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"""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
<COOrdinate sparse matrix of dtype 'float64'
with 3 stored elements and shape (3, 4)>
>>> 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
<COOrdinate sparse matrix of dtype 'float64'
with 3 stored elements and shape (3, 4)>
>>> 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()
<COOrdinate sparse matrix of dtype 'int64'
with 2 stored elements and shape (4, 1)>
"""
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
Sindbad File Manager Version 1.0, Coded By Sindbad EG ~ The Terrorists