481 lines
16 KiB
Python
481 lines
16 KiB
Python
|
import re
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from pandas._libs.sparse import IntIndex
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas import isna
|
||
|
|
import pandas._testing as tm
|
||
|
|
from pandas.core.arrays.sparse import (
|
||
|
|
SparseArray,
|
||
|
|
SparseDtype,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture
|
||
|
|
def arr_data():
|
||
|
|
"""Fixture returning numpy array with valid and missing entries"""
|
||
|
|
return np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture
|
||
|
|
def arr(arr_data):
|
||
|
|
"""Fixture returning SparseArray from 'arr_data'"""
|
||
|
|
return SparseArray(arr_data)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture
|
||
|
|
def zarr():
|
||
|
|
"""Fixture returning SparseArray with integer entries and 'fill_value=0'"""
|
||
|
|
return SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
|
||
|
|
|
||
|
|
|
||
|
|
class TestSparseArray:
|
||
|
|
@pytest.mark.parametrize("fill_value", [0, None, np.nan])
|
||
|
|
def test_shift_fill_value(self, fill_value):
|
||
|
|
# GH #24128
|
||
|
|
sparse = SparseArray(np.array([1, 0, 0, 3, 0]), fill_value=8.0)
|
||
|
|
res = sparse.shift(1, fill_value=fill_value)
|
||
|
|
if isna(fill_value):
|
||
|
|
fill_value = res.dtype.na_value
|
||
|
|
exp = SparseArray(np.array([fill_value, 1, 0, 0, 3]), fill_value=8.0)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
def test_set_fill_value(self):
|
||
|
|
arr = SparseArray([1.0, np.nan, 2.0], fill_value=np.nan)
|
||
|
|
arr.fill_value = 2
|
||
|
|
assert arr.fill_value == 2
|
||
|
|
|
||
|
|
arr = SparseArray([1, 0, 2], fill_value=0, dtype=np.int64)
|
||
|
|
arr.fill_value = 2
|
||
|
|
assert arr.fill_value == 2
|
||
|
|
|
||
|
|
# TODO: this seems fine? You can construct an integer
|
||
|
|
# sparsearray with NaN fill value, why not update one?
|
||
|
|
# coerces to int
|
||
|
|
# msg = "unable to set fill_value 3\\.1 to int64 dtype"
|
||
|
|
# with pytest.raises(ValueError, match=msg):
|
||
|
|
arr.fill_value = 3.1
|
||
|
|
assert arr.fill_value == 3.1
|
||
|
|
|
||
|
|
# msg = "unable to set fill_value nan to int64 dtype"
|
||
|
|
# with pytest.raises(ValueError, match=msg):
|
||
|
|
arr.fill_value = np.nan
|
||
|
|
assert np.isnan(arr.fill_value)
|
||
|
|
|
||
|
|
arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
|
||
|
|
arr.fill_value = True
|
||
|
|
assert arr.fill_value
|
||
|
|
|
||
|
|
# FIXME: don't leave commented-out
|
||
|
|
# coerces to bool
|
||
|
|
# TODO: we can construct an sparse array of bool
|
||
|
|
# type and use as fill_value any value
|
||
|
|
# msg = "fill_value must be True, False or nan"
|
||
|
|
# with pytest.raises(ValueError, match=msg):
|
||
|
|
# arr.fill_value = 0
|
||
|
|
|
||
|
|
# msg = "unable to set fill_value nan to bool dtype"
|
||
|
|
# with pytest.raises(ValueError, match=msg):
|
||
|
|
arr.fill_value = np.nan
|
||
|
|
assert np.isnan(arr.fill_value)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("val", [[1, 2, 3], np.array([1, 2]), (1, 2, 3)])
|
||
|
|
def test_set_fill_invalid_non_scalar(self, val):
|
||
|
|
arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
|
||
|
|
msg = "fill_value must be a scalar"
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
arr.fill_value = val
|
||
|
|
|
||
|
|
def test_copy(self, arr):
|
||
|
|
arr2 = arr.copy()
|
||
|
|
assert arr2.sp_values is not arr.sp_values
|
||
|
|
assert arr2.sp_index is arr.sp_index
|
||
|
|
|
||
|
|
def test_values_asarray(self, arr_data, arr):
|
||
|
|
