Backport PR #48601 on branch 1.5.x (CI: Fix matplolib release issues) (#48617)

Backport PR #48601: CI: Fix matplolib release issues

Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com>

Author: meeseeksmachine

doc/source/user_guide/visualization.rst

@@ -625,6 +625,7 @@ To plot multiple column groups in a single axes, repeat ``plot`` method specifyi
 It is recommended to specify ``color`` and ``label`` keywords to distinguish each groups.
 
 .. ipython:: python
+   :okwarning:
 
    ax = df.plot.scatter(x="a", y="b", color="DarkBlue", label="Group 1")
    @savefig scatter_plot_repeated.png

pandas/plotting/_core.py

@@ -1016,7 +1016,7 @@ def __call__(self, *args, **kwargs):
 
             >>> s = pd.Series([1, 3, 2])
             >>> s.plot.line()
-            <AxesSubplot:ylabel='Density'>
+            <AxesSubplot: ylabel='Density'>
 
         .. plot::
             :context: close-figs

pandas/plotting/_matplotlib/compat.py

@@ -19,3 +19,4 @@ def inner():
 
 mpl_ge_3_4_0 = _mpl_version("3.4.0", operator.ge)
 mpl_ge_3_5_0 = _mpl_version("3.5.0", operator.ge)
+mpl_ge_3_6_0 = _mpl_version("3.6.0", operator.ge)

pandas/plotting/_matplotlib/core.py

@@ -14,6 +14,7 @@
 )
 import warnings
 
+import matplotlib as mpl
 from matplotlib.artist import Artist
 import numpy as np
 
@@ -54,6 +55,7 @@
 from pandas.core.frame import DataFrame
 
 from pandas.io.formats.printing import pprint_thing
+from pandas.plotting._matplotlib.compat import mpl_ge_3_6_0
 from pandas.plotting._matplotlib.converter import register_pandas_matplotlib_converters
 from pandas.plotting._matplotlib.groupby import reconstruct_data_with_by
 from pandas.plotting._matplotlib.misc import unpack_single_str_list
@@ -1205,9 +1207,6 @@ def _make_plot(self):
 
         color_by_categorical = c_is_column and is_categorical_dtype(self.data[c])
 
-        # pandas uses colormap, matplotlib uses cmap.
-        cmap = self.colormap or "Greys"
-        cmap = self.plt.cm.get_cmap(cmap)
         color = self.kwds.pop("color", None)
         if c is not None and color is not None:
             raise TypeError("Specify exactly one of `c` and `color`")
@@ -1222,6 +1221,17 @@ def _make_plot(self):
         else:
             c_values = c
 
+        # cmap is only used if c_values are integers, otherwise UserWarning
+        if is_integer_dtype(c_values):
+            # pandas uses colormap, matplotlib uses cmap.
+            cmap = self.colormap or "Greys"
+            if mpl_ge_3_6_0():
+                cmap = mpl.colormaps[cmap]
+            else:
+                cmap = self.plt.cm.get_cmap(cmap)
+        else:
+            cmap = None
+
         if color_by_categorical:
             from matplotlib import colors
 
@@ -1286,7 +1296,10 @@ def _make_plot(self):
         ax = self.axes[0]
         # pandas uses colormap, matplotlib uses cmap.
         cmap = self.colormap or "BuGn"
-        cmap = self.plt.cm.get_cmap(cmap)
+        if mpl_ge_3_6_0():
+            cmap = mpl.colormaps[cmap]
+        else:
+            cmap = self.plt.cm.get_cmap(cmap)
         cb = self.kwds.pop("colorbar", True)
 
         if C is None:

pandas/plotting/_matplotlib/style.py

@@ -12,6 +12,7 @@
 )
 import warnings
 
+import matplotlib as mpl
 import matplotlib.cm as cm
 import matplotlib.colors
 import numpy as np
@@ -22,6 +23,8 @@
 
 import pandas.core.common as com
 
+from pandas.plotting._matplotlib.compat import mpl_ge_3_6_0
+
 if TYPE_CHECKING:
     from matplotlib.colors import Colormap
 
@@ -155,7 +158,10 @@ def _get_cmap_instance(colormap: str | Colormap) -> Colormap:
     """Get instance of matplotlib colormap."""
     if isinstance(colormap, str):
         cmap = colormap
-        colormap = cm.get_cmap(colormap)
+        if mpl_ge_3_6_0():
+            colormap = mpl.colormaps[colormap]
+        else:
+            colormap = cm.get_cmap(colormap)
         if colormap is None:
             raise ValueError(f"Colormap {cmap} is not recognized")
     return colormap

