API: Restore implicit converter registration

ci/check_imports.py

@@ -9,7 +9,6 @@
     'ipython',
     'jinja2'
     'lxml',
-    'matplotlib',
     'numexpr',
     'openpyxl',
     'py',

doc/source/api.rst

@@ -2375,6 +2375,17 @@ Style Export and Import
    Styler.use
    Styler.to_excel
 
+Plotting
+~~~~~~~~
+
+.. currentmodule:: pandas
+
+.. autosummary::
+   :toctree: generated/
+
+   plotting.register_matplotlib_converters
+   plotting.deregister_matplotlib_converters
+
 .. currentmodule:: pandas
 
 General utility functions

doc/source/whatsnew/v0.21.1.txt

@@ -7,6 +7,39 @@ This is a minor release from 0.21.1 and includes a number of deprecations, new
 features, enhancements, and performance improvements along with a large number
 of bug fixes. We recommend that all users upgrade to this version.
 
+.. _whatsnew_0211.special:
+
+Restore Matplotlib datetime Converter Registration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Pandas implements some matplotlib converters for nicely formatting the axis
+labels on plots with ``datetime`` or ``Period`` values. Prior to pandas 0.21.0,
+these were implicitly registered with matplotlib, as a side effect of
+``import pandas``. In pandas 0.21.0, we required users to explicitly register
+the converter.
+
+.. code-block:: python
+
+   >>> from pandas.plotting import register_matplotlib_converters
+   >>> register_matplotlib_converters()
+
+This caused problems for some users who relied on those converters being present
+for regular ``matplotlib.pyplot`` plotting methods, so we're temporarily
+reverting that change; pandas will again register the converters on import.
+Pandas plotting functions, such as ``Series.plot``, will continue to register
+them for you.
+
+We've added a new option to control the converters,
+``pd.options.plotting.matplotlib.register_converters``. By default, they are
+registered. Toggling this to ``False`` removes pandas' formatters and restore
+any converters we overwrote when registering them (:issue:`18301`).
+
+We're working with the matplotlib developers to make this easier. We're trying
+to balance user convenience (automatically registering the converters) with
+import performance and best practices (importing pandas shouldn't have the side
+effect of overwriting any custom converters you've already set). Apologies for
+any bumps along the way.
+
 .. _whatsnew_0211.enhancements:
 
