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

Issue:
New API: `combine_first` for Series and DataFrame

### Describe the problem
Once #993 is implemented, this should be trivial to implement.


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
@@ -713,7 +713,7 @@ def combine(self, other, func, fill_value=None, **kwargs):
         )
 
     def combine_first(self, other):
-        return self._default_to_pandas("combine_first", other=other)
+        return self._binary_op("combine_first", other, _axis=0)
 
     def compound(self, axis=None, skipna=None, level=None):
         return self._default_to_pandas(


Test patch:
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -2236,11 +2236,15 @@ def test_boxplot(self, data):
         assert modin_df.boxplot() == to_pandas(modin_df).boxplot()
 
     def test_combine_first(self):
-        df1 = pd.DataFrame({"A": [None, 0], "B": [None, 4]})
-        df2 = pd.DataFrame({"A": [1, 1], "B": [3, 3]})
-
-        with pytest.warns(UserWarning):
-            df1.combine_first(df2)
+        data1 = {"A": [None, 0], "B": [None, 4]}
+        modin_df1 = pd.DataFrame(data1)
+        pandas_df1 = pandas.DataFrame(data1)
+        data2 = {"A": [1, 1], "B": [3, 3]}
+        modin_df2 = pd.DataFrame(data2)
+        pandas_df2 = pandas.DataFrame(data2)
+        df_equals(
+            modin_df1.combine_first(modin_df2), pandas_df1.combine_first(pandas_df2)
+        )
 
     def test_compound(self):
         data = test_data_values[0]
diff --git a/modin/pandas/test/test_series.py b/modin/pandas/test/test_series.py
--- a/modin/pandas/test/test_series.py
+++ b/modin/pandas/test/test_series.py
@@ -960,10 +960,12 @@ def test_combine(data):
 
 @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
 def test_combine_first(data):
-    modin_series, _ = create_test_series(data)  # noqa: F841
+    modin_series, pandas_series = create_test_series(data)
     modin_series2 = modin_series % (max(modin_series) // 2)
-    with pytest.warns(UserWarning):
-        modin_series.combine_first(modin_series2)
+    pandas_series2 = pandas_series % (max(pandas_series) // 2)
+    modin_result = modin_series.combine_first(modin_series2)
+    pandas_result = pandas_series.combine_first(pandas_series2)
+    df_equals(modin_result, pandas_result)
 
 
 @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
Repository: modin-project/modin

Issue:
New API: `assign` on `DataFrame`

### Describe the problem
<!-- Describe the problem clearly here. -->
Implementing `assign` should be relatively easy. All of the functionality already exists to support `assign`, it is just a matter of applying it to this function.




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
@@ -537,7 +537,13 @@ def append(self, other, ignore_index=False, verify_integrity=False, sort=None):
         return DataFrame(query_compiler=query_compiler)
 
     def assign(self, **kwargs):
-        return self._default_to_pandas(pandas.DataFrame.assign, **kwargs)
+        df = self.copy()
+        for k, v in kwargs.items():
+            if callable(v):
+                df[k] = v(df)
+            else:
+                df[k] = v
+        return df
 
     def boxplot(
         self,


Test patch:
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -2158,9 +2158,17 @@ def test_assign(self):
         data = test_data_values[0]
         modin_df = pd.DataFrame(data)
         pandas_df = pandas.DataFrame(data)
-        with pytest.warns(UserWarning):
-            modin_result = modin_df.assign(new_column=pd.Series(modin_df.iloc[:, 0]))
-        pandas_result = pandas_df.assign(new_column=pd.Series(pandas_df.iloc[:, 0]))
+        modin_result = modin_df.assign(new_column=pd.Series(modin_df.iloc[:, 0]))
+        pandas_result = pandas_df.assign(new_column=pandas.Series(pandas_df.iloc[:, 0]))
+        df_equals(modin_result, pandas_result)
+        modin_result = modin_df.assign(
+            new_column=pd.Series(modin_df.iloc[:, 0]),
+            new_column2=pd.Series(modin_df.iloc[:, 1]),
+        )
+        pandas_result = pandas_df.assign(
+            new_column=pandas.Series(pandas_df.iloc[:, 0]),
+            new_column2=pandas.Series(pandas_df.iloc[:, 1]),
+        )
         df_equals(modin_result, pandas_result)
 
     def test_at_time(self):
Repository: modin-project/modin

Issue:
New API: `droplevel` for Series and DataFrame

### Describe the problem
<!-- Describe the problem clearly here. -->
Implementing `droplevel` for `Series` and `DataFrame` should be relatively straightforward because it only affects and modifies the index.



