DEPR: remove tsplot (pandas-dev#29726)

doc/source/whatsnew/v1.0.0.rst

@@ -318,6 +318,7 @@ or ``matplotlib.Axes.plot``. See :ref:`plotting.formatters` for more.
 - Removed the previously deprecated ``IntervalIndex.from_intervals`` in favor of the :class:`IntervalIndex` constructor (:issue:`19263`)
 - Changed the default value for the "keep_tz" argument in :meth:`DatetimeIndex.to_series` to ``True`` (:issue:`23739`)
 - Ability to read pickles containing :class:`Categorical` instances created with pre-0.16 version of pandas has been removed (:issue:`27538`)
+- Removed previously deprecated :func:`pandas.tseries.plotting.tsplot` (:issue:`18627`)
 - Removed the previously deprecated ``reduce`` and ``broadcast`` arguments from :meth:`DataFrame.apply` (:issue:`18577`)
 - Removed the previously deprecated ``assert_raises_regex`` function in ``pandas.util.testing`` (:issue:`29174`)
 - Removed :meth:`Index.is_lexsorted_for_tuple` (:issue:`29305`)

pandas/plotting/__init__.py

@@ -38,7 +38,6 @@
 - hist_series and hist_frame (for `Series.hist` and `DataFrame.hist`)
 - boxplot (`pandas.plotting.boxplot(df)` equivalent to `DataFrame.boxplot`)
 - boxplot_frame and boxplot_frame_groupby
-- tsplot (deprecated)
 - register and deregister (register converters for the tick formats)
 - Plots not called as `Series` and `DataFrame` methods:
   - table

pandas/plotting/_misc.py

@@ -1,5 +1,4 @@
 from contextlib import contextmanager
-import warnings
 
 from pandas.util._decorators import deprecate_kwarg
 
@@ -426,33 +425,6 @@ def autocorrelation_plot(series, ax=None, **kwargs):
     return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
 
 
-def tsplot(series, plotf, ax=None, **kwargs):
-    """
-    Plots a Series on the given Matplotlib axes or the current axes
-
-    Parameters
-    ----------
-    axes : Axes
-    series : Series
-
-    Notes
-    _____
-    Supports same kwargs as Axes.plot
-
-
-    .. deprecated:: 0.23.0
-       Use Series.plot() instead
-    """
-    warnings.warn(
-        "'tsplot' is deprecated and will be removed in a "
-        "future version. Please use Series.plot() instead.",
-        FutureWarning,
-        stacklevel=2,
-    )
-    plot_backend = _get_plot_backend("matplotlib")
-    return plot_backend.tsplot(series=series, plotf=plotf, ax=ax, **kwargs)
-
-
 class _Options(dict):
     """
     Stores pandas plotting options.

pandas/tests/plotting/test_datetimelike.py

@@ -99,33 +99,12 @@ def test_nonnumeric_exclude(self):
         with pytest.raises(TypeError, match=msg):
             df["A"].plot()
 
-    def test_tsplot_deprecated(self):
-        from pandas.tseries.plotting import tsplot
-
-        _, ax = self.plt.subplots()
-        ts = tm.makeTimeSeries()
-
-        with tm.assert_produces_warning(FutureWarning):
-            tsplot(ts, self.plt.Axes.plot, ax=ax)
-
     @pytest.mark.slow
     def test_tsplot(self):
 
-        from pandas.tseries.plotting import tsplot
-
         _, ax = self.plt.subplots()
         ts = tm.makeTimeSeries()
 
-        def f(*args, **kwds):
-            with tm.assert_produces_warning(FutureWarning):
-                return tsplot(s, self.plt.Axes.plot, *args, **kwds)
-
-        for s in self.period_ser:
-            _check_plot_works(f, s.index.freq, ax=ax, series=s)
-
-        for s in self.datetime_ser:
-            _check_plot_works(f, s.index.freq.rule_code, ax=ax, series=s)
-
         for s in self.period_ser:
             _check_plot_works(s.plot, ax=ax)
 
@@ -194,17 +173,6 @@ def check_format_of_first_point(ax, expected_string):
         check_format_of_first_point(ax, "t = 2014-01-01  y = 1.000000")
         tm.close()
 
-        # tsplot
-        from pandas.tseries.plotting import tsplot
-
-        _, ax = self.plt.subplots()
-        with tm.assert_produces_warning(FutureWarning):
-            tsplot(annual, self.plt.Axes.plot, ax=ax)
-        check_format_of_first_point(ax, "t = 2014  y = 1.000000")
-        with tm.assert_produces_warning(FutureWarning):
-            tsplot(daily, self.plt.Axes.plot, ax=ax)
-        check_format_of_first_point(ax, "t = 2014-01-01  y = 1.000000")
-
     @pytest.mark.slow
     def test_line_plot_period_series(self):
         for s in self.period_ser:
@@ -892,16 +860,6 @@ def test_to_weekly_resampling(self):
         for l in ax.get_lines():
             assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
 
-        _, ax = self.plt.subplots()
-        from pandas.tseries.plotting import tsplot
-
-        with tm.assert_produces_warning(FutureWarning):
-            tsplot(high, self.plt.Axes.plot, ax=ax)
-        with tm.assert_produces_warning(FutureWarning):
-            lines = tsplot(low, self.plt.Axes.plot, ax=ax)
-        for l in lines:
-            assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
-
     @pytest.mark.slow
     def test_from_weekly_resampling(self):
         idxh = date_range("1/1/1999", periods=52, freq="W")
@@ -926,21 +884,6 @@ def test_from_weekly_resampling(self):
                 tm.assert_numpy_array_equal(xdata, expected_h)
         tm.close()
 
-        _, ax = self.plt.subplots()
-        from pandas.tseries.plotting import tsplot
-
-        with tm.assert_produces_warning(FutureWarning):
-            tsplot(low, self.plt.Axes.plot, ax=ax)
-        with tm.assert_produces_warning(FutureWarning):
-            lines = tsplot(high, self.plt.Axes.plot, ax=ax)
-        for l in lines:
-            assert PeriodIndex(data=l.get_xdata()).freq == idxh.freq
-            xdata = l.get_xdata(orig=False)
-            if len(xdata) == 12:  # idxl lines
-                tm.assert_numpy_array_equal(xdata, expected_l)
-            else:
-                tm.assert_numpy_array_equal(xdata, expected_h)
-
     @pytest.mark.slow
     def test_from_resampling_area_line_mixed(self):
         idxh = date_range("1/1/1999", periods=52, freq="W")