API: Change matplotlib formatter registration

ci/code_checks.sh

@@ -204,7 +204,7 @@ import sys
 import pandas
 
 blacklist = {'bs4', 'gcsfs', 'html5lib', 'http', 'ipython', 'jinja2', 'hypothesis',
-             'lxml', 'numexpr', 'openpyxl', 'py', 'pytest', 's3fs', 'scipy',
+             'lxml', 'matplotlib', 'numexpr', 'openpyxl', 'py', 'pytest', 's3fs', 'scipy',
              'tables', 'urllib.request', 'xlrd', 'xlsxwriter', 'xlwt'}
 
 # GH#28227 for some of these check for top-level modules, while others are

doc/source/whatsnew/v1.0.0.rst

@@ -101,6 +101,10 @@ Backwards incompatible API changes
 
    pd.arrays.IntervalArray.from_tuples([(0, 1), (2, 3)])
 
+- Changed the default configuration value for ``options.matplotlib.register_converters`` from ``True`` to ``"auto"`` (:issue:`18720`).
+  Now, pandas custom formatters will only be applied to plots created by pandas, through :meth:`~DataFrame.plot`.
+  Previously, pandas' formatters would be applied to all plots created *after* a :meth:`~DataFrame.plot`.
+
 
 .. _whatsnew_1000.api.other:
 
@@ -109,7 +113,6 @@ Other API changes
 
 - :meth:`pandas.api.types.infer_dtype` will now return "integer-na" for integer and ``np.nan`` mix (:issue:`27283`)
 - :meth:`MultiIndex.from_arrays` will no longer infer names from arrays if ``names=None`` is explicitly provided (:issue:`27292`)
--
 
 .. _whatsnew_1000.deprecations:
 
@@ -133,6 +136,25 @@ with migrating existing code.
 Removal of prior version deprecations/changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+**Matplotlib unit registration**
+
+Previously, pandas would register converters with matplotlib as a side effect of importing pandas (:issue:`18720`).
+This changed the output of plots made via matplotlib plots after pandas was imported, even if you were using
+matplotlib directly rather than rather than :meth:`~DataFrame.plot`.
+
+To use pandas formatters with a matplotlib plot, specify
+
+.. code-block:: python
+
+   >>> import pandas as pd
+   >>> pd.options.plotting.matplotlib.register_converters = True
+
+Note that plots created by :meth:`DataFrame.plot` and :meth:`Series.plot` *do* register the converters
+automatically. The only behavior change is when plotting a date-like object via ``matplotlib.pyplot.plot``
+or ``matplotlib.Axes.plot``.
+
+**Other removals**
+
 - Removed the previously deprecated :meth:`Series.get_value`, :meth:`Series.set_value`, :meth:`DataFrame.get_value`, :meth:`DataFrame.set_value` (:issue:`17739`)
 - Changed the the default value of `inplace` in :meth:`DataFrame.set_index` and :meth:`Series.set_axis`. It now defaults to False (:issue:`27600`)
 - :meth:`pandas.Series.str.cat` now defaults to aligning ``others``, using ``join='left'`` (:issue:`27611`)

pandas/core/config_init.py

@@ -599,7 +599,7 @@ def register_plotting_backend_cb(key):
 
 
 register_converter_doc = """
-: bool
+: bool or 'auto'.
     Whether to register converters with matplotlib's units registry for
     dates, times, datetimes, and Periods. Toggling to False will remove
     the converters, restoring any converters that pandas overwrote.
@@ -619,8 +619,8 @@ def register_converter_cb(key):
 with cf.config_prefix("plotting.matplotlib"):
     cf.register_option(
         "register_converters",
-        True,
+        "auto",
         register_converter_doc,
-        validator=bool,
+        validator=is_one_of_factory(["auto", True, False]),
         cb=register_converter_cb,
     )

pandas/plotting/_core.py

@@ -3,19 +3,13 @@
 
 from pandas._config import get_option
 
-from pandas.compat._optional import import_optional_dependency
 from pandas.util._decorators import Appender
 
 from pandas.core.dtypes.common import is_integer, is_list_like
 from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
 
 from pandas.core.base import PandasObject
 
-# Trigger matplotlib import, which implicitly registers our
-# converts. Implicit registration is deprecated, and when enforced
-# we can lazily import matplotlib.
-import_optional_dependency("pandas.plotting._matplotlib", raise_on_missing=False)
-
 
 def hist_series(
     self,

pandas/plotting/_matplotlib/__init__.py

@@ -1,5 +1,3 @@
-from pandas._config import get_option
-
 from pandas.plotting._matplotlib.boxplot import (
     BoxPlot,
     boxplot,
@@ -42,9 +40,6 @@
     "hexbin": HexBinPlot,
 }
 
-if get_option("plotting.matplotlib.register_converters"):
-    register(explicit=False)
-
 
 def plot(data, kind, **kwargs):
     # Importing pyplot at the top of the file (before the converters are

pandas/plotting/_matplotlib/boxplot.py

@@ -11,7 +11,6 @@
 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
@@ -366,7 +365,6 @@ def boxplot_frame(
 ):
     import matplotlib.pyplot as plt
 
-    converter._WARN = False  # no warning for pandas plots
     ax = boxplot(
         self,
         column=column,
@@ -398,7 +396,6 @@ def boxplot_frame_groupby(
     sharey=True,
     **kwds
 ):
-    converter._WARN = False  # no warning for pandas plots
     if subplots is True:
         naxes = len(grouped)
         fig, axes = _subplots(

pandas/plotting/_matplotlib/converter.py

@@ -1,6 +1,7 @@
+import contextlib
 import datetime as pydt
 from datetime import datetime, timedelta
-import warnings
+import functools
 
 from dateutil.relativedelta import relativedelta
 import matplotlib.dates as dates
@@ -23,6 +24,7 @@
 )
 from pandas.core.dtypes.generic import ABCSeries
 
+from pandas import get_option
 import pandas.core.common as com
 from pandas.core.index import Index
 from pandas.core.indexes.datetimes import date_range
@@ -39,7 +41,6 @@
 
 MUSEC_PER_DAY = 1e6 * SEC_PER_DAY
 
-_WARN = True  # Global for whether pandas has registered the units explicitly
 _mpl_units = {}  # Cache for units overwritten by us
 
 
@@ -55,13 +56,43 @@ def get_pairs():
     return pairs
 
 
-def register(explicit=True):
-    # Renamed in pandas.plotting.__init__
-    global _WARN
+def pandas_converters_deco(func):
+    """
+    Decorator applying pandas_converters.
+    """
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        with pandas_converters():
+            return func(*args, **kwargs)
 
-    if explicit:
-        _WARN = False
+    return wrapper
 
+
+@contextlib.contextmanager
+def pandas_converters():
+    """
+    Context manager registering pandas' converters for a plot.
+
+    When
+    See Also
+    --------
+    pandas_converters_deco : Decorator that applies this.
+    """
+    value = get_option("plotting.matplotlib.register_converters")
+
+    if value:
+        # register for True or "auto"
+        register()
+    try:
+        yield
+    finally:
+        if value == "auto":
+            # only deregister for "auto"
+            deregister()
+
+
+def register():
     pairs = get_pairs()
     for type_, cls in pairs:
         # Cache previous converter if present
@@ -86,24 +117,6 @@ def deregister():
             units.registry[unit] = formatter
 
 
-def _check_implicitly_registered():
-    global _WARN
-
-    if _WARN:
-        msg = (
-            "Using an implicitly registered datetime converter for a "
-            "matplotlib plotting method. The converter was registered "
-            "by pandas on import. Future versions of pandas will require "
-            "you to explicitly register matplotlib converters.\n\n"
-            "To register the converters:\n\t"
-            ">>> from pandas.plotting import register_matplotlib_converters"
-            "\n\t"
-            ">>> register_matplotlib_converters()"
-        )
-        warnings.warn(msg, FutureWarning)
-        _WARN = False
-
-
 def _to_ordinalf(tm):
     tot_sec = tm.hour * 3600 + tm.minute * 60 + tm.second + float(tm.microsecond / 1e6)
     return tot_sec
@@ -253,7 +266,6 @@ class DatetimeConverter(dates.DateConverter):
     @staticmethod
     def convert(values, unit, axis):
         # values might be a 1-d array, or a list-like of arrays.
-        _check_implicitly_registered()
         if is_nested_list_like(values):
             values = [DatetimeConverter._convert_1d(v, unit, axis) for v in values]
         else:
@@ -330,7 +342,6 @@ def __init__(self, locator, tz=None, defaultfmt="%Y-%m-%d"):
 class PandasAutoDateLocator(dates.AutoDateLocator):
     def get_locator(self, dmin, dmax):
         """Pick the best locator based on a distance."""
-        _check_implicitly_registered()
         delta = relativedelta(dmax, dmin)
 
         num_days = (delta.years * 12.0 + delta.months) * 31.0 + delta.days
@@ -372,7 +383,6 @@ def get_unit_generic(freq):
 
     def __call__(self):
         # if no data have been set, this will tank with a ValueError
-        _check_implicitly_registered()
         try:
             dmin, dmax = self.viewlim_to_dt()
         except ValueError:
@@ -990,7 +1000,6 @@ def _get_default_locs(self, vmin, vmax):
     def __call__(self):
         "Return the locations of the ticks."
         # axis calls Locator.set_axis inside set_m<xxxx>_formatter
-        _check_implicitly_registered()
 
         vi = tuple(self.axis.get_view_interval())
         if vi != self.plot_obj.view_interval:
@@ -1075,7 +1084,6 @@ def set_locs(self, locs):
         "Sets the locations of the ticks"
         # don't actually use the locs. This is just needed to work with
         # matplotlib. Force to use vmin, vmax
-        _check_implicitly_registered()
 
         self.locs = locs
 
@@ -1088,7 +1096,6 @@ def set_locs(self, locs):
         self._set_default_format(vmin, vmax)
 
     def __call__(self, x, pos=0):
-        _check_implicitly_registered()
 
         if self.formatdict is None:
             return ""
@@ -1120,7 +1127,6 @@ def format_timedelta_ticks(x, pos, n_decimals):
         return s
 
     def __call__(self, x, pos=0):
-        _check_implicitly_registered()
         (vmin, vmax) = tuple(self.axis.get_view_interval())
         n_decimals = int(np.ceil(np.log10(100 * 1e9 / (vmax - vmin))))
         if n_decimals > 9:

pandas/plotting/_matplotlib/core.py

@@ -4,8 +4,6 @@
 
 import numpy as np
 
-from pandas._config import get_option
-
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import cache_readonly
 
@@ -28,8 +26,8 @@
 import pandas.core.common as com
 
 from pandas.io.formats.printing import pprint_thing
-from pandas.plotting._matplotlib import converter
 from pandas.plotting._matplotlib.compat import _mpl_ge_3_0_0
+from pandas.plotting._matplotlib.converter import pandas_converters_deco
 from pandas.plotting._matplotlib.style import _get_standard_colors
 from pandas.plotting._matplotlib.tools import (
     _flatten,
@@ -41,9 +39,6 @@
     table,
 )
 
-if get_option("plotting.matplotlib.register_converters"):
-    converter.register(explicit=False)
-
 
 class MPLPlot:
     """
@@ -112,7 +107,6 @@ def __init__(
 
         import matplotlib.pyplot as plt
 
-        converter._WARN = False  # no warning for pandas plots
         self.data = data
         self.by = by
 
@@ -648,6 +642,7 @@ def _get_xticks(self, convert_period=False):
         return x
 
     @classmethod
+    @pandas_converters_deco
     def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds):
         mask = isna(y)
         if mask.any():

pandas/plotting/_matplotlib/hist.py

@@ -9,7 +9,6 @@
 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
 
@@ -255,7 +254,6 @@ def _grouped_hist(
     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(
@@ -365,7 +363,6 @@ def hist_frame(
     bins=10,
     **kwds
 ):
-    converter._WARN = False  # no warning for pandas plots
     if by is not None:
         axes = _grouped_hist(
             data,

pandas/plotting/_matplotlib/timeseries.py

@@ -25,6 +25,7 @@
     TimeSeries_DateFormatter,
     TimeSeries_DateLocator,
     TimeSeries_TimedeltaFormatter,
+    pandas_converters_deco,
 )
 import pandas.tseries.frequencies as frequencies
 from pandas.tseries.offsets import DateOffset
@@ -33,6 +34,7 @@
 # Plotting functions and monkey patches
 
 
+@pandas_converters_deco
 def tsplot(series, plotf, ax=None, **kwargs):
     """
     Plots a Series on the given Matplotlib axes or the current axes
@@ -56,7 +58,7 @@ def tsplot(series, plotf, ax=None, **kwargs):
         "'tsplot' is deprecated and will be removed in a "
         "future version. Please use Series.plot() instead.",
         FutureWarning,
-        stacklevel=2,
+        stacklevel=3,
     )
 
     # Used inferred freq is possible, need a test case for inferred