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
@@ -1087,7 +1087,14 @@ def droplevel(self, level, axis=0):
         Returns:
             Index or MultiIndex
         """
-        return self._default_to_pandas("droplevel", level, axis=axis)
+        axis = self._get_axis_number(axis)
+        new_axis = self.axes[axis].droplevel(level)
+        result = self.copy()
+        if axis == 0:
+            result.index = new_axis
+        else:
+            result.columns = new_axis
+        return result
 
     def drop_duplicates(self, keep="first", inplace=False, **kwargs):
         """Return DataFrame with duplicate rows removed, optionally only considering certain columns


Test patch:
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -1137,12 +1137,8 @@ def test_droplevel(self):
         df.columns = pd.MultiIndex.from_tuples(
             [("c", "e"), ("d", "f")], names=["level_1", "level_2"]
         )
-
-        with pytest.warns(UserWarning):
-            df.droplevel("a")
-
-        with pytest.warns(UserWarning):
-            df.droplevel("level_2", axis=1)
+        df.droplevel("a")
+        df.droplevel("level_2", axis=1)
 
     @pytest.mark.parametrize(
         "data", test_data_with_duplicates_values, ids=test_data_with_duplicates_keys
Repository: modin-project/modin

Issue:
New API: `first` for Series and DataFrame

### Describe the problem
<!-- Describe the problem clearly here. -->
Similar to #989 and #990, however the utilities on a `pandas.DatetimeIndex` does not support a `first` operator, so we will probably need an additional step.



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
@@ -1301,7 +1301,7 @@ def f(x):
         return self[self.columns[bool_arr]]
 
     def first(self, offset):
-        return self._default_to_pandas("first", offset)
+        return self.loc[pandas.Series(index=self.index).first(offset).index]
 
     def first_valid_index(self):
         """Return index for first non-NA/null value.


Test patch:
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -2317,10 +2317,13 @@ def test_explode(self, data):
             modin_df.explode(modin_df.columns[0])
 
     def test_first(self):
-        i = pd.date_range("2018-04-09", periods=4, freq="2D")
-        ts = pd.DataFrame({"A": [1, 2, 3, 4]}, index=i)
-        with pytest.warns(UserWarning):
-            ts.first("3D")
+        i = pd.date_range("2010-04-09", periods=400, freq="2D")
+        modin_df = pd.DataFrame({"A": list(range(400)), "B": list(range(400))}, index=i)
+        pandas_df = pandas.DataFrame(
+            {"A": list(range(400)), "B": list(range(400))}, index=i
+        )
+        df_equals(modin_df.first("3D"), pandas_df.first("3D"))
+        df_equals(modin_df.first("20D"), pandas_df.first("20D"))
 
     @pytest.mark.skip(reason="Defaulting to Pandas")
     @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
diff --git a/modin/pandas/test/test_series.py b/modin/pandas/test/test_series.py
--- a/modin/pandas/test/test_series.py
+++ b/modin/pandas/test/test_series.py
@@ -1368,10 +1368,11 @@ def test_filter():
 
 
 def test_first():
-    i = pd.date_range("2018-04-09", periods=4, freq="2D")
-    ts = pd.Series([1, 2, 3, 4], index=i)
-    with pytest.warns(UserWarning):
-        ts.first("3D")
+    i = pd.date_range("2010-04-09", periods=400, freq="2D")
+    modin_series = pd.Series(list(range(400)), index=i)
+    pandas_series = pandas.Series(list(range(400)), index=i)
+    df_equals(modin_series.first("3D"), pandas_series.first("3D"))
+    df_equals(modin_series.first("20D"), pandas_series.first("20D"))
 
 
 @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
Repository: modin-project/modin

Issue:
New API: `last` for Series and DataFrame
Describe the problem
Similar to #989 and #990, however the utilities on a pandas.DatetimeIndex does not support a `last`operator, so we will probably need an additional step.


