DEPS: drop numpy < 1.12

ci/azure-macos-35.yaml

@@ -11,7 +11,7 @@ dependencies:
   - matplotlib
   - nomkl
   - numexpr
-  - numpy=1.10.4
+  - numpy=1.12.0
   - openpyxl=2.5.5
   - pytables
   - python=3.5*

ci/circle-27-compat.yaml

@@ -3,11 +3,11 @@ channels:
   - defaults
   - conda-forge
 dependencies:
-  - bottleneck=1.0.0
+  - bottleneck=1.2.0
   - cython=0.28.2
   - jinja2=2.8
-  - numexpr=2.4.4 # we test that we correctly don't use an unsupported numexpr
-  - numpy=1.9.3
+  - numexpr=2.6.2
+  - numpy=1.12.0
   - openpyxl=2.5.5
   - psycopg2
   - pytables=3.2.2

ci/requirements-optional-conda.txt

@@ -1,6 +1,6 @@
 beautifulsoup4>=4.2.1
 blosc
-bottleneck
+bottleneck>=1.2.0
 fastparquet
 feather-format
 gcsfs
@@ -9,9 +9,9 @@ ipython>=5.6.0
 ipykernel
 jinja2
 lxml
-matplotlib
+matplotlib>=2.0.0
 nbsphinx
-numexpr
+numexpr>=2.6.2
 openpyxl=2.5.5
 pyarrow
 pymysql

ci/requirements-optional-pip.txt

@@ -2,7 +2,7 @@
 # Do not modify directly
 beautifulsoup4>=4.2.1
 blosc
-bottleneck
+bottleneck>=1.2.0
 fastparquet
 feather-format
 gcsfs
@@ -11,9 +11,9 @@ ipython>=5.6.0
 ipykernel
 jinja2
 lxml
-matplotlib
+matplotlib>=2.0.0
 nbsphinx
-numexpr
+numexpr>=2.6.2
 openpyxl==2.5.5
 pyarrow
 pymysql

ci/travis-27-locale.yaml

@@ -3,11 +3,11 @@ channels:
   - defaults
   - conda-forge
 dependencies:
-  - bottleneck=1.0.0
+  - bottleneck=1.2.0
   - cython=0.28.2
   - lxml
-  - matplotlib=1.4.3
-  - numpy=1.9.3
+  - matplotlib=2.0.0
+  - numpy=1.12.0
   - openpyxl=2.4.0
   - python-dateutil
   - python-blosc

doc/source/install.rst

@@ -225,7 +225,7 @@ Dependencies
 ------------
 
 * `setuptools <https://setuptools.readthedocs.io/en/latest/>`__: 24.2.0 or higher
-* `NumPy <http://www.numpy.org>`__: 1.9.0 or higher
+* `NumPy <http://www.numpy.org>`__: 1.12.0 or higher
 * `python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__: 2.5.0 or higher
 * `pytz <http://pytz.sourceforge.net/>`__
 
@@ -236,11 +236,11 @@ Recommended Dependencies
 
 * `numexpr <https://github.com/pydata/numexpr>`__: for accelerating certain numerical operations.
   ``numexpr`` uses multiple cores as well as smart chunking and caching to achieve large speedups.
-  If installed, must be Version 2.4.6 or higher.
+  If installed, must be Version 2.6.2 or higher.
 
 * `bottleneck <https://github.com/kwgoodman/bottleneck>`__: for accelerating certain types of ``nan``
   evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups. If installed,
-  must be Version 1.0.0 or higher.
+  must be Version 1.2.0 or higher.
 
 .. note::
 
@@ -266,7 +266,7 @@ Optional Dependencies
     * `pymysql <https://github.com/PyMySQL/PyMySQL>`__: for MySQL.
     * `SQLite <https://docs.python.org/3/library/sqlite3.html>`__: for SQLite, this is included in Python's standard library by default.
 
