PyLint

Orange/preprocess/discretize.py

@@ -1,7 +1,7 @@
 import numpy as np
 import scipy.sparse as sp
 
-from Orange.data import DiscreteVariable, Domain, Table
+from Orange.data import DiscreteVariable, Domain
 from Orange.data.sql.table import SqlTable
 from Orange.preprocess.util import _RefuseDataInConstructor
 from Orange.statistics import distribution, contingency

Orange/preprocess/impute.py

@@ -157,7 +157,7 @@ def __call__(self, data):
             column = np.array([float(data[self.variable])])
         else:
             column = np.array(data.get_column_view(self.variable)[0],
-                                 copy=True)
+                              copy=True)
 
         mask = np.isnan(column)
         if not np.any(mask):

Orange/tests/test_domain.py

@@ -11,7 +11,7 @@
 from numpy.testing import assert_array_equal
 
 from Orange.data import (
-    ContinuousVariable, DiscreteVariable,  StringVariable, TimeVariable,
+    ContinuousVariable, DiscreteVariable, StringVariable, TimeVariable,
     Variable, Domain, Table, DomainConversion)
 from Orange.data.domain import filter_visible
 from Orange.preprocess import Continuize, Impute
@@ -165,7 +165,8 @@ def test_from_numpy_values(self):
                                             (0, 2, DiscreteVariable),
                                             (18, 23, ContinuousVariable)]:
             n_rows, n_cols, = aran_max - aran_min, 1
-            d = Domain.from_numpy(np.zeros((1, 1)), np.arange(aran_min, aran_max).reshape(n_rows, n_cols))
+            d = Domain.from_numpy(np.zeros((1, 1)),
+                                  np.arange(aran_min, aran_max).reshape(n_rows, n_cols))
             self.assertTrue(d.anonymous)
             self.assertIsInstance(d.class_var, vartype)
             if isinstance(vartype, DiscreteVariable):
@@ -402,14 +403,14 @@ def test_conversion(self):
         assert_array_equal(y, np.array([0]))
         metas_exp = [gender.Unknown, education.Unknown, ssn.Unknown]
 
-        def eq(a, b):
+        def equal(a, b):
             if isinstance(a, Real) and isinstance(b, Real) and \
                     np.isnan(a) and np.isnan(b):
                 return True
             else:
                 return a == b
 
-        self.assertTrue(all(starmap(eq, zip(metas, metas_exp))))
+        self.assertTrue(all(starmap(equal, zip(metas, metas_exp))))
 
         x, y, metas = domain.convert([42, 13, "White", "M", "HS", "1234567"])
         assert_array_equal(x, np.array([42, 13]))

Orange/tests/test_remove.py

@@ -9,6 +9,7 @@
 from Orange.preprocess import Remove
 from Orange.tests import test_filename
 
+
 class TestRemover(unittest.TestCase):
 
     @classmethod

Orange/tests/test_table.py

@@ -1777,7 +1777,7 @@ def test_can_filter_row_with_slice(self):
                 new_table, self.table, rows=slice_)
 
     def test_can_use_attributes_as_new_columns(self):
-        a, c, m = column_sizes(self.table)
+        a, _, _ = column_sizes(self.table)
         order = [random.randrange(a) for _ in self.domain.attributes]
         new_attributes = [self.domain.attributes[i] for i in order]
         new_domain = self.create_domain(
@@ -1788,7 +1788,7 @@ def test_can_use_attributes_as_new_columns(self):
             new_table, self.table, xcols=order, ycols=order, mcols=order)
 
     def test_can_use_class_vars_as_new_columns(self):
-        a, c, m = column_sizes(self.table)
+        a, c, _ = column_sizes(self.table)
         order = [random.randrange(a, a + c) for _ in self.domain.class_vars]
         new_classes = [self.domain.class_vars[i - a] for i in order]
         new_domain = self.create_domain(new_classes, new_classes, new_classes)
@@ -1798,7 +1798,7 @@ def test_can_use_class_vars_as_new_columns(self):
             new_table, self.table, xcols=order, ycols=order, mcols=order)
 
     def test_can_use_metas_as_new_columns(self):
-        a, c, m = column_sizes(self.table)
+        _, _, m = column_sizes(self.table)
         order = [random.randrange(-m + 1, 0) for _ in self.domain.metas]
         new_metas = [self.domain.metas[::-1][i] for i in order]
         new_domain = self.create_domain(new_metas, new_metas, new_metas)
@@ -2049,7 +2049,7 @@ def test_can_assign_values(self):
         self.assertAlmostEqual(self.table.X[0, 0], 42.)
 
     def test_can_assign_values_to_classes(self):
-        a, c, m = column_sizes(self.table)
+        a, _, _ = column_sizes(self.table)
         self.table[0, a] = 42.
         self.assertAlmostEqual(self.table.Y[0], 42.)
 
@@ -2067,7 +2067,7 @@ def test_can_assign_rows_to_rows(self):
             self.table.metas[0], self.table.metas[1])
 
     def test_can_assign_lists(self):
-        a, c, m = column_sizes(self.table)
+        a, _, _ = column_sizes(self.table)
         new_example = [float(i)
                        for i in range(len(self.attributes + self.class_vars))]
         self.table[0] = new_example
@@ -2077,7 +2077,7 @@ def test_can_assign_lists(self):
             self.table.Y[0], np.array(new_example[a:]))
 
     def test_can_assign_np_array(self):
-        a, c, m = column_sizes(self.table)
+        a, _, _ = column_sizes(self.table)
         new_example = \
             np.array([float(i)
                       for i in range(len(self.attributes + self.class_vars))])
@@ -2199,7 +2199,7 @@ def test_delete_rows(self):
     def test_clear(self):
         self.table.clear()
         self.assertEqual(len(self.table), 0)
-        for i in self.table:
+        for _ in self.table:
             self.fail("Table should not contain any rows.")
 
     def test_subclasses(self):

Orange/widgets/visualize/tests/test_owscatterplot.py

@@ -2,7 +2,6 @@
 # pylint: disable=missing-docstring
 from unittest.mock import MagicMock
 import numpy as np
-import scipy.sparse as sp
 
 from AnyQt.QtCore import QRectF, Qt