tm.assert_almost_equal(arr.to_dense(), arr_data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data,shape,dtype",
|
||
|
|
[
|
||
|
|
([0, 0, 0, 0, 0], (5,), None),
|
||
|
|
([], (0,), None),
|
||
|
|
([0], (1,), None),
|
||
|
|
(["A", "A", np.nan, "B"], (4,), object),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_shape(self, data, shape, dtype):
|
||
|
|
# GH 21126
|
||
|
|
out = SparseArray(data, dtype=dtype)
|
||
|
|
assert out.shape == shape
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"vals",
|
||
|
|
[
|
||
|
|
[np.nan, np.nan, np.nan, np.nan, np.nan],
|
||
|
|
[1, np.nan, np.nan, 3, np.nan],
|
||
|
|
[1, np.nan, 0, 3, 0],
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("fill_value", [None, 0])
|
||
|
|
def test_dense_repr(self, vals, fill_value):
|
||
|
|
vals = np.array(vals)
|
||
|
|
arr = SparseArray(vals, fill_value=fill_value)
|
||
|
|
|
||
|
|
res = arr.to_dense()
|
||
|
|
tm.assert_numpy_array_equal(res, vals)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("fix", ["arr", "zarr"])
|
||
|
|
def test_pickle(self, fix, request):
|
||
|
|
obj = request.getfixturevalue(fix)
|
||
|
|
unpickled = tm.round_trip_pickle(obj)
|
||
|
|
tm.assert_sp_array_equal(unpickled, obj)
|
||
|
|
|
||
|
|
def test_generator_warnings(self):
|
||
|
|
sp_arr = SparseArray([1, 2, 3])
|
||
|
|
with tm.assert_produces_warning(None):
|
||
|
|
for _ in sp_arr:
|
||
|
|
pass
|
||
|
|
|
||
|
|
def test_where_retain_fill_value(self):
|
||
|
|
# GH#45691 don't lose fill_value on _where
|
||
|
|
arr = SparseArray([np.nan, 1.0], fill_value=0)
|
||
|
|
|
||
|
|
mask = np.array([True, False])
|
||
|
|
|
||
|
|
res = arr._where(~mask, 1)
|
||
|
|
exp = SparseArray([1, 1.0], fill_value=0)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
ser = pd.Series(arr)
|
||
|
|
res = ser.where(~mask, 1)
|
||
|
|
tm.assert_series_equal(res, pd.Series(exp))
|
||
|
|
|
||
|
|
def test_fillna(self):
|
||
|
|
s = SparseArray([1, np.nan, np.nan, 3, np.nan])
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1, dtype=np.float64)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([1, -1, -1, 3, -1], fill_value=0, dtype=np.float64)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
s = SparseArray([1, np.nan, 0, 3, 0])
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1, dtype=np.float64)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0)
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([1, -1, 0, 3, 0], fill_value=0, dtype=np.float64)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
s = SparseArray([np.nan, np.nan, np.nan, np.nan])
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([-1, -1, -1, -1], fill_value=-1, dtype=np.float64)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0)
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([-1, -1, -1, -1], fill_value=0, dtype=np.float64)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
# float dtype's fill_value is np.nan, replaced by -1
|
||
|
|
s = SparseArray([0.0, 0.0, 0.0, 0.0])
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([0.0, 0.0, 0.0, 0.0], fill_value=-1)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
# int dtype shouldn't have missing. No changes.
|
||
|
|
s = SparseArray([0, 0, 0, 0])
|
||
|
|
assert s.dtype == SparseDtype(np.int64)
|
||
|
|
assert s.fill_value == 0
|
||
|
|
res = s.fillna(-1)
|
||
|
|
tm.assert_sp_array_equal(res, s)
|
||
|
|
|
||
|
|
s = SparseArray([0, 0, 0, 0], fill_value=0)
|
||
|
|
assert s.dtype == SparseDtype(np.int64)
|
||
|
|
assert s.fill_value == 0
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([0, 0, 0, 0], fill_value=0)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
# fill_value can be nan if there is no missing hole.
|
||
|
|
# only fill_value will be changed
|
||
|
|
s = SparseArray([0, 0, 0, 0], fill_value=np.nan)
|
||
|
|
assert s.dtype == SparseDtype(np.int64, fill_value=np.nan)
|
||
|
|
assert np.isnan(s.fill_value)
|
||
|
|
res = s.fillna(-1)
|
||
|
|
exp = SparseArray([0, 0, 0, 0], fill_value=-1)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
def test_fillna_overlap(self):
|
||
|
|
s = SparseArray([1, np.nan, np.nan, 3, np.nan])
|
||
|
|
# filling with existing value doesn't replace existing value with
|
||
|
|
# fill_value, i.e. existing 3 remains in sp_values
|
||
|
|
res = s.fillna(3)
|
||
|
|
exp = np.array([1, 3, 3, 3, 3], dtype=np.float64)
|
||
|
|
tm.assert_numpy_array_equal(res.to_dense(), exp)
|
||
|
|
|
||
|
|
s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
|
||
|
|
res = s.fillna(3)
|
||
|
|
exp = SparseArray([1, 3, 3, 3, 3], fill_value=0, dtype=np.float64)
|
||
|
|
tm.assert_sp_array_equal(res, exp)
|
||
|
|
|
||
|
|
def test_nonzero(self):
|
||
|
|
# Tests regression #21172.
|
||
|
|
sa = SparseArray([float("nan"), float("nan"), 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
|
||
|
|
expected = np.array([2, 5, 9], dtype=np.int32)
|
||
|
|
(result,) = sa.nonzero()
|
||
|
|
tm.assert_numpy_array_equal(expected, result)
|
||
|
|
|
||
|
|
sa = SparseArray([0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
|
||
|
|
(result,) = sa.nonzero()
|
||
|
|
tm.assert_numpy_array_equal(expected, result)
|
||
|
|
|
||
|
|
|
||
|
|
class TestSparseArrayAnalytics:
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data,expected",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
np.array([1, 2, 3, 4, 5], dtype=float), # non-null data
|
||
|
|
SparseArray(np.array([1.0, 3.0, 6.0, 10.0, 15.0])),
|
||
|
|
),
|
||
|
|
(
|
||
|
|
np.array([1, 2, np.nan, 4, 5], dtype=float), # null data
|
||
|
|
SparseArray(np.array([1.0, 3.0, np.nan, 7.0, 12.0])),
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("numpy", [True, False])
|
||
|
|
def test_cumsum(self, data, expected, numpy):
|
||
|
|
cumsum = np.cumsum if numpy else lambda s: s.cumsum()
|
||
|
|
|
||
|
|
out = cumsum(SparseArray(data))
|
||
|
|
tm.assert_sp_array_equal(out, expected)
|
||
|
|
|
||
|
|
out = cumsum(SparseArray(data, fill_value=np.nan))
|
||
|
|
tm.assert_sp_array_equal(out, expected)
|
||
|
|
|
||
|
|
out = cumsum(SparseArray(data, fill_value=2))
|
||
|
|
tm.assert_sp_array_equal(out, expected)
|
||
|
|
|
||
|
|
if numpy: # numpy compatibility checks.
|
||
|
|
msg = "the 'dtype' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
np.cumsum(SparseArray(data), dtype=np.int64)
|
||
|
|
|
||
|
|
msg = "the 'out' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
np.cumsum(SparseArray(data), out=out)
|
||
|
|
else:
|
||
|
|
axis = 1 # SparseArray currently 1-D, so only axis = 0 is valid.
|
||
|
|
msg = re.escape(f"axis(={axis}) out of bounds")
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
SparseArray(data).cumsum(axis=axis)
|
||
|
|
|
||
|
|
def test_ufunc(self):
|
||
|
|
# GH 13853 make sure ufunc is applied to fill_value
|
||
|
|
sparse = SparseArray([1, np.nan, 2, np.nan, -2])
|
||
|
|
result = SparseArray([1, np.nan, 2, np.nan, 2])
|
||
|
|
tm.assert_sp_array_equal(abs(sparse), result)
|
||
|
|
tm.assert_sp_array_equal(np.abs(sparse), result)
|
||
|
|
|
||
|
|
sparse = SparseArray([1, -1, 2, -2], fill_value=1)
|
||
|
|
result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index, fill_value=1)
|
||
|
|
tm.assert_sp_array_equal(abs(sparse), result)
|
||
|
|
