Repository: pandas-dev/pandas (python)

Issue:
DOC: Specify what "non-null" means in DataFrame.info()
### Pandas version checks

- [x] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)


### Location of the documentation

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.info.html

### Documentation problem

Non-null is not specific

### Suggested fix for documentation

Link to documentation or specify exactly what non-null means. In particular, for float64s NaN are considered "null". And does it also represent NULLs in the Nullable integer types? https://pandas.pydata.org/docs/user_guide/integer_na.html

Pandas is not consistent with its terminology of NA, NULL, and NaN. 
NaN is a floating point value that is not in the IEEE standard as a missing value.
R uses NA consistently and SQL uses NULL consistently in 3VL. 

Pull request:
Changed term non-null to NA
- [ ] closes #60802

Changed the term non-null to NA, to reflect pandas' docs standard


Patch:
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index 29be9a7341..184ca58190 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -68,6 +68,7 @@ Other enhancements
 - :func:`read_parquet` accepts ``to_pandas_kwargs`` which are forwarded to :meth:`pyarrow.Table.to_pandas` which enables passing additional keywords to customize the conversion to pandas, such as ``maps_as_pydicts`` to read the Parquet map data type as python dictionaries (:issue:`56842`)
 - :meth:`.DataFrameGroupBy.transform`, :meth:`.SeriesGroupBy.transform`, :meth:`.DataFrameGroupBy.agg`, :meth:`.SeriesGroupBy.agg`, :meth:`.SeriesGroupBy.apply`, :meth:`.DataFrameGroupBy.apply` now support ``kurt`` (:issue:`40139`)
 - :meth:`DataFrame.apply` supports using third-party execution engines like the Bodo.ai JIT compiler (:issue:`60668`)
+- :meth:`DataFrame.iloc` and :meth:`Series.iloc` now support boolean masks in ``__getitem__`` for more consistent indexing behavior (:issue:`60994`)
 - :meth:`DataFrameGroupBy.transform`, :meth:`SeriesGroupBy.transform`, :meth:`DataFrameGroupBy.agg`, :meth:`SeriesGroupBy.agg`, :meth:`RollingGroupby.apply`, :meth:`ExpandingGroupby.apply`, :meth:`Rolling.apply`, :meth:`Expanding.apply`, :meth:`DataFrame.apply` with ``engine="numba"`` now supports positional arguments passed as kwargs (:issue:`58995`)
 - :meth:`Rolling.agg`, :meth:`Expanding.agg` and :meth:`ExponentialMovingWindow.agg` now accept :class:`NamedAgg` aggregations through ``**kwargs`` (:issue:`28333`)
 - :meth:`Series.map` can now accept kwargs to pass on to func (:issue:`59814`)
@@ -762,6 +763,7 @@ Plotting
 - Bug in :meth:`DataFrame.plot.bar` with ``stacked=True`` where labels on stacked bars with zero-height segments were incorrectly positioned at the base instead of the label position of the previous segment (:issue:`59429`)
 - Bug in :meth:`DataFrame.plot.line` raising ``ValueError`` when set both color and a ``dict`` style (:issue:`59461`)
 - Bug in :meth:`DataFrame.plot` that causes a shift to the right when the frequency multiplier is greater than one. (:issue:`57587`)
+- Bug in :meth:`Series.plot` preventing a line and scatter plot from being aligned (:issue:`61005`)
 - Bug in :meth:`Series.plot` with ``kind="pie"`` with :class:`ArrowDtype` (:issue:`59192`)
 
 Groupby/resample/rolling
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index bbbcc4da9f..34a437ba40 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -1582,11 +1582,7 @@ class _iLocIndexer(_LocationIndexer):
         if com.is_bool_indexer(key):
             if hasattr(key, "index") and isinstance(key.index, Index):
                 if key.index.inferred_type == "integer":
-                    raise NotImplementedError(
-                        "iLocation based boolean "
-                        "indexing on an integer type "
-                        "is not available"
-                    )
+                    return
                 raise ValueError(
                     "iLocation based boolean indexing cannot use an indexable as a mask"
                 )
diff --git a/pandas/io/formats/info.py b/pandas/io/formats/info.py
index c9a6e94a0c..eb579f7149 100644
--- a/pandas/io/formats/info.py
+++ b/pandas/io/formats/info.py
@@ -249,7 +249,7 @@ INFO_DOCSTRING = dedent(
     Print a concise summary of a {klass}.
 
     This method prints information about a {klass} including
-    the index dtype{type_sub}, non-null values and memory usage.
+    the index dtype{type_sub}, non-NA values and memory usage.
     {version_added_sub}\
 
     Parameters
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index 1035150302..24aa848de1 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -64,10 +64,9 @@ from pandas.plotting._matplotlib.groupby import reconstruct_data_with_by
 from pandas.plotting._matplotlib.misc import unpack_single_str_list
 from pandas.plotting._matplotlib.style import get_standard_colors
 from pandas.plotting._matplotlib.timeseries import (
-    decorate_axes,
     format_dateaxis,
     maybe_convert_index,
-    maybe_resample,
+    prepare_ts_data,
     use_dynamic_x,
 )
 from pandas.plotting._matplotlib.tools import (
@@ -288,6 +287,21 @@ class MPLPlot(ABC):
 
         self.data = self._ensure_frame(self.data)
 
+        from pandas.plotting import plot_params
+
+        self.x_compat = plot_params["x_compat"]
+        if "x_compat" in self.kwds:
+            self.x_compat = bool(self.kwds.pop("x_compat"))
+
+    @final
+    def _is_ts_plot(self) -> bool:
+        # this is slightly deceptive
+        return not self.x_compat and self.use_index and self._use_dynamic_x()
+
+    @final
+    def _use_dynamic_x(self) -> bool:
+        return use_dynamic_x(self._get_ax(0), self.data.index)
+
     @final
     @staticmethod
     def _validate_sharex(sharex: bool | None, ax, by) -> bool:
@@ -1324,10 +1338,20 @@ class ScatterPlot(PlanePlot):
             c = self.data.columns[c]
         self.c = c
 
+    @register_pandas_matplotlib_converters
     def _make_plot(self, fig: Figure) -> None:
         x, y, c, data = self.x, self.y, self.c, self.data
         ax = self.axes[0]
 
+        from pandas import Series
+
+        x_data = data[x]
+        s = Series(index=x_data)
+        if use_dynamic_x(ax, s.index):
+            s = maybe_convert_index(ax, s)
+            freq, s = prepare_ts_data(s, ax, self.kwds)
+            x_data = s.index
+
         c_is_column = is_hashable(c) and c in self.data.columns
 
         color_by_categorical = c_is_column and isinstance(
@@ -1344,7 +1368,7 @@ class ScatterPlot(PlanePlot):
         else:
             label = None
 
-        # if a list of non color strings is passed in as c, color points
+        # if a list of non-color strings is passed in as c, color points
         # by uniqueness of the strings, such same strings get same color
         create_colors = not self._are_valid_colors(c_values)
         if create_colors:
@@ -1360,7 +1384,7 @@ class ScatterPlot(PlanePlot):
             )
 
         scatter = ax.scatter(
-            data[x].values,
+            x_data.values,
             data[y].values,
             c=c_values,
             label=label,
@@ -1520,23 +1544,9 @@ class LinePlot(MPLPlot):
         return "line"
 
     def __init__(self, data, **kwargs) -> None:
-        from pandas.plotting import plot_params
-
         MPLPlot.__init__(self, data, **kwargs)
         if self.stacked:
             self.data = self.data.fillna(value=0)
-        self.x_compat = plot_params["x_compat"]
-        if "x_compat" in self.kwds:
-            self.x_compat = bool(self.kwds.pop("x_compat"))
-
-    @final
-    def _is_ts_plot(self) -> bool:
-        # this is slightly deceptive
-        return not self.x_compat and self.use_index and self._use_dynamic_x()
-
-    @final
-    def _use_dynamic_x(self) -> bool:
-        return use_dynamic_x(self._get_ax(0), self.data)
 
     def _make_plot(self, fig: Figure) -> None:
         if self._is_ts_plot():
@@ -1626,15 +1636,8 @@ class LinePlot(MPLPlot):
         # accept x to be consistent with normal plot func,
         # x is not passed to tsplot as it uses data.index as x coordinate
         # column_num must be in kwds for stacking purpose
-        freq, data = maybe_resample(data, ax, kwds)
+        freq, data = prepare_ts_data(data, ax, kwds)
 
