update minimum versions and associated code cleanup

.travis.yml

@@ -14,8 +14,6 @@ matrix:
     env: CONDA_ENV=py27-min
   - python: 2.7
     env: CONDA_ENV=py27-cdat+iris+pynio
-  - python: 3.4
-    env: CONDA_ENV=py34
   - python: 3.5
     env: CONDA_ENV=py35
   - python: 3.6
@@ -102,7 +100,7 @@ install:
 
 script:
   # TODO: restore this check once the upstream pandas issue is fixed:
-  # https://github.com/pandas-dev/pandas/issues/21071 
+  # https://github.com/pandas-dev/pandas/issues/21071
   # - python -OO -c "import xarray"
   - if [[ "$CONDA_ENV" == "docs" ]]; then
       conda install -c conda-forge sphinx sphinx_rtd_theme sphinx-gallery numpydoc;

ci/requirements-py27-min.yml

@@ -4,8 +4,8 @@ dependencies:
   - pytest
   - flake8
   - mock
-  - numpy==1.11
-  - pandas==0.18.0
+  - numpy=1.12
+  - pandas=0.19
   - pip:
     - coveralls
     - pytest-cov

ci/requirements-py35.yml

@@ -4,11 +4,10 @@ channels:
 dependencies:
   - python=3.5
   - cftime
-  - dask
-  - distributed
+  - dask=0.16
   - h5py
   - h5netcdf
-  - matplotlib
+  - matplotlib=1.5
   - netcdf4
   - pytest
   - flake8

doc/installing.rst

@@ -6,9 +6,9 @@ Installation
 Required dependencies
 ---------------------
 
-- Python 2.7 [1]_, 3.4, 3.5, or 3.6
-- `numpy <http://www.numpy.org/>`__ (1.11 or later)
-- `pandas <http://pandas.pydata.org/>`__ (0.18.0 or later)
+- Python 2.7 [1]_, 3.5, or 3.6
+- `numpy <http://www.numpy.org/>`__ (1.12 or later)
+- `pandas <http://pandas.pydata.org/>`__ (0.19.2 or later)
 
 Optional dependencies
 ---------------------
@@ -45,13 +45,14 @@ For accelerating xarray
 For parallel computing
 ~~~~~~~~~~~~~~~~~~~~~~
 
-- `dask.array <http://dask.pydata.org>`__ (0.9.0 or later): required for
+- `dask.array <http://dask.pydata.org>`__ (0.16 or later): required for
   :ref:`dask`.
 
 For plotting
 ~~~~~~~~~~~~
 
 - `matplotlib <http://matplotlib.org/>`__: required for :ref:`plotting`
+  (1.5 or later)
 - `cartopy <http://scitools.org.uk/cartopy/>`__: recommended for
   :ref:`plot-maps`
 - `seaborn <https://stanford.edu/~mwaskom/software/seaborn/>`__: for better

doc/whats-new.rst

@@ -66,6 +66,18 @@ Bug fixes
   weren't monotonic (:issue:`2250`).
   By `Fabien Maussion <https://github.com/fmaussion>`_.
 
+Breaking changes
+~~~~~~~~~~~~~~~~
+
+- Xarray no longer supports python 3.4. Additionally, the minimum supported
+  versions of the following dependencies has been updated and/or clarified:
+
+  - Pandas: 0.18 -> 0.19
+  - NumPy: 1.11 -> 1.12
+  - Dask: 0.9 -> 0.16
+  - Matplotlib: unspecified -> 1.5
+
+  (:issue:`2204`). By `Joe Hamman <https://github.com/jhamman>`_.
 
 .. _whats-new.0.10.7:
 

setup.py

@@ -71,4 +71,4 @@
       tests_require=TESTS_REQUIRE,
       url=URL,
       packages=find_packages(),
-      package_data={'xarray': ['tests/data/*', 'plot/default_colormap.csv']})
+      package_data={'xarray': ['tests/data/*']})

xarray/core/duck_array_ops.py

@@ -363,9 +363,9 @@ def f(values, axis=None, skipna=None, **kwargs):
 median = _create_nan_agg_method('median', numeric_only=True)
 prod = _create_nan_agg_method('prod', numeric_only=True, no_bottleneck=True)
 cumprod_1d = _create_nan_agg_method(
-    'cumprod', numeric_only=True, np_compat=True, no_bottleneck=True)
+    'cumprod', numeric_only=True, no_bottleneck=True)
 cumsum_1d = _create_nan_agg_method(
-    'cumsum', numeric_only=True, np_compat=True, no_bottleneck=True)
+    'cumsum', numeric_only=True, no_bottleneck=True)
 
 
 def _nd_cum_func(cum_func, array, axis, **kwargs):

xarray/core/missing.py

@@ -8,7 +8,6 @@
 
 from . import rolling
 from .computation import apply_ufunc
-from .npcompat import flip
 from .pycompat import iteritems
 from .utils import is_scalar, OrderedSet
 from .variable import Variable, broadcast_variables
@@ -245,13 +244,13 @@ def _bfill(arr, n=None, axis=-1):
     '''inverse of ffill'''
     import bottleneck as bn
 
-    arr = flip(arr, axis=axis)
+    arr = np.flip(arr, axis=axis)
 
     # fill
     arr = bn.push(arr, axis=axis, n=n)
 
     # reverse back to original
-    return flip(arr, axis=axis)
+    return np.flip(arr, axis=axis)
 
 
 def ffill(arr, dim=None, limit=None):