BUG: Handle iterable of arrays in convert (#16026)

* BUG: Handle iterable of arrays in convert

DatetimeConverter.convert can take an array or iterable of arrays.
Fixed the converter to detect which case we're in and then re-use
the existing logic.

doc/source/whatsnew/v0.20.0.txt

@@ -1566,6 +1566,7 @@ Plotting
 
 - Bug in ``DataFrame.hist`` where ``plt.tight_layout`` caused an ``AttributeError``  (use ``matplotlib >= 2.0.1``) (:issue:`9351`)
 - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`)
+- Bug in the date and time converters pandas registers with matplotlib not handling multiple dimensions (:issue:`16026`)
 - Bug in ``pd.scatter_matrix()`` could accept either ``color`` or ``c``, but not both (:issue:`14855`)
 
 Groupby/Resample/Rolling

pandas/core/dtypes/inference.py

@@ -273,6 +273,50 @@ def is_list_like(obj):
             not isinstance(obj, string_and_binary_types))
 
 
+def is_nested_list_like(obj):
+    """
+    Check if the object is list-like, and that all of its elements
+    are also list-like.
+
+    .. versionadded:: 0.20.0
+
+    Parameters
+    ----------
+    obj : The object to check.
+
+    Returns
+    -------
+    is_list_like : bool
+        Whether `obj` has list-like properties.
+
+    Examples
+    --------
+    >>> is_nested_list_like([[1, 2, 3]])
+    True
+    >>> is_nested_list_like([{1, 2, 3}, {1, 2, 3}])
+    True
+    >>> is_nested_list_like(["foo"])
+    False
+    >>> is_nested_list_like([])
+    False
+    >>> is_nested_list_like([[1, 2, 3], 1])
+    False
+
+    Notes
+    -----
+    This won't reliably detect whether a consumable iterator (e. g.
+    a generator) is a nested-list-like without consuming the iterator.
+    To avoid consuming it, we always return False if the outer container
+    doesn't define `__len__`.
+
+    See Also
+    --------
+    is_list_like
+    """
+    return (is_list_like(obj) and hasattr(obj, '__len__') and
+            len(obj) > 0 and all(is_list_like(item) for item in obj))
+
+
 def is_dict_like(obj):
     """
     Check if the object is dict-like.

pandas/plotting/_converter.py

@@ -10,13 +10,14 @@
 from matplotlib.ticker import Formatter, AutoLocator, Locator
 from matplotlib.transforms import nonsingular
 
-
 from pandas.core.dtypes.common import (
     is_float, is_integer,
     is_integer_dtype,
     is_float_dtype,
     is_datetime64_ns_dtype,
-    is_period_arraylike)
+    is_period_arraylike,
+    is_nested_list_like
+)
 
 from pandas.compat import lrange
 import pandas.compat as compat
@@ -127,6 +128,15 @@ class PeriodConverter(dates.DateConverter):
 
     @staticmethod
     def convert(values, units, axis):
+        if is_nested_list_like(values):
+            values = [PeriodConverter._convert_1d(v, units, axis)
+                      for v in values]
+        else:
+            values = PeriodConverter._convert_1d(values, units, axis)
+        return values
+
+    @staticmethod
+    def _convert_1d(values, units, axis):
         if not hasattr(axis, 'freq'):
             raise TypeError('Axis must have `freq` set to convert to Periods')
         valid_types = (compat.string_types, datetime,
@@ -178,6 +188,16 @@ class DatetimeConverter(dates.DateConverter):
 
     @staticmethod
     def convert(values, unit, axis):
+        # values might be a 1-d array, or a list-like of arrays.
+        if is_nested_list_like(values):
+            values = [DatetimeConverter._convert_1d(v, unit, axis)
+                      for v in values]
+        else:
+            values = DatetimeConverter._convert_1d(values, unit, axis)
+        return values
+
+    @staticmethod
+    def _convert_1d(values, unit, axis):
         def try_parse(values):
             try:
                 return _dt_to_float_ordinal(tools.to_datetime(values))

pandas/tests/dtypes/test_inference.py

@@ -11,6 +11,7 @@
 from datetime import datetime, date, timedelta, time
 import numpy as np
 import pytz
+import pytest
 
 import pandas as pd
 from pandas._libs import tslib, lib
@@ -66,6 +67,27 @@ def test_is_list_like():
         assert not inference.is_list_like(f)
 
 
+@pytest.mark.parametrize('inner', [
+    [], [1], (1, ), (1, 2), {'a': 1}, set([1, 'a']), Series([1]),
+    Series([]), Series(['a']).str, (x for x in range(5))
+])
+@pytest.mark.parametrize('outer', [
+    list, Series, np.array, tuple
+])
+def test_is_nested_list_like_passes(inner, outer):
+    result = outer([inner for _ in range(5)])
+    assert inference.is_list_like(result)
+
+
+@pytest.mark.parametrize('obj', [
+    'abc', [], [1], (1,), ['a'], 'a', {'a'},
+    [1, 2, 3], Series([1]), DataFrame({"A": [1]}),
+    ([1, 2] for _ in range(5)),
+])
+def test_is_nested_list_like_fails(obj):
+    assert not inference.is_nested_list_like(obj)
+
+
 def test_is_dict_like():
     passes = [{}, {'A': 1}, Series([1])]
     fails = ['1', 1, [1, 2], (1, 2), range(2), Index([1])]

pandas/tests/plotting/test_converter.py

@@ -138,6 +138,13 @@ def _assert_less(ts1, ts2):
         _assert_less(ts, ts + Milli())
         _assert_less(ts, ts + Micro(50))
 
+    def test_convert_nested(self):
+        inner = [Timestamp('2017-01-01', Timestamp('2017-01-02'))]
+        data = [inner, inner]
+        result = self.dtc.convert(data, None, None)
+        expected = [self.dtc.convert(x, None, None) for x in data]
+        assert result == expected
+
 
 class TestPeriodConverter(tm.TestCase):
 
@@ -196,3 +203,9 @@ def test_integer_passthrough(self):
         rs = self.pc.convert([0, 1], None, self.axis)
         xp = [0, 1]
         self.assertEqual(rs, xp)
+
+    def test_convert_nested(self):
+        data = ['2012-1-1', '2012-1-2']
+        r1 = self.pc.convert([data, data], None, self.axis)
+        r2 = [self.pc.convert(data, None, self.axis) for _ in range(2)]
+        assert r1 == r2

pandas/tests/plotting/test_datetimelike.py

@@ -1334,6 +1334,14 @@ def test_timedelta_plot(self):
         s = Series(np.random.randn(len(index)), index)
         _check_plot_works(s.plot)
 
+    def test_hist(self):
+        # https://github.com/matplotlib/matplotlib/issues/8459
+        rng = date_range('1/1/2011', periods=10, freq='H')
+        x = rng
+        w1 = np.arange(0, 1, .1)
+        w2 = np.arange(0, 1, .1)[::-1]
+        self.plt.hist([x, x], weights=[w1, w2])
+
 
 def _check_plot_works(f, freq=None, series=None, *args, **kwargs):
     import matplotlib.pyplot as plt