupby("col 1")[["col 3", "col 2"]].count()
+    )
+
+
 @pytest.mark.parametrize(
     "groupby_kwargs",
     [
Repository: modin-project/modin

Issue:
FIX: fix values=None in pivot()
port this portion: https://github.com/modin-project/modin/pull/6135#discussion_r1194667470
from this commit:  https://github.com/ponder-org/modin-public/commit/40932f3586270454f0512a20cc41ecda9b3b4f04


Discussion:

Resolving patch:
diff --git a/modin/pandas/dataframe.py b/modin/pandas/dataframe.py
--- a/modin/pandas/dataframe.py
+++ b/modin/pandas/dataframe.py
@@ -1482,6 +1482,15 @@ def pivot(self, index=None, columns=None, values=None):  # noqa: PR01, RT01, D20
         """
         Return reshaped ``DataFrame`` organized by given index / column values.
         """
+        # if values is not specified, it should be the remaining columns not in
+        # index or columns
+        if values is None:
+            values = list(self.columns)
+            if index:
+                values = [v for v in values if v not in index]
+            if columns:
+                values = [v for v in values if v not in columns]
+
         return self.__constructor__(
             query_compiler=self._query_compiler.pivot(
                 index=index, columns=columns, values=values


Test patch:
diff --git a/modin/pandas/test/test_general.py b/modin/pandas/test/test_general.py
--- a/modin/pandas/test/test_general.py
+++ b/modin/pandas/test/test_general.py
@@ -544,6 +544,19 @@ def test_pivot():
         pd.pivot(test_df["bar"], index="foo", columns="bar", values="baz")
 
 
+def test_pivot_values_is_none():
+    test_df = pd.DataFrame(
+        {
+            "foo": ["one", "one", "one", "two", "two", "two"],
+            "bar": ["A", "B", "C", "A", "B", "C"],
+            "baz": [1, 2, 3, 4, 5, 6],
+            "zoo": ["x", "y", "z", "q", "w", "t"],
+        }
+    )
+    df = pd.pivot(test_df, index="foo", columns="bar")
+    assert isinstance(df, pd.DataFrame)
+
+
 def test_pivot_table():
     test_df = pd.DataFrame(
         {
Repository: modin-project/modin

Issue:
REFACTOR: Add query compiler methods for groupby idxmax, idxmin
upstreaming https://github.com/ponder-org/modin-public/commit/489622c622618c62f4402442b9aeffe82db2855d




Discussion:

Resolving patch:
diff --git a/modin/core/storage_formats/base/query_compiler.py b/modin/core/storage_formats/base/query_compiler.py
--- a/modin/core/storage_formats/base/query_compiler.py
+++ b/modin/core/storage_formats/base/query_compiler.py
@@ -2790,6 +2790,42 @@ def groupby_any(
             drop=drop,
         )
 
+    @doc_utils.doc_groupby_method(
+        action="get the index of the minimum value",
+        result="index of minimum value",
+        refer_to="idxmin",
+    )
+    def groupby_idxmin(
+        self, by, axis, groupby_kwargs, agg_args, agg_kwargs, drop=False
+    ):
+        return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.idxmin)(
+            self,
+            by=by,
+            axis=axis,
+            groupby_kwargs=groupby_kwargs,
+            agg_args=agg_args,
+            agg_kwargs=agg_kwargs,
+            drop=drop,
+        )
+
+    @doc_utils.doc_groupby_method(
+        action="get the index of the maximum value",
+        result="index of maximum value",
+        refer_to="idxmax",
+    )
+    def groupby_idxmax(
+        self, by, axis, groupby_kwargs, agg_args, agg_kwargs, drop=False
+    ):
+        return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.idxmax)(
+            self,
+            by=by,
+            axis=axis,
+            groupby_kwargs=groupby_kwargs,
+            agg_args=agg_args,
+            agg_kwargs=agg_kwargs,
+            drop=drop,
+        )
+
     @doc_utils.doc_groupby_method(
         action="get the minimum value", result="minimum value", refer_to="min"
     )
diff --git a/modin/pandas/groupby.py b/modin/pandas/groupby.py
--- a/modin/pandas/groupby.py
+++ b/modin/pandas/groupby.py
@@ -339,8 +339,12 @@ def min(self, numeric_only=False, min_count=-1):
             agg_kwargs=dict(min_count=min_count),
         )
 
-    def idxmax(self):
-        return self._default_to_pandas(lambda df: df.idxmax())
+    def idxmax(self, axis=0, skipna=True, numeric_only=True):
+        return self._wrap_aggregation(
+            type(self._query_compiler).groupby_idxmax,
+            agg_kwargs=dict(axis=axis, skipna=skipna),
+            numeric_only=numeric_only,
+        )
 
     @property
     def ndim(self):
@@ -679,8 +683,12 @@ def bfill(self, limit=None):
         )
         return self.fillna(limit=limit, method="bfill")
 
-    def idxmin(self):
-        return self._default_to_pandas(lambda df: df.idxmin())
+    def idxmin(self, axis=0, skipna=True, numeric_only=True):
+        return self._wrap_aggregation(
+            type(self._query_compiler).groupby_idxmin,
+            agg_kwargs=dict(axis=axis, skipna=skipna),
+            numeric_only=numeric_only,
+        )
 
     def prod(self, numeric_only=None, min_count=0):
         return self._wrap_aggregation(


Test patch:
Repository: modin-project/modin

Issue:
BUG: Failed to pass storage_options parameter to the to_csv function of PandasOnUnidistIO class with fsspec
Similar to #6097.


