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Oct 3, 2017
Merged

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

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32 changes: 14 additions & 18 deletions pandas/tests/indexing/test_categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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):
Expand All @@ -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)

Expand Down Expand Up @@ -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'])
Expand All @@ -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
Expand Down
11 changes: 5 additions & 6 deletions pandas/tests/reshape/test_merge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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]})


Expand Down Expand Up @@ -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
Expand Down
9 changes: 5 additions & 4 deletions pandas/tests/reshape/test_pivot.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@

from datetime import datetime, date, timedelta

import pytest
Expand All @@ -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):
Expand Down Expand Up @@ -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],
Expand Down
29 changes: 15 additions & 14 deletions pandas/tests/reshape/test_tile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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):
Expand All @@ -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):
Expand Down Expand Up @@ -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.])
Expand All @@ -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.])
Expand All @@ -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])
Expand All @@ -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])
Expand All @@ -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])
Expand All @@ -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):
Expand Down Expand Up @@ -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)

Expand Down Expand Up @@ -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]
Expand Down