def test_missing(self):
diclist = [deepcopy(basic_dict) for _ in range(10)]
diclist[5]['a'] = nan
result = dictionary.pack(diclist)
expected = {key: list((i,) * 10) for
i, key in enumerate(names)}
expected['self'] = deepcopy(expected)
expected['a'][5] = nan
np.testing.assert_equal(result, expected)
del diclist[5]['b']
expected['b'][5] = nan
result = dictionary.pack(diclist)
np.testing.assert_equal(result, expected)
def test_numpy(self):
diclist = [basic_dict for _ in range(10)]
expected = {key: np.array(list((i,) * 10)) for
i, key in enumerate(names)}
expected['self'] = dict(expected)
result = dictionary.pack(diclist, dtype=int)
np.testing.assert_equal(result, expected)
def test_basic(self):
diclist = [basic_dict for _ in range(10)]
expected = {key: list((i,) * 10) for
i, key in enumerate(names)}
expected['self'] = dict(expected)
result = dictionary.pack(diclist)
self.assertDictEqual(result, expected)
def _dataframe_dict(data, index=None, filler='', header=None):
if isinstance(data, dict):
try:
if depth(data, isiter=True) < 2:
return data
except TypeError:
return data
if not isinstance(data, dict):
header = resolve_header(header)
if header is None:
header = get_header(data[0])
data = pack(data, header)
data = flatten(data)
data = fill_keys(data, filler)
return data
def test_embedded_types(self):
dtype = {key: int for key in basic_dict}
dtype['a'] = float
dtype['self'] = dict(dtype)
base = deepcopy(basic_dict)
base['a'] = 5.5
base['self']['a'] = 5.5
diclist = [base for _ in range(10)]
result = dictionary.pack(diclist, header=diclist[0], dtype=dtype)
expected = {key: np.array(list((i,) * 10), dtype=int) for
i, key in enumerate(names)}
expected['a'] = np.array(list((5.5,) * 10), dtype=float)
expected['self'] = dict(expected)
np.testing.assert_equal(result, expected)
def test_dict(self):
header = get_header(testdata[0])
custom_data = fill_keys(flatten(pack(testdata, header)), '')
result = dataframe_dict(testdata)
expected = dataframe_dict(custom_data)
assert_frame_equal(result, expected, check_names=True)
