Merge branch 'main' into change-closed-docs

Author: SebastianOuslis

doc/source/whatsnew/v3.0.0.rst

@@ -766,6 +766,7 @@ Reshaping
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when ``sort=False`` (:issue:`54987`, :issue:`55516`)
 - Bug in :meth:`DataFrame.merge` when merging two :class:`DataFrame` on ``intc`` or ``uintc`` types on Windows (:issue:`60091`, :issue:`58713`)
 - Bug in :meth:`DataFrame.pivot_table` incorrectly subaggregating results when called without an ``index`` argument (:issue:`58722`)
+- Bug in :meth:`DataFrame.stack` with the new implementation where ``ValueError`` is raised when ``level=[]`` (:issue:`60740`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)
 
 Sparse

pandas/core/frame.py

@@ -3205,9 +3205,13 @@ def to_html(
             Convert the characters <, >, and & to HTML-safe sequences.
         notebook : {True, False}, default False
             Whether the generated HTML is for IPython Notebook.
-        border : int
-            A ``border=border`` attribute is included in the opening
-            `<table>` tag. Default ``pd.options.display.html.border``.
+        border : int or bool
+            When an integer value is provided, it sets the border attribute in
+            the opening tag, specifying the thickness of the border.
+            If ``False`` or ``0`` is passed, the border attribute will not
+            be present in the ``<table>`` tag.
+            The default value for this parameter is governed by
+            ``pd.options.display.html.border``.
         table_id : str, optional
             A css id is included in the opening `<table>` tag if specified.
         render_links : bool, default False

pandas/core/reshape/reshape.py

@@ -929,6 +929,8 @@ def _reorder_for_extension_array_stack(
 def stack_v3(frame: DataFrame, level: list[int]) -> Series | DataFrame:
     if frame.columns.nunique() != len(frame.columns):
         raise ValueError("Columns with duplicate values are not supported in stack")
+    if not len(level):
+        return frame
     set_levels = set(level)
     stack_cols = frame.columns._drop_level_numbers(
         [k for k in range(frame.columns.nlevels - 1, -1, -1) if k not in set_levels]

pandas/io/formats/format.py

@@ -897,9 +897,13 @@ def to_html(
             ``<table>`` tag, in addition to the default "dataframe".
         notebook : {True, False}, optional, default False
             Whether the generated HTML is for IPython Notebook.
-        border : int
-            A ``border=border`` attribute is included in the opening
-            ``<table>`` tag. Default ``pd.options.display.html.border``.
+        border : int or bool
+            When an integer value is provided, it sets the border attribute in
+            the opening tag, specifying the thickness of the border.
+            If ``False`` or ``0`` is passed, the border attribute will not
+            be present in the ``<table>`` tag.
+            The default value for this parameter is governed by
+            ``pd.options.display.html.border``.
         table_id : str, optional
             A css id is included in the opening `<table>` tag if specified.
         render_links : bool, default False

pandas/tests/frame/test_stack_unstack.py

@@ -1452,6 +1452,25 @@ def test_stack_empty_frame(dropna, future_stack):
         tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+@pytest.mark.parametrize("dropna", [True, False, lib.no_default])
+def test_stack_empty_level(dropna, future_stack, int_frame):
+    # GH 60740
+    if future_stack and dropna is not lib.no_default:
+        with pytest.raises(ValueError, match="dropna must be unspecified"):
+            DataFrame(dtype=np.int64).stack(dropna=dropna, future_stack=future_stack)
+    else:
+        expected = int_frame
+        result = int_frame.copy().stack(
+            level=[], dropna=dropna, future_stack=future_stack
+        )
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame()
+        result = DataFrame().stack(level=[], dropna=dropna, future_stack=future_stack)
+        tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
 @pytest.mark.parametrize("dropna", [True, False, lib.no_default])
 @pytest.mark.parametrize("fill_value", [None, 0])

web/pandas/community/ecosystem.md

@@ -496,17 +496,29 @@ You can find more information about the Hugging Face Dataset Hub in the [documen
 
 ## Out-of-core
 
-### [Bodo](https://bodo.ai/)
+### [Bodo](https://github.com/bodo-ai/Bodo)
 
-Bodo is a high-performance Python computing engine that automatically parallelizes and
-optimizes your code through compilation using HPC (high-performance computing) techniques.
-Designed to operate with native pandas dataframes, Bodo compiles your pandas code to execute
-across multiple cores on a single machine or distributed clusters of multiple compute nodes efficiently.
-Bodo also makes distributed pandas dataframes queryable with SQL.
 
-The community edition of Bodo is free to use on up to 8 cores. Beyond that, Bodo offers a paid
-enterprise edition. Free licenses of Bodo (for more than 8 cores) are available
-[upon request](https://www.bodo.ai/contact) for academic and non-profit use.
+Bodo is a high-performance compute engine for Python data processing.
+Using an auto-parallelizing just-in-time (JIT) compiler, Bodo simplifies scaling Pandas
+workloads from laptops to clusters without major code changes.
+Under the hood, Bodo relies on MPI-based high-performance computing (HPC) technology—making it
+both easier to use and often much faster than alternatives.
+Bodo also provides a SQL engine that can query distributed pandas dataframes efficiently.
+
+```python
+import pandas as pd
+import bodo
+
+@bodo.jit
+def process_data():
+    df = pd.read_parquet("my_data.pq")
+    df2 = pd.DataFrame({"A": df.apply(lambda r: 0 if r.A == 0 else (r.B // r.A), axis=1)})
+    df2.to_parquet("out.pq")
+
+process_data()
+```
+
 
 ### [Cylon](https://cylondata.org/)