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