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| import string
|
| import numpy as np
| import pytest
|
| import pandas.util._test_decorators as td
|
| import pandas as pd
| import pandas._testing as tm
| from pandas.core.arrays.sparse import (
| SparseArray,
| SparseDtype,
| )
|
|
| class TestSeriesAccessor:
| def test_to_dense(self):
| ser = pd.Series([0, 1, 0, 10], dtype="Sparse[int64]")
| result = ser.sparse.to_dense()
| expected = pd.Series([0, 1, 0, 10])
| tm.assert_series_equal(result, expected)
|
| @pytest.mark.parametrize("attr", ["npoints", "density", "fill_value", "sp_values"])
| def test_get_attributes(self, attr):
| arr = SparseArray([0, 1])
| ser = pd.Series(arr)
|
| result = getattr(ser.sparse, attr)
| expected = getattr(arr, attr)
| assert result == expected
|
| @td.skip_if_no_scipy
| def test_from_coo(self):
| import scipy.sparse
|
| row = [0, 3, 1, 0]
| col = [0, 3, 1, 2]
| data = [4, 5, 7, 9]
| # TODO(scipy#13585): Remove dtype when scipy is fixed
| # https://github.com/scipy/scipy/issues/13585
| sp_array = scipy.sparse.coo_matrix((data, (row, col)), dtype="int")
| result = pd.Series.sparse.from_coo(sp_array)
|
| index = pd.MultiIndex.from_arrays(
| [
| np.array([0, 0, 1, 3], dtype=np.int32),
| np.array([0, 2, 1, 3], dtype=np.int32),
| ],
| )
| expected = pd.Series([4, 9, 7, 5], index=index, dtype="Sparse[int]")
| tm.assert_series_equal(result, expected)
|
| @td.skip_if_no_scipy
| @pytest.mark.parametrize(
| "sort_labels, expected_rows, expected_cols, expected_values_pos",
| [
| (
| False,
| [("b", 2), ("a", 2), ("b", 1), ("a", 1)],
| [("z", 1), ("z", 2), ("x", 2), ("z", 0)],
| {1: (1, 0), 3: (3, 3)},
| ),
| (
| True,
| [("a", 1), ("a", 2), ("b", 1), ("b", 2)],
| [("x", 2), ("z", 0), ("z", 1), ("z", 2)],
| {1: (1, 2), 3: (0, 1)},
| ),
| ],
| )
| def test_to_coo(
| self, sort_labels, expected_rows, expected_cols, expected_values_pos
| ):
| import scipy.sparse
|
| values = SparseArray([0, np.nan, 1, 0, None, 3], fill_value=0)
| index = pd.MultiIndex.from_tuples(
| [
| ("b", 2, "z", 1),
| ("a", 2, "z", 2),
| ("a", 2, "z", 1),
| ("a", 2, "x", 2),
| ("b", 1, "z", 1),
| ("a", 1, "z", 0),
| ]
| )
| ss = pd.Series(values, index=index)
|
| expected_A = np.zeros((4, 4))
| for value, (row, col) in expected_values_pos.items():
| expected_A[row, col] = value
|
| A, rows, cols = ss.sparse.to_coo(
| row_levels=(0, 1), column_levels=(2, 3), sort_labels=sort_labels
| )
| assert isinstance(A, scipy.sparse.coo_matrix)
| tm.assert_numpy_array_equal(A.toarray(), expected_A)
| assert rows == expected_rows
| assert cols == expected_cols
|
| def test_non_sparse_raises(self):
| ser = pd.Series([1, 2, 3])
| with pytest.raises(AttributeError, match=".sparse"):
| ser.sparse.density
|
|
| class TestFrameAccessor:
| def test_accessor_raises(self):
| df = pd.DataFrame({"A": [0, 1]})
| with pytest.raises(AttributeError, match="sparse"):
| df.sparse
|
| @pytest.mark.parametrize("format", ["csc", "csr", "coo"])
| @pytest.mark.parametrize("labels", [None, list(string.ascii_letters[:10])])
| @pytest.mark.parametrize("dtype", ["float64", "int64"])
| @td.skip_if_no_scipy
| def test_from_spmatrix(self, format, labels, dtype):
| import scipy.sparse
|
| sp_dtype = SparseDtype(dtype, np.array(0, dtype=dtype).item())
|
| mat = scipy.sparse.eye(10, format=format, dtype=dtype)
| result = pd.DataFrame.sparse.from_spmatrix(mat, index=labels, columns=labels)
| expected = pd.DataFrame(
| np.eye(10, dtype=dtype), index=labels, columns=labels
| ).astype(sp_dtype)
| tm.assert_frame_equal(result, expected)
|
| @pytest.mark.parametrize("format", ["csc", "csr", "coo"])
| @td.skip_if_no_scipy
| def test_from_spmatrix_including_explicit_zero(self, format):
| import scipy.sparse
|
| mat = scipy.sparse.random(10, 2, density=0.5, format=format)
| mat.data[0] = 0
| result = pd.DataFrame.sparse.from_spmatrix(mat)
| dtype = SparseDtype("float64", 0.0)
