Refactor time series plotting logic for improved clarity

Extract and streamline time series preparation steps into `prepare_ts_data`, replacing redundant logic across methods. Simplifies axis frequency handling and improves code readability while maintaining functionality.

Author: MartinBraquet

pandas/plotting/_matplotlib/core.py

@@ -54,6 +54,7 @@
 )
 from pandas.core.dtypes.missing import isna
 
+from pandas import Series
 import pandas.core.common as com
 from pandas.util.version import Version
 
@@ -64,10 +65,9 @@
 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 (
@@ -95,7 +95,6 @@
     from pandas import (
         DataFrame,
         Index,
-        Series,
     )
 
 
@@ -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 +1338,17 @@ 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]
 
+        x_data = Series(index=data[x])
+        if use_dynamic_x(ax, x_data.index):
+            x_data = maybe_convert_index(ax, x_data)
+            freq, x_data = prepare_ts_data(x_data, ax, self.kwds)
+        x_data = x_data.index
+
         c_is_column = is_hashable(c) and c in self.data.columns
 
         color_by_categorical = c_is_column and isinstance(
@@ -1344,7 +1365,7 @@ 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 +1381,7 @@ 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 +1541,9 @@ 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 +1633,8 @@ 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 +364,17 @@ def format_dateaxis(
         raise TypeError("index type not supported")
 
     plt.draw_if_interactive()
+
+
+def prepare_ts_data(data, ax, kwds):
+    freq, data = maybe_resample(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)
+
+    return freq, data