Discussion:

Resolving patch:
diff --git a/modin/core/execution/unidist/implementations/pandas_on_unidist/io/io.py b/modin/core/execution/unidist/implementations/pandas_on_unidist/io/io.py
--- a/modin/core/execution/unidist/implementations/pandas_on_unidist/io/io.py
+++ b/modin/core/execution/unidist/implementations/pandas_on_unidist/io/io.py
@@ -159,7 +159,9 @@ def func(df, **kw):  # pragma: no cover
             path_or_buf = csv_kwargs["path_or_buf"]
             is_binary = "b" in csv_kwargs["mode"]
             csv_kwargs["path_or_buf"] = io.BytesIO() if is_binary else io.StringIO()
+            storage_options = csv_kwargs.pop("storage_options", None)
             df.to_csv(**csv_kwargs)
+            csv_kwargs.update({"storage_options": storage_options})
             content = csv_kwargs["path_or_buf"].getvalue()
             csv_kwargs["path_or_buf"].close()
 


Test patch:
Repository: modin-project/modin

Issue:
DOCS: Misleading default values for MODIN_CPUS and MODIN_NPARTITIONS
[**Is your feature request related to a problem? Please describe.**]
Seems [here ](https://modin.readthedocs.io/en/stable/flow/modin/config.html#modin-configs-list) we have wrong default values for MODIN_CPUS and MODIN_NPARTITIONS


Discussion:

Resolving patch:
diff --git a/modin/config/__main__.py b/modin/config/__main__.py
--- a/modin/config/__main__.py
+++ b/modin/config/__main__.py
@@ -45,6 +45,11 @@ def export_config_help(filename: str) -> None:
         Name of the file to export configs data.
     """
     configs_data = []
+    default_values = dict(
+        RayRedisPassword="random string",
+        CpuCount="multiprocessing.cpu_count()",
+        NPartitions="equals to MODIN_CPUS env",
+    )
     for objname in sorted(globals()):
         obj = globals()[objname]
         if isinstance(obj, type) and issubclass(obj, Parameter) and not obj.is_abstract:
@@ -53,9 +58,7 @@ def export_config_help(filename: str) -> None:
                 "Env. Variable Name": getattr(
                     obj, "varname", "not backed by environment"
                 ),
-                "Default Value": obj._get_default()
-                if obj.__name__ != "RayRedisPassword"
-                else "random string",
+                "Default Value": default_values.get(obj.__name__, obj._get_default()),
                 # `Notes` `-` underlining can't be correctly parsed inside csv table by sphinx
                 "Description": dedent(obj.__doc__ or "").replace(
                     "Notes\n-----", "Notes:\n"


Test patch:
Repository: modin-project/modin

Issue:
REFACTOR: rewrite query compiler droplevel() so it doesn't rely on pandas index droplevel
porting:
- https://github.com/ponder-org/modin-public/commit/93e308487fa44bc4f7c582d34eaade12ecb21991
- droplevel part of https://github.com/ponder-org/modin-public/commit/3c10b00db493aceb5f9e57572530f1777ce01018

change the modin implementation to do the whole reset_index(), drop(), set_index() thing, which is probably about as expensive as the current implementation in OSS.


Discussion:

Resolving patch:
diff --git a/modin/pandas/base.py b/modin/pandas/base.py
--- a/modin/pandas/base.py
+++ b/modin/pandas/base.py
@@ -1416,12 +1416,33 @@ def droplevel(self, level, axis=0):  # noqa: PR01, RT01, D200
         Return `BasePandasDataset` with requested index / column level(s) removed.
         """
         axis = self._get_axis_number(axis)
-        new_axis = self.axes[axis].droplevel(level)
         result = self.copy()
         if axis == 0:
-            result.index = new_axis
+            index_columns = result.index.names.copy()
+            if is_integer(level):
+                level = index_columns[level]
+            elif is_list_like(level):
+                level = [
+                    index_columns[lev] if is_integer(lev) else lev for lev in level
+                ]
+            if is_list_like(level):
+                for lev in level:
+                    index_columns.remove(lev)
+            else:
+                index_columns.remove(level)
+            if len(result.columns.names) > 1:
+                # In this case, we are dealing with a MultiIndex column, so we need to
+                # be careful when dropping the additional index column.
+                if is_list_like(level):
+                    drop_labels = [(lev, "") for lev in level]
+                else:
+                    drop_labels = [(level, "")]
+                result = result.reset_index().drop(columns=drop_labels)
+            else:
+                result = result.reset_index().drop(columns=level)
+            result = result.set_index(index_columns)
         else:
-            result.columns = new_axis
+            result.columns = self.columns.droplevel(level)
         return result
 
     def drop_duplicates(


Test patch:
Repository: modin-project/modin

Issue:
BUG: tz_convert and tz_localize ignore `copy` argument.
### Modin version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest released version of Modin.

- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)


### Reproducible Example

```python
They ignore `copy`.
```


### Issue Description

They ignore `copy` and pass `True`. They should not.

### Expected Behavior

Should not ignore copy.

### Error Logs

<details>

```python-traceback

Replace this line with the error backtrace (if applicable).

```

</details>


### Installed Versions

<details>

Replace this line with the output of pd.show_versions()

</details>



Discussion:

Resolving patch:
diff --git a/modin/pandas/base.py b/modin/pandas/base.py
--- a/modin/pandas/base.py
+++ b/modin/pandas/base.py
@@ -3491,7 +3491,7 @@ def tz_convert(self, tz, axis=0, level=None, copy=True):  # noqa: PR01, RT01, D2
         """
         return self._create_or_update_from_compiler(
             self._query_compiler.tz_convert(
-                tz, axis=self._get_axis_number(axis), level=level, copy=True
+                tz, axis=self._get_axis_number(axis), level=level, copy=copy
             ),
             inplace=(not copy),
         )
@@ -3507,7 +3507,7 @@ def tz_localize(
                 tz,
                 axis=self._get_axis_number(axis),
                 level=level,
-                copy=True,
+                copy=copy,
                 ambiguous=ambiguous,
                 nonexistent=nonexistent,
             ),


Test patch:
Repository: modin-project/modin

Issue:
BUG: NumPy should reset index and columns no matter storage format
### Modin version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest released version of Modin.

- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)


### Reproducible Example

```python
NumPy should reset index + columns no matter the storage format.
```


### Issue Description

NumPy should reset index + columns no matter the storage format.

### Expected Behavior

NumPy should reset index + columns no matter the storage format.

### Error Logs

<details>

```python-traceback

Replace this line with the error backtrace (if applicable).

```

</details>


### Installed Versions

<details>

Replace this line with the output of pd.show_versions()

</details>



Discussion:

Resolving patch:
diff --git a/modin/numpy/arr.py b/modin/numpy/arr.py
--- a/modin/numpy/arr.py
+++ b/modin/numpy/arr.py
@@ -27,7 +27,6 @@
     Reduce,
     Binary,
 )
-from modin.config import StorageFormat
 
 from .utils import try_convert_from_interoperable_type
 
@@ -220,14 +219,13 @@ def __init__(
 
             self._query_compiler = pd.DataFrame(arr)._query_compiler
             new_dtype = arr.dtype
-        if StorageFormat.get() == "Pandas":
-            # These two lines are necessary so that our query compiler does not keep track of indices
-            # and try to map like indices to like indices. (e.g. if we multiply two arrays that used
-            # to be dataframes, and the dataframes had the same column names but ordered differently
-            # we want to do a simple broadcast where we only consider position, as numpy would, rather
-            # than pair columns with the same name and multiply them.)
-            self._query_compiler = self._query_compiler.reset_index(drop=True)
-            self._query_compiler.columns = range(len(self._query_compiler.columns))
+        # These two lines are necessary so that our query compiler does not keep track of indices
+        # and try to map like indices to like indices. (e.g. if we multiply two arrays that used
+        # to be dataframes, and the dataframes had the same column names but ordered differently
+        # we want to do a simple broadcast where we only consider position, as numpy would, rather
+        # than pair columns with the same name and multiply them.)
+        self._query_compiler = self._query_compiler.reset_index(drop=True)
+        self._query_compiler.columns = range(len(self._query_compiler.columns))
         new_dtype = new_dtype if dtype is None else dtype
         cols_with_wrong_dtype = self._query_compiler.dtypes != new_dtype
         if cols_with_wrong_dtype.any():


Test patch:
Repository: modin-project/modin

Issue:
BUG: Modin.Numpy should use np.float64 instead of pandas.Float64Dtype()
### Modin version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest released version of Modin.

- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)


### Reproducible Example

```python
use np.float64 instead of pandas extended dtype
```


### Issue Description

using pandas extended dtype may not work on all backends.

### Expected Behavior

use np.float64 instead of pandas extended dtype

### Error Logs

<details>

```python-traceback

Replace this line with the error backtrace (if applicable).

```

</details>


### Installed Versions

<details>

Replace this line with the output of pd.show_versions()

</details>



Discussion:

Resolving patch:
diff --git a/modin/numpy/arr.py b/modin/numpy/arr.py
--- a/modin/numpy/arr.py
+++ b/modin/numpy/arr.py
@@ -229,6 +229,8 @@ def __init__(
             self._query_compiler = self._query_compiler.reset_index(drop=True)
             self._query_compiler.columns = range(len(self._query_compiler.columns))
         new_dtype = new_dtype if dtype is None else dtype
+        if isinstance(new_dtype, pandas.Float64Dtype):
+            new_dtype = numpy.float64
         cols_with_wrong_dtype = self._query_compiler.dtypes != new_dtype
         if cols_with_wrong_dtype.any():
             self._query_compiler = self._query_compiler.astype(


Test patch: