Python 3.6

.travis.yml

@@ -7,26 +7,26 @@ matrix:
   include:
     - python: 2.7
       env:
-    - python: 3.4
-      env:
     - python: 3.5
       env:
+    - python: 3.6
+      env:
     - python: 2.7
       env: PANDAS_VERSION="git+https://github.com/pydata/pandas"
-    - python: 3.4
-      env: PANDAS_VERSION="git+https://github.com/pydata/pandas"
     - python: 3.5
       env: PANDAS_VERSION="git+https://github.com/pydata/pandas"
+    - python: 3.6
+      env: PANDAS_VERSION="git+https://github.com/pydata/pandas"
   allow_failures:
     - python: 2.7
       env: PANDAS_VERSION="git+https://github.com/pydata/pandas"
-    - python: 3.4
-      env: PANDAS_VERSION="git+https://github.com/pydata/pandas"
     - python: 3.5
       env: PANDAS_VERSION="git+https://github.com/pydata/pandas"
+    - python: 3.6
+      env: PANDAS_VERSION="git+https://github.com/pydata/pandas"
 
 addons:
-  postgresql: "9.3"
+  postgresql: "9.5"
 
 services:
     - mongodb
@@ -40,6 +40,25 @@ install:
   - conda config --set always_yes yes --set changeps1 no
   - conda update conda
 
+<<<<<<< HEAD
+  # Install dependencies
+  # Use conda **ONLY** for numpy and pandas (if not pulling from master), this
+  # speeds up the builds a lot. Use the normal pip install for the rest.
+  - conda create -n odo numpy=1.11.2
+  - source activate odo
+  # update setuptools and pip
+  - conda update setuptools pip
+  - if [ -n "$PANDAS_VERSION" ]; then pip install cython==0.27.3; pip install $PANDAS_VERSION; else conda install pandas=0.22.0; fi
+
+  # install the frozen ci dependencies
+  - pip install -e .[ci]
+
+  # datashape
+  - pip install git+git://github.com/blaze/datashape.git
+
+  # redshift sqlalchemy dialect
+  - pip install --upgrade git+git://github.com/graingert/redshift_sqlalchemy
+
   # install python stuff
   - source etc/ci-install.sh
 

conda.recipe/meta.yaml

@@ -8,7 +8,7 @@ build:
   {% else %}string: py{{ environ.get('PY_VER').replace('.', '') }}_{{ environ.get('GIT_BUILD_STR', 'GIT_STUB') }}{% endif %}
 
 source:
-  git_url: ../
+  path: ../
 
 requirements:
   build:
@@ -18,7 +18,7 @@ requirements:
     - pandas >=0.15.0
     - toolz >=0.7.3
     - multipledispatch >=0.4.7
-    - networkx
+    - networkx >1.0,<2.0
     - dask >=0.11.1
 
   run:
@@ -28,7 +28,7 @@ requirements:
     - pandas >=0.15.0
     - toolz >=0.7.3
     - multipledispatch >=0.4.7
-    - networkx
+    - networkx >1.0,<2.0
     - dask >=0.11.1
 
 test:
@@ -38,8 +38,8 @@ test:
     - pytables >=3.0.0
     - sqlalchemy >=0.8.0
     - bcolz
+    - pymongo # [not (py35 or win)]
     - mock
-    - pymongo >=2.8,<3 # [not (py35 or win)]
     - dask
     - paramiko # [not win]
     - sqlite3 # [win]
@@ -50,7 +50,7 @@ test:
     - psycopg2 # [unix]
 
   commands:
-    - pip install sas7bdat && py.test -vx --pyargs odo -rsxX
+    - pip install sas7bdat && py.test -v --pyargs odo -rsxX
 
 about:
   home: https://github.com/blaze/odo/

odo/backends/pandas.py

@@ -22,7 +22,7 @@
 
 
 def dshape_from_pandas(col):
-    if isinstance(col.dtype, categorical):
+    if col.dtype.name == 'category':
         return Categorical(col.cat.categories.tolist())
     elif col.dtype.kind == 'M':
         tz = getattr(col.dtype, 'tz', None)

odo/backends/tests/test_bokeh.py

@@ -4,6 +4,8 @@
 bokeh = pytest.importorskip('bokeh')
 
 from odo.backends.bokeh import convert, pd, ColumnDataSource
+import numpy as np
+import numpy.testing as nt
 import pandas.util.testing as tm
 
 

odo/backends/tests/test_dask_array.py

@@ -59,14 +59,14 @@ def test_into_inplace():
 
 
 def test_insert_to_ooc():
-    x = np.arange(600).reshape((20, 30))
-    y = np.empty(shape=x.shape, dtype=x.dtype)
-    a = convert(Array, x, chunks=(4, 5))
+    in_arr = np.arange(600).reshape((20, 30))
+    out_arr = np.empty(shape=in_arr.shape, dtype=in_arr.dtype)
+    d_arr = convert(Array, in_arr, chunks=(4, 5))
 
-    dsk = insert_to_ooc(y, a)
-    core.get(merge(dsk, a.dask), list(dsk.keys()))
+    dsk = insert_to_ooc(d_arr, out_arr)
+    core.get(merge(dsk, d_arr.dask), list(dsk.keys()))
 
-    assert eq(y, x)
+    assert eq(out_arr, in_arr)
 
 
 def test_array_interface():

odo/backends/tests/test_dask_dataframe.py

@@ -24,7 +24,7 @@ def test_discover():
     ddf = dd.from_pandas(df, npartitions=2)
     assert_dshape_equal(discover(ddf),
                         var * Record([('x', Categorical(['a', 'b', 'c'])),
-                                            ('y', int64), ('z', float64)]))
+                                      ('y', int64), ('z', float64)]))
     assert_dshape_equal(discover(ddf.x), var * Categorical(['a', 'b', 'c']))
 
 

odo/backends/tests/test_sqlite.py

@@ -86,14 +86,14 @@ def test_different_encoding():
 
     NULL = u''
 
-    expected = [(u'1958.001.500131-1A', 1, NULL, NULL, 899),
-                (u'1958.001.500156-6', 1, NULL, NULL, 899),
-                (u'1958.001.500162-1', 1, NULL, NULL, 899),
-                (u'1958.001.500204-2', 1, NULL, NULL, 899),
-                (u'1958.001.500204-2A', 1, NULL, NULL, 899),
-                (u'1958.001.500204-2B', 1, NULL, NULL, 899),
-                (u'1958.001.500223-6', 1, NULL, NULL, 9610),
-                (u'1958.001.500233-9', 1, NULL, NULL, 4703),
+    expected = [(u'1958.001.500131-1A', 1, None, None, 899),
+                (u'1958.001.500156-6', 1, None, None, 899),
+                (u'1958.001.500162-1', 1, None, None, 899),
+                (u'1958.001.500204-2', 1, None, None, 899),
+                (u'1958.001.500204-2A', 1, None, None, 899),
+                (u'1958.001.500204-2B', 1, None, None, 899),
+                (u'1958.001.500223-6', 1, None, None, 9610),
+                (u'1958.001.500233-9', 1, None, None, 4703),
                 (u'1909.017.000018-3', 1, 30.0, u'sumaria', 899)]
     assert result == expected
 

odo/tests/test_convert.py

@@ -165,7 +165,7 @@ def test_numpy_launders_python_types():
 
 def test_numpy_asserts_type_after_dataframe():
     df = pd.DataFrame({'name': ['Alice'], 'amount': [100]})
-    ds = datashape.dshape('1 * {name: string[10, "ascii"], amount: int32}')
+    ds = datashape.dshape('1 * {amount: int32, name: string[10, "ascii"]}')
     x = convert(np.ndarray, df, dshape=ds)
     assert discover(x) == ds