You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
470 lines
15 KiB
470 lines
15 KiB
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
|
|
|
|
class BaseGetitemTests:
|
|
"""Tests for ExtensionArray.__getitem__."""
|
|
|
|
def test_iloc_series(self, data):
|
|
ser = pd.Series(data)
|
|
result = ser.iloc[:4]
|
|
expected = pd.Series(data[:4])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = ser.iloc[[0, 1, 2, 3]]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_iloc_frame(self, data):
|
|
df = pd.DataFrame({"A": data, "B": np.arange(len(data), dtype="int64")})
|
|
expected = pd.DataFrame({"A": data[:4]})
|
|
|
|
# slice -> frame
|
|
result = df.iloc[:4, [0]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# sequence -> frame
|
|
result = df.iloc[[0, 1, 2, 3], [0]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = pd.Series(data[:4], name="A")
|
|
|
|
# slice -> series
|
|
result = df.iloc[:4, 0]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# sequence -> series
|
|
result = df.iloc[:4, 0]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# GH#32959 slice columns with step
|
|
result = df.iloc[:, ::2]
|
|
tm.assert_frame_equal(result, df[["A"]])
|
|
result = df[["B", "A"]].iloc[:, ::2]
|
|
tm.assert_frame_equal(result, df[["B"]])
|
|
|
|
def test_iloc_frame_single_block(self, data):
|
|
# GH#32959 null slice along index, slice along columns with single-block
|
|
df = pd.DataFrame({"A": data})
|
|
|
|
result = df.iloc[:, :]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.iloc[:, :1]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.iloc[:, :2]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.iloc[:, ::2]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.iloc[:, 1:2]
|
|
tm.assert_frame_equal(result, df.iloc[:, :0])
|
|
|
|
result = df.iloc[:, -1:]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
def test_loc_series(self, data):
|
|
ser = pd.Series(data)
|
|
result = ser.loc[:3]
|
|
expected = pd.Series(data[:4])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = ser.loc[[0, 1, 2, 3]]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_frame(self, data):
|
|
df = pd.DataFrame({"A": data, "B": np.arange(len(data), dtype="int64")})
|
|
expected = pd.DataFrame({"A": data[:4]})
|
|
|
|
# slice -> frame
|
|
result = df.loc[:3, ["A"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# sequence -> frame
|
|
result = df.loc[[0, 1, 2, 3], ["A"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = pd.Series(data[:4], name="A")
|
|
|
|
# slice -> series
|
|
result = df.loc[:3, "A"]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# sequence -> series
|
|
result = df.loc[:3, "A"]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_iloc_frame_single_dtype(self, data):
|
|
# GH#27110 bug in ExtensionBlock.iget caused df.iloc[n] to incorrectly
|
|
# return a scalar
|
|
df = pd.DataFrame({"A": data})
|
|
expected = pd.Series([data[2]], index=["A"], name=2, dtype=data.dtype)
|
|
|
|
result = df.loc[2]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
expected = pd.Series(
|
|
[data[-1]], index=["A"], name=len(data) - 1, dtype=data.dtype
|
|
)
|
|
result = df.iloc[-1]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_getitem_scalar(self, data):
|
|
result = data[0]
|
|
assert isinstance(result, data.dtype.type)
|
|
|
|
result = pd.Series(data)[0]
|
|
assert isinstance(result, data.dtype.type)
|
|
|
|
def test_getitem_invalid(self, data):
|
|
# TODO: box over scalar, [scalar], (scalar,)?
|
|
|
|
msg = (
|
|
r"only integers, slices \(`:`\), ellipsis \(`...`\), numpy.newaxis "
|
|
r"\(`None`\) and integer or boolean arrays are valid indices"
|
|
)
|
|
with pytest.raises(IndexError, match=msg):
|
|
data["foo"]
|
|
with pytest.raises(IndexError, match=msg):
|
|
data[2.5]
|
|
|
|
ub = len(data)
|
|
msg = "|".join(
|
|
[
|
|
"list index out of range", # json
|
|
"index out of bounds", # pyarrow
|
|
"Out of bounds access", # Sparse
|
|
f"loc must be an integer between -{ub} and {ub}", # Sparse
|
|
f"index {ub+1} is out of bounds for axis 0 with size {ub}",
|
|
f"index -{ub+1} is out of bounds for axis 0 with size {ub}",
|
|
]
|
|
)
|
|
with pytest.raises(IndexError, match=msg):
|
|
data[ub + 1]
|
|
with pytest.raises(IndexError, match=msg):
|
|
data[-ub - 1]
|
|
|
|
def test_getitem_scalar_na(self, data_missing, na_cmp, na_value):
|
|
result = data_missing[0]
|
|
assert na_cmp(result, na_value)
|
|
|
|
def test_getitem_empty(self, data):
|
|
# Indexing with empty list
|
|
result = data[[]]
|
|
assert len(result) == 0
|
|
assert isinstance(result, type(data))
|
|
|
|
expected = data[np.array([], dtype="int64")]
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_getitem_mask(self, data):
|
|
# Empty mask, raw array
|
|
mask = np.zeros(len(data), dtype=bool)
|
|
result = data[mask]
|
|
assert len(result) == 0
|
|
assert isinstance(result, type(data))
|
|
|
|
# Empty mask, in series
|
|
mask = np.zeros(len(data), dtype=bool)
|
|
result = pd.Series(data)[mask]
|
|
assert len(result) == 0
|
|
assert result.dtype == data.dtype
|
|
|
|
# non-empty mask, raw array
|
|
mask[0] = True
|
|
result = data[mask]
|
|
assert len(result) == 1
|
|
assert isinstance(result, type(data))
|
|
|
|
# non-empty mask, in series
|
|
result = pd.Series(data)[mask]
|
|
assert len(result) == 1
|
|
assert result.dtype == data.dtype
|
|
|
|
def test_getitem_mask_raises(self, data):
|
|
mask = np.array([True, False])
|
|
msg = f"Boolean index has wrong length: 2 instead of {len(data)}"
|
|
with pytest.raises(IndexError, match=msg):
|
|
data[mask]
|
|
|
|
mask = pd.array(mask, dtype="boolean")
|
|
with pytest.raises(IndexError, match=msg):
|
|
data[mask]
|
|
|
|
def test_getitem_boolean_array_mask(self, data):
|
|
mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
|
|
result = data[mask]
|
|
assert len(result) == 0
|
|
assert isinstance(result, type(data))
|
|
|
|
result = pd.Series(data)[mask]
|
|
assert len(result) == 0
|
|
assert result.dtype == data.dtype
|
|
|
|
mask[:5] = True
|
|
expected = data.take([0, 1, 2, 3, 4])
|
|
result = data[mask]
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
expected = pd.Series(expected)
|
|
result = pd.Series(data)[mask]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_getitem_boolean_na_treated_as_false(self, data):
|
|
# https://github.com/pandas-dev/pandas/issues/31503
|
|
mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
|
|
mask[:2] = pd.NA
|
|
mask[2:4] = True
|
|
|
|
result = data[mask]
|
|
expected = data[mask.fillna(False)]
|
|
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
s = pd.Series(data)
|
|
|
|
result = s[mask]
|
|
expected = s[mask.fillna(False)]
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"idx",
|
|
[[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
|
|
ids=["list", "integer-