BLD: Numexpr 2.4.6 required

closes pandas-dev#15213

Author: Francesc Alted <francesc@continuum.io>

Closes pandas-dev#15383 from FrancescAlted/numexpr-2.4.6 and squashes the following commits:

c417fe2 [Francesc Alted] Simplify and remove UserWarning testing on numexpr import
e1b34a9 [Francesc Alted] Force a reload of pd.computation for actually triggering the UserWarning
c081199 [Francesc Alted] Relax the exact message for the ImportError
73f0319 [Francesc Alted] numexpr requisite raised to 2.4.6
0d4ab9a [Francesc Alted] Restored the old numexpr version dependencies to adjust for old requirements
c1aae19 [Francesc Alted] Fixed a lint error
7575ba2 [Francesc Alted] Using constants instead of literals for numexpr version
7a275ce [Francesc Alted] Fixed a typo
93f54aa [Francesc Alted] numexpr section moved to Other API changes section
3b6e58b [Francesc Alted] Removed recomendation for numexpr 2.6.2
f225598 [Francesc Alted] Updated test_compat for numexpr 2.4.6
8bd4ed1 [Francesc Alted] numexpr 2.4.6 requirement moved to other enhancements section
e45b742 [Francesc Alted] Moved pinned versions in CI folder to 2.4.6
6e12e29 [Francesc Alted] Added a notice on the recommended numexpr version
ac62653 [Francesc Alted] Require numexpr 2.4.6
ab79c54 [Francesc Alted] Require numexpr 2.6.2

ci/requirements-3.4_SLOW.run

@@ -9,7 +9,7 @@ html5lib
 patsy
 beautiful-soup
 scipy
-numexpr=2.4.4
+numexpr=2.4.6
 pytables
 matplotlib
 lxml

doc/source/install.rst

@@ -226,7 +226,7 @@ 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 2.1 or higher (excluding a buggy 2.4.4). Version 2.4.6 or higher is highly recommended.
+  If installed, must be Version 2.4.6 or higher.
 
 * `bottleneck <http://berkeleyanalytics.com/bottleneck>`__: for accelerating certain types of ``nan``
   evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups.

doc/source/whatsnew/v0.20.0.txt

@@ -1,6 +1,6 @@
 .. _whatsnew_0200:
 
-v0.20.0 (????, 2016)
+v0.20.0 (????, 2017)
 --------------------
 
 This is a major release from 0.19 and includes a small number of API changes, several new features,
@@ -158,6 +158,7 @@ Other enhancements
 
 .. _whatsnew_0200.api_breaking:
 
+
 Backwards incompatible API changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -429,6 +430,7 @@ Other API Changes
 - ``DataFrame.asof()`` will return a null filled ``Series`` instead the scalar ``NaN`` if a match is not found (:issue:`15118`)
 - The :func:`pd.read_gbq` method now stores ``INTEGER`` columns as ``dtype=object`` if they contain ``NULL`` values. Otherwise they are stored as ``int64``. This prevents precision lost for integers greather than 2**53. Furthermore ``FLOAT`` columns with values above 10**4 are no more casted to ``int64`` which also caused precision lost (:issue: `14064`, :issue:`14305`).
 - Reorganization of timeseries development tests (:issue:`14854`)
+- ``numexpr`` version is now required to be >= 2.4.6 and it will not be used at all if this requisite is not fulfilled (:issue:`15213`).
 
 .. _whatsnew_0200.deprecations:
 

pandas/computation/__init__.py

@@ -3,26 +3,19 @@
 from distutils.version import LooseVersion
 
 _NUMEXPR_INSTALLED = False
+_MIN_NUMEXPR_VERSION = "2.4.6"
 
 try:
     import numexpr as ne
     ver = ne.__version__
-    _NUMEXPR_INSTALLED = ver >= LooseVersion('2.1')
+    _NUMEXPR_INSTALLED = ver >= LooseVersion(_MIN_NUMEXPR_VERSION)
 
-    # we specifically disallow 2.4.4 as
-    # has some hard-to-diagnose bugs
-    if ver == LooseVersion('2.4.4'):
-        _NUMEXPR_INSTALLED = False
-        warnings.warn(
-            "The installed version of numexpr {ver} is not supported "
-            "in pandas and will be not be used\n".format(ver=ver),
-            UserWarning)
-
-    elif not _NUMEXPR_INSTALLED:
+    if not _NUMEXPR_INSTALLED:
         warnings.warn(
             "The installed version of numexpr {ver} is not supported "
             "in pandas and will be not be used\nThe minimum supported "
-            "version is 2.1\n".format(ver=ver), UserWarning)
+            "version is {min_ver}\n".format(
+                ver=ver, min_ver=_MIN_NUMEXPR_VERSION), UserWarning)
 
 except ImportError:  # pragma: no cover
     pass

pandas/tests/computation/test_compat.py

@@ -10,6 +10,7 @@
 
 from pandas.computation.engines import _engines
 import pandas.computation.expr as expr
+from pandas.computation import _MIN_NUMEXPR_VERSION
 
 ENGINES_PARSERS = list(product(_engines, expr._parsers))
 
@@ -21,15 +22,10 @@ def test_compat():
     try:
         import numexpr as ne
         ver = ne.__version__
-        if ver == LooseVersion('2.4.4'):
+        if ver < LooseVersion(_MIN_NUMEXPR_VERSION):
             assert not _NUMEXPR_INSTALLED
-        elif ver < LooseVersion('2.1'):
-            with tm.assert_produces_warning(UserWarning,
-                                            check_stacklevel=False):
-                assert not _NUMEXPR_INSTALLED
         else:
             assert _NUMEXPR_INSTALLED
-
     except ImportError:
         pytest.skip("not testing numexpr version compat")
 
@@ -51,12 +47,9 @@ def testit():
         except ImportError:
             pytest.skip("no numexpr")
         else:
-            if ne.__version__ < LooseVersion('2.1'):
-                with tm.assertRaisesRegexp(ImportError, "'numexpr' version is "
-                                           ".+, must be >= 2.1"):
+            if ne.__version__ < LooseVersion(_MIN_NUMEXPR_VERSION):
+                with tm.assertRaises(ImportError):
                     testit()
-            elif ne.__version__ == LooseVersion('2.4.4'):
-                pytest.skip("numexpr version==2.4.4")
             else:
                 testit()
     else: