def test_empty_iterator_to_chunks_ndarray():
ds = dshape('var * {x: int}')
result = convert(chunks(np.ndarray), iter([]), dshape=ds)
data = convert(np.ndarray, result)
assert isinstance(data, np.ndarray)
assert len(data) == 0
assert data.dtype.names == ('x',)
def test_empty_iterator_to_chunks_ndarray():
ds = dshape('var * {x: int}')
result = convert(chunks(np.ndarray), iter([]), dshape=ds)
data = convert(np.ndarray, result)
assert isinstance(data, np.ndarray)
assert len(data) == 0
assert data.dtype.names == ('x', )
def test_ndarray_to_df_preserves_field_names():
ds = dshape('2 * {a: int, b: int}')
arr = np.array([[0, 1], [2, 3]])
# dshape explicitly sets field names.
assert (convert(pd.DataFrame, arr, dshape=ds).columns == ['a', 'b']).all()
# no dshape is passed.
assert (convert(pd.DataFrame, arr).columns == [0, 1]).all()
def test_ndarray_to_df_preserves_field_names():
ds = dshape('2 * {a: int, b: int}')
arr = np.array([[0, 1], [2, 3]])
# dshape explicitly sets field names.
assert (convert(pd.DataFrame, arr, dshape=ds).columns == ['a', 'b']).all()
# no dshape is passed.
assert (convert(pd.DataFrame, arr).columns == [0, 1]).all()
def test_recarray():
data = np.array([(1, 1.), (2, 2.)], dtype=[('a', 'i4'), ('b', 'f4')])
result = convert(np.recarray, data)
assert isinstance(result, np.recarray)
assert eq(result.a, data['a'])
result2 = convert(np.ndarray, data)
assert not isinstance(result2, np.recarray)
assert eq(result2, data)
def test_recarray():
data = np.array([(1, 1.), (2, 2.)], dtype=[('a', 'i4'), ('b', 'f4')])
result = convert(np.recarray, data)
assert isinstance(result, np.recarray)
assert eq(result.a, data['a'])
result2 = convert(np.ndarray, data)
assert not isinstance(result2, np.recarray)
assert eq(result2, data)
def test_Series_to_ndarray():
assert eq(convert(np.ndarray, pd.Series([1, 2, 3]), dshape='3 * float64'),
np.array([1.0, 2.0, 3.0]))
assert eq(convert(np.ndarray, pd.Series(['aa', 'bbb', 'ccccc']),
dshape='3 * string[5, "A"]'),
np.array(['aa', 'bbb', 'ccccc'], dtype='S5'))
assert eq(convert(np.ndarray, pd.Series(['aa', 'bbb', 'ccccc']),
dshape='3 * ?string'),
np.array(['aa', 'bbb', 'ccccc'], dtype='O'))
def test_dataframe_and_series():
s = pd.Series([1, 2, 3], name='foo')
df = convert(pd.DataFrame, s)
assert isinstance(df, pd.DataFrame)
assert list(df.columns) == ['foo']
s2 = convert(pd.Series, df)
assert isinstance(s2, pd.Series)
assert s2.name == 'foo'
def test_dataframe_and_series():
s = pd.Series([1, 2, 3], name='foo')
df = convert(pd.DataFrame, s)
assert isinstance(df, pd.DataFrame)
assert list(df.columns) == ['foo']
s2 = convert(pd.Series, df)
assert isinstance(s2, pd.Series)
assert s2.name == 'foo'
def test_Series_to_ndarray():
assert eq(convert(np.ndarray, pd.Series([1, 2, 3]), dshape='3 * float64'),
np.array([1.0, 2.0, 3.0]))
assert eq(convert(np.ndarray, pd.Series(['aa', 'bbb', 'ccccc']),
dshape='3 * string[5, "A"]'),
np.array(['aa', 'bbb', 'ccccc'], dtype='S5'))
assert eq(convert(np.ndarray, pd.Series(['aa', 'bbb', 'ccccc']),
dshape='3 * ?string'),
np.array(['aa', 'bbb', 'ccccc'], dtype='O'))
def test_iterator_to_df():
ds = dshape('var * int32')
it = iter([1, 2, 3])
df = convert(pd.DataFrame, it, dshape=ds)
assert df[0].tolist() == [1, 2, 3]
it = iter([1, 2, 3])
df = convert(pd.DataFrame, it, dshape=None)
assert df[0].tolist() == [1, 2, 3]
it = iter([1, 2, 3])
df = convert(pd.DataFrame, it)
assert df[0].tolist() == [1, 2, 3]
def test_chunks_numpy_pandas():
x = np.array([('Alice', 100), ('Bob', 200)],
dtype=[('name', 'S7'), ('amount', 'i4')])
n = chunks(np.ndarray)([x, x])
pan = convert(chunks(pd.DataFrame), n)
num = convert(chunks(np.ndarray), pan)
assert isinstance(pan, chunks(pd.DataFrame))
assert all(isinstance(chunk, pd.DataFrame) for chunk in pan)
assert isinstance(num, chunks(np.ndarray))
assert all(isinstance(chunk, np.ndarray) for chunk in num)
def test_chunks_numpy_pandas():
x = np.array([('Alice', 100), ('Bob', 200)],
dtype=[('name', 'S7'), ('amount', 'i4')])
n = chunks(np.ndarray)([x, x])
pan = convert(chunks(pd.DataFrame), n)
num = convert(chunks(np.ndarray), pan)
assert isinstance(pan, chunks(pd.DataFrame))
assert all(isinstance(chunk, pd.DataFrame) for chunk in pan)
assert isinstance(num, chunks(np.ndarray))
assert all(isinstance(chunk, np.ndarray) for chunk in num)
def test_iterator_to_df():
ds = dshape('var * int32')
it = iter([1, 2, 3])
df = convert(pd.DataFrame, it, dshape=ds)
assert df[0].tolist() == [1, 2, 3]
it = iter([1, 2, 3])
df = convert(pd.DataFrame, it, dshape=None)
assert df[0].tolist() == [1, 2, 3]
it = iter([1, 2, 3])
df = convert(pd.DataFrame, it)
assert df[0].tolist() == [1, 2, 3]
def test_iterator_and_numpy_chunks():
c = iterator_to_numpy_chunks([1, 2, 3], chunksize=2)
assert isinstance(c, chunks(np.ndarray))
assert all(isinstance(chunk, np.ndarray) for chunk in c)
c = iterator_to_numpy_chunks([1, 2, 3], chunksize=2)
L = convert(list, c)
assert L == [1, 2, 3]
def test_iterator_to_DataFrame_chunks():
data = ((0, 1), (2, 3), (4, 5), (6, 7))
df1 = pd.DataFrame(list(data))
df2 = iterator_to_DataFrame_chunks(data, chunksize=2, add_index=True)
df2 = pd.concat(df2, axis=0)
tm.assert_frame_equal(df1, df2)
df2 = convert(pd.DataFrame, data, chunksize=2, add_index=True)
tm.assert_almost_equal(df1, df2)
def test_list_of_dicts_with_missing_to_numpy():
data = [{'name': 'Alice', 'amount': 100}, {'name': 'Bob'}, {'amount': 200}]
result = convert(np.ndarray, data)
assert result.dtype.names == ('amount', 'name')
expected = np.array([(100.0, 'Alice'), (np.nan, 'Bob'), (200.0, None)],
dtype=[('amount', 'float64'), ('name', 'O')])
assert np.all((result == expected)
| ((result != result) & (expected != expected)))
def test_iterator_to_DataFrame_chunks():
data = ((0, 1), (2, 3), (4, 5), (6, 7))
df1 = pd.DataFrame(list(data))
df2 = iterator_to_DataFrame_chunks(data, chunksize=2, add_index=True)
df2 = pd.concat(df2, axis=0)
tm.assert_frame_equal(df1, df2)
df2 = convert(pd.DataFrame, data, chunksize=2, add_index=True)
tm.assert_almost_equal(df1, df2)
def test_iterator_and_numpy_chunks():
c = iterator_to_numpy_chunks([1, 2, 3], chunksize=2)
assert isinstance(c, chunks(np.ndarray))
assert all(isinstance(chunk, np.ndarray) for chunk in c)
c = iterator_to_numpy_chunks([1, 2, 3], chunksize=2)
L = convert(list, c)
assert L == [1, 2, 3]
def test_datetimes_persist():
typs = [list, tuple, np.ndarray, tuple]
L = [datetime.datetime.now()] * 3
ds = discover(L)
x = L
for cls in typs:
x = convert(cls, x)
assert discover(x) == ds
def test_datetimes_persist():
typs = [list, tuple, np.ndarray, tuple]
L = [datetime.datetime.now()] * 3
ds = discover(L)
x = L
for cls in typs:
x = convert(cls, x)
assert discover(x) == ds
def test_list_of_dicts_with_missing_to_numpy():
data = [{'name': 'Alice', 'amount': 100},
{'name': 'Bob'},
{'amount': 200}]
result = convert(np.ndarray, data)
assert result.dtype.names == ('amount', 'name')
expected = np.array([(100.0, 'Alice'),
(np.nan, 'Bob'),
(200.0, None)],
dtype=[('amount', 'float64'), ('name', 'O')])
assert np.all((result == expected) |
((result != result) & (expected != expected)))
def test_numpy_launders_python_types():
ds = datashape.dshape('3 * int32')
x = convert(np.ndarray, ['1', '2', '3'], dshape=ds)
assert convert(list, x) == [1, 2, 3]
def test_array_to_set():
assert convert(set, np.array([1, 2, 3])) == set([1, 2, 3])
def test_list_of_lists_to_set_creates_tuples():
assert convert(set, [[1], [2]]) == set([(1, ), (2, )])
def test_set_to_Series():
assert eq(convert(pd.Series, set([1, 2, 3])), pd.Series([1, 2, 3]))
def test_list_of_lists_to_set_creates_tuples():
assert convert(set, [[1], [2]]) == set([(1,), (2,)])
def test_set_to_Series():
assert eq(convert(pd.Series, set([1, 2, 3])),
pd.Series([1, 2, 3]))
def test_Series_to_set():
assert convert(set, pd.Series([1, 2, 3])) == set([1, 2, 3])
def test_Series_to_object_ndarray():
ds = datashape.dshape('{amount: float64, name: string, id: int64}')
expected = np.array([1.0, 'Alice', 3], dtype='object')
result = convert(np.ndarray, pd.Series(expected), dshape=ds)
np.testing.assert_array_equal(result, expected)
def test_Series_to_datetime64_ndarray():
s = pd.Series(pd.date_range(start='now', freq='N', periods=10).values)
expected = s.values
result = convert(np.ndarray, s.values)
np.testing.assert_array_equal(result, expected)
def test_noop():
assert convert(list, [1, 2, 3]) == [1, 2, 3]
def test_generator_is_iterator():
g = (1 for i in range(3))
L = convert(list, g)
assert L == [1, 1, 1]
def test_chunks_of_lists_and_iterators():
L = [1, 2], [3, 4]
cl = chunks(list)(L)
assert convert(list, cl) == [1, 2, 3, 4]
assert list(convert(Iterator, cl)) == [1, 2, 3, 4]
assert len(list(convert(chunks(Iterator), cl))) == 2
def test_list_to_numpy_on_dicts():
data = [{'name': 'Alice', 'amount': 100},
{'name': 'Bob', 'amount': 200}]
ds = datashape.dshape('var * {name: string[5], amount: int}')
x = list_to_numpy(data, dshape=ds)
assert convert(list, x) == [('Alice', 100), ('Bob', 200)]
def test_list_to_dataframe_without_datashape():
data = [('Alice', 100), ('Bob', 200)]
df = convert(pd.DataFrame, data)
assert isinstance(df, pd.DataFrame)
assert list(df.columns) != ['Alice', 100]
assert convert(list, df) == data
def test_Series_to_object_ndarray():
ds = datashape.dshape('{amount: float64, name: string, id: int64}')
expected = np.array([1.0, 'Alice', 3], dtype='object')
result = convert(np.ndarray, pd.Series(expected), dshape=ds)
np.testing.assert_array_equal(result, expected)
def test_basic():
assert convert(tuple, [1, 2, 3]) == (1, 2, 3)
def test_cannot_convert_to_series_from_more_than_one_column():
data = [(1, 2), (2, 3), (3, 4)]
ds = datashape.dshape('3 * {id: int64, id2: int64}')
with pytest.raises(ValueError):
convert(pd.Series, data, dshape=ds)
def test_cannot_convert_to_series_from_more_than_one_column():
data = [(1, 2), (2, 3), (3, 4)]
ds = datashape.dshape('3 * {id: int64, id2: int64}')
with pytest.raises(ValueError):
convert(pd.Series, data, dshape=ds)
def test_generator_is_iterator():
g = (1 for i in range(3))
L = convert(list, g)
assert L == [1, 1, 1]