-* `matplotlib <http://matplotlib.org/>`__: for plotting, Version 1.4.3 or higher.
+* `matplotlib <http://matplotlib.org/>`__: for plotting, Version 2.0.0 or higher.
 * For Excel I/O:
 
     * `xlrd/xlwt <http://www.python-excel.org/>`__: Excel reading (xlrd) and writing (xlwt)

doc/source/whatsnew/v0.24.0.txt

@@ -199,8 +199,28 @@ Other Enhancements
 
 Backwards incompatible API changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 - A newly constructed empty :class:`DataFrame` with integer as the ``dtype`` will now only be cast to ``float64`` if ``index`` is specified (:issue:`22858`)
 
+.. _whatsnew_0240.api_breaking.deps:
+
+Dependencies have increased minimum versions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We have updated our minimum supported versions of dependencies (:issue:`21242`).
+If installed, we now require:
+
++-----------------+-----------------+----------+---------------+
+| Package         | Minimum Version | Required |     Issue     |
++=================+=================+==========+===============+
+| numpy           | 1.12.0          |    X     | :issue:`21242`|
++-----------------+-----------------+----------+---------------+
+| bottleneck      | 1.2.0           |          | :issue:`21242`|
++-----------------+-----------------+----------+---------------+
+| matplotlib      | 2.0.0           |          | :issue:`21242`|
++-----------------+-----------------+----------+---------------+
+| numexpr         | 2.6.2           |          | :issue:`21242`|
++-----------------+-----------------+----------+---------------+
 
 .. _whatsnew_0240.api_breaking.interval_values:
 

pandas/compat/numpy/__init__.py

@@ -9,19 +9,16 @@
 # numpy versioning
 _np_version = np.__version__
 _nlv = LooseVersion(_np_version)
-_np_version_under1p10 = _nlv < LooseVersion('1.10')
-_np_version_under1p11 = _nlv < LooseVersion('1.11')
-_np_version_under1p12 = _nlv < LooseVersion('1.12')
 _np_version_under1p13 = _nlv < LooseVersion('1.13')
 _np_version_under1p14 = _nlv < LooseVersion('1.14')
 _np_version_under1p15 = _nlv < LooseVersion('1.15')
 
 
-if _nlv < '1.9':
+if _nlv < '1.12':
     raise ImportError('this version of pandas is incompatible with '
-                      'numpy < 1.9.0\n'
+                      'numpy < 1.12.0\n'
                       'your numpy version is {0}.\n'
-                      'Please upgrade numpy to >= 1.9.0 to use '
+                      'Please upgrade numpy to >= 1.12.0 to use '
                       'this pandas version'.format(_np_version))
 
 
@@ -43,9 +40,7 @@ def np_datetime64_compat(s, *args, **kwargs):
     tz-changes in 1.11 that make '2015-01-01 09:00:00Z' show a deprecation
     warning, when need to pass '2015-01-01 09:00:00'
     """
-
-    if not _np_version_under1p11:
-        s = tz_replacer(s)
+    s = tz_replacer(s)
     return np.datetime64(s, *args, **kwargs)
 
 
@@ -56,23 +51,17 @@ def np_array_datetime64_compat(arr, *args, **kwargs):
     tz-changes in 1.11 that make '2015-01-01 09:00:00Z' show a deprecation
     warning, when need to pass '2015-01-01 09:00:00'
     """
-
-    if not _np_version_under1p11:
-
-        # is_list_like
-        if hasattr(arr, '__iter__') and not \
-           isinstance(arr, string_and_binary_types):
-            arr = [tz_replacer(s) for s in arr]
-        else:
-            arr = tz_replacer(arr)
+    # is_list_like
+    if hasattr(arr, '__iter__') and not \
+       isinstance(arr, string_and_binary_types):
+        arr = [tz_replacer(s) for s in arr]
+    else:
+        arr = tz_replacer(arr)
 
     return np.array(arr, *args, **kwargs)
 
