TST: update incorrectly defined fixtures

pandas/tests/extension/json/array.py

@@ -10,7 +10,12 @@
 in that case. We *want* the dictionaries to be treated as scalars, so we
 hack around pandas by using UserDicts.
 """
-import collections
+try:
+ from collections.abc import Iterable, Mapping, Sequence
+except AttributeError:
+ from collections import Iterable, Mapping, Sequence
+from collections import UserDict
+
 import itertools
 import numbers
 import random
@@ -24,10 +29,10 @@
 
 
 class JSONDtype(ExtensionDtype):
- type = collections.Mapping
+ type = Mapping
  name = 'json'
  try:
- na_value = collections.UserDict()
+ na_value = UserDict()
  except AttributeError:
  # source compatibility with Py2.
  na_value = {}
@@ -64,14 +69,14 @@ def _from_sequence(cls, scalars):
 
  @classmethod
  def _from_factorized(cls, values, original):
- return cls([collections.UserDict(x) for x in values if x != ()])
+ return cls([UserDict(x) for x in values if x != ()])
 
  def __getitem__(self, item):
  if isinstance(item, numbers.Integral):
  return self.data[item]
  elif isinstance(item, np.ndarray) and item.dtype == 'bool':
  return self._from_sequence([x for x, m in zip(self, item) if m])
- elif isinstance(item, collections.Iterable):
+ elif isinstance(item, Iterable):
  # fancy indexing
  return type(self)([self.data[i] for i in item])
  else:
@@ -82,8 +87,7 @@ def __setitem__(self, key, value):
  if isinstance(key, numbers.Integral):
  self.data[key] = value
  else:
- if not isinstance(value, (type(self),
- collections.Sequence)):
+ if not isinstance(value, (type(self), Sequence)):
  # broadcast value
  value = itertools.cycle([value])
 
@@ -174,6 +178,6 @@ def _values_for_argsort(self):
 
 def make_data():
  # TODO: Use a regular dict. See _NDFrameIndexer._setitem_with_indexer
- return [collections.UserDict([
+ return [UserDict([
  (random.choice(string.ascii_letters), random.randint(0, 100))
  for _ in range(random.randint(0, 10))]) for _ in range(100)]

pandas/tests/groupby/test_whitelist.py

@@ -91,6 +91,19 @@
  'is_monotonic_decreasing',
 ])
 
+def df_letters_fn():
+ letters = np.array(list(ascii_lowercase))
+ N = 10
+ random_letters = letters.take(np.random.randint(0, 26, N))
+ df = DataFrame({'floats': N / 10 * Series(np.random.random(N)),
+ 'letters': Series(random_letters)})
+ return df
+
+df_letters_var = df_letters_fn()
+
+
+
+
 
 @pytest.fixture
 def mframe():
@@ -114,16 +127,16 @@ def df():
 
 @pytest.fixture
 def df_letters():
- letters = np.array(list(ascii_lowercase))
- N = 10
- random_letters = letters.take(np.random.randint(0, 26, N))
- df = DataFrame({'floats': N / 10 * Series(np.random.random(N)),
- 'letters': Series(random_letters)})
- return df
+ return df_letters_var()
+
+
+@pytest.fixture
+def df_letters_floats():
+ return df_letters_var().floats
 
 
 @pytest.mark.parametrize(
- "obj, whitelist", zip((df_letters(), df_letters().floats),
+ "obj, whitelist", zip((df_letters, df_letters_floats),
  (df_whitelist, s_whitelist)))
 def test_groupby_whitelist(df_letters, obj, whitelist):
  df = df_letters

pandas/tests/indexes/datetimes/test_tools.py

@@ -1528,6 +1528,9 @@ def test_normalize_date():
  assert (result == datetime(2012, 9, 7))
 
 
+the_year_1960 = Timestamp('1960-01-01')
+
+
 @pytest.fixture(params=['D', 's', 'ms', 'us', 'ns'])
 def units(request):
  return request.param
@@ -1536,18 +1539,31 @@ def units(request):
 @pytest.fixture
 def epoch_1960():
  # for origin as 1960-01-01
- return Timestamp('1960-01-01')
+ return the_year_1960
+
+@pytest.fixture
+def epoch_1960_as_pydatetime():
+ # for origin as 1960-01-01
+ return the_year_1960.to_pydatetime()
+
+@pytest.fixture
+def epoch_1960_as_datetime64():
+ # for origin as 1960-01-01
+ return the_year_1960.to_datetime64()
+
+
+
 
 
 @pytest.fixture
 def units_from_epochs():
  return list(range(5))
 
 
-@pytest.fixture(params=[epoch_1960(),
- epoch_1960().to_pydatetime(),
- epoch_1960().to_datetime64(),
- str(epoch_1960())])
+@pytest.fixture(params=[epoch_1960,
+ epoch_1960_as_pydatetime,
+ epoch_1960_as_datetime64,
+ str(epoch_1960)])
 def epochs(request):
  return request.param
 

pandas/tests/series/test_analytics.py

@@ -1768,8 +1768,7 @@ def test_value_counts_categorical_not_ordered(self):
  tm.assert_series_equal(idx.value_counts(normalize=True), exp)
 
 
-@pytest.fixture
-def s_main_dtypes():
+def s_main_dtypes_var():
  df = pd.DataFrame(
  {'datetime': pd.to_datetime(['2003', '2002',
  '2001', '2002',
@@ -1789,6 +1788,23 @@ def s_main_dtypes():
  return df
 
 
+@pytest.fixture
+def s_main_dtypes():
+ df = s_main_dtypes_var()
+ return df 
+
+def s_main_dtypes_items():
+ adf = s_main_dtypes_var()
+ # pytest.set_trace()
+ a_list = [v for k, v in adf.iteritems()]
+ return a_list
+
+
+@pytest.fixture(name="s_main_dtypes_items")
+def s_main_dtypes_iteritems():
+ a_list = s_main_dtypes_items
+ return a_list
+
 class TestMode(object):
 
  @pytest.mark.parametrize('dropna, expected', [
@@ -1992,9 +2008,10 @@ def test_error(self, r):
  with tm.assert_raises_regex(TypeError, msg):
  method(arg)
 
+ @pytest.fixture(name="s_main_dtypes_iteritems")
  @pytest.mark.parametrize(
  "s",
- [v for k, v in s_main_dtypes().iteritems()])
+ s_main_dtypes_iteritems)
  def test_nsmallest_nlargest(self, s):
  # float, int, datetime64 (use i8), timedelts64 (same),
  # object that are numbers, object that are strings