DEPS: drop numpy < 1.12

ci/azure-macos-35.yaml

@@ -8,10 +8,10 @@ dependencies:
   - html5lib
   - jinja2
   - lxml
-  - matplotlib
+  - matplotlib=2.2.0
   - nomkl
   - numexpr
-  - numpy=1.10.4
+  - numpy=1.12.0
   - openpyxl=2.5.5
   - pytables
   - python=3.5*

ci/azure-windows-27.yaml

@@ -10,7 +10,7 @@ dependencies:
   - html5lib
   - jinja2=2.8
   - lxml
-  - matplotlib
+  - matplotlib=2.0.1
   - numexpr
   - numpy=1.12*
   - openpyxl=2.5.5

ci/azure/macos.yml

@@ -9,7 +9,7 @@ jobs:
   strategy:
     maxParallel: 11
     matrix:
-      py35_np_110:
+      py35_np_120:
         ENV_FILE: ci/azure-macos-35.yaml
         CONDA_PY: "35"
         CONDA_ENV: pandas

ci/azure/windows-py27.yml

@@ -9,7 +9,7 @@ jobs:
   strategy:
     maxParallel: 11
     matrix:
-      py36_np14:
+      py36_np121:
         ENV_FILE: ci/azure-windows-27.yaml
         CONDA_PY: "27"
         CONDA_ENV: pandas

ci/circle-27-compat.yaml

@@ -3,18 +3,18 @@ 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=2.6.1
+  - numpy=1.12.0
   - openpyxl=2.5.5
   - psycopg2
-  - pytables=3.2.2
+  - pytables=3.4.2
   - python-dateutil=2.5.0
   - python=2.7*
   - pytz=2013b
-  - scipy=0.14.0
+  - scipy=0.18.1
   - sqlalchemy=0.7.8
   - xlrd=0.9.2
   - xlsxwriter=0.5.2

ci/requirements-optional-conda.txt

@@ -1,6 +1,6 @@
 beautifulsoup4>=4.2.1
 blosc
-bottleneck
+bottleneck>=1.2.0
 fastparquet
 feather-format
 gcsfs
@@ -9,17 +9,17 @@ ipython>=5.6.0
 ipykernel
 jinja2
 lxml
-matplotlib
+matplotlib>=2.0.0
 nbsphinx
-numexpr
+numexpr>=2.6.1
 openpyxl=2.5.5
 pyarrow
 pymysql
-pytables
+pytables>=3.4.2
 pytest-cov
 pytest-xdist
 s3fs
-scipy
+scipy>=0.18.1
 seaborn
 sqlalchemy
 statsmodels

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
@@ -11,17 +11,17 @@ ipython>=5.6.0
 ipykernel
 jinja2
 lxml
-matplotlib
+matplotlib>=2.0.0
 nbsphinx
-numexpr
+numexpr>=2.6.1
 openpyxl==2.5.5
 pyarrow
 pymysql
 tables
 pytest-cov
 pytest-xdist
 s3fs
-scipy
+scipy>=0.18.1
 seaborn
 sqlalchemy
 statsmodels

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

ci/travis-27.yaml

@@ -14,7 +14,7 @@ dependencies:
   - jemalloc=4.5.0.post
   - jinja2=2.8
   - lxml
-  - matplotlib
+  - matplotlib=2.2.2
   - mock
   - nomkl
   - numexpr

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 2.6.1 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::
 
@@ -255,9 +255,9 @@ Optional Dependencies
 
 * `Cython <http://www.cython.org>`__: Only necessary to build development
   version. Version 0.28.2 or higher.
-* `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions, Version 0.14.0 or higher
+* `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions, Version 0.18.1 or higher
 * `xarray <http://xarray.pydata.org>`__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended.
-* `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage. Version 3.0.0 or higher required, Version 3.2.1 or higher highly recommended.
+* `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage, Version 3.4.2 or higher
 * `Feather Format <https://github.com/wesm/feather>`__: necessary for feather-based storage, version 0.3.1 or higher.
 * `Apache Parquet <https://parquet.apache.org/>`__, either `pyarrow <http://arrow.apache.org/docs/python/>`__ (>= 0.4.1) or `fastparquet <https://fastparquet.readthedocs.io/en/latest>`__ (>= 0.0.6) for parquet-based storage. The `snappy <https://pypi.org/project/python-snappy>`__ and `brotli <https://pypi.org/project/brotlipy>`__ are available for compression support.
 * `SQLAlchemy <http://www.sqlalchemy.org>`__: for SQL database support. Version 0.8.1 or higher recommended. Besides SQLAlchemy, you also need a database specific driver. You can find an overview of supported drivers for each SQL dialect in the `SQLAlchemy docs <http://docs.sqlalchemy.org/en/latest/dialects/index.html>`__. Some common drivers are:
@@ -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

@@ -206,8 +206,32 @@ 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 |
++=================+=================+==========+
+| numpy           | 1.12.0          |    X     |
++-----------------+-----------------+----------+
+| bottleneck      | 1.2.0           |          |
++-----------------+-----------------+----------+
+| matplotlib      | 2.0.0           |          |
++-----------------+-----------------+----------+
+| numexpr         | 2.6.1           |          |
++-----------------+-----------------+----------+
+| pytables        | 3.4.2           |          |
++-----------------+-----------------+----------+
+| scipy           | 0.18.1          |          |
++-----------------+-----------------+----------+
 
 .. _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
@@ -910,26 +909,12 @@ def checked_add_with_arr(arr, b, arr_mask=None, b_mask=None):
     ------
     OverflowError if any x + y exceeds the maximum or minimum int64 value.
     """
-    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
-
     # For performance reasons, we broadcast 'b' to the new array 'b2'
     # so that it has the same size as 'arr'.
-    b2 = _broadcast(b, arr.shape)
+    b2 = np.broadcast_to(b, arr.shape)
     if b_mask is not None:
         # We do the same broadcasting for b_mask as well.
-        b2_mask = _broadcast(b_mask, arr.shape)
+        b2_mask = np.broadcast_to(b_mask, arr.shape)
     else:
         b2_mask = None
 

pandas/core/computation/check.py

@@ -2,7 +2,7 @@
 from distutils.version import LooseVersion
 
 _NUMEXPR_INSTALLED = False
-_MIN_NUMEXPR_VERSION = "2.4.6"
+_MIN_NUMEXPR_VERSION = "2.6.1"
 
 try:
     import numexpr as ne

pandas/core/ops.py

@@ -1805,12 +1805,7 @@ def to_series(right):
 
             elif right.shape[0] == left.shape[0] and right.shape[1] == 1:
                 # Broadcast across columns
-                try:
-                    right = np.broadcast_to(right, left.shape)
-                except AttributeError:
-                    # numpy < 1.10.0
-                    right = np.tile(right, (1, left.shape[1]))
-
+                right = np.broadcast_to(right, left.shape)
                 right = left._constructor(right,
                                           index=left.index,
                                           columns=left.columns)

pandas/plotting/_compat.py

@@ -18,15 +18,8 @@ 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)
+_mpl_ge_2_2_2 = _mpl_version('2.2.2', operator.ge)
 _mpl_ge_3_0_0 = _mpl_version('3.0.0', 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):