-        # Set ax with freq info
-        decorate_axes(ax, freq)
-        # digging deeper
-        if hasattr(ax, "left_ax"):
-            decorate_axes(ax.left_ax, freq)
-        if hasattr(ax, "right_ax"):
-            decorate_axes(ax.right_ax, freq)
         # TODO #54485
         ax._plot_data.append((data, self._kind, kwds))  # type: ignore[attr-defined]
 
diff --git a/pandas/plotting/_matplotlib/timeseries.py b/pandas/plotting/_matplotlib/timeseries.py
index d95ccad2da..beaf5b6259 100644
--- a/pandas/plotting/_matplotlib/timeseries.py
+++ b/pandas/plotting/_matplotlib/timeseries.py
@@ -48,7 +48,6 @@ if TYPE_CHECKING:
     from pandas._typing import NDFrameT
 
     from pandas import (
-        DataFrame,
         DatetimeIndex,
         Index,
         PeriodIndex,
@@ -231,8 +230,8 @@ def _get_freq(ax: Axes, series: Series):
     return freq, ax_freq
 
 
-def use_dynamic_x(ax: Axes, data: DataFrame | Series) -> bool:
-    freq = _get_index_freq(data.index)
+def use_dynamic_x(ax: Axes, index: Index) -> bool:
+    freq = _get_index_freq(index)
     ax_freq = _get_ax_freq(ax)
 
     if freq is None:  # convert irregular if axes has freq info
@@ -250,16 +249,15 @@ def use_dynamic_x(ax: Axes, data: DataFrame | Series) -> bool:
         return False
 
     # FIXME: hack this for 0.10.1, creating more technical debt...sigh
-    if isinstance(data.index, ABCDatetimeIndex):
+    if isinstance(index, ABCDatetimeIndex):
         # error: "BaseOffset" has no attribute "_period_dtype_code"
         freq_str = OFFSET_TO_PERIOD_FREQSTR.get(freq_str, freq_str)
         base = to_offset(freq_str, is_period=True)._period_dtype_code  # type: ignore[attr-defined]
-        x = data.index
         if base <= FreqGroup.FR_DAY.value:
-            return x[:1].is_normalized
-        period = Period(x[0], freq_str)
+            return index[:1].is_normalized
+        period = Period(index[0], freq_str)
         assert isinstance(period, Period)
-        return period.to_timestamp().tz_localize(x.tz) == x[0]
+        return period.to_timestamp().tz_localize(index.tz) == index[0]
     return True
 
 
@@ -366,3 +364,19 @@ def format_dateaxis(
         raise TypeError("index type not supported")
 
     plt.draw_if_interactive()
+
+
+def prepare_ts_data(
+    series: Series, ax: Axes, kwargs: dict[str, Any]
+) -> tuple[BaseOffset | str, Series]:
+    freq, data = maybe_resample(series, ax, kwargs)
+
+    # Set ax with freq info
+    decorate_axes(ax, freq)
+    # digging deeper
+    if hasattr(ax, "left_ax"):
+        decorate_axes(ax.left_ax, freq)
+    if hasattr(ax, "right_ax"):
+        decorate_axes(ax.right_ax, freq)
+
+    return freq, data
diff --git a/pyproject.toml b/pyproject.toml
index d3bcd7bdb0..825fb67133 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -148,7 +148,7 @@ setup = ['--vsenv'] # For Windows
 
 [tool.cibuildwheel]
 skip = "cp36-* cp37-* cp38-* cp39-* pp* *_i686 *_ppc64le *_s390x"
-build-verbosity = "3"
+build-verbosity = 3
 environment = {LDFLAGS="-Wl,--strip-all"}
 test-requires = "hypothesis>=6.84.0 pytest>=7.3.2 pytest-xdist>=3.4.0"
 test-command = """
@@ -160,6 +160,7 @@ free-threaded-support = true
 before-build = "PACKAGE_DIR={package} bash {package}/scripts/cibw_before_build.sh"
 
 [tool.cibuildwheel.windows]
+environment = {}
 before-build = "pip install delvewheel && bash {package}/scripts/cibw_before_build_windows.sh"
 before-test = "bash {package}/scripts/cibw_before_test_windows.sh"
 test-command = """
diff --git a/scripts/cibw_before_build_windows.sh b/scripts/cibw_before_build_windows.sh
index f9e1e68d8e..dbf1d95d91 100644
--- a/scripts/cibw_before_build_windows.sh
+++ b/scripts/cibw_before_build_windows.sh
@@ -5,10 +5,11 @@ for file in $PACKAGE_DIR/LICENSES/*; do
 done
 