pandas/plotting/_misc.py

@@ -139,22 +139,22 @@ def scatter_matrix(
 
         >>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
         >>> pd.plotting.scatter_matrix(df, alpha=0.2)
-        array([[<AxesSubplot:xlabel='A', ylabel='A'>,
-            <AxesSubplot:xlabel='B', ylabel='A'>,
-            <AxesSubplot:xlabel='C', ylabel='A'>,
-            <AxesSubplot:xlabel='D', ylabel='A'>],
-           [<AxesSubplot:xlabel='A', ylabel='B'>,
-            <AxesSubplot:xlabel='B', ylabel='B'>,
-            <AxesSubplot:xlabel='C', ylabel='B'>,
-            <AxesSubplot:xlabel='D', ylabel='B'>],
-           [<AxesSubplot:xlabel='A', ylabel='C'>,
-            <AxesSubplot:xlabel='B', ylabel='C'>,
-            <AxesSubplot:xlabel='C', ylabel='C'>,
-            <AxesSubplot:xlabel='D', ylabel='C'>],
-           [<AxesSubplot:xlabel='A', ylabel='D'>,
-            <AxesSubplot:xlabel='B', ylabel='D'>,
-            <AxesSubplot:xlabel='C', ylabel='D'>,
-            <AxesSubplot:xlabel='D', ylabel='D'>]], dtype=object)
+        array([[<AxesSubplot: xlabel='A', ylabel='A'>,
+            <AxesSubplot: xlabel='B', ylabel='A'>,
+            <AxesSubplot: xlabel='C', ylabel='A'>,
+            <AxesSubplot: xlabel='D', ylabel='A'>],
+           [<AxesSubplot: xlabel='A', ylabel='B'>,
+            <AxesSubplot: xlabel='B', ylabel='B'>,
+            <AxesSubplot: xlabel='C', ylabel='B'>,
+            <AxesSubplot: xlabel='D', ylabel='B'>],
+           [<AxesSubplot: xlabel='A', ylabel='C'>,
+            <AxesSubplot: xlabel='B', ylabel='C'>,
+            <AxesSubplot: xlabel='C', ylabel='C'>,
+            <AxesSubplot: xlabel='D', ylabel='C'>],
+           [<AxesSubplot: xlabel='A', ylabel='D'>,
+            <AxesSubplot: xlabel='B', ylabel='D'>,
+            <AxesSubplot: xlabel='C', ylabel='D'>,
+            <AxesSubplot: xlabel='D', ylabel='D'>]], dtype=object)
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.scatter_matrix(
@@ -247,7 +247,7 @@ def radviz(
         ...     }
         ... )
         >>> pd.plotting.radviz(df, 'Category')
-        <AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>
+        <AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.radviz(
@@ -311,7 +311,7 @@ def andrews_curves(
         ...     'pandas/main/pandas/tests/io/data/csv/iris.csv'
         ... )
         >>> pd.plotting.andrews_curves(df, 'Name')
-        <AxesSubplot:title={'center':'width'}>
+        <AxesSubplot: title={'center': 'width'}>
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.andrews_curves(
@@ -445,7 +445,7 @@ def parallel_coordinates(
         >>> pd.plotting.parallel_coordinates(
         ...     df, 'Name', color=('#556270', '#4ECDC4', '#C7F464')
         ... )
-        <AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>
+        <AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.parallel_coordinates(
@@ -494,15 +494,15 @@ def lag_plot(series: Series, lag: int = 1, ax: Axes | None = None, **kwds) -> Ax
         >>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))
         >>> s = pd.Series(x)
         >>> s.plot()
-        <AxesSubplot:xlabel='Midrange'>
+        <AxesSubplot: xlabel='Midrange'>
 
     A lag plot with ``lag=1`` returns
 
     .. plot::
         :context: close-figs
 
         >>> pd.plotting.lag_plot(s, lag=1)
-        <AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>
+        <AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
@@ -536,7 +536,7 @@ def autocorrelation_plot(series: Series, ax: Axes | None = None, **kwargs) -> Ax
         >>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
         >>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
         >>> pd.plotting.autocorrelation_plot(s)
-        <AxesSubplot:title={'center':'width'}, xlabel='Lag', ylabel='Autocorrelation'>
+        <AxesSubplot: title={'center': 'width'}, xlabel='Lag', ylabel='Autocorrelation'>
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)

pandas/tests/plotting/frame/test_frame.py

@@ -32,9 +32,15 @@
 from pandas.io.formats.printing import pprint_thing
 import pandas.plotting as plotting
 
+try:
+    from pandas.plotting._matplotlib.compat import mpl_ge_3_6_0
+except ImportError:
+    mpl_ge_3_6_0 = lambda: True
+
 
 @td.skip_if_no_mpl
 class TestDataFramePlots(TestPlotBase):
+    @pytest.mark.xfail(mpl_ge_3_6_0(), reason="Api changed")
     @pytest.mark.slow
     def test_plot(self):
         df = tm.makeTimeDataFrame()
@@ -733,7 +739,7 @@ def test_plot_scatter_with_c(self):
         from pandas.plotting._matplotlib.compat import mpl_ge_3_4_0
 
         df = DataFrame(
-            np.random.randn(6, 4),
+            np.random.randint(low=0, high=100, size=(6, 4)),
             index=list(string.ascii_letters[:6]),
             columns=["x", "y", "z", "four"],
         )
@@ -1533,17 +1539,17 @@ def test_errorbar_plot_iterator(self):
 
     def test_errorbar_with_integer_column_names(self):
         # test with integer column names
-        df = DataFrame(np.random.randn(10, 2))
-        df_err = DataFrame(np.random.randn(10, 2))
+        df = DataFrame(np.abs(np.random.randn(10, 2)))
+        df_err = DataFrame(np.abs(np.random.randn(10, 2)))
         ax = _check_plot_works(df.plot, yerr=df_err)
         self._check_has_errorbars(ax, xerr=0, yerr=2)
         ax = _check_plot_works(df.plot, y=0, yerr=1)
         self._check_has_errorbars(ax, xerr=0, yerr=1)
 