tm.assert_sp_array_equal(np.abs(sparse), result)
|
||
|
|
|
||
|
|
sparse = SparseArray([1, -1, 2, -2], fill_value=-1)
|
||
|
|
exp = SparseArray([1, 1, 2, 2], fill_value=1)
|
||
|
|
tm.assert_sp_array_equal(abs(sparse), exp)
|
||
|
|
tm.assert_sp_array_equal(np.abs(sparse), exp)
|
||
|
|
|
||
|
|
sparse = SparseArray([1, np.nan, 2, np.nan, -2])
|
||
|
|
result = SparseArray(np.sin([1, np.nan, 2, np.nan, -2]))
|
||
|
|
tm.assert_sp_array_equal(np.sin(sparse), result)
|
||
|
|
|
||
|
|
sparse = SparseArray([1, -1, 2, -2], fill_value=1)
|
||
|
|
result = SparseArray(np.sin([1, -1, 2, -2]), fill_value=np.sin(1))
|
||
|
|
tm.assert_sp_array_equal(np.sin(sparse), result)
|
||
|
|
|
||
|
|
sparse = SparseArray([1, -1, 0, -2], fill_value=0)
|
||
|
|
result = SparseArray(np.sin([1, -1, 0, -2]), fill_value=np.sin(0))
|
||
|
|
tm.assert_sp_array_equal(np.sin(sparse), result)
|
||
|
|
|
||
|
|
def test_ufunc_args(self):
|
||
|
|
# GH 13853 make sure ufunc is applied to fill_value, including its arg
|
||
|
|
sparse = SparseArray([1, np.nan, 2, np.nan, -2])
|
||
|
|
result = SparseArray([2, np.nan, 3, np.nan, -1])
|
||
|
|
tm.assert_sp_array_equal(np.add(sparse, 1), result)
|
||
|
|
|
||
|
|
sparse = SparseArray([1, -1, 2, -2], fill_value=1)
|
||
|
|
result = SparseArray([2, 0, 3, -1], fill_value=2)
|
||
|
|
tm.assert_sp_array_equal(np.add(sparse, 1), result)
|
||
|
|
|
||
|
|
sparse = SparseArray([1, -1, 0, -2], fill_value=0)
|
||
|
|
result = SparseArray([2, 0, 1, -1], fill_value=1)
|
||
|
|
tm.assert_sp_array_equal(np.add(sparse, 1), result)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("fill_value", [0.0, np.nan])
|
||
|
|
def test_modf(self, fill_value):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/26946
|
||
|
|
sparse = SparseArray([fill_value] * 10 + [1.1, 2.2], fill_value=fill_value)
|
||
|
|
r1, r2 = np.modf(sparse)
|
||
|
|
e1, e2 = np.modf(np.asarray(sparse))
|
||
|
|
tm.assert_sp_array_equal(r1, SparseArray(e1, fill_value=fill_value))
|
||
|
|
tm.assert_sp_array_equal(r2, SparseArray(e2, fill_value=fill_value))
|
||
|
|
|
||
|
|
def test_nbytes_integer(self):
|
||
|
|
arr = SparseArray([1, 0, 0, 0, 2], kind="integer")
|
||
|
|
result = arr.nbytes
|
||
|
|
# (2 * 8) + 2 * 4
|
||
|
|
assert result == 24
|
||
|
|
|
||
|
|
def test_nbytes_block(self):
|
||
|
|
arr = SparseArray([1, 2, 0, 0, 0], kind="block")
|
||
|
|
result = arr.nbytes
|
||
|
|
# (2 * 8) + 4 + 4
|
||
|
|
# sp_values, blocs, blengths
|
||
|
|
assert result == 24
|
||
|
|
|
||
|
|
def test_asarray_datetime64(self):
|
||
|
|
s = SparseArray(pd.to_datetime(["2012", None, None, "2013"]))
|
||
|
|
np.asarray(s)
|
||
|
|
|
||
|
|
def test_density(self):
|
||
|
|
arr = SparseArray([0, 1])
|
||
|
|
assert arr.density == 0.5
|
||
|
|
|
||
|
|
def test_npoints(self):
|
||
|
|
arr = SparseArray([0, 1])
|
||
|
|
assert arr.npoints == 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_setting_fill_value_fillna_still_works():
|
||
|
|
# This is why letting users update fill_value / dtype is bad
|
||
|
|
# astype has the same problem.
|
||
|
|
arr = SparseArray([1.0, np.nan, 1.0], fill_value=0.0)
|
||
|
|
arr.fill_value = np.nan
|
||
|
|
result = arr.isna()
|
||
|
|
# Can't do direct comparison, since the sp_index will be different
|
||
|
|
# So let's convert to ndarray and check there.
|
||
|
|
result = np.asarray(result)
|
||
|
|
|
||
|
|
expected = np.array([False, True, False])
|
||
|
|
tm.assert_numpy_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_setting_fill_value_updates():
|
||
|
|
arr = SparseArray([0.0, np.nan], fill_value=0)
|
||
|
|
arr.fill_value = np.nan
|
||
|
|
# use private constructor to get the index right
|
||
|
|
# otherwise both nans would be un-stored.
|
||
|
|
expected = SparseArray._simple_new(
|
||
|
|
sparse_array=np.array([np.nan]),
|
||
|
|
sparse_index=IntIndex(2, [1]),
|
||
|
|
dtype=SparseDtype(float, np.nan),
|
||
|
|
)
|
||
|
|
tm.assert_sp_array_equal(arr, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"arr,fill_value,loc",
|
||
|
|
[
|
||
|
|
([None, 1, 2], None, 0),
|
||
|
|
([0, None, 2], None, 1),
|
||
|
|
([0, 1, None], None, 2),
|
||
|
|
([0, 1, 1, None, None], None, 3),
|
||
|
|
([1, 1, 1, 2], None, -1),
|
||
|
|
([], None, -1),
|
||
|
|
([None, 1, 0, 0, None, 2], None, 0),
|
||
|
|
([None, 1, 0, 0, None, 2], 1, 1),
|
||
|
|
([None, 1, 0, 0, None, 2], 2, 5),
|
||
|
|
([None, 1, 0, 0, None, 2], 3, -1),
|
||
|
|
([None, 0, 0, 1, 2, 1], 0, 1),
|
||
|
|
([None, 0, 0, 1, 2, 1], 1, 3),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_first_fill_value_loc(arr, fill_value, loc):
|
||
|
|
result = SparseArray(arr, fill_value=fill_value)._first_fill_value_loc()
|
||
|
|
assert result == loc
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"arr",
|
||
|
|
[
|
||
|
|
[1, 2, np.nan, np.nan],
|
||
|
|
[1, np.nan, 2, np.nan],
|
||
|
|
[1, 2, np.nan],
|
||
|
|
[np.nan, 1, 0, 0, np.nan, 2],
|
||
|
|
[np.nan, 0, 0, 1, 2, 1],
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("fill_value", [np.nan, 0, 1])
|
||
|
|
def test_unique_na_fill(arr, fill_value):
|
||
|
|
a = SparseArray(arr, fill_value=fill_value).unique()
|
||
|
|
b = pd.Series(arr).unique()
|
||
|
|
assert isinstance(a, SparseArray)
|
||
|
|
a = np.asarray(a)
|
||
|
|
tm.assert_numpy_array_equal(a, b)
|
||
|
|
|
||
|
|
|
||
|
|
def test_unique_all_sparse():
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/23168
|
||
|
|
arr = SparseArray([0, 0])
|
||
|
|
result = arr.unique()
|
||
|
|
expected = SparseArray([0])
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map():
|
||
|
|
arr = SparseArray([0, 1, 2])
|
||
|
|
expected = SparseArray([10, 11, 12], fill_value=10)
|
||
|
|
|
||
|
|
# dict
|
||
|
|
result = arr.map({0: 10, 1: 11, 2: 12})
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
# series
|
||
|
|
result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
# function
|
||
|
|
result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
|
||
|
|
expected = SparseArray([10, 11, 12], fill_value=10)
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_map_missing():
|
||
|
|
arr = SparseArray([0, 1, 2])
|
||
|
|
expected = SparseArray([10, 11, None], fill_value=10)
|
||
|
|
|
||
|
|
result = arr.map({0: 10, 1: 11})
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("fill_value", [np.nan, 1])
|
||
|
|
def test_dropna(fill_value):
|
||
|
|
# GH-28287
|
||
|
|
arr = SparseArray([np.nan, 1], fill_value=fill_value)
|
||
|
|
exp = SparseArray([1.0], fill_value=fill_value)
|
||
|
|
tm.assert_sp_array_equal(arr.dropna(), exp)
|
||
|
|
|
||
|
|
df = pd.DataFrame({"a": [0, 1], "b": arr})
|
||
|
|
expected_df = pd.DataFrame({"a": [1], "b": exp}, index=pd.Index([1]))
|
||
|
|
tm.assert_equal(df.dropna(), expected_df)
|
||
|
|
|
||
|
|
|
||
|
|
def test_drop_duplicates_fill_value():
|
||
|
|
# GH 11726
|
||
|
|
df = pd.DataFrame(np.zeros((5, 5))).apply(lambda x: SparseArray(x, fill_value=0))
|
||
|
|
result = df.drop_duplicates()
|
||
|
|
expected = pd.DataFrame({i: SparseArray([0.0], fill_value=0) for i in range(5)})
|
||
|
|
tm.assert_frame_equal(result, expected)
|