def _asarray_tuplesafe(values, dtype=None):
if not isinstance(values, (list, tuple, np.ndarray)):
values = list(values)
if isinstance(values, list) and dtype in [np.object_, object]:
return lib.list_to_object_array(values)
result = np.asarray(values, dtype=dtype)
if issubclass(result.dtype.type, basestring):
result = np.asarray(values, dtype=object)
if result.ndim == 2:
if isinstance(values, list):
return lib.list_to_object_array(values)
else:
# give it our best shot
result = np.empty(len(values), dtype=object)
result[:] = values
return result
def _asarray_tuplesafe(values, dtype=None):
if not isinstance(values, (list, tuple, np.ndarray)):
values = list(values)
if isinstance(values, list) and dtype in [np.object_, object]:
return lib.list_to_object_array(values)
result = np.asarray(values, dtype=dtype)
if issubclass(result.dtype.type, basestring):
result = np.asarray(values, dtype=object)
if result.ndim == 2:
if isinstance(values, list):
return lib.list_to_object_array(values)
else:
# give it our best shot
result = np.empty(len(values), dtype=object)
result[:] = values
return result
def _asarray_tuplesafe(values, dtype=None):
if not isinstance(values, (list, tuple, np.ndarray)):
values = list(values)
if isinstance(values, list) and dtype in [np.object_, object]:
return lib.list_to_object_array(values)
result = np.asarray(values, dtype=dtype)
if issubclass(result.dtype.type, basestring):
result = np.asarray(values, dtype=object)
if result.ndim == 2:
if isinstance(values, list):
return lib.list_to_object_array(values)
else:
# Making a 1D array that safely contains tuples is a bit tricky
# in numpy, leading to the following
result = np.empty(len(values), dtype=object)
result[:] = values
return result
def _asarray_tuplesafe(values, dtype=None):
if not isinstance(values, (list, tuple, np.ndarray)):
values = list(values)
if isinstance(values, list) and dtype in [np.object_, object]:
return lib.list_to_object_array(values)
result = np.asarray(values, dtype=dtype)
if issubclass(result.dtype.type, basestring):
result = np.asarray(values, dtype=object)
if result.ndim == 2:
if isinstance(values, list):
return lib.list_to_object_array(values)
else:
# Making a 1D array that safely contains tuples is a bit tricky
# in numpy, leading to the following
result = np.empty(len(values), dtype=object)
result[:] = values
return result
def unique1d(values):
"""
Hash table-based unique
"""
if np.issubdtype(values.dtype, np.floating):
table = lib.Float64HashTable(len(values))
uniques = np.array(table.unique(com._ensure_float64(values)), dtype=np.float64)
elif np.issubdtype(values.dtype, np.integer):
table = lib.Int64HashTable(len(values))
uniques = np.array(table.unique(com._ensure_int64(values)), dtype=np.int64)
if values.dtype == np.datetime64:
uniques = uniques.view("M8[us]")
else:
table = lib.PyObjectHashTable(len(values))
uniques = table.unique(com._ensure_object(values))
uniques = lib.list_to_object_array(uniques)
return uniques
def unique1d(values):
"""
Hash table-based unique
"""
if issubclass(values.dtype.type, np.floating):
if values.dtype != np.float64:
values = values.astype(np.float64)
table = lib.Float64HashTable(len(values))
uniques = np.array(table.unique(values), dtype=np.float64)
elif issubclass(values.dtype.type, np.integer):
if values.dtype != np.int64:
values = values.astype(np.int64)
table = lib.Int64HashTable(len(values))
uniques = np.array(table.unique(values), dtype=np.int64)
else:
if not values.dtype == np.object_:
values = values.astype(np.object_)
table = lib.PyObjectHashTable(len(values))
uniques = lib.list_to_object_array(table.unique(values))
return uniques
def unique1d(values):
"""
Hash table-based unique
"""
if issubclass(values.dtype.type, np.floating):
if values.dtype != np.float64:
values = values.astype(np.float64)
table = lib.Float64HashTable(len(values))
uniques = np.array(table.unique(values), dtype=np.float64)
elif issubclass(values.dtype.type, np.integer):
if values.dtype != np.int64:
values = values.astype(np.int64)
table = lib.Int64HashTable(len(values))
uniques = np.array(table.unique(values), dtype=np.int64)
else:
if not values.dtype == np.object_:
values = values.astype(np.object_)
table = lib.PyObjectHashTable(len(values))
uniques = lib.list_to_object_array(table.unique(values))
return uniques
def _zip2(*args):
return lib.list_to_object_array(zip(*args))
def _zip2(*args):
return lib.list_to_object_array(zip(*args))