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
@@ -1518,7 +1518,7 @@ def kurtosis(self, axis=None, skipna=None, level=None, numeric_only=None, **kwar
         )
 
     def last(self, offset):
-        return self._default_to_pandas("last", offset)
+        return self.loc[pandas.Series(index=self.index).last(offset).index]
 
     def last_valid_index(self):
         """Return index for last non-NA/null value.


Test patch:
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -2380,10 +2380,16 @@ def test_kurtosis(self):
             pd.DataFrame(data).kurtosis()
 
     def test_last(self):
-        i = pd.date_range("2018-04-09", periods=4, freq="2D")
-        ts = pd.DataFrame({"A": [1, 2, 3, 4]}, index=i)
-        with pytest.warns(UserWarning):
-            ts.last("3D")
+        modin_index = pd.date_range("2010-04-09", periods=400, freq="2D")
+        pandas_index = pandas.date_range("2010-04-09", periods=400, freq="2D")
+        modin_df = pd.DataFrame(
+            {"A": list(range(400)), "B": list(range(400))}, index=modin_index
+        )
+        pandas_df = pandas.DataFrame(
+            {"A": list(range(400)), "B": list(range(400))}, index=pandas_index
+        )
+        df_equals(modin_df.last("3D"), pandas_df.last("3D"))
+        df_equals(modin_df.last("20D"), pandas_df.last("20D"))
 
     def test_lookup(self):
         data = test_data_values[0]
diff --git a/modin/pandas/test/test_series.py b/modin/pandas/test/test_series.py
--- a/modin/pandas/test/test_series.py
+++ b/modin/pandas/test/test_series.py
@@ -1688,10 +1688,12 @@ def test_kurtosis(data):
 
 
 def test_last():
-    i = pd.date_range("2018-04-09", periods=4, freq="2D")
-    ts = pd.Series([1, 2, 3, 4], index=i)
-    with pytest.warns(UserWarning):
-        ts.last("3D")
+    modin_index = pd.date_range("2010-04-09", periods=400, freq="2D")
+    pandas_index = pandas.date_range("2010-04-09", periods=400, freq="2D")
+    modin_series = pd.Series(list(range(400)), index=modin_index)
+    pandas_series = pandas.Series(list(range(400)), index=pandas_index)
+    df_equals(modin_series.last("3D"), pandas_series.last("3D"))
+    df_equals(modin_series.last("20D"), pandas_series.last("20D"))
 
 
 @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
Repository: modin-project/modin

Issue:
New API: `swapaxes` for DataFrame and Series

### Describe the problem
<!-- Describe the problem clearly here. -->
This is either `copy` or `transpose` depending on the args. For `Series`, there is only one axis.



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
@@ -2800,7 +2800,13 @@ def sum(
         )
 
     def swapaxes(self, axis1, axis2, copy=True):
-        return self._default_to_pandas("swapaxes", axis1, axis2, copy=copy)
+        axis1 = self._get_axis_number(axis1)
+        axis2 = self._get_axis_number(axis2)
+        if axis1 != axis2:
+            return self.transpose()
+        if copy:
+            return self.copy()
+        return self
 
     def swaplevel(self, i=-2, j=-1, axis=0):
         return self._default_to_pandas("swaplevel", i=i, j=j, axis=axis)


Test patch:
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -2532,10 +2532,20 @@ def test_style(self):
         with pytest.warns(UserWarning):
             pd.DataFrame(data).style
 
-    def test_swapaxes(self):
-        data = test_data_values[0]
-        with pytest.warns(UserWarning):
-            pd.DataFrame(data).swapaxes(0, 1)
+    @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
+    @pytest.mark.parametrize("axis1", [0, 1, "columns", "index"])
+    @pytest.mark.parametrize("axis2", [0, 1, "columns", "index"])
+    def test_swapaxes(self, data, axis1, axis2):
+        modin_df = pd.DataFrame(data)
+        pandas_df = pandas.DataFrame(data)
+        try:
+            pandas_result = pandas_df.swapaxes(axis1, axis2)
+        except Exception as e:
+            with pytest.raises(type(e)):
+                modin_df.swapaxes(axis1, axis2)
+        else:
+            modin_result = modin_df.swapaxes(axis1, axis2)
+            df_equals(modin_result, pandas_result)
 
     def test_swaplevel(self):
         df = pd.DataFrame(
diff --git a/modin/pandas/test/test_series.py b/modin/pandas/test/test_series.py
--- a/modin/pandas/test/test_series.py
+++ b/modin/pandas/test/test_series.py
@@ -2459,10 +2459,18 @@ def test_sum(data, skipna, min_count):
 
 
 @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
-def test_swapaxes(data):
+@pytest.mark.parametrize("axis1", [0, 1, "columns", "index"])
+@pytest.mark.parametrize("axis2", [0, 1, "columns", "index"])
+def test_swapaxes(data, axis1, axis2):
     modin_series, pandas_series = create_test_series(data)
-    with pytest.warns(UserWarning):
-        modin_series.swapaxes(0, 0)
+    try:
+        pandas_result = pandas_series.swapaxes(axis1, axis2)
+    except Exception as e:
+        with pytest.raises(type(e)):
+            modin_series.swapaxes(axis1, axis2)
+    else:
+        modin_result = modin_series.swapaxes(axis1, axis2)
+        df_equals(modin_result, pandas_result)
 
 
 def test_swaplevel():
Repository: modin-project/modin

Issue:
New API: `tshift` for Series and DataFrame

### Describe the problem
<!-- Describe the problem clearly here. -->
`tshift` only affects the index, so it should be trivial to implement.



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
@@ -3148,7 +3148,9 @@ def truncate(self, before=None, after=None, axis=None, copy=True):
         )
 
     def tshift(self, periods=1, freq=None, axis=0):
-        return self._default_to_pandas("tshift", periods=periods, freq=freq, axis=axis)
+        axis = self._get_axis_number(axis)
+        new_labels = self.axes[axis].shift(periods, freq=freq)
+        return self.set_axis(new_labels, axis=axis, inplace=False)
 
     def transform(self, func, axis=0, *args, **kwargs):
         kwargs["is_transform"] = True


Test patch:
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -2619,10 +2619,10 @@ def test_truncate(self):
 
     def test_tshift(self):
         idx = pd.date_range("1/1/2012", periods=5, freq="M")
-        df = pd.DataFrame(np.random.randint(0, 100, size=(len(idx), 4)), index=idx)
-
-        with pytest.warns(UserWarning):
-            df.to_period().tshift()
+        data = np.random.randint(0, 100, size=(len(idx), 4))
+        modin_df = pd.DataFrame(data, index=idx)
+        pandas_df = pandas.DataFrame(data, index=idx)
+        df_equals(modin_df.tshift(4), pandas_df.tshift(4))
 
     def test_tz_convert(self):
         modin_idx = pd.date_range(
diff --git a/modin/pandas/test/test_series.py b/modin/pandas/test/test_series.py
--- a/modin/pandas/test/test_series.py
+++ b/modin/pandas/test/test_series.py
@@ -2600,9 +2600,10 @@ def test_truncate(data):
 
 def test_tshift():
     idx = pd.date_range("1/1/2012", periods=5, freq="M")
-    modin_series = pd.Series(np.random.randint(0, 100, size=len(idx)), index=idx)
-    with pytest.warns(UserWarning):
-        modin_series.to_period().tshift()
+    data = np.random.randint(0, 100, size=len(idx))
+    modin_series = pd.Series(data, index=idx)
+    pandas_series = pandas.Series(data, index=idx)
+    df_equals(modin_series.tshift(4), pandas_series.tshift(4))
 
 
 def test_tz_convert():
Repository: modin-project/modin

Issue:
pd.rename() is failing to rename columns
The following code snippet fails to rename the columns 'a' and 'b' to 'x' and 'y'. Changing to regular pandas works fine, and also not doing the operation inplace results in the same

```
import modin.pandas as pd

print(pd.__version__)

df = pd.DataFrame({
    'a': [1, 2, 3],
    'b': [4, 5, 6]
})

df.rename({'a': 'x', 'b': 'y'}, axis=1, inplace=True)

print(df)
```

Output:

```
0.6.3
UserWarning: Distributing <class 'dict'> object. This may take some time.
   a  b
0  1  4
1  2  5
2  3  6

```
Also, I am using modin==0.7.0, but it is printing version 0.6.3.