 
 __all__ = ['np',
-           '_np_version_under1p10',
-           '_np_version_under1p11',
-           '_np_version_under1p12',
            '_np_version_under1p13',
            '_np_version_under1p14',
            '_np_version_under1p15'

pandas/core/algorithms.py

@@ -30,7 +30,6 @@
     ensure_platform_int, ensure_object,
     ensure_float64, ensure_uint64,
     ensure_int64)
-from pandas.compat.numpy import _np_version_under1p10
 from pandas.core.dtypes.missing import isna, na_value_for_dtype
 
 from pandas.core import common as com
@@ -914,15 +913,7 @@ def _broadcast(arr_or_scalar, shape):
         """
         Helper function to broadcast arrays / scalars to the desired shape.
         """
-        if _np_version_under1p10:
-            if is_scalar(arr_or_scalar):
-                out = np.empty(shape)
-                out.fill(arr_or_scalar)
-            else:
-                out = arr_or_scalar
-        else:
-            out = np.broadcast_to(arr_or_scalar, shape)
-        return out
+        return np.broadcast_to(arr_or_scalar, shape)
 
     # For performance reasons, we broadcast 'b' to the new array 'b2'
     # so that it has the same size as 'arr'.

pandas/plotting/_compat.py

@@ -18,14 +18,6 @@ def inner():
     return inner
 
 
-_mpl_ge_1_2_1 = _mpl_version('1.2.1', operator.ge)
-_mpl_le_1_2_1 = _mpl_version('1.2.1', operator.le)
-_mpl_ge_1_3_1 = _mpl_version('1.3.1', operator.ge)
-_mpl_ge_1_4_0 = _mpl_version('1.4.0', operator.ge)
-_mpl_ge_1_4_1 = _mpl_version('1.4.1', operator.ge)
-_mpl_ge_1_5_0 = _mpl_version('1.5.0', operator.ge)
-_mpl_ge_2_0_0 = _mpl_version('2.0.0', operator.ge)
-_mpl_le_2_0_0 = _mpl_version('2.0.0', operator.le)
 _mpl_ge_2_0_1 = _mpl_version('2.0.1', operator.ge)
 _mpl_ge_2_1_0 = _mpl_version('2.1.0', operator.ge)
 _mpl_ge_2_2_0 = _mpl_version('2.2.0', operator.ge)

pandas/plotting/_converter.py

@@ -35,8 +35,6 @@
 from pandas.tseries.frequencies import FreqGroup
 from pandas.core.indexes.period import Period, PeriodIndex
 
-from pandas.plotting._compat import _mpl_le_2_0_0
-
 # constants
 HOURS_PER_DAY = 24.
 MIN_PER_HOUR = 60.
@@ -371,13 +369,6 @@ def __init__(self, locator, tz=None, defaultfmt='%Y-%m-%d'):
         if self._tz is dates.UTC:
             self._tz._utcoffset = self._tz.utcoffset(None)
 
-        # For mpl > 2.0 the format strings are controlled via rcparams
-        # so do not mess with them.  For mpl < 2.0 change the second
-        # break point and add a musec break point
-        if _mpl_le_2_0_0():
-            self.scaled[1. / SEC_PER_DAY] = '%H:%M:%S'
-            self.scaled[1. / MUSEC_PER_DAY] = '%H:%M:%S.%f'
-
 
 class PandasAutoDateLocator(dates.AutoDateLocator):
 

pandas/plotting/_core.py

@@ -30,10 +30,7 @@
 
 from pandas.io.formats.printing import pprint_thing
 
-from pandas.plotting._compat import (_mpl_ge_1_3_1,
-                                     _mpl_ge_1_5_0,
-                                     _mpl_ge_2_0_0,
-                                     _mpl_ge_3_0_0)
+from pandas.plotting._compat import _mpl_ge_3_0_0
 from pandas.plotting._style import (plot_params,
                                     _get_standard_colors)
 from pandas.plotting._tools import (_subplots, _flatten, table,
@@ -551,14 +548,6 @@ def plt(self):
         import matplotlib.pyplot as plt
         return plt
 
