TST: remove bunch of warnings for .astype(.....), xref #17636 (#17759)

Author: jreback

pandas/tests/indexing/test_categorical.py

@@ -8,6 +8,7 @@
  Categorical, CategoricalIndex)
 from pandas.util.testing import assert_series_equal, assert_frame_equal
 from pandas.util import testing as tm
+from pandas.api.types import CategoricalDtype as CDT
 
 
 class TestCategoricalIndex(object):
@@ -16,36 +17,32 @@ def setup_method(self, method):
 
  self.df = DataFrame({'A': np.arange(6, dtype='int64'),
  'B': Series(list('aabbca')).astype(
- 'category', categories=list(
- 'cab'))}).set_index('B')
+ CDT(list('cab')))}).set_index('B')
  self.df2 = DataFrame({'A': np.arange(6, dtype='int64'),
  'B': Series(list('aabbca')).astype(
- 'category', categories=list(
- 'cabe'))}).set_index('B')
+ CDT(list('cabe')))}).set_index('B')
  self.df3 = DataFrame({'A': np.arange(6, dtype='int64'),
  'B': (Series([1, 1, 2, 1, 3, 2])
- .astype('category', categories=[3, 2, 1],
-  ordered=True))}).set_index('B')
+ .astype(CDT([3, 2, 1], ordered=True)))
+ }).set_index('B')
  self.df4 = DataFrame({'A': np.arange(6, dtype='int64'),
  'B': (Series([1, 1, 2, 1, 3, 2])
- .astype('category', categories=[3, 2, 1],
-  ordered=False))}).set_index('B')
+ .astype(CDT([3, 2, 1], ordered=False)))
+ }).set_index('B')
 
  def test_loc_scalar(self):
  result = self.df.loc['a']
  expected = (DataFrame({'A': [0, 1, 5],
  'B': (Series(list('aaa'))
- .astype('category',
- categories=list('cab')))})
+ .astype(CDT(list('cab'))))})
  .set_index('B'))
  assert_frame_equal(result, expected)
 
  df = self.df.copy()
  df.loc['a'] = 20
  expected = (DataFrame({'A': [20, 20, 2, 3, 4, 20],
  'B': (Series(list('aabbca'))
- .astype('category',
- categories=list('cab')))})
+ .astype(CDT(list('cab'))))})
  .set_index('B'))
  assert_frame_equal(df, expected)
 
@@ -319,13 +316,13 @@ def test_reindexing(self):
  result = self.df2.reindex(Categorical(['a', 'd'], categories=cats))
  expected = DataFrame({'A': [0, 1, 5, np.nan],
  'B': Series(list('aaad')).astype(
- 'category', categories=cats)}).set_index('B')
+ CDT(cats))}).set_index('B')
  assert_frame_equal(result, expected, check_index_type=True)
 
  result = self.df2.reindex(Categorical(['a'], categories=cats))
  expected = DataFrame({'A': [0, 1, 5],
  'B': Series(list('aaa')).astype(
- 'category', categories=cats)}).set_index('B')
+ CDT(cats))}).set_index('B')
  assert_frame_equal(result, expected, check_index_type=True)
 
  result = self.df2.reindex(['a', 'b', 'e'])
@@ -348,16 +345,15 @@ def test_reindexing(self):
  ['a', 'd'], categories=cats, ordered=True))
  expected = DataFrame(
  {'A': [0, 1, 5, np.nan],
- 'B': Series(list('aaad')).astype('category', categories=cats,
-  ordered=True)}).set_index('B')
+ 'B': Series(list('aaad')).astype(
+ CDT(cats, ordered=True))}).set_index('B')
  assert_frame_equal(result, expected, check_index_type=True)
 
  result = self.df2.reindex(Categorical(
  ['a', 'd'], categories=['a', 'd']))
  expected = DataFrame({'A': [0, 1, 5, np.nan],
  'B': Series(list('aaad')).astype(
- 'category', categories=['a', 'd'
- ])}).set_index('B')
+ CDT(['a', 'd']))}).set_index('B')
  assert_frame_equal(result, expected, check_index_type=True)
 
  # passed duplicate indexers are not allowed

pandas/tests/reshape/test_merge.py

@@ -16,6 +16,7 @@
 from pandas.core.dtypes.common import is_categorical_dtype, is_object_dtype
 from pandas import DataFrame, Index, MultiIndex, Series, Categorical
 import pandas.util.testing as tm
+from pandas.api.types import CategoricalDtype as CDT
 
 
 N = 50
@@ -1414,16 +1415,15 @@ def left():
  return DataFrame(
  {'X': Series(np.random.choice(
  ['foo', 'bar'],
- size=(10,))).astype('category', categories=['foo', 'bar']),
+ size=(10,))).astype(CDT(['foo', 'bar'])),
  'Y': np.random.choice(['one', 'two', 'three'], size=(10,))})
 
 
 @pytest.fixture
 def right():
  np.random.seed(1234)
  return DataFrame(
- {'X': Series(['foo', 'bar']).astype('category',
- categories=['foo', 'bar']),
+ {'X': Series(['foo', 'bar']).astype(CDT(['foo', 'bar'])),
  'Z': [1, 2]})
 
 
@@ -1468,9 +1468,8 @@ def test_other_columns(self, left, right):
 
  @pytest.mark.parametrize(
  'change', [lambda x: x,
- lambda x: x.astype('category',
- categories=['foo', 'bar', 'bah']),
- lambda x: x.astype('category', ordered=True)])
+ lambda x: x.astype(CDT(['foo', 'bar', 'bah'])),
+ lambda x: x.astype(CDT(ordered=True))])
  @pytest.mark.parametrize('how', ['inner', 'outer', 'left', 'right'])
  def test_dtype_on_merged_different(self, change, how, left, right):
  # our merging columns, X now has 2 different dtypes

pandas/tests/reshape/test_pivot.py

@@ -1,3 +1,4 @@
+
 from datetime import datetime, date, timedelta
 
 import pytest
@@ -13,6 +14,7 @@
 from pandas.compat import range, product
 import pandas.util.testing as tm
 from pandas.tseries.util import pivot_annual, isleapyear
+from pandas.api.types import CategoricalDtype as CDT
 
 
 class TestPivotTable(object):
@@ -98,13 +100,12 @@ def test_pivot_table_dropna_categoricals(self):
  'B': [1, 2, 3, 1, 2, 3, 1, 2, 3],
  'C': range(0, 9)})
 
- df['A'] = df['A'].astype('category', ordered=False,
- categories=categories)
+ df['A'] = df['A'].astype(CDT(categories, ordered=False))
  result_true = df.pivot_table(index='B', columns='A', values='C',
  dropna=True)
  expected_columns = Series(['a', 'b', 'c'], name='A')
- expected_columns = expected_columns.astype('category', ordered=False,
-  categories=categories)
+ expected_columns = expected_columns.astype(
+ CDT(categories, ordered=False))
  expected_index = Series([1, 2, 3], name='B')
  expected_true = DataFrame([[0.0, 3.0, 6.0],
  [1.0, 4.0, 7.0],

pandas/tests/reshape/test_tile.py

@@ -9,6 +9,7 @@
  Interval, IntervalIndex, Categorical,
  cut, qcut, date_range)
 import pandas.util.testing as tm
+from pandas.api.types import CategoricalDtype as CDT
 
 from pandas.core.algorithms import quantile
 import pandas.core.reshape.tile as tmod
@@ -299,7 +300,7 @@ def test_cut_return_intervals(self):
  exp_bins = np.linspace(0, 8, num=4).round(3)
  exp_bins[0] -= 0.008
  exp = Series(IntervalIndex.from_breaks(exp_bins, closed='right').take(
- [0, 0, 0, 1, 1, 1, 2, 2, 2])).astype('category', ordered=True)
+ [0, 0, 0, 1, 1, 1, 2, 2, 2])).astype(CDT(ordered=True))
  tm.assert_series_equal(res, exp)
 
  def test_qcut_return_intervals(self):
@@ -308,22 +309,22 @@ def test_qcut_return_intervals(self):
  exp_levels = np.array([Interval(-0.001, 2.664),
  Interval(2.664, 5.328), Interval(5.328, 8)])
  exp = Series(exp_levels.take([0, 0, 0, 1, 1, 1, 2, 2, 2])).astype(
- 'category', ordered=True)
+ CDT(ordered=True))
  tm.assert_series_equal(res, exp)
 