Discussion:
It does work if I pass the dict to the `columns` parameter, instead of specifying `mapper` and `axis`.
Try specifying the axis as either index or column like so

df.rename({'a': 'x', 'b': 'y'}, axis='column', inplace=True)

Resolving patch:
diff --git a/modin/pandas/dataframe.py b/modin/pandas/dataframe.py
--- a/modin/pandas/dataframe.py
+++ b/modin/pandas/dataframe.py
@@ -1364,11 +1364,13 @@ def rename(
         # inplace should always be true because this is just a copy, and we will use the
         # results after.
         kwargs["inplace"] = False
-        if index is not None:
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+        if index is not None or (mapper is not None and axis == 0):
             new_index = pandas.DataFrame(index=self.index).rename(**kwargs).index
         else:
             new_index = self.index
-        if columns is not None:
+        if columns is not None or (mapper is not None and axis == 1):
             new_columns = (
                 pandas.DataFrame(columns=self.columns).rename(**kwargs).columns
             )


Test patch:
diff --git a/modin/pandas/test/test_dataframe.py b/modin/pandas/test/test_dataframe.py
--- a/modin/pandas/test/test_dataframe.py
+++ b/modin/pandas/test/test_dataframe.py
@@ -4639,6 +4639,16 @@ def test_rename_sanity(self):
         tm.assert_index_equal(
             modin_df.rename(index=str.upper).index, df.rename(index=str.upper).index
         )
+
+        # Using the `mapper` functionality with `axis`
+        tm.assert_index_equal(
+            modin_df.rename(str.upper, axis=0).index, df.rename(str.upper, axis=0).index
+        )
+        tm.assert_index_equal(
+            modin_df.rename(str.upper, axis=1).columns,
+            df.rename(str.upper, axis=1).columns,
+        )
+
         # have to pass something
         with pytest.raises(TypeError):
             modin_df.rename()
Repository: modin-project/modin

Issue:
Documentation for 0.7 says that it depends on pandas==0.23.4, but when I import modin it says it requires pandas==0.25
In the [modin documentation for 0.7]( https://modin.readthedocs.io/en/latest/installation.html#dependencies), it says that it depends on `pandas==0.23.4`, but when I install `modin==0.7` and try to import it, the following import error is thrown:

    ImportError: The pandas version installed does not match the required pandas version in Modin. Please install pandas 0.25.3 to use Modin.

Is this an error in the documentation? Is there anyway I can use `modin==0.7` with `pandas==0.23.4` as I am using Dataiku DSS v6.0, which requires `pandas==0.23.4` and cannot be upgraded.


Discussion:
Hi @KevOBrien, this is a typo in the documentation. The pandas API has changed some since the 0.23 release, and as of right now there's no way to ignore the pinned version.

The reason for the pin is that we can only guarantee API identity on a single version, so we choose the latest. Since pandas release 1.0 last week, I still need to update it within Modin.

I suppose I could create a workaround for the pandas version. We don't have the manpower to support all pandas versions in the same way, so might be some issues that pop up with older versions. Either way I need to update the documentation to be up to date. Let me know if you have any opinion.
What sort of workaround for the pandas version do you have in mind? It would be great if I could run modin 0.6 or 0.7 with pandas 0.23, and I would fully understand that there is a risk of incompatability issues, which I would have to test for myself
I could just give a warning instead of throwing an exception. That way you could use it at your own risk without any changes in your behavior.
Yes that would be great. Thank you

Resolving patch:
diff --git a/modin/pandas/__init__.py b/modin/pandas/__init__.py
--- a/modin/pandas/__init__.py
+++ b/modin/pandas/__init__.py
@@ -3,10 +3,11 @@
 __pandas_version__ = "0.25.3"
 
 if pandas.__version__ != __pandas_version__:
-    raise ImportError(
-        "The pandas version installed does not match the required pandas "
-        "version in Modin. Please install pandas {} to use "
-        "Modin.".format(__pandas_version__)
+    import warnings
+
+    warnings.warn(
+        "The pandas version installed does not match the required pandas version in "
+        "Modin. This may cause undesired side effects!".format(__pandas_version__)
     )
 
 from pandas import (


Test patch: