DEPS: set min versions

closes pandas-dev#15206, numpy >= 1.9
closes pandas-dev#15543, matplotlib >= 1.4.3
closes pandas-dev#15214, bottleneck >= 1.0.0
scipy >= 0.14.0

Author: jreback

Diff for: ci/requirements-2.7_COMPAT.build

@@ -1,5 +1,5 @@
 python=2.7*
-numpy=1.7.1
+numpy=1.9.1
 cython=0.23
 dateutil=1.5
 pytz=2013b

Diff for: ci/requirements-2.7_COMPAT.run

@@ -1,10 +1,10 @@
-numpy=1.7.1
+numpy=1.9.1
 dateutil=1.5
 pytz=2013b
-scipy=0.11.0
+scipy=0.14.0
 xlwt=0.7.5
 xlrd=0.9.2
-bottleneck=0.8.0
+bottleneck=1.0.0
 numexpr=2.2.2
 pytables=3.0.0
 html5lib=1.0b2

Diff for: ci/requirements-2.7_LOCALE.build

@@ -1,5 +1,5 @@
 python=2.7*
 python-dateutil
 pytz=2013b
-numpy=1.8.2
+numpy=1.9.1
 cython=0.23

Diff for: ci/requirements-2.7_LOCALE.run

@@ -1,12 +1,12 @@
 python-dateutil
 pytz=2013b
-numpy=1.8.2
+numpy=1.9.1
 xlwt=0.7.5
 openpyxl=1.6.2
 xlsxwriter=0.4.6
 xlrd=0.9.2
-bottleneck=0.8.0
-matplotlib=1.3.1
+bottleneck=1.0.0
+matplotlib=1.4.3
 sqlalchemy=0.8.1
 html5lib=1.0b2
 lxml=3.2.1

Diff for: ci/requirements-2.7_SLOW.build

@@ -1,5 +1,5 @@
 python=2.7*
 python-dateutil
 pytz
-numpy=1.8.2
+numpy=1.9.1
 cython

Diff for: ci/requirements-2.7_SLOW.run

@@ -1,7 +1,7 @@
 python-dateutil
 pytz
-numpy=1.8.2
-matplotlib=1.3.1
+numpy=1.9.1
+matplotlib=1.4.3
 scipy
 patsy
 xlwt

Diff for: pandas/_libs/sparse.pyx

@@ -12,8 +12,6 @@ from distutils.version import LooseVersion
 
 # numpy versioning
 _np_version = np.version.short_version
-_np_version_under1p8 = LooseVersion(_np_version) < '1.8'
-_np_version_under1p9 = LooseVersion(_np_version) < '1.9'
 _np_version_under1p10 = LooseVersion(_np_version) < '1.10'
 _np_version_under1p11 = LooseVersion(_np_version) < '1.11'
 

Diff for: pandas/compat/numpy/__init__.py

@@ -9,19 +9,18 @@
 # numpy versioning
 _np_version = np.__version__
 _nlv = LooseVersion(_np_version)
-_np_version_under1p8 = _nlv < '1.8'
-_np_version_under1p9 = _nlv < '1.9'
 _np_version_under1p10 = _nlv < '1.10'
 _np_version_under1p11 = _nlv < '1.11'
 _np_version_under1p12 = _nlv < '1.12'
 _np_version_under1p13 = _nlv < '1.13'
 _np_version_under1p14 = _nlv < '1.14'
+_np_version_under1p15 = _nlv < '1.15'
 
-if _nlv < '1.7.0':
+if _nlv < '1.9':
     raise ImportError('this version of pandas is incompatible with '
-                      'numpy < 1.7.0\n'
+                      'numpy < 1.9.0\n'
                       'your numpy version is {0}.\n'
-                      'Please upgrade numpy to >= 1.7.0 to use '
+                      'Please upgrade numpy to >= 1.9.0 to use '
                       'this pandas version'.format(_np_version))
 
 
@@ -70,11 +69,10 @@ def np_array_datetime64_compat(arr, *args, **kwargs):
 
 
 __all__ = ['np',
-           '_np_version_under1p8',
-           '_np_version_under1p9',
            '_np_version_under1p10',
            '_np_version_under1p11',
            '_np_version_under1p12',
            '_np_version_under1p13',
-           '_np_version_under1p14'
+           '_np_version_under1p14',
+           '_np_version_under1p15'
            ]

Diff for: pandas/core/algorithms.py

@@ -6,7 +6,7 @@
 from warnings import warn, catch_warnings
 import numpy as np
 
-from pandas import compat, _np_version_under1p8
+from pandas import compat
 from pandas.core.dtypes.cast import maybe_promote
 from pandas.core.dtypes.generic import (
     ABCSeries, ABCIndex,
@@ -398,11 +398,9 @@ def isin(comps, values):
     comps, dtype, _ = _ensure_data(comps)
     values, _, _ = _ensure_data(values, dtype=dtype)
 
-    # GH11232
-    # work-around for numpy < 1.8 and comparisions on py3
     # faster for larger cases to use np.in1d
     f = lambda x, y: htable.ismember_object(x, values)
-    if (_np_version_under1p8 and compat.PY3) or len(comps) > 1000000:
+    if len(comps) > 1000000:
         f = lambda x, y: np.in1d(x, y)
     elif is_integer_dtype(comps):
         try:

Diff for: pandas/core/generic.py

@@ -1744,11 +1744,8 @@ def _box_item_values(self, key, values):
 
     def _maybe_cache_changed(self, item, value):
         """The object has called back to us saying maybe it has changed.
-
-        numpy < 1.8 has an issue with object arrays and aliasing
-        GH6026
         """
-        self._data.set(item, value, check=pd._np_version_under1p8)
+        self._data.set(item, value, check=False)
 
     @property
     def _is_cached(self):

Diff for: pandas/core/groupby.py

@@ -13,7 +13,7 @@
 )
 
 from pandas import compat
-from pandas.compat.numpy import function as nv, _np_version_under1p8
+from pandas.compat.numpy import function as nv
 from pandas.compat import set_function_name
 
 from pandas.core.dtypes.common import (
@@ -3256,11 +3256,7 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
             d = np.diff(np.r_[idx, len(ids)])
             if dropna:
                 m = ids[lab == -1]
-                if _np_version_under1p8:
-                    mi, ml = algorithms.factorize(m)
-                    d[ml] = d[ml] - np.bincount(mi)
-                else:
-                    np.add.at(d, m, -1)
+                np.add.at(d, m, -1)
                 acc = rep(d)[mask]
             else:
                 acc = rep(d)

Diff for: pandas/core/internals.py

@@ -66,8 +66,7 @@
 import pandas.core.computation.expressions as expressions
 from pandas.util._decorators import cache_readonly
 from pandas.util._validators import validate_bool_kwarg
-
-from pandas import compat, _np_version_under1p9
+from pandas import compat
 from pandas.compat import range, map, zip, u
 
 
@@ -1327,15 +1326,7 @@ def quantile(self, qs, interpolation='linear', axis=0, mgr=None):
         tuple of (axis, block)
 
         """
-        if _np_version_under1p9:
-            if interpolation != 'linear':
-                raise ValueError("Interpolation methods other than linear "
-                                 "are not supported in numpy < 1.9.")
-
-        kw = {}
-        if not _np_version_under1p9:
-            kw.update({'interpolation': interpolation})
-
+        kw = {'interpolation': interpolation}
         values = self.get_values()
         values, _, _, _ = self._try_coerce_args(values, values)
 

Diff for: pandas/tests/frame/test_quantile.py

@@ -12,7 +12,6 @@
 from pandas.util.testing import assert_series_equal, assert_frame_equal
 
 import pandas.util.testing as tm
-from pandas import _np_version_under1p9
 
 from pandas.tests.frame.common import TestData
 
@@ -103,9 +102,6 @@ def test_quantile_axis_parameter(self):
 
     def test_quantile_interpolation(self):
         # see gh-10174
-        if _np_version_under1p9:
-            pytest.skip("Numpy version under 1.9")
-
         from numpy import percentile
 
         # interpolation = linear (default case)
@@ -166,44 +162,6 @@ def test_quantile_interpolation(self):
                                  index=[.25, .5], columns=['a', 'b', 'c'])
         assert_frame_equal(result, expected)
 
-    def test_quantile_interpolation_np_lt_1p9(self):
-        # see gh-10174
-        if not _np_version_under1p9:
-            pytest.skip("Numpy version is greater than 1.9")
-
-        from numpy import percentile
-
-        # interpolation = linear (default case)
-        q = self.tsframe.quantile(0.1, axis=0, interpolation='linear')
-        assert q['A'] == percentile(self.tsframe['A'], 10)
-        q = self.intframe.quantile(0.1)
-        assert q['A'] == percentile(self.intframe['A'], 10)
-
-        # test with and without interpolation keyword
-        q1 = self.intframe.quantile(0.1)
-        assert q1['A'] == np.percentile(self.intframe['A'], 10)
-        assert_series_equal(q, q1)
-
-        # interpolation method other than default linear
-        msg = "Interpolation methods other than linear"
-        df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3])
-        with tm.assert_raises_regex(ValueError, msg):
-            df.quantile(.5, axis=1, interpolation='nearest')
-
-        with tm.assert_raises_regex(ValueError, msg):
-            df.quantile([.5, .75], axis=1, interpolation='lower')
-
-        # test degenerate case
-        df = DataFrame({'x': [], 'y': []})
-        with tm.assert_raises_regex(ValueError, msg):
-            q = df.quantile(0.1, axis=0, interpolation='higher')
-
-        # multi
-        df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]],
-                       columns=['a', 'b', 'c'])
-        with tm.assert_raises_regex(ValueError, msg):
-            df.quantile([.25, .5], interpolation='midpoint')
-
     def test_quantile_multi(self):
         df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]],
                        columns=['a', 'b', 'c'])