  def test_series_retbins(self):
  # GH 8589
  s = Series(np.arange(4))
  result, bins = cut(s, 2, retbins=True)
  expected = Series(IntervalIndex.from_breaks(
- [-0.003, 1.5, 3], closed='right').repeat(2)).astype('category',
-  ordered=True)
+ [-0.003, 1.5, 3], closed='right').repeat(2)).astype(
+ CDT(ordered=True))
  tm.assert_series_equal(result, expected)
 
  result, bins = qcut(s, 2, retbins=True)
  expected = Series(IntervalIndex.from_breaks(
- [-0.001, 1.5, 3], closed='right').repeat(2)).astype('category',
-  ordered=True)
+ [-0.001, 1.5, 3], closed='right').repeat(2)).astype(
+ CDT(ordered=True))
  tm.assert_series_equal(result, expected)
 
  def test_qcut_duplicates_bin(self):
@@ -351,7 +352,7 @@ def test_single_quantile(self):
  result = qcut(s, 1)
  intervals = IntervalIndex([Interval(8.999, 9.0),
  Interval(8.999, 9.0)], closed='right')
- expected = Series(intervals).astype('category', ordered=True)
+ expected = Series(intervals).astype(CDT(ordered=True))
  tm.assert_series_equal(result, expected)
 
  s = Series([-9., -9.])
@@ -361,7 +362,7 @@ def test_single_quantile(self):
  result = qcut(s, 1)
  intervals = IntervalIndex([Interval(-9.001, -9.0),
  Interval(-9.001, -9.0)], closed='right')
- expected = Series(intervals).astype('category', ordered=True)
+ expected = Series(intervals).astype(CDT(ordered=True))
  tm.assert_series_equal(result, expected)
 
  s = Series([0., 0.])
@@ -371,7 +372,7 @@ def test_single_quantile(self):
  result = qcut(s, 1)
  intervals = IntervalIndex([Interval(-0.001, 0.0),
  Interval(-0.001, 0.0)], closed='right')
- expected = Series(intervals).astype('category', ordered=True)
+ expected = Series(intervals).astype(CDT(ordered=True))
  tm.assert_series_equal(result, expected)
 
  s = Series([9])
@@ -380,7 +381,7 @@ def test_single_quantile(self):
  tm.assert_series_equal(result, expected)
  result = qcut(s, 1)
  intervals = IntervalIndex([Interval(8.999, 9.0)], closed='right')
- expected = Series(intervals).astype('category', ordered=True)
+ expected = Series(intervals).astype(CDT(ordered=True))
  tm.assert_series_equal(result, expected)
 
  s = Series([-9])
@@ -389,7 +390,7 @@ def test_single_quantile(self):
  tm.assert_series_equal(result, expected)
  result = qcut(s, 1)
  intervals = IntervalIndex([Interval(-9.001, -9.0)], closed='right')
- expected = Series(intervals).astype('category', ordered=True)
+ expected = Series(intervals).astype(CDT(ordered=True))
  tm.assert_series_equal(result, expected)
 
  s = Series([0])
@@ -398,7 +399,7 @@ def test_single_quantile(self):
  tm.assert_series_equal(result, expected)
  result = qcut(s, 1)
  intervals = IntervalIndex([Interval(-0.001, 0.0)], closed='right')
- expected = Series(intervals).astype('category', ordered=True)
+ expected = Series(intervals).astype(CDT(ordered=True))
  tm.assert_series_equal(result, expected)
 
  def test_single_bin(self):
@@ -450,7 +451,7 @@ def test_datetime_cut(self):
  Timestamp('2013-01-02 08:00:00')),
  Interval(Timestamp('2013-01-02 08:00:00'),
  Timestamp('2013-01-03 00:00:00'))]))
- .astype('category', ordered=True))
+ .astype(CDT(ordered=True)))
 
  tm.assert_series_equal(result, expected)
 
@@ -479,7 +480,7 @@ def test_datetime_bin(self):
  Series(IntervalIndex.from_intervals([
  Interval(Timestamp(bin_data[0]), Timestamp(bin_data[1])),
  Interval(Timestamp(bin_data[1]), Timestamp(bin_data[2]))]))
- .astype('category', ordered=True))
+ .astype(CDT(ordered=True)))
 
  for conv in [Timestamp, Timestamp, np.datetime64]:
  bins = [conv(v) for v in bin_data]