def testRepr(self):
# test tensor repr
with np.printoptions(threshold=100):
arr = np.random.randint(1000, size=(11, 4, 13))
t = mt.tensor(arr, chunk_size=3)
result = repr(t.execute())
expected = repr(arr)
self.assertEqual(result, expected)
for size in (5, 58, 60, 62, 64):
pdf = pd.DataFrame(np.random.randint(1000, size=(size, 10)))
# test DataFrame repr
df = md.DataFrame(pdf, chunk_size=size // 2)
result = repr(df.execute())
expected = repr(pdf)
self.assertEqual(result, expected,
f'failed repr for DataFrame when size = {size}')
# test DataFrame _repr_html_
result = df.execute()._repr_html_()
expected = pdf._repr_html_()
self.assertEqual(
result, expected,
f'failed repr html for DataFrame when size = {size}')
# test Series repr
ps = pdf[0]
s = md.Series(ps, chunk_size=size // 2)
result = repr(s.execute())
expected = repr(ps)
self.assertEqual(result, expected,
f'failed repr for Series when size = {size}')
# test Index repr
pind = pd.date_range('2020-1-1', periods=10)
ind = md.Index(pind, chunk_size=5)
self.assertIn('DatetimeIndex', repr(ind.execute()))
# test groupby repr
df = md.DataFrame(
pd.DataFrame(np.random.rand(100, 3), columns=list('abc')))
grouped = df.groupby(['a', 'b']).execute()
self.assertIn('DataFrameGroupBy', repr(grouped))
# test Categorical repr
c = md.qcut(range(5), 3)
self.assertIn('Categorical', repr(c))
self.assertIn('Categorical', str(c))
self.assertEqual(repr(c.execute()), repr(pd.qcut(range(5), 3)))
def test_repr(setup):
# test tensor repr
with np.printoptions(threshold=100):
arr = np.random.randint(1000, size=(11, 4, 13))
t = mt.tensor(arr, chunk_size=3)
result = repr(t.execute())
expected = repr(arr)
assert result == expected
for size in (5, 58, 60, 62, 64):
pdf = pd.DataFrame(np.random.randint(1000, size=(size, 10)))
# test DataFrame repr
df = md.DataFrame(pdf, chunk_size=size // 2)
result = repr(df.execute())
expected = repr(pdf)
assert result == expected
# test DataFrame _repr_html_
result = df.execute()._repr_html_()
expected = pdf._repr_html_()
assert result == expected
# test Series repr
ps = pdf[0]
s = md.Series(ps, chunk_size=size // 2)
result = repr(s.execute())
expected = repr(ps)
assert result == expected
# test Index repr
pind = pd.date_range('2020-1-1', periods=10)
ind = md.Index(pind, chunk_size=5)
assert 'DatetimeIndex' in repr(ind.execute())
# test groupby repr
df = md.DataFrame(pd.DataFrame(np.random.rand(100, 3),
columns=list('abc')))
grouped = df.groupby(['a', 'b']).execute()
assert 'DataFrameGroupBy' in repr(grouped)
# test Categorical repr
c = md.qcut(range(5), 3)
assert 'Categorical' in repr(c)
assert 'Categorical' in str(c)
assert repr(c.execute()) == repr(pd.qcut(range(5), 3))
