BUG: Handle overlapping line and scatter on the same plot

doc/source/whatsnew/v3.0.0.rst

@@ -762,6 +762,7 @@ 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

pandas/plotting/_matplotlib/core.py

@@ -64,10 +64,9 @@
 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 @@ def __init__(
 
         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 @@ def __init__(
             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 @@ def _make_plot(self, fig: Figure) -> None:
         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 @@ def _make_plot(self, fig: Figure) -> None:
             )
 
         scatter = ax.scatter(
-            data[x].values,
+            x_data.values,
             data[y].values,
             c=c_values,
             label=label,
@@ -1520,23 +1544,9 @@ def _kind(self) -> Literal["line", "area", "hist", "kde", "box"]:
         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 @@ def _ts_plot(self, ax: Axes, x, data: Series, style=None, **kwds):
         # 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]
 

pandas/plotting/_matplotlib/timeseries.py

@@ -48,7 +48,6 @@
     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

pandas/tests/plotting/frame/test_frame.py

@@ -840,14 +840,26 @@ def test_plot_scatter_shape(self):
         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):