diff --git a/ci/requirements-2.7_COMPAT.build b/ci/requirements-2.7_COMPAT.build
index 0e1ccf9eac9bf1..0159505a2c2851 100644
--- a/ci/requirements-2.7_COMPAT.build
+++ b/ci/requirements-2.7_COMPAT.build
@@ -1,5 +1,6 @@
python=2.7*
-numpy=1.7.1
+numpy=1.9.1
+libgfortran=3.0.0
cython=0.23
dateutil=1.5
pytz=2013b
diff --git a/ci/requirements-2.7_COMPAT.run b/ci/requirements-2.7_COMPAT.run
index d27b6a72c2d158..63c5d95bbb900d 100644
--- a/ci/requirements-2.7_COMPAT.run
+++ b/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 --git a/ci/requirements-2.7_LOCALE.build b/ci/requirements-2.7_LOCALE.build
index 4a37ce8fbe1613..7e309681909a49 100644
--- a/ci/requirements-2.7_LOCALE.build
+++ b/ci/requirements-2.7_LOCALE.build
@@ -1,5 +1,6 @@
python=2.7*
python-dateutil
pytz=2013b
-numpy=1.8.2
+numpy=1.9.1
+libgfortran=3.0.0
cython=0.23
diff --git a/ci/requirements-2.7_LOCALE.run b/ci/requirements-2.7_LOCALE.run
index 5d7cc31b7d55ed..f7533e98d58894 100644
--- a/ci/requirements-2.7_LOCALE.run
+++ b/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 --git a/ci/requirements-2.7_SLOW.build b/ci/requirements-2.7_SLOW.build
index 0f4a2c6792e6b1..6e988104c340b4 100644
--- a/ci/requirements-2.7_SLOW.build
+++ b/ci/requirements-2.7_SLOW.build
@@ -1,5 +1,6 @@
python=2.7*
python-dateutil
pytz
-numpy=1.8.2
+numpy=1.9.1
+libgfortran=3.0.0
cython
diff --git a/ci/requirements-2.7_SLOW.run b/ci/requirements-2.7_SLOW.run
index c2d2a14285ad6b..ca930c25609450 100644
--- a/ci/requirements-2.7_SLOW.run
+++ b/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 --git a/doc/source/install.rst b/doc/source/install.rst
index 48d51e1200447f..7962f0f4333d37 100644
--- a/doc/source/install.rst
+++ b/doc/source/install.rst
@@ -203,7 +203,7 @@ Dependencies
------------
* `setuptools `__
-* `NumPy `__: 1.7.1 or higher
+* `NumPy `__: 1.9.0 or higher
* `python-dateutil `__: 1.5 or higher
* `pytz `__: Needed for time zone support
@@ -217,7 +217,7 @@ Recommended Dependencies
If installed, must be Version 2.4.6 or higher.
* `bottleneck `__: for accelerating certain types of ``nan``
- evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups.
+ evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups, Version 1.0.0 or higher.
.. note::
@@ -232,7 +232,7 @@ Optional Dependencies
* `Cython `__: Only necessary to build development
version. Version 0.23 or higher.
-* `SciPy `__: miscellaneous statistical functions
+* `SciPy `__: miscellaneous statistical functions, Version 0.14.0 or higher
* `xarray `__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended.
* `PyTables `__: necessary for HDF5-based storage. Version 3.0.0 or higher required, Version 3.2.1 or higher highly recommended.
* `Feather Format `__: necessary for feather-based storage, version 0.3.1 or higher.
@@ -242,7 +242,7 @@ Optional Dependencies
* `pymysql `__: for MySQL.
* `SQLite `__: for SQLite, this is included in Python's standard library by default.
-* `matplotlib `__: for plotting
+* `matplotlib `__: for plotting, Version 1.4.3 or higher.
* For Excel I/O:
* `xlrd/xlwt `__: Excel reading (xlrd) and writing (xlwt)
diff --git a/doc/source/whatsnew/v0.21.0.txt b/doc/source/whatsnew/v0.21.0.txt
index c63d4575bac43a..d4d9d5477d269d 100644
--- a/doc/source/whatsnew/v0.21.0.txt
+++ b/doc/source/whatsnew/v0.21.0.txt
@@ -46,6 +46,26 @@ Other Enhancements
Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. _whatsnew_0210.api_breaking.deps:
+
+Dependencies have increased minimum versions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We have updated our minimum supported versions of dependencies (:issue:`15206`, :issue:`15543`, :issue:`15214`). We now require:
+
+ +--------------+-----------------+
+ | Package | Minimum Version |
+ +======================+=========+
+ | Numpy | 1.9.1 |
+ +--------------+-----------------+
+ | Matplotlib | 1.4.3 |
+ +--------------+-----------------+
+ | Scipy | 0.14.0 |
+ +--------------+-----------------+
+ | Bottleneck | 1.0.0 |
+ +--------------+-----------------+
+
.. _whatsnew_0210.api_breaking.pandas_eval:
Improved error handling during item assignment in pd.eval
diff --git a/pandas/_libs/sparse.pyx b/pandas/_libs/sparse.pyx
index 0c2e056ead7fac..1cc7f5ace95ea5 100644
--- a/pandas/_libs/sparse.pyx
+++ b/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 --git a/pandas/compat/numpy/__init__.py b/pandas/compat/numpy/__init__.py
index 2c5a18973afa8f..5112957b498751 100644
--- a/pandas/compat/numpy/__init__.py
+++ b/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 --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index b490bf787a0376..ec27e276db2f5c 100644
--- a/pandas/core/algorithms.py
+++ b/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 --git a/pandas/core/generic.py b/pandas/core/generic.py
index f12592feaa4c36..44f38211f03e20 100644
--- a/pandas/core/generic.py
+++ b/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 --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index daf3381ae4e890..20f197ca60d3f5 100644
--- a/pandas/core/groupby.py
+++ b/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 --git a/pandas/core/internals.py b/pandas/core/internals.py
index f2a7ac76481d4a..77214ddec4bac4 100644
--- a/pandas/core/internals.py
+++ b/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 --git a/pandas/tests/frame/test_quantile.py b/pandas/tests/frame/test_quantile.py
index 2482e493dbefdc..2f264874378bce 100644
--- a/pandas/tests/frame/test_quantile.py
+++ b/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 --git a/pandas/tests/indexes/datetimes/test_datetime.py b/pandas/tests/indexes/datetimes/test_datetime.py
index f99dcee9e5c8ab..47f53f53cfd021 100644
--- a/pandas/tests/indexes/datetimes/test_datetime.py
+++ b/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 --git a/pandas/tests/indexes/period/test_indexing.py b/pandas/tests/indexes/period/test_indexing.py
index d4dac1cf88fffb..efc13a56cd77e4 100644
--- a/pandas/tests/indexes/period/test_indexing.py
+++ b/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 --git a/pandas/tests/indexes/timedeltas/test_timedelta.py b/pandas/tests/indexes/timedeltas/test_timedelta.py
index 59e4b1432b8bc1..0b3bd0b03bccfd 100644
--- a/pandas/tests/indexes/timedeltas/test_timedelta.py
+++ b/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 --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py
index 2e400812e0331f..a51d71ddbdd3ec 100644
--- a/pandas/tests/series/test_operators.py
+++ b/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)
diff --git a/pandas/tests/series/test_quantile.py b/pandas/tests/series/test_quantile.py
index 2d02260ac7303f..c0a26f3c1c554c 100644
--- a/pandas/tests/series/test_quantile.py
+++ b/pandas/tests/series/test_quantile.py
@@ -1,11 +1,10 @@
# coding=utf-8
# pylint: disable-msg=E1101,W0612
-import pytest
import numpy as np
import pandas as pd
-from pandas import (Index, Series, _np_version_under1p9)
+from pandas import Index, Series
from pandas.core.indexes.datetimes import Timestamp
from pandas.core.dtypes.common import is_integer
import pandas.util.testing as tm
@@ -68,8 +67,6 @@ def test_quantile_multi(self):
[], dtype=float))
tm.assert_series_equal(result, expected)
- @pytest.mark.skipif(_np_version_under1p9,
- reason="Numpy version is under 1.9")
def test_quantile_interpolation(self):
# see gh-10174
@@ -82,8 +79,6 @@ def test_quantile_interpolation(self):
# test with and without interpolation keyword
assert q == q1
- @pytest.mark.skipif(_np_version_under1p9,
- reason="Numpy version is under 1.9")
def test_quantile_interpolation_dtype(self):
# GH #10174
@@ -96,26 +91,6 @@ def test_quantile_interpolation_dtype(self):
assert q == np.percentile(np.array([1, 3, 4]), 50)
assert is_integer(q)
- @pytest.mark.skipif(not _np_version_under1p9,
- reason="Numpy version is greater 1.9")
- def test_quantile_interpolation_np_lt_1p9(self):
- # GH #10174
-
- # interpolation = linear (default case)
- q = self.ts.quantile(0.1, interpolation='linear')
- assert q == np.percentile(self.ts.valid(), 10)
- q1 = self.ts.quantile(0.1)
- assert q1 == np.percentile(self.ts.valid(), 10)
-
- # interpolation other than linear
- msg = "Interpolation methods other than "
- with tm.assert_raises_regex(ValueError, msg):
- self.ts.quantile(0.9, interpolation='nearest')
-
- # object dtype
- with tm.assert_raises_regex(ValueError, msg):
- Series(self.ts, dtype=object).quantile(0.7, interpolation='higher')
-
def test_quantile_nan(self):
# GH 13098
diff --git a/pandas/tests/sparse/test_array.py b/pandas/tests/sparse/test_array.py
index 4ce03f72dbba6e..b0a9182a265fe8 100644
--- a/pandas/tests/sparse/test_array.py
+++ b/pandas/tests/sparse/test_array.py
@@ -8,7 +8,6 @@
from numpy import nan
import numpy as np
-from pandas import _np_version_under1p8
from pandas.core.sparse.api import SparseArray, SparseSeries
from pandas._libs.sparse import IntIndex
from pandas.util.testing import assert_almost_equal
@@ -150,10 +149,8 @@ def test_take(self):
assert np.isnan(self.arr.take(0))
assert np.isscalar(self.arr.take(2))
- # np.take in < 1.8 doesn't support scalar indexing
- if not _np_version_under1p8:
- assert self.arr.take(2) == np.take(self.arr_data, 2)
- assert self.arr.take(6) == np.take(self.arr_data, 6)
+ assert self.arr.take(2) == np.take(self.arr_data, 2)
+ assert self.arr.take(6) == np.take(self.arr_data, 6)
exp = SparseArray(np.take(self.arr_data, [2, 3]))
tm.assert_sp_array_equal(self.arr.take([2, 3]), exp)
diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index 6798e64b01d7e2..8c9aa648e1274c 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -8,7 +8,7 @@
import numpy as np
import pandas as pd
-from pandas import Series, isnull, _np_version_under1p9
+from pandas import Series, isnull
from pandas.core.dtypes.common import is_integer_dtype
import pandas.core.nanops as nanops
import pandas.util.testing as tm
@@ -340,15 +340,13 @@ def test_nanmean_overflow(self):
# In the previous implementation mean can overflow for int dtypes, it
# is now consistent with numpy
- # numpy < 1.9.0 is not computing this correctly
- if not _np_version_under1p9:
- for a in [2 ** 55, -2 ** 55, 20150515061816532]:
- s = Series(a, index=range(500), dtype=np.int64)
- result = s.mean()
- np_result = s.values.mean()
- assert result == a
- assert result == np_result
- assert result.dtype == np.float64
+ for a in [2 ** 55, -2 ** 55, 20150515061816532]:
+ s = Series(a, index=range(500), dtype=np.int64)
+ result = s.mean()
+ np_result = s.values.mean()
+ assert result == a
+ assert result == np_result
+ assert result.dtype == np.float64
def test_returned_dtype(self):
diff --git a/pandas/tests/tools/test_numeric.py b/pandas/tests/tools/test_numeric.py
index 664a97640387ef..1d13ba93ba7592 100644
--- a/pandas/tests/tools/test_numeric.py
+++ b/pandas/tests/tools/test_numeric.py
@@ -3,7 +3,7 @@
import numpy as np
import pandas as pd
-from pandas import to_numeric, _np_version_under1p9
+from pandas import to_numeric
from pandas.util import testing as tm
from numpy import iinfo
@@ -355,9 +355,6 @@ def test_downcast(self):
def test_downcast_limits(self):
# Test the limits of each downcast. Bug: #14401.
- # Check to make sure numpy is new enough to run this test.
- if _np_version_under1p9:
- pytest.skip("Numpy version is under 1.9")
i = 'integer'
u = 'unsigned'
diff --git a/setup.py b/setup.py
index 31a3cddc3f9fdf..947eb2fd3723d8 100755
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def is_platform_mac():
_have_setuptools = False
setuptools_kwargs = {}
-min_numpy_ver = '1.7.0'
+min_numpy_ver = '1.9.0'
if sys.version_info[0] >= 3:
setuptools_kwargs = {