"""Sparse accessor""" from __future__ import annotations from typing import TYPE_CHECKING import numpy as np from pandas.compat._optional import import_optional_dependency from pandas.core.dtypes.cast import find_common_type from pandas.core.dtypes.dtypes import SparseDtype from pandas.core.accessor import ( PandasDelegate, delegate_names, ) from pandas.core.arrays.sparse.array import SparseArray if TYPE_CHECKING: from pandas import ( DataFrame, Series, ) class BaseAccessor: _validation_msg = "Can only use the '.sparse' accessor with Sparse data." def __init__(self, data=None) -> None: self._parent = data self._validate(data) def _validate(self, data): raise NotImplementedError @delegate_names( SparseArray, ["npoints", "density", "fill_value", "sp_values"], typ="property" ) class SparseAccessor(BaseAccessor, PandasDelegate): """ Accessor for SparseSparse from other sparse matrix data types. Examples -------- >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]") >>> ser.sparse.density 0.6 >>> ser.sparse.sp_values array([2, 2, 2]) """ def _validate(self, data): if not isinstance(data.dtype, SparseDtype): raise AttributeError(self._validation_msg) def _delegate_property_get(self, name: str, *args, **kwargs): return getattr(self._parent.array, name) def _delegate_method(self, name: str, *args, **kwargs): if name == "from_coo": return self.from_coo(*args, **kwargs) elif name == "to_coo": return self.to_coo(*args, **kwargs) else: raise ValueError @classmethod def from_coo(cls, A, dense_index: bool = False) -> Series: """ Create a Series with sparse values from a scipy.sparse.coo_matrix. Parameters ---------- A : scipy.sparse.coo_matrix dense_index : bool, default False If False (default), the index consists of only the coords of the non-null entries of the original coo_matrix. If True, the index consists of the full sorted (row, col) coordinates of the coo_matrix. Returns ------- s : Series A Series with sparse values. Examples -------- >>> from scipy import sparse >>> A = sparse.coo_matrix( ... ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4) ... ) >>> A <3x4 sparse matrix of type '' with 3 stored elements in COOrdinate format> >>> A.todense() matrix([[0., 0., 1., 2.], [3., 0., 0., 0.], [0., 0., 0., 0.]]) >>> ss = pd.Series.sparse.from_coo(A) >>> ss 0 2 1.0 3 2.0 1 0 3.0 dtype: Sparse[float64, nan] """ from pandas import Series from pandas.core.arrays.sparse.scipy_sparse import coo_to_sparse_series result = coo_to_sparse_series(A, dense_index=dense_index) result = Series(result.array, index=result.index, copy=False) return result def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels: bool = False): """ Create a scipy.sparse.coo_matrix from a Series with MultiIndex. Use row_levels and column_levels to determine the row and column coordinates respectively. row_levels and column_levels are the names (labels) or numbers of the levels. {row_levels, column_levels} must be a partition of the MultiIndex level names (or numbers). Parameters ---------- row_levels : tuple/list column_levels : tuple/list sort_labels : bool, default False Sort the row and column labels before forming the sparse matrix. When `row_levels` and/or `column_levels` refer to a single level, set to `True` for a faster execution. Returns ------- y : scipy.sparse.coo_matrix rows : list (row labels) columns : list (column labels) Examples -------- >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan]) >>> s.index = pd.MultiIndex.from_tuples( ... [ ... (1, 2, "a", 0), ... (1, 2, "a", 1), ... (1, 1, "b", 0), ... (1, 1, "b", 1), ... (2, 1, "b", 0), ... (2, 1, "b", 1) ... ], ... names=["A", "B", "C", "D"], ... ) >>> s A B C D 1 2 a 0 3.0 1 NaN 1 b 0 1.0 1 3.0 2 1 b 0 NaN 1 NaN dtype: float64 >>> ss = s.astype("Sparse") >>> ss A B C D 1 2 a 0 3.0 1 NaN 1 b 0 1.0 1 3.0 2 1 b 0 NaN 1 NaN dtype: Sparse[float64, nan] >>> A, rows, columns = ss.sparse.to_coo( ... row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True ... ) >>> A <3x4 sparse matrix of type '' with 3 stored elements in COOrdinate format> >>> A.todense() matrix([[0., 0., 1., 3.], [3., 0., 0., 0.], [0., 0., 0., 0.]]) >>> rows [(1, 1), (1, 2), (2, 1)] >>> columns [('a', 0), ('a', 1), ('b', 0), ('b', 1)] """ from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo A, rows, columns = sparse_series_to_coo( self._parent, row_levels, column_levels, sort_labels=sort_labels ) return A, rows, columns def to_dense(self) -> Series: """ Convert a Series from sparse values to dense. Returns ------- Series: A Series with the same values, stored as a dense array. Examples -------- >>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0])) >>> series 0 0 1 1 2 0 dtype: Sparse[int64, 0] >>> series.sparse.to_dense() 0 0 1 1 2 0 dtype: int64 """ from pandas import Series return Series( self._parent.array.to_dense(), index=self._parent.index, name=self._parent.name, copy=False, ) class SparseFrameAccessor(BaseAccessor, PandasDelegate): """ DataFrame accessor for sparse data. Examples -------- >>> df = pd.DataFrame({"a": [1, 2, 0, 0], ... "b": [3, 0, 0, 4]}, dtype="Sparse[int]") >>> df.sparse.density 0.5 """ def _validate(self, data): dtypes = data.dtypes if not all(isinstance(t, SparseDtype) for t in dtypes): raise AttributeError(self._validation_msg) @classmethod def from_spmatrix(cls, data, index=None, columns=None) -> DataFrame: """ Create a new DataFrame from a scipy sparse matrix. Parameters ---------- data : scipy.sparse.spmatrix Must be convertible to csc format. index, columns : Index, optional Row and column labels to use for the resulting DataFrame. Defaults to a RangeIndex. Returns ------- DataFrame Each column of the DataFrame is stored as a :class:`arrays.SparseArray`. Examples -------- >>> import scipy.sparse >>> mat = scipy.sparse.eye(3, dtype=float) >>> pd.DataFrame.sparse.from_spmatrix(mat) 0 1 2 0 1.0 0 0 1 0 1.0 0 2 0 0 1.0 """ from pandas._libs.sparse import IntIndex from pandas import DataFrame data = data.tocsc() index, columns = cls._prep_index(data, index, columns) n_rows, n_columns = data.shape # We need to make sure indices are sorted, as we create # IntIndex with no input validation (i.e. check_integrity=False ). # Indices may already be sorted in scipy in which case this adds # a small overhead. data.sort_indices() indices = data.indices indptr = data.indptr array_data = data.data dtype = SparseDtype(array_data.dtype, 0) arrays = [] for i in range(n_columns): sl = slice(indptr[i], indptr[i + 1]) idx = IntIndex(n_rows, indices[sl], check_integrity=False) arr = SparseArray._simple_new(array_data[sl], idx, dtype) arrays.append(arr) return DataFrame._from_arrays( arrays, columns=columns, index=index, verify_integrity=False ) def to_dense(self) -> DataFrame: """ Convert a DataFrame with sparse values to dense. Returns ------- DataFrame A DataFrame with the same values stored as dense arrays. Examples -------- >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])}) >>> df.sparse.to_dense() A 0 0 1 1 2 0 """ from pandas import DataFrame data = {k: v.array.to_dense() for k, v in self._parent.items()} return DataFrame(data, index=self._parent.index, columns=self._parent.columns) def to_coo(self): """ Return the contents of the frame as a sparse SciPy COO matrix. Returns ------- scipy.sparse.spmatrix If the caller is heterogeneous and contains booleans or objects, the result will be of dtype=object. See Notes. Notes ----- The dtype will be the lowest-common-denominator type (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. By numpy.find_common_type convention, mixing int64 and and uint64 will result in a float64 dtype. Examples -------- >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])}) >>> df.sparse.to_coo() <4x1 sparse matrix of type '' with 2 stored elements in COOrdinate format> """ import_optional_dependency("scipy") from scipy.sparse import coo_matrix dtype = find_common_type(self._parent.dtypes.to_list()) if isinstance(dtype, SparseDtype): dtype = dtype.subtype cols, rows, data = [], [], [] for col, (_, ser) in enumerate(self._parent.items()): sp_arr = ser.array if sp_arr.fill_value != 0: raise ValueError("fill value must be 0 when converting to COO matrix") row = sp_arr.sp_index.indices cols.append(np.repeat(col, len(row))) rows.append(row) data.append(sp_arr.sp_values.astype(dtype, copy=False)) cols = np.concatenate(cols) rows = np.concatenate(rows) data = np.concatenate(data) return coo_matrix((data, (rows, cols)), shape=self._parent.shape) @property def density(self) -> float: """ Ratio of non-sparse points to total (dense) data points. Examples -------- >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])}) >>> df.sparse.density 0.5 """ tmp = np.mean([column.array.density for _, column in self._parent.items()]) return tmp @staticmethod def _prep_index(data, index, columns): from pandas.core.indexes.api import ( default_index, ensure_index, ) N, K = data.shape if index is None: index = default_index(N) else: index = ensure_index(index) if columns is None: columns = default_index(K) else: columns = ensure_index(columns) if len(columns) != K: raise ValueError(f"Column length mismatch: {len(columns)} vs. {K}") if len(index) != N: raise ValueError(f"Index length mismatch: {len(index)} vs. {N}") return index, columns