 # TODO: Delete when there's a PyPI Cython release that supports free-threaded Python 3.13
-# and a NumPy Windows wheel for the free-threaded build on PyPI.
 FREE_THREADED_BUILD="$(python -c"import sysconfig; print(bool(sysconfig.get_config_var('Py_GIL_DISABLED')))")"
 if [[ $FREE_THREADED_BUILD == "True" ]]; then
     python -m pip install -U pip
-    python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy cython
-    python -m pip install ninja meson-python versioneer[toml]
+    # python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple cython
+    # TODO: Remove below and uncomment above once https://github.com/cython/cython/pull/6717 no longer breaks tests
+    python -m pip install git+https://github.com/cython/cython.git@3276b588720a053c78488e5de788605950f4b136
+    python -m pip install ninja meson-python versioneer[toml] numpy
 fi
diff --git a/web/pandas/community/ecosystem.md b/web/pandas/community/ecosystem.md
index 4863093387..3555d67c70 100644
--- a/web/pandas/community/ecosystem.md
+++ b/web/pandas/community/ecosystem.md
@@ -469,7 +469,7 @@ read_record.data
 df.dtypes
 ```
 
-ArcticDB also supports appending, updating, and querying data from storage to a pandas DataFrame. Please find more information [here](https://docs.arcticdb.io/latest/api/query_builder/).
+ArcticDB also supports appending, updating, and querying data from storage to a pandas DataFrame. Please find more information [here](https://docs.arcticdb.io/latest/api/processing/#arcticdb.QueryBuilder).
 
 ### [Hugging Face](https://huggingface.co/datasets)
 


Test patch:
diff --git a/pandas/tests/indexing/test_iloc.py b/pandas/tests/indexing/test_iloc.py
index 2f6998a85c..3be69617ca 100644
--- a/pandas/tests/indexing/test_iloc.py
+++ b/pandas/tests/indexing/test_iloc.py
@@ -726,15 +726,16 @@ class TestiLocBaseIndependent:
 
     @pytest.mark.filterwarnings("ignore::UserWarning")
     def test_iloc_mask(self):
-        # GH 3631, iloc with a mask (of a series) should raise
+        # GH 60994, iloc with a mask (of a series) should return accordingly
         df = DataFrame(list(range(5)), index=list("ABCDE"), columns=["a"])
         mask = df.a % 2 == 0
         msg = "iLocation based boolean indexing cannot use an indexable as a mask"
         with pytest.raises(ValueError, match=msg):
             df.iloc[mask]
+
         mask.index = range(len(mask))
-        msg = "iLocation based boolean indexing on an integer type is not available"
-        with pytest.raises(NotImplementedError, match=msg):
+        msg = "Unalignable boolean Series provided as indexer"
+        with pytest.raises(IndexingError, match=msg):
             df.iloc[mask]
 
         # ndarray ok
@@ -753,18 +754,13 @@ class TestiLocBaseIndependent:
             (None, ".iloc"): "0b1100",
             ("index", ""): "0b11",
             ("index", ".loc"): "0b11",
-            ("index", ".iloc"): (
-                "iLocation based boolean indexing cannot use an indexable as a mask"
-            ),
-            ("locs", ""): "Unalignable boolean Series provided as indexer "
-            "(index of the boolean Series and of the indexed "
-            "object do not match).",
-            ("locs", ".loc"): "Unalignable boolean Series provided as indexer "
-            "(index of the boolean Series and of the "
-            "indexed object do not match).",
-            ("locs", ".iloc"): (
-                "iLocation based boolean indexing on an integer type is not available"
-            ),
+            (
+                "index",
+                ".iloc",
+            ): "iLocation based boolean indexing cannot use an indexable as a mask",
+            ("locs", ""): "Unalignable boolean Series provided as indexer",
+            ("locs", ".loc"): "Unalignable boolean Series provided as indexer",
+            ("locs", ".iloc"): "Unalignable boolean Series provided as indexer",
         }
 
         # UserWarnings from reindex of a boolean mask
@@ -780,18 +776,52 @@ class TestiLocBaseIndependent:
                     else:
                         accessor = df
                     answer = str(bin(accessor[mask]["nums"].sum()))
-                except (ValueError, IndexingError, NotImplementedError) as err:
