def make_sparse(arr: np.ndarray,
kind="block",
fill_value=None,
dtype=None,
copy=False):
"""
Convert ndarray to sparse format
Parameters
----------
arr : ndarray
kind : {'block', 'integer'}
fill_value : NaN or another value
dtype : np.dtype, optional
copy : bool, default False
Returns
-------
(sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)
"""
assert isinstance(arr, np.ndarray)
if arr.ndim > 1:
raise TypeError("expected dimension <= 1 data")
if fill_value is None:
fill_value = na_value_for_dtype(arr.dtype)
if isna(fill_value):
mask = notna(arr)
else:
# cast to object comparison to be safe
if is_string_dtype(arr):
arr = arr.astype(object)
if is_object_dtype(arr.dtype):
# element-wise equality check method in numpy doesn't treat
# each element type, eg. 0, 0.0, and False are treated as
# same. So we have to check the both of its type and value.
mask = splib.make_mask_object_ndarray(arr, fill_value)
else:
mask = arr != fill_value
length = len(arr)
if length != len(mask):
# the arr is a SparseArray
indices = mask.sp_index.indices
else:
indices = mask.nonzero()[0].astype(np.int32)
index = _make_index(length, indices, kind)
sparsified_values = arr[mask]
if dtype is not None:
sparsified_values = astype_nansafe(sparsified_values, dtype=dtype)
# TODO: copy
return sparsified_values, index, fill_value
def make_sparse(arr, kind='block', fill_value=None, dtype=None, copy=False):
"""
Convert ndarray to sparse format
Parameters
----------
arr : ndarray
kind : {'block', 'integer'}
fill_value : NaN or another value
dtype : np.dtype, optional
copy : bool, default False
Returns
-------
(sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)
"""
arr = _sanitize_values(arr)
if arr.ndim > 1:
raise TypeError("expected dimension <= 1 data")
if fill_value is None:
fill_value = na_value_for_dtype(arr.dtype)
if isna(fill_value):
mask = notna(arr)
else:
# For str arrays in NumPy 1.12.0, operator!= below isn't
# element-wise but just returns False if fill_value is not str,
# so cast to object comparison to be safe
if is_string_dtype(arr):
arr = arr.astype(object)
if is_object_dtype(arr.dtype):
# element-wise equality check method in numpy doesn't treat
# each element type, eg. 0, 0.0, and False are treated as
# same. So we have to check the both of its type and value.
mask = splib.make_mask_object_ndarray(arr, fill_value)
else:
mask = arr != fill_value
length = len(arr)
if length != len(mask):
# the arr is a SparseArray
indices = mask.sp_index.indices
else:
indices = mask.nonzero()[0].astype(np.int32)
index = _make_index(length, indices, kind)
sparsified_values = arr[mask]
if dtype is not None:
sparsified_values = astype_nansafe(sparsified_values, dtype=dtype)
# TODO: copy
return sparsified_values, index, fill_value
def make_sparse(arr, kind='block', fill_value=None):
"""
Convert ndarray to sparse format
Parameters
----------
arr : ndarray
kind : {'block', 'integer'}
fill_value : NaN or another value
Returns
-------
(sparse_values, index) : (ndarray, SparseIndex)
"""
arr = _sanitize_values(arr)
if arr.ndim > 1:
raise TypeError("expected dimension <= 1 data")
if fill_value is None:
fill_value = na_value_for_dtype(arr.dtype)
if isna(fill_value):
mask = notna(arr)
else:
# For str arrays in NumPy 1.12.0, operator!= below isn't
# element-wise but just returns False if fill_value is not str,
# so cast to object comparison to be safe
if is_string_dtype(arr):
arr = arr.astype(object)
if is_object_dtype(arr.dtype):
# element-wise equality check method in numpy doesn't treat
# each element type, eg. 0, 0.0, and False are treated as
# same. So we have to check the both of its type and value.
mask = splib.make_mask_object_ndarray(arr, fill_value)
else:
mask = arr != fill_value
length = len(arr)
if length != mask.size:
# the arr is a SparseArray
indices = mask.sp_index.indices
else:
indices = mask.nonzero()[0].astype(np.int32)
index = _make_index(length, indices, kind)
sparsified_values = arr[mask]
return sparsified_values, index, fill_value
