93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
import pytest
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"values, dtype",
|
|
[
|
|
([], "object"),
|
|
([1, 2, 3], "int64"),
|
|
([1.0, 2.0, 3.0], "float64"),
|
|
(["a", "b", "c"], "object"),
|
|
(["a", "b", "c"], "string"),
|
|
([1, 2, 3], "datetime64[ns]"),
|
|
([1, 2, 3], "datetime64[ns, CET]"),
|
|
([1, 2, 3], "timedelta64[ns]"),
|
|
(["2000", "2001", "2002"], "Period[D]"),
|
|
([1, 0, 3], "Sparse"),
|
|
([pd.Interval(0, 1), pd.Interval(1, 2), pd.Interval(3, 4)], "interval"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"mask", [[True, False, False], [True, True, True], [False, False, False]]
|
|
)
|
|
@pytest.mark.parametrize("indexer_class", [list, pd.array, pd.Index, pd.Series])
|
|
@pytest.mark.parametrize("frame", [True, False])
|
|
def test_series_mask_boolean(values, dtype, mask, indexer_class, frame):
|
|
# In case len(values) < 3
|
|
index = ["a", "b", "c"][: len(values)]
|
|
mask = mask[: len(values)]
|
|
|
|
obj = pd.Series(values, dtype=dtype, index=index)
|
|
if frame:
|
|
if len(values) == 0:
|
|
# Otherwise obj is an empty DataFrame with shape (0, 1)
|
|
obj = pd.DataFrame(dtype=dtype)
|
|
else:
|
|
obj = obj.to_frame()
|
|
|
|
if indexer_class is pd.array:
|
|
mask = pd.array(mask, dtype="boolean")
|
|
elif indexer_class is pd.Series:
|
|
mask = pd.Series(mask, index=obj.index, dtype="boolean")
|
|
else:
|
|
mask = indexer_class(mask)
|
|
|
|
expected = obj[mask]
|
|
|
|
result = obj[mask]
|
|
tm.assert_equal(result, expected)
|
|
|
|
if indexer_class is pd.Series:
|
|
msg = "iLocation based boolean indexing cannot use an indexable as a mask"
|
|
with pytest.raises(ValueError, match=msg):
|
|
result = obj.iloc[mask]
|
|
tm.assert_equal(result, expected)
|
|
else:
|
|
result = obj.iloc[mask]
|
|
tm.assert_equal(result, expected)
|
|
|
|
result = obj.loc[mask]
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("frame", [True, False])
|
|
def test_na_treated_as_false(frame):
|
|
# https://github.com/pandas-dev/pandas/issues/31503
|
|
s = pd.Series([1, 2, 3], name="name")
|
|
|
|
if frame:
|
|
s = s.to_frame()
|
|
|
|
mask = pd.array([True, False, None], dtype="boolean")
|
|
|
|
result = s[mask]
|
|
expected = s[mask.fillna(False)]
|
|
|
|
result_loc = s.loc[mask]
|
|
expected_loc = s.loc[mask.fillna(False)]
|
|
|
|
result_iloc = s.iloc[mask]
|
|
expected_iloc = s.iloc[mask.fillna(False)]
|
|
|
|
if frame:
|
|
tm.assert_frame_equal(result, expected)
|
|
tm.assert_frame_equal(result_loc, expected_loc)
|
|
tm.assert_frame_equal(result_iloc, expected_iloc)
|
|
else:
|
|
tm.assert_series_equal(result, expected)
|
|
tm.assert_series_equal(result_loc, expected_loc)
|
|
tm.assert_series_equal(result_iloc, expected_iloc)
|