     @pytest.mark.slow
     def test_errorbar_with_partial_columns(self):
-        df = DataFrame(np.random.randn(10, 3))
-        df_err = DataFrame(np.random.randn(10, 2), columns=[0, 2])
+        df = DataFrame(np.abs(np.random.randn(10, 3)))
+        df_err = DataFrame(np.abs(np.random.randn(10, 2)), columns=[0, 2])
         kinds = ["line", "bar"]
         for kind in kinds:
             ax = _check_plot_works(df.plot, yerr=df_err, kind=kind)
@@ -1631,9 +1637,11 @@ def test_table(self):
             assert len(ax.tables) == 1
 
     def test_errorbar_scatter(self):
-        df = DataFrame(np.random.randn(5, 2), index=range(5), columns=["x", "y"])
+        df = DataFrame(
+            np.abs(np.random.randn(5, 2)), index=range(5), columns=["x", "y"]
+        )
         df_err = DataFrame(
-            np.random.randn(5, 2) / 5, index=range(5), columns=["x", "y"]
+            np.abs(np.random.randn(5, 2)) / 5, index=range(5), columns=["x", "y"]
         )
 
         ax = _check_plot_works(df.plot.scatter, x="x", y="y")
@@ -1660,7 +1668,9 @@ def _check_errorbar_color(containers, expected, has_err="has_xerr"):
             )
 
         # GH 8081
-        df = DataFrame(np.random.randn(10, 5), columns=["a", "b", "c", "d", "e"])
+        df = DataFrame(
+            np.abs(np.random.randn(10, 5)), columns=["a", "b", "c", "d", "e"]
+        )
         ax = df.plot.scatter(x="a", y="b", xerr="d", yerr="e", c="red")
         self._check_has_errorbars(ax, xerr=1, yerr=1)
         _check_errorbar_color(ax.containers, "red", has_err="has_xerr")

pandas/tests/plotting/frame/test_frame_color.py

@@ -655,6 +655,6 @@ def test_colors_of_columns_with_same_name(self):
 
     def test_invalid_colormap(self):
         df = DataFrame(np.random.randn(3, 2), columns=["A", "B"])
-        msg = "'invalid_colormap' is not a valid value for name; supported values are "
-        with pytest.raises(ValueError, match=msg):
+        msg = "(is not a valid value)|(is not a known colormap)"
+        with pytest.raises((ValueError, KeyError), match=msg):
             df.plot(colormap="invalid_colormap")

pandas/tests/plotting/test_datetimelike.py

@@ -39,6 +39,11 @@
 from pandas.core.indexes.timedeltas import timedelta_range
 from pandas.tests.plotting.common import TestPlotBase
 
+try:
+    from pandas.plotting._matplotlib.compat import mpl_ge_3_6_0
+except ImportError:
+    mpl_ge_3_6_0 = lambda: True
+
 from pandas.tseries.offsets import WeekOfMonth
 
 
@@ -260,6 +265,7 @@ def test_plot_multiple_inferred_freq(self):
         ser = Series(np.random.randn(len(dr)), index=dr)
         _check_plot_works(ser.plot)
 
+    @pytest.mark.xfail(mpl_ge_3_6_0(), reason="Api changed")
     def test_uhf(self):
         import pandas.plotting._matplotlib.converter as conv
 
@@ -1209,6 +1215,7 @@ def test_secondary_legend(self):
         # TODO: color cycle problems
         assert len(colors) == 4
 
+    @pytest.mark.xfail(mpl_ge_3_6_0(), reason="Api changed")
     def test_format_date_axis(self):
         rng = date_range("1/1/2012", periods=12, freq="M")
         df = DataFrame(np.random.randn(len(rng), 3), rng)

pandas/tests/plotting/test_hist_method.py

@@ -18,6 +18,11 @@
     _check_plot_works,
 )
 
+try:
+    from pandas.plotting._matplotlib.compat import mpl_ge_3_6_0
+except ImportError:
+    mpl_ge_3_6_0 = lambda: True
+
 
 @pytest.fixture
 def ts():
@@ -191,6 +196,7 @@ def test_hist_kwargs(self, ts):
         ax = ts.plot.hist(align="left", stacked=True, ax=ax)
         tm.close()
 
+    @pytest.mark.xfail(mpl_ge_3_6_0(), reason="Api changed")
     @td.skip_if_no_scipy
     def test_hist_kde(self, ts):