def test_max_len_string_array(self):
arr = a = np.array(["foo", "b", np.nan], dtype="object")
assert libwriters.max_len_string_array(arr) == 3
# unicode
arr = a.astype("U").astype(object)
assert libwriters.max_len_string_array(arr) == 3
# bytes for python3
arr = a.astype("S").astype(object)
assert libwriters.max_len_string_array(arr) == 3
# raises
with pytest.raises(TypeError):
libwriters.max_len_string_array(arr.astype("U"))
def test_max_len_string_array(self):
arr = a = np.array(['foo', 'b', np.nan], dtype='object')
assert libwriters.max_len_string_array(arr) == 3
# unicode
arr = a.astype('U').astype(object)
assert libwriters.max_len_string_array(arr) == 3
# bytes for python3
arr = a.astype('S').astype(object)
assert libwriters.max_len_string_array(arr) == 3
# raises
with pytest.raises(TypeError):
libwriters.max_len_string_array(arr.astype('U'))
def test_max_len_string_array(self):
arr = a = np.array(['foo', 'b', np.nan], dtype='object')
assert libwriters.max_len_string_array(arr) == 3
# unicode
arr = a.astype('U').astype(object)
assert libwriters.max_len_string_array(arr) == 3
# bytes for python3
arr = a.astype('S').astype(object)
assert libwriters.max_len_string_array(arr) == 3
# raises
pytest.raises(TypeError,
lambda: libwriters.max_len_string_array(arr.astype('U')))
def _convert_types(self, a):
"""
Converts object arrays of strings to numpy string arrays
"""
# No conversion for scalar type
if a.dtype != 'object':
return a, None
# We can't infer the type of an empty array, so just
# assume strings
if len(a) == 0:
return a.astype('U1'), None
# Compute a mask of missing values. Replace NaNs and Nones with
# empty strings so that type inference has a chance.
mask = pd.isnull(a)
if mask.sum() > 0:
a = a.copy()
np.putmask(a, mask, '')
else:
mask = None
if infer_dtype(a, skipna=False) == 'mixed':
# assume its a string, otherwise raise an error
try:
a = np.array([s.encode('ascii') for s in a])
a = a.astype('O')
except:
raise ValueError(
"Column of type 'mixed' cannot be converted to string")
type_ = infer_dtype(a, skipna=False)
if type_ in ['unicode', 'string']:
max_len = max_len_string_array(a)
return a.astype('U{:d}'.format(max_len)), mask
else:
raise ValueError('Cannot store arrays with {} dtype'.format(type_))
def _convert_types(self, a):
"""
Converts object arrays of strings to numpy string arrays
"""
# No conversion for scalar type
if a.dtype != 'object':
return a, None
# We can't infer the type of an empty array, so just
# assume strings
if len(a) == 0:
return a.astype('U1'), None
# Compute a mask of missing values. Replace NaNs and Nones with
# empty strings so that type inference has a chance.
mask = pd.isnull(a)
if mask.sum() > 0:
a = a.copy()
np.putmask(a, mask, '')
else:
mask = None
if infer_dtype(a, skipna=False) == 'mixed':
# assume its a string, otherwise raise an error
try:
a = np.array([s.encode('ascii') for s in a])
a = a.astype('O')
except:
raise ValueError("Column of type 'mixed' cannot be converted to string")
type_ = infer_dtype(a, skipna=False)
if type_ in ['unicode', 'string']:
max_len = max_len_string_array(a)
return a.astype('U{:d}'.format(max_len)), mask
else:
raise ValueError('Cannot store arrays with {} dtype'.format(type_))
