def set(self, item, value):
"""
Modify Block in-place with new item value
Returns
-------
None
"""
loc = self.items.get_loc(item)
if value.dtype != _NS_DTYPE:
value = tslib.cast_to_nanoseconds(value)
self.values[loc] = value
def set(self, item, value):
"""
Modify Block in-place with new item value
Returns
-------
None
"""
loc = self.items.get_loc(item)
if value.dtype != _NS_DTYPE:
value = tslib.cast_to_nanoseconds(value)
self.values[loc] = value
def __init__(self, values, items, ref_items, ndim=2):
if values.dtype != _NS_DTYPE:
values = tslib.cast_to_nanoseconds(values)
Block.__init__(self, values, items, ref_items, ndim=ndim)
def form_blocks(arrays, names, axes):
# pre-filter out items if we passed it
items = axes[0]
if len(arrays) < len(items):
extra_items = items - Index(names)
else:
extra_items = []
# put "leftover" items in float bucket, where else?
# generalize?
float_items = []
complex_items = []
int_items = []
bool_items = []
object_items = []
datetime_items = []
for k, v in zip(names, arrays):
if issubclass(v.dtype.type, np.floating):
float_items.append((k, v))
elif issubclass(v.dtype.type, np.complexfloating):
complex_items.append((k, v))
elif issubclass(v.dtype.type, np.datetime64):
if v.dtype != _NS_DTYPE:
v = tslib.cast_to_nanoseconds(v)
if hasattr(v, 'tz') and v.tz is not None:
object_items.append((k, v))
else:
datetime_items.append((k, v))
elif issubclass(v.dtype.type, np.integer):
if v.dtype == np.uint64:
# HACK #2355 definite overflow
if (v > 2**63 - 1).any():
object_items.append((k, v))
continue
int_items.append((k, v))
elif v.dtype == np.bool_:
bool_items.append((k, v))
else:
object_items.append((k, v))
blocks = []
if len(float_items):
float_block = _simple_blockify(float_items, items, np.float64)
blocks.append(float_block)
if len(complex_items):
complex_block = _simple_blockify(complex_items, items, np.complex128)
blocks.append(complex_block)
if len(int_items):
int_block = _simple_blockify(int_items, items, np.int64)
blocks.append(int_block)
if len(datetime_items):
datetime_block = _simple_blockify(datetime_items, items, _NS_DTYPE)
blocks.append(datetime_block)
if len(bool_items):
bool_block = _simple_blockify(bool_items, items, np.bool_)
blocks.append(bool_block)
if len(object_items) > 0:
object_block = _simple_blockify(object_items, items, np.object_)
blocks.append(object_block)
if len(extra_items):
shape = (len(extra_items),) + tuple(len(x) for x in axes[1:])
# empty items -> dtype object
block_values = np.empty(shape, dtype=object)
block_values.fill(nan)
na_block = make_block(block_values, extra_items, items)
blocks.append(na_block)
blocks = _consolidate(blocks, items)
return blocks
def __init__(self, values, items, ref_items, ndim=2):
if values.dtype != _NS_DTYPE:
values = tslib.cast_to_nanoseconds(values)
Block.__init__(self, values, items, ref_items, ndim=ndim)
def form_blocks(arrays, names, axes):
# pre-filter out items if we passed it
items = axes[0]
if len(arrays) < len(items):
extra_items = items - Index(names)
else:
extra_items = []
# put "leftover" items in float bucket, where else?
# generalize?
float_items = []
complex_items = []
int_items = []
bool_items = []
object_items = []
datetime_items = []
for k, v in zip(names, arrays):
if issubclass(v.dtype.type, np.floating):
float_items.append((k, v))
elif issubclass(v.dtype.type, np.complexfloating):
complex_items.append((k, v))
elif issubclass(v.dtype.type, np.datetime64):
if v.dtype != _NS_DTYPE:
v = tslib.cast_to_nanoseconds(v)
if hasattr(v, 'tz') and v.tz is not None:
object_items.append((k, v))
else:
datetime_items.append((k, v))
elif issubclass(v.dtype.type, np.integer):
if v.dtype == np.uint64:
# HACK #2355 definite overflow
if (v > 2**63 - 1).any():
object_items.append((k, v))
continue
int_items.append((k, v))
elif v.dtype == np.bool_:
bool_items.append((k, v))
else:
object_items.append((k, v))
blocks = []
if len(float_items):
float_block = _simple_blockify(float_items, items, np.float64)
blocks.append(float_block)
if len(complex_items):
complex_block = _simple_blockify(complex_items, items, np.complex128)
blocks.append(complex_block)
if len(int_items):
int_block = _simple_blockify(int_items, items, np.int64)
blocks.append(int_block)
if len(datetime_items):
datetime_block = _simple_blockify(datetime_items, items, _NS_DTYPE)
blocks.append(datetime_block)
if len(bool_items):
bool_block = _simple_blockify(bool_items, items, np.bool_)
blocks.append(bool_block)
if len(object_items) > 0:
object_block = _simple_blockify(object_items, items, np.object_)
blocks.append(object_block)
if len(extra_items):
shape = (len(extra_items), ) + tuple(len(x) for x in axes[1:])
# empty items -> dtype object
block_values = np.empty(shape, dtype=object)
block_values.fill(nan)
na_block = make_block(block_values, extra_items, items)
blocks.append(na_block)
blocks = _consolidate(blocks, items)
return blocks
def test_index_cast_datetime64_other_units(self):
arr = np.arange(0, 100, 10, dtype=np.int64).view('M8[D]')
idx = Index(arr)
self.assertTrue((idx.values == tslib.cast_to_nanoseconds(arr)).all())
def test_index_cast_datetime64_other_units(self):
arr = np.arange(0, 100, 10, dtype=np.int64).view('M8[D]')
idx = Index(arr)
self.assertTrue((idx.values == tslib.cast_to_nanoseconds(arr)).all())