| expected = pd.DataFrame(mat.todense()).astype(dtype)
| tm.assert_frame_equal(result, expected)
|
| @pytest.mark.parametrize(
| "columns",
| [["a", "b"], pd.MultiIndex.from_product([["A"], ["a", "b"]]), ["a", "a"]],
| )
| @td.skip_if_no_scipy
| def test_from_spmatrix_columns(self, columns):
| import scipy.sparse
|
| dtype = SparseDtype("float64", 0.0)
|
| mat = scipy.sparse.random(10, 2, density=0.5)
| result = pd.DataFrame.sparse.from_spmatrix(mat, columns=columns)
| expected = pd.DataFrame(mat.toarray(), columns=columns).astype(dtype)
| tm.assert_frame_equal(result, expected)
|
| @pytest.mark.parametrize(
| "colnames", [("A", "B"), (1, 2), (1, pd.NA), (0.1, 0.2), ("x", "x"), (0, 0)]
| )
| @td.skip_if_no_scipy
| def test_to_coo(self, colnames):
| import scipy.sparse
|
| df = pd.DataFrame(
| {colnames[0]: [0, 1, 0], colnames[1]: [1, 0, 0]}, dtype="Sparse[int64, 0]"
| )
| result = df.sparse.to_coo()
| expected = scipy.sparse.coo_matrix(np.asarray(df))
| assert (result != expected).nnz == 0
|
| @pytest.mark.parametrize("fill_value", [1, np.nan])
| @td.skip_if_no_scipy
| def test_to_coo_nonzero_fill_val_raises(self, fill_value):
| df = pd.DataFrame(
| {
| "A": SparseArray(
| [fill_value, fill_value, fill_value, 2], fill_value=fill_value
| ),
| "B": SparseArray(
| [fill_value, 2, fill_value, fill_value], fill_value=fill_value
| ),
| }
| )
| with pytest.raises(ValueError, match="fill value must be 0"):
| df.sparse.to_coo()
|
| @td.skip_if_no_scipy
| def test_to_coo_midx_categorical(self):
| # GH#50996
| import scipy.sparse
|
| midx = pd.MultiIndex.from_arrays(
| [
| pd.CategoricalIndex(list("ab"), name="x"),
| pd.CategoricalIndex([0, 1], name="y"),
| ]
| )
|
| ser = pd.Series(1, index=midx, dtype="Sparse[int]")
| result = ser.sparse.to_coo(row_levels=["x"], column_levels=["y"])[0]
| expected = scipy.sparse.coo_matrix(
| (np.array([1, 1]), (np.array([0, 1]), np.array([0, 1]))), shape=(2, 2)
| )
| assert (result != expected).nnz == 0
|
| def test_to_dense(self):
| df = pd.DataFrame(
| {
| "A": SparseArray([1, 0], dtype=SparseDtype("int64", 0)),
| "B": SparseArray([1, 0], dtype=SparseDtype("int64", 1)),
| "C": SparseArray([1.0, 0.0], dtype=SparseDtype("float64", 0.0)),
| },
| index=["b", "a"],
| )
| result = df.sparse.to_dense()
| expected = pd.DataFrame(
| {"A": [1, 0], "B": [1, 0], "C": [1.0, 0.0]}, index=["b", "a"]
| )
| tm.assert_frame_equal(result, expected)
|
| def test_density(self):
| df = pd.DataFrame(
| {
| "A": SparseArray([1, 0, 2, 1], fill_value=0),
| "B": SparseArray([0, 1, 1, 1], fill_value=0),
| }
| )
| res = df.sparse.density
| expected = 0.75
| assert res == expected
|
| @pytest.mark.parametrize("dtype", ["int64", "float64"])
| @pytest.mark.parametrize("dense_index", [True, False])
| @td.skip_if_no_scipy
| def test_series_from_coo(self, dtype, dense_index):
| import scipy.sparse
|
| A = scipy.sparse.eye(3, format="coo", dtype=dtype)
| result = pd.Series.sparse.from_coo(A, dense_index=dense_index)
|
| index = pd.MultiIndex.from_tuples(
| [
| np.array([0, 0], dtype=np.int32),
| np.array([1, 1], dtype=np.int32),
| np.array([2, 2], dtype=np.int32),
| ],
| )
| expected = pd.Series(SparseArray(np.array([1, 1, 1], dtype=dtype)), index=index)
| if dense_index:
| expected = expected.reindex(pd.MultiIndex.from_product(index.levels))
|
| tm.assert_series_equal(result, expected)
|
| @td.skip_if_no_scipy
| def test_series_from_coo_incorrect_format_raises(self):
| # gh-26554
| import scipy.sparse
|
| m = scipy.sparse.csr_matrix(np.array([[0, 1], [0, 0]]))
| with pytest.raises(
| TypeError, match="Expected coo_matrix. Got csr_matrix instead."
| ):
| pd.Series.sparse.from_coo(m)
|
| def test_with_column_named_sparse(self):
| # https://github.com/pandas-dev/pandas/issues/30758
| df = pd.DataFrame({"sparse": pd.arrays.SparseArray([1, 2])})
| assert isinstance(df.sparse, pd.core.arrays.sparse.accessor.SparseFrameAccessor)
|
|