def test_list_to_numpy_on_tuples():
data = [['a', 1], ['b', 2], ['c', 3]]
ds = datashape.dshape('var * (string[1], int32)')
x = list_to_numpy(data, dshape=ds)
assert convert(list, x) == [('a', 1), ('b', 2), ('c', 3)]
def test_list_of_strings_to_set():
assert convert(set, ['Alice', 'Bob']) == set(['Alice', 'Bob'])
def test_list_to_numpy_on_dicts():
data = [{'name': 'Alice', 'amount': 100}, {'name': 'Bob', 'amount': 200}]
ds = datashape.dshape('var * {name: string[5], amount: int}')
x = list_to_numpy(data, dshape=ds)
assert convert(list, x) == [('Alice', 100), ('Bob', 200)]
def test_numpy_to_list_preserves_ns_datetimes():
x = np.array([(0, 0)], dtype=[('a', 'M8[ns]'), ('b', 'i4')])
assert convert(list, x) == [(datetime.datetime(1970, 1, 1, 0, 0), 0)]
def test_list_of_strings_to_set():
assert convert(set, ['Alice', 'Bob']) == set(['Alice', 'Bob'])
def test_array_to_set():
assert convert(set, np.array([1, 2, 3])) == set([1, 2, 3])
def test_numpy_asserts_type_after_dataframe():
df = pd.DataFrame({'name': ['Alice'], 'amount': [100]})
ds = datashape.dshape('1 * {name: string[10, "ascii"], amount: int32}')
x = convert(np.ndarray, df, dshape=ds)
assert discover(x) == ds
def test_empty_iterator_to_chunks_dataframe():
ds = dshape('var * {x: int}')
result = convert(chunks(pd.DataFrame), iter([]), dshape=ds)
data = convert(pd.DataFrame, result)
assert isinstance(data, pd.DataFrame)
assert list(data.columns) == ['x']
def test_numpy_launders_python_types():
ds = datashape.dshape('3 * int32')
x = convert(np.ndarray, ['1', '2', '3'], dshape=ds)
assert convert(list, x) == [1, 2, 3]
def test_chunks_of_lists_and_iterators():
L = [1, 2], [3, 4]
cl = chunks(list)(L)
assert convert(list, cl) == [1, 2, 3, 4]
assert list(convert(Iterator, cl)) == [1, 2, 3, 4]
assert len(list(convert(chunks(Iterator), cl))) == 2
def test_numpy_to_list_preserves_ns_datetimes():
x = np.array([(0, 0)], dtype=[('a', 'M8[ns]'), ('b', 'i4')])
assert convert(list, x) == [(datetime.datetime(1970, 1, 1, 0, 0), 0)]
def test_list_to_numpy_on_tuples():
data = [['a', 1], ['b', 2], ['c', 3]]
ds = datashape.dshape('var * (string[1], int32)')
x = list_to_numpy(data, dshape=ds)
assert convert(list, x) == [('a', 1), ('b', 2), ('c', 3)]
def test_basic():
assert convert(tuple, [1, 2, 3]) == (1, 2, 3)
def test_Series_to_datetime64_ndarray():
s = pd.Series(pd.date_range(start='now', freq='N', periods=10).values)
expected = s.values
result = convert(np.ndarray, s.values)
np.testing.assert_array_equal(result, expected)
def test_list_to_dataframe_without_datashape():
data = [('Alice', 100), ('Bob', 200)]
df = convert(pd.DataFrame, data)
assert isinstance(df, pd.DataFrame)
assert list(df.columns) != ['Alice', 100]
assert convert(list, df) == data
def test_Series_to_set():
assert convert(set, pd.Series([1, 2, 3])) == set([1, 2, 3])
def test_noop():
assert convert(list, [1, 2, 3]) == [1, 2, 3]
def test_numpy_asserts_type_after_dataframe():
df = pd.DataFrame({'name': ['Alice'], 'amount': [100]})
ds = datashape.dshape('1 * {name: string[10, "ascii"], amount: int32}')
x = convert(np.ndarray, df, dshape=ds)
assert discover(x) == ds
def test_empty_iterator_to_chunks_dataframe():
ds = dshape('var * {x: int}')
result = convert(chunks(pd.DataFrame), iter([]), dshape=ds)
data = convert(pd.DataFrame, result)
assert isinstance(data, pd.DataFrame)
assert list(data.columns) == ['x']