Diff for: pandas/tests/indexes/datetimes/test_datetime.py

@@ -9,7 +9,7 @@
 from pandas.compat import lrange
 from pandas.compat.numpy import np_datetime64_compat
 from pandas import (DatetimeIndex, Index, date_range, Series, DataFrame,
-                    Timestamp, datetime, offsets, _np_version_under1p8)
+                    Timestamp, datetime, offsets)
 
 from pandas.util.testing import assert_series_equal, assert_almost_equal
 
@@ -276,11 +276,7 @@ def test_comparisons_nat(self):
                          np_datetime64_compat('2014-06-01 00:00Z'),
                          np_datetime64_compat('2014-07-01 00:00Z')])
 
-        if _np_version_under1p8:
-            # cannot test array because np.datetime('nat') returns today's date
-            cases = [(fidx1, fidx2), (didx1, didx2)]
-        else:
-            cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
+        cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
 
         # Check pd.NaT is handles as the same as np.nan
         with tm.assert_produces_warning(None):

Diff for: pandas/tests/indexes/period/test_indexing.py

@@ -8,7 +8,7 @@
 from pandas.compat import lrange
 from pandas._libs import tslib
 from pandas import (PeriodIndex, Series, DatetimeIndex,
-                    period_range, Period, _np_version_under1p9)
+                    period_range, Period)
 
 
 class TestGetItem(object):
@@ -149,16 +149,12 @@ def test_getitem_seconds(self):
             values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H',
                       '2013/02/01 09:00']
             for v in values:
-                if _np_version_under1p9:
-                    with pytest.raises(ValueError):
-                        idx[v]
-                else:
-                    # GH7116
-                    # these show deprecations as we are trying
-                    # to slice with non-integer indexers
-                    # with pytest.raises(IndexError):
-                    #    idx[v]
-                    continue
+                # GH7116
+                # these show deprecations as we are trying
+                # to slice with non-integer indexers
+                # with pytest.raises(IndexError):
+                #    idx[v]
+                continue
 
             s = Series(np.random.rand(len(idx)), index=idx)
             tm.assert_series_equal(s['2013/01/01 10:00'], s[3600:3660])
@@ -178,16 +174,12 @@ def test_getitem_day(self):
                       '2013/02/01 09:00']
             for v in values:
 
-                if _np_version_under1p9:
-                    with pytest.raises(ValueError):
-                        idx[v]
-                else:
-                    # GH7116
-                    # these show deprecations as we are trying
-                    # to slice with non-integer indexers
-                    # with pytest.raises(IndexError):
-                    #    idx[v]
-                    continue
+                # GH7116
+                # these show deprecations as we are trying
+                # to slice with non-integer indexers
+                # with pytest.raises(IndexError):
+                #    idx[v]
+                continue
 
             s = Series(np.random.rand(len(idx)), index=idx)
             tm.assert_series_equal(s['2013/01'], s[0:31])

Diff for: pandas/tests/indexes/timedeltas/test_timedelta.py

@@ -7,7 +7,7 @@
 import pandas.util.testing as tm
 from pandas import (timedelta_range, date_range, Series, Timedelta,
                     DatetimeIndex, TimedeltaIndex, Index, DataFrame,
-                    Int64Index, _np_version_under1p8)
+                    Int64Index)
 from pandas.util.testing import (assert_almost_equal, assert_series_equal,
                                  assert_index_equal)
 
@@ -379,11 +379,7 @@ def test_comparisons_nat(self):
                           np.timedelta64(1, 'D') + np.timedelta64(2, 's'),
                           np.timedelta64(5, 'D') + np.timedelta64(3, 's')])
 
-        if _np_version_under1p8:
-            # cannot test array because np.datetime('nat') returns today's date
-            cases = [(tdidx1, tdidx2)]
-        else:
-            cases = [(tdidx1, tdidx2), (tdidx1, tdarr)]
+        cases = [(tdidx1, tdidx2), (tdidx1, tdarr)]
 
         # Check pd.NaT is handles as the same as np.nan
         for idx1, idx2 in cases:

Diff for: pandas/tests/series/test_operators.py

@@ -14,8 +14,7 @@
 import pandas as pd
 
 from pandas import (Index, Series, DataFrame, isnull, bdate_range,
-                    NaT, date_range, timedelta_range,
-                    _np_version_under1p8)
+                    NaT, date_range, timedelta_range)
 from pandas.core.indexes.datetimes import Timestamp
 from pandas.core.indexes.timedeltas import Timedelta
 import pandas.core.nanops as nanops
@@ -687,14 +686,13 @@ def run_ops(ops, get_ser, test_ser):
         assert_series_equal(result, exp)
 
         # odd numpy behavior with scalar timedeltas
-        if not _np_version_under1p8:
-            result = td1[0] + dt1
-            exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz)
-            assert_series_equal(result, exp)
+        result = td1[0] + dt1
+        exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz)
+        assert_series_equal(result, exp)
 
-            result = td2[0] + dt2
-            exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz)
-            assert_series_equal(result, exp)
+        result = td2[0] + dt2
+        exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz)
+        assert_series_equal(result, exp)
 
         result = dt1 - td1[0]
         exp = (dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize(tz)