 New features
@@ -30,9 +63,8 @@ Other Enhancements
 Deprecations
 ~~~~~~~~~~~~
 
--
--
--
+- ``pandas.tseries.register`` has been renamed to
+  :func:`pandas.plotting.register_matplotlib_converters`` (:issue:`18301`)
 
 .. _whatsnew_0211.performance:
 

pandas/core/config_init.py

@@ -480,3 +480,29 @@ def use_inf_as_na_cb(key):
     cf.register_option(
         'engine', 'auto', parquet_engine_doc,
         validator=is_one_of_factory(['auto', 'pyarrow', 'fastparquet']))
+
+# --------
+# Plotting
+# ---------
+
+register_converter_doc = """
+: bool
+    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.
+"""
+
+
+def register_converter_cb(key):
+    from pandas.plotting import register_matplotlib_converters
+    from pandas.plotting import deregister_matplotlib_converters
+
+    if cf.get_option(key):
+        register_matplotlib_converters()
+    else:
+        deregister_matplotlib_converters()
+
+
+with cf.config_prefix("plotting.matplotlib"):
+    cf.register_option("register_converters", True, register_converter_doc,
+                       validator=bool, cb=register_converter_cb)

pandas/plotting/__init__.py

@@ -11,3 +11,10 @@
 from pandas.plotting._core import boxplot
 from pandas.plotting._style import plot_params
 from pandas.plotting._tools import table
+try:
+    from pandas.plotting._converter import \
+        register as register_matplotlib_converters
+    from pandas.plotting._converter import \
+        deregister as deregister_matplotlib_converters
+except ImportError:
+    pass

pandas/plotting/_converter.py

@@ -1,3 +1,4 @@
+import warnings
 from datetime import datetime, timedelta
 import datetime as pydt
 import numpy as np
@@ -45,14 +46,96 @@
 
 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
 
-def register():
-    units.registry[lib.Timestamp] = DatetimeConverter()
-    units.registry[Period] = PeriodConverter()
-    units.registry[pydt.datetime] = DatetimeConverter()
-    units.registry[pydt.date] = DatetimeConverter()
-    units.registry[pydt.time] = TimeConverter()
-    units.registry[np.datetime64] = DatetimeConverter()
+
+def get_pairs():
+    pairs = [
+        (lib.Timestamp, DatetimeConverter),
+        (Period, PeriodConverter),
+        (pydt.datetime, DatetimeConverter),
+        (pydt.date, DatetimeConverter),
+        (pydt.time, TimeConverter),
+        (np.datetime64, DatetimeConverter),
+    ]
+    return pairs
+
+
+def register(explicit=False):
+    """Register Pandas Formatters and Converters with matplotlib
+
+    This function modifies the global ``matplotlib.units.registry``
+    dictionary. Pandas adds custom converters for
+
+    * pd.Timestamp
+    * pd.Period
+    * np.datetime64
+    * datetime.datetime
+    * datetime.date
+    * datetime.time
+
+    See Also
+    --------
+    deregister_matplotlib_converter
+    """
+    # Renamed in pandas.plotting.__init__
+    global _WARN
+
+    if explicit:
+        _WARN = False
+
+    pairs = get_pairs()
+    for type_, cls in pairs:
+        converter = cls()
+        if type_ in units.registry:
+            previous = units.registry[type_]
+            _mpl_units[type_] = previous
+        units.registry[type_] = converter
+
+
+def deregister():
+    """Remove pandas' formatters and converters
+
+    Removes the custom converters added by :func:`register`. This
+    attempts to set the state of the registry back to the state before
+    pandas registered its own units. Converters for pandas' own types like
+    Timestamp and Period are removed completely. Converters for types
+    pandas overwrites, like ``datetime.datetime``, are restored to their
+    original value.
+
+    See Also
+    --------
+    deregister_matplotlib_converters
+    """
+    # Renamed in pandas.plotting.__init__
+    for type_, cls in get_pairs():
+        # We use type to catch our classes directly, no inheritance
+        if type(units.registry.get(type_)) is cls:
+            units.registry.pop(type_)
+
+    # restore the old keys
+    for unit, formatter in _mpl_units.items():
+        if type(formatter) not in {DatetimeConverter, PeriodConverter,
+                                   TimeConverter}:
+            # make it idempotent by excluding ours.
+            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):
@@ -190,6 +273,7 @@ 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]
@@ -274,6 +358,7 @@ 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
@@ -315,6 +400,7 @@ 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:
@@ -917,6 +1003,8 @@ 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:
             self.plot_obj.date_axis_info = None
@@ -1001,6 +1089,8 @@ 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
 
         (vmin, vmax) = vi = tuple(self.axis.get_view_interval())
@@ -1012,6 +1102,8 @@ 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 ''
         else:
@@ -1042,6 +1134,7 @@ 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/_core.py

@@ -11,6 +11,7 @@
 
 from pandas.util._decorators import cache_readonly
 from pandas.core.base import PandasObject
+from pandas.core.config import get_option
 from pandas.core.dtypes.missing import isna, notna, remove_na_arraylike
 from pandas.core.dtypes.common import (
     is_list_like,
@@ -40,16 +41,13 @@
                                     _get_xlim, _set_ticks_props,
                                     format_date_labels)
 
-_registered = False
-
-
-def _setup():
-    # delay the import of matplotlib until nescessary
-    global _registered
-    if not _registered:
-        from pandas.plotting import _converter
-        _converter.register()
-        _registered = True
+try:
+    from pandas.plotting import _converter
+except ImportError:
+    pass
+else:
+    if get_option('plotting.matplotlib.register_converters'):
+        _converter.register(explicit=True)
 
 
 def _get_standard_kind(kind):
@@ -99,7 +97,7 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=None,
                  secondary_y=False, colormap=None,
                  table=False, layout=None, **kwds):
 
-        _setup()
+        _converter._WARN = False
         self.data = data
         self.by = by
 
@@ -2064,7 +2062,7 @@ 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
-    _setup()
+    _converter._WARN = False
     ax = boxplot(self, column=column, by=by, ax=ax, fontsize=fontsize,
                  grid=grid, rot=rot, figsize=figsize, layout=layout,
                  return_type=return_type, **kwds)
@@ -2160,7 +2158,7 @@ def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
     `**kwds` : other plotting keyword arguments
         To be passed to hist function
     """
-    _setup()
+    _converter._WARN = False
     if by is not None:
         axes = grouped_hist(data, column=column, by=by, ax=ax, grid=grid,
                             figsize=figsize, sharex=sharex, sharey=sharey,
@@ -2294,6 +2292,8 @@ def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None,
     -------
     axes: collection of Matplotlib Axes
     """
+    _converter._WARN = False
+
     def plot_group(group, ax):
         ax.hist(group.dropna().values, bins=bins, **kwargs)
 
@@ -2358,7 +2358,7 @@ def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
     >>> grouped = df.unstack(level='lvl1').groupby(level=0, axis=1)
     >>> boxplot_frame_groupby(grouped, subplots=False)
     """
-    _setup()
+    _converter._WARN = False
     if subplots is True:
         naxes = len(grouped)
         fig, axes = _subplots(naxes=naxes, squeeze=False,