-    @staticmethod
-    def mpl_ge_1_3_1():
-        return _mpl_ge_1_3_1()
-
-    @staticmethod
-    def mpl_ge_1_5_0():
-        return _mpl_ge_1_5_0()
-
     _need_to_set_index = False
 
     def _get_xticks(self, convert_period=False):
@@ -908,8 +897,7 @@ def _make_plot(self):
         scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
                              label=label, cmap=cmap, **self.kwds)
         if cb:
-            if self.mpl_ge_1_3_1():
-                cbar_label = c if c_is_column else ''
+            cbar_label = c if c_is_column else ''
             self._plot_colorbar(ax, label=cbar_label)
 
         if label is not None:
@@ -1012,10 +1000,9 @@ def _make_plot(self):
                              **kwds)
             self._add_legend_handle(newlines[0], label, index=i)
 
-            if not _mpl_ge_2_0_0():
-                lines = _get_all_lines(ax)
-                left, right = _get_xlim(lines)
-                ax.set_xlim(left, right)
+            lines = _get_all_lines(ax)
+            left, right = _get_xlim(lines)
+            ax.set_xlim(left, right)
 
     @classmethod
     def _plot(cls, ax, x, y, style=None, column_num=None,
@@ -1141,8 +1128,7 @@ def _plot(cls, ax, x, y, style=None, column_num=None,
 
         # need to remove label, because subplots uses mpl legend as it is
         line_kwds = kwds.copy()
-        if cls.mpl_ge_1_5_0():
-            line_kwds.pop('label')
+        line_kwds.pop('label')
         lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds)
 
         # get data from the line to get coordinates for fill_between
@@ -1165,17 +1151,10 @@ def _plot(cls, ax, x, y, style=None, column_num=None,
         cls._update_stacker(ax, stacking_id, y)
 
         # LinePlot expects list of artists
-        res = [rect] if cls.mpl_ge_1_5_0() else lines
+        res = [rect]
         return res
 
     def _add_legend_handle(self, handle, label, index=None):
-        if not self.mpl_ge_1_5_0():
-            from matplotlib.patches import Rectangle
-            # Because fill_between isn't supported in legend,
-            # specifically add Rectangle handle here
-            alpha = self.kwds.get('alpha', None)
-            handle = Rectangle((0, 0), 1, 1, fc=handle.get_color(),
-                               alpha=alpha)
         LinePlot._add_legend_handle(self, handle, label, index=index)
 
     def _post_plot_logic(self, ax, data):

pandas/plotting/_style.py

@@ -4,14 +4,12 @@
 
 import warnings
 from contextlib import contextmanager
-import re
 
 import numpy as np
 
 from pandas.core.dtypes.common import is_list_like
 from pandas.compat import lrange, lmap
 import pandas.compat as compat
-from pandas.plotting._compat import _mpl_ge_2_0_0
 
 
 def _get_standard_colors(num_colors=None, colormap=None, color_type='default',
@@ -72,18 +70,9 @@ def _maybe_valid_colors(colors):
         # check whether each character can be convertible to colors
         maybe_color_cycle = _maybe_valid_colors(list(colors))
         if maybe_single_color and maybe_color_cycle and len(colors) > 1:
-            # Special case for single str 'CN' match and convert to hex
-            # for supporting matplotlib < 2.0.0
-            if re.match(r'\AC[0-9]\Z', colors) and _mpl_ge_2_0_0():
-                hex_color = [c['color']
-                             for c in list(plt.rcParams['axes.prop_cycle'])]
-                colors = [hex_color[int(colors[1])]]
-            else:
-                # this may no longer be required
-                msg = ("'{0}' can be parsed as both single color and "
-                       "color cycle. Specify each color using a list "
-                       "like ['{0}'] or {1}")
-                raise ValueError(msg.format(colors, list(colors)))
+            hex_color = [c['color']
+                         for c in list(plt.rcParams['axes.prop_cycle'])]
+            colors = [hex_color[int(colors[1])]]
         elif maybe_single_color:
             colors = [colors]
         else: