Fix matplotlib converter registering warning

pandas/plotting/_core.py

@@ -5,19 +5,16 @@
 from pandas.core.dtypes.common import is_integer, is_list_like
 from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
 
-import pandas
 from pandas.core.base import PandasObject
 from pandas.core.generic import _shared_doc_kwargs, _shared_docs
 
-# Automatically registering converters was deprecated in 0.21, but
-# the deprecation warning wasn't showing until 0.24
-# This block will be eventually removed, but it's not clear when
-if pandas.get_option('plotting.matplotlib.register_converters'):
-    try:
-        from .misc import register
-        register(explicit=False)
-    except ImportError:
-        pass
+# Trigger matplotlib import, which implicitly registers our
+# converts. Implicit registration is deprecated, and when enforced
+# we can lazily import matplotlib.
+try:
+    import pandas.plotting._matplotlib  # noqa
+except ImportError:
+    pass
 
 df_kind = """- 'scatter' : scatter plot
         - 'hexbin' : hexbin plot"""

pandas/plotting/_matplotlib/__init__.py

@@ -1,3 +1,5 @@
+from pandas._config import get_option
+
 from pandas.plotting._matplotlib.boxplot import (
     BoxPlot, boxplot, boxplot_frame, boxplot_frame_groupby)
 from pandas.plotting._matplotlib.converter import deregister, register
@@ -11,6 +13,10 @@
 from pandas.plotting._matplotlib.timeseries import tsplot
 from pandas.plotting._matplotlib.tools import table
 
+if get_option("plotting.matplotlib.register_converters"):
+    register(explicit=False)
+
+
 __all__ = ['LinePlot', 'BarPlot', 'BarhPlot', 'HistPlot', 'BoxPlot', 'KdePlot',
            'AreaPlot', 'PiePlot', 'ScatterPlot', 'HexBinPlot', 'hist_series',
            'hist_frame', 'boxplot', 'boxplot_frame', 'boxplot_frame_groupby',

pandas/plotting/_matplotlib/boxplot.py

@@ -1,7 +1,6 @@
 from collections import namedtuple
 import warnings
 
-from matplotlib import pyplot as plt
 from matplotlib.artist import setp
 import numpy as np
 
@@ -11,6 +10,7 @@
 import pandas as pd
 
 from pandas.io.formats.printing import pprint_thing
+from pandas.plotting._matplotlib import converter
 from pandas.plotting._matplotlib.core import LinePlot, MPLPlot
 from pandas.plotting._matplotlib.style import _get_standard_colors
 from pandas.plotting._matplotlib.tools import _flatten, _subplots
@@ -215,6 +215,7 @@ def boxplot(data, column=None, by=None, ax=None, fontsize=None,
             rot=0, grid=True, figsize=None, layout=None, return_type=None,
             **kwds):
 
+    import matplotlib.pyplot as plt
     # validate return_type:
     if return_type not in BoxPlot._valid_return_types:
         raise ValueError("return_type must be {'axes', 'dict', 'both'}")
@@ -296,6 +297,8 @@ def plot_group(keys, values, ax):
 def boxplot_frame(self, column=None, by=None, ax=None, fontsize=None, rot=0,
                   grid=True, figsize=None, layout=None,
                   return_type=None, **kwds):
+    import matplotlib.pyplot as plt
+    converter._WARN = False  # no warning for pandas plots
     ax = boxplot(self, column=column, by=by, ax=ax, fontsize=fontsize,
                  grid=grid, rot=rot, figsize=figsize, layout=layout,
                  return_type=return_type, **kwds)
@@ -306,6 +309,7 @@ def boxplot_frame(self, column=None, by=None, ax=None, fontsize=None, rot=0,
 def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
                           rot=0, grid=True, ax=None, figsize=None,
                           layout=None, sharex=False, sharey=True, **kwds):
+    converter._WARN = False  # no warning for pandas plots
     if subplots is True:
         naxes = len(grouped)
         fig, axes = _subplots(naxes=naxes, squeeze=False,

pandas/plotting/_matplotlib/core.py

@@ -2,7 +2,6 @@
 from typing import Optional  # noqa
 import warnings
 
-import matplotlib.pyplot as plt
 import numpy as np
 
 from pandas._config import get_option
@@ -61,6 +60,8 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=None,
                  secondary_y=False, colormap=None,
                  table=False, layout=None, **kwds):
 
+        import matplotlib.pyplot as plt
+        converter._WARN = False  # no warning for pandas plots
         self.data = data
         self.by = by
 
@@ -103,7 +104,7 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=None,
             self.rot = self._default_rot
 
         if grid is None:
-            grid = False if secondary_y else self.plt.rcParams['axes.grid']
+            grid = False if secondary_y else plt.rcParams['axes.grid']
 
         self.grid = grid
         self.legend = legend
@@ -618,6 +619,8 @@ def _get_ax(self, i):
 
     @classmethod
     def get_default_ax(cls, ax):
+        import matplotlib.pyplot as plt
+
         if ax is None and len(plt.get_fignums()) > 0:
             with plt.rc_context():
                 ax = plt.gca()

pandas/plotting/_matplotlib/hist.py

@@ -1,6 +1,5 @@
 import warnings
 
-import matplotlib.pyplot as plt
 import numpy as np
 
 from pandas.core.dtypes.common import is_integer, is_list_like
@@ -10,6 +9,7 @@
 import pandas.core.common as com
 
 from pandas.io.formats.printing import pprint_thing
+from pandas.plotting._matplotlib import converter
 from pandas.plotting._matplotlib.core import LinePlot, MPLPlot
 from pandas.plotting._matplotlib.tools import (
     _flatten, _set_ticks_props, _subplots)
@@ -203,6 +203,7 @@ def _grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None,
     def plot_group(group, ax):
         ax.hist(group.dropna().values, bins=bins, **kwargs)
 
+    converter._WARN = False  # no warning for pandas plots
     xrot = xrot or rot
 
     fig, axes = _grouped_plot(plot_group, data, column=column,
@@ -220,6 +221,7 @@ def plot_group(group, ax):
 def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None,
                 xrot=None, ylabelsize=None, yrot=None, figsize=None,
                 bins=10, **kwds):
+    import matplotlib.pyplot as plt
     if by is None:
         if kwds.get('layout', None) is not None:
             raise ValueError("The 'layout' keyword is not supported when "
@@ -261,6 +263,7 @@ def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None,
 def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
                xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False,
                sharey=False, figsize=None, layout=None, bins=10, **kwds):
+    converter._WARN = False  # no warning for pandas plots
     if by is not None:
         axes = _grouped_hist(data, column=column, by=by, ax=ax, grid=grid,
                              figsize=figsize, sharex=sharex, sharey=sharey,

pandas/plotting/_matplotlib/misc.py

@@ -2,7 +2,6 @@
 
 import matplotlib.lines as mlines
 import matplotlib.patches as patches
-import matplotlib.pyplot as plt
 import numpy as np
 
 from pandas.core.dtypes.missing import notna
@@ -105,6 +104,7 @@ def _get_marker_compat(marker):
 
 
 def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
+    import matplotlib.pyplot as plt
 
     def normalize(series):
         a = min(series)
@@ -169,6 +169,7 @@ def normalize(series):
 
 def andrews_curves(frame, class_column, ax=None, samples=200, color=None,
                    colormap=None, **kwds):
+    import matplotlib.pyplot as plt
 
     def function(amplitudes):
         def f(t):
@@ -224,6 +225,7 @@ def f(t):
 
 def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
 
+    import matplotlib.pyplot as plt
     # random.sample(ndarray, int) fails on python 3.3, sigh
     data = list(series.values)
     samplings = [random.sample(data, size) for _ in range(samples)]
@@ -270,6 +272,7 @@ def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None,
                          use_columns=False, xticks=None, colormap=None,
                          axvlines=True, axvlines_kwds=None, sort_labels=False,
                          **kwds):
+    import matplotlib.pyplot as plt
     if axvlines_kwds is None:
         axvlines_kwds = {'linewidth': 1, 'color': 'black'}
 
@@ -336,6 +339,7 @@ def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None,
 
 def lag_plot(series, lag=1, ax=None, **kwds):
     # workaround because `c='b'` is hardcoded in matplotlibs scatter method
+    import matplotlib.pyplot as plt
     kwds.setdefault('c', plt.rcParams['patch.facecolor'])
 
     data = series.values
@@ -350,6 +354,8 @@ def lag_plot(series, lag=1, ax=None, **kwds):
 
 
 def autocorrelation_plot(series, ax=None, **kwds):
+    import matplotlib.pyplot as plt
+
     n = len(series)
     data = np.asarray(series)
     if ax is None:

pandas/plotting/_matplotlib/style.py

@@ -3,7 +3,6 @@
 
 import matplotlib.cm as cm
 import matplotlib.colors
-import matplotlib.pyplot as plt
 import numpy as np
 
 from pandas.core.dtypes.common import is_list_like
@@ -13,6 +12,7 @@
 
 def _get_standard_colors(num_colors=None, colormap=None, color_type='default',
                          color=None):
+    import matplotlib.pyplot as plt
     if color is None and colormap is not None:
         if isinstance(colormap, str):
             cmap = colormap

pandas/plotting/_matplotlib/timeseries.py

@@ -3,8 +3,6 @@
 import functools
 import warnings
 
-from matplotlib import pylab
-import matplotlib.pyplot as plt
 import numpy as np
 
 from pandas._libs.tslibs.frequencies import (
@@ -42,6 +40,7 @@ def tsplot(series, plotf, ax=None, **kwargs):
     .. deprecated:: 0.23.0
        Use Series.plot() instead
     """
+    import matplotlib.pyplot as plt
     warnings.warn("'tsplot' is deprecated and will be removed in a "
                   "future version. Please use Series.plot() instead.",
                   FutureWarning, stacklevel=2)
@@ -323,6 +322,7 @@ def format_dateaxis(subplot, freq, index):
     default, changing the limits of the x axis will intelligently change
     the positions of the ticks.
     """
+    from matplotlib import pylab
 
     # handle index specific formatting
     # Note: DatetimeIndex does not use this

pandas/plotting/_matplotlib/tools.py

@@ -2,7 +2,6 @@
 from math import ceil
 import warnings
 
-import matplotlib.pyplot as plt
 import matplotlib.table
 import matplotlib.ticker as ticker
 import numpy as np
@@ -168,6 +167,7 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
     # Four polar axes
     plt.subplots(2, 2, subplot_kw=dict(polar=True))
     """
+    import matplotlib.pyplot as plt
     if subplot_kw is None:
         subplot_kw = {}
 
@@ -345,6 +345,7 @@ def _get_xlim(lines):
 
 def _set_ticks_props(axes, xlabelsize=None, xrot=None,
                      ylabelsize=None, yrot=None):
+    import matplotlib.pyplot as plt
     for ax in _flatten(axes):
         if xlabelsize is not None:
             plt.setp(ax.get_xticklabels(), fontsize=xlabelsize)

pandas/tests/plotting/test_converter.py

@@ -12,11 +12,30 @@
 from pandas import Index, Period, Series, Timestamp, date_range
 import pandas.util.testing as tm
 
+from pandas.plotting import (
+    deregister_matplotlib_converters, register_matplotlib_converters)
 from pandas.tseries.offsets import Day, Micro, Milli, Second
 
-converter = pytest.importorskip('pandas.plotting._converter')
-from pandas.plotting import (deregister_matplotlib_converters,  # isort:skip
-                             register_matplotlib_converters)
+try:
+    from pandas.plotting._matplotlib import converter
+except ImportError:
+    # try / except, rather than skip, to avoid internal refactoring
+    # causing an improprer skip
+    pass
+
+pytest.importorskip('matplotlib.pyplot')
+
+
+def test_initial_warning():
+    code = (
+        "import pandas as pd; import matplotlib.pyplot as plt; "
+        "s = pd.Series(1, pd.date_range('2000', periods=12)); "
+        "fig, ax = plt.subplots(); "
+        "ax.plot(s.index, s.values)"
+    )
+    call = [sys.executable, '-c', code]
+    out = subprocess.check_output(call, stderr=subprocess.STDOUT).decode()
+    assert 'Using an implicitly' in out
 
 
 def test_timtetonum_accepts_unicode():