+                except (ValueError, IndexingError) as err:
                     answer = str(err)
 
                 key = (
                     idx,
                     method,
                 )
-                r = expected.get(key)
-                if r != answer:
-                    raise AssertionError(
-                        f"[{key}] does not match [{answer}], received [{r}]"
+                expected_result = expected.get(key)
+
+                # Fix the assertion to check for substring match
+                if (
+                    idx is None or (idx == "index" and method != ".iloc")
+                ) and "0b" in expected_result:
+                    # For successful numeric results, exact match is needed
+                    assert expected_result == answer, (
+                        f"[{key}] does not match [{answer}]"
                     )
+                else:
+                    # For error messages, substring match is sufficient
+                    assert expected_result in answer, f"[{key}] not found in [{answer}]"
+
+    def test_iloc_with_numpy_bool_array(self):
+        df = DataFrame(list(range(5)), index=list("ABCDE"), columns=["a"])
+        result = df.iloc[np.array([True, False, True, False, True], dtype=bool)]
+        expected = DataFrame({"a": [0, 2, 4]}, index=["A", "C", "E"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_iloc_series_mask_with_index_mismatch_raises(self):
+        df = DataFrame(list(range(5)), index=list("ABCDE"), columns=["a"])
+        mask = df.a % 2 == 0
+        msg = "Unalignable boolean Series provided as indexer"
+        with pytest.raises(IndexingError, match=msg):
+            df.iloc[Series([True] * len(mask), dtype=bool)]
+
+    def test_iloc_series_mask_all_true(self):
+        df = DataFrame(list(range(5)), columns=["a"])
+        mask = Series([True] * len(df), dtype=bool)
+        result = df.iloc[mask]
+        tm.assert_frame_equal(result, df)
+
+    def test_iloc_series_mask_alternate_true(self):
+        df = DataFrame(list(range(5)), columns=["a"])
+        mask = Series([True, False, True, False, True], dtype=bool)
+        result = df.iloc[mask]
+        expected = DataFrame({"a": [0, 2, 4]}, index=[0, 2, 4])
+        tm.assert_frame_equal(result, expected)
 
     def test_iloc_non_unique_indexing(self):
         # GH 4017, non-unique indexing (on the axis)
diff --git a/pandas/tests/plotting/frame/test_frame.py b/pandas/tests/plotting/frame/test_frame.py
index d18f098267..3f274a336a 100644
--- a/pandas/tests/plotting/frame/test_frame.py
+++ b/pandas/tests/plotting/frame/test_frame.py
@@ -840,14 +840,26 @@ class TestDataFramePlots:
         axes = df.plot(x="x", y="y", kind="scatter", subplots=True)
         _check_axes_shape(axes, axes_num=1, layout=(1, 1))
 
-    def test_raise_error_on_datetime_time_data(self):
-        # GH 8113, datetime.time type is not supported by matplotlib in scatter
+    def test_scatter_on_datetime_time_data(self):
+        # datetime.time type is now supported in scatter, since a converter
+        # is implemented in ScatterPlot
         df = DataFrame(np.random.default_rng(2).standard_normal(10), columns=["a"])
         df["dtime"] = date_range(start="2014-01-01", freq="h", periods=10).time
-        msg = "must be a string or a (real )?number, not 'datetime.time'"
+        df.plot(kind="scatter", x="dtime", y="a")
 
-        with pytest.raises(TypeError, match=msg):
-            df.plot(kind="scatter", x="dtime", y="a")
+    def test_scatter_line_xticks(self):
+        # GH#61005
+        df = DataFrame(
+            [(datetime(year=2025, month=1, day=1, hour=n), n) for n in range(3)],
+            columns=["datetime", "y"],
+        )
+        fig, ax = plt.subplots(2, sharex=True)
+        df.plot.scatter(x="datetime", y="y", ax=ax[0])
+        scatter_xticks = ax[0].get_xticks()
+        df.plot(x="datetime", y="y", ax=ax[1])
+        line_xticks = ax[1].get_xticks()
+        assert scatter_xticks[0] == line_xticks[0]
+        assert scatter_xticks[-1] == line_xticks[-1]
 
     @pytest.mark.parametrize("x, y", [("dates", "vals"), (0, 1)])
     def test_scatterplot_datetime_data(self, x, y):