Merge pull request #154 from arielrossanigo/python36_in_tox

Python 3.6, numpy 1.14 and some fix in tests

Author: dukebody

README.rst

@@ -57,7 +57,7 @@ Normally you'll read the data from a file, but for demonstration purposes we'll
 
     >>> data = pd.DataFrame({'pet':      ['cat', 'dog', 'dog', 'fish', 'cat', 'dog', 'cat', 'fish'],
     ...                      'children': [4., 6, 3, 3, 2, 3, 5, 4],
-    ...                      'salary':   [90, 24, 44, 27, 32, 59, 36, 27]})
+    ...                      'salary':   [90., 24, 44, 27, 32, 59, 36, 27]})
 
 Transformation Mapping
 ----------------------
@@ -106,7 +106,7 @@ Now that the transformation is trained, we confirm that it works on new data::
 
     >>> sample = pd.DataFrame({'pet': ['cat'], 'children': [5.]})
     >>> np.round(mapper.transform(sample), 2)
-    array([[ 1.  ,  0.  ,  0.  ,  1.04]])
+    array([[1.  , 0.  , 0.  , 1.04]])
 
 
 Output features names
@@ -251,14 +251,14 @@ Only columns that are listed in the DataFrameMapper are kept. To keep a column b
     ...     ('children', None)
     ... ])
     >>> np.round(mapper3.fit_transform(data.copy()))
-    array([[ 1.,  0.,  0.,  4.],
-           [ 0.,  1.,  0.,  6.],
-           [ 0.,  1.,  0.,  3.],
-           [ 0.,  0.,  1.,  3.],
-           [ 1.,  0.,  0.,  2.],
-           [ 0.,  1.,  0.,  3.],
-           [ 1.,  0.,  0.,  5.],
-           [ 0.,  0.,  1.,  4.]])
+    array([[1., 0., 0., 4.],
+           [0., 1., 0., 6.],
+           [0., 1., 0., 3.],
+           [0., 0., 1., 3.],
+           [1., 0., 0., 2.],
+           [0., 1., 0., 3.],
+           [1., 0., 0., 5.],
+           [0., 0., 1., 4.]])
 
 Applying a default transformer
 ******************************
@@ -329,11 +329,11 @@ Then the following code could be used to override default imputing strategy:
     ...     'col3': [0, 0, 0, None, None]
     ... })
     >>> mapper6.fit_transform(data6)
-    array([[ 1.,  1.,  0.],
-           [ 1.,  0.,  0.],
-           [ 1.,  1.,  0.],
-           [ 2.,  1.,  0.],
-           [ 3.,  1.,  0.]])
+    array([[1., 1., 0.],
+           [1., 0., 0.],
+           [1., 1., 0.],
+           [2., 1., 0.],
+           [3., 1., 0.]])
 
 
 Feature selection and other supervised transformations
@@ -344,14 +344,14 @@ Feature selection and other supervised transformations
     >>> from sklearn.feature_selection import SelectKBest, chi2
     >>> mapper_fs = DataFrameMapper([(['children','salary'], SelectKBest(chi2, k=1))])
     >>> mapper_fs.fit_transform(data[['children','salary']], data['pet'])
-    array([[ 90.],
-           [ 24.],
-           [ 44.],
-           [ 27.],
-           [ 32.],
-           [ 59.],
-           [ 36.],
-           [ 27.]])
+    array([[90.],
+           [24.],
+           [44.],
+           [27.],
+           [32.],
+           [59.],
+           [36.],
+           [27.]])
 
 Working with sparse features
 ****************************

tests/test_categorical_imputer.py

@@ -29,7 +29,7 @@ def test_unit(input_type, none_value):
 
     Xt = CategoricalImputer().fit_transform(X)
 
-    assert (np.asarray(X) == np.asarray(Xc)).all()
+    assert pd.core.common.array_equivalent(np.asarray(X), np.asarray(Xc))
     assert isinstance(Xt, np.ndarray)
     assert (Xt == ['a', 'b', 'b', 'b']).all()
 
@@ -151,7 +151,7 @@ def test_custom_replacement(replacement_value, input_type):
         replacement=replacement_value
     ).fit_transform(X)
 
-    assert (np.asarray(X) == np.asarray(Xc)).all()
+    assert pd.core.common.array_equivalent(np.asarray(X), np.asarray(Xc))
     assert isinstance(Xt, np.ndarray)
     assert (Xt == ['a', replacement_value, 'b', 'b']).all()
 

tests/test_dataframe_mapper.py

@@ -625,7 +625,8 @@ def test_list_transformers():
     Specifying a list of transformers applies them sequentially to the
     selected column.
     """
-    dataframe = pd.DataFrame({"a": [1, np.nan, 3], "b": [1, 5, 7]})
+    dataframe = pd.DataFrame({"a": [1, np.nan, 3], "b": [1, 5, 7]},
+                             dtype=np.float64)
 
     mapper = DataFrameMapper([
         (["a"], [Imputer(), StandardScaler()]),

tox.ini

@@ -1,13 +1,13 @@
 [tox]
-envlist = {py27,py35}-sklearn{17,18}
+envlist = {py27,py36}-sklearn{17,18}
 
 [testenv]
 deps =
     pytest==3.2.1
-    setuptools==16.0
+    setuptools==39.0
     wheel==0.24.0
     flake8==2.4.1
-    numpy==1.11.3
+    numpy==1.14.3
     scipy==0.18.1
     pandas==0.19.2
     sklearn17: scikit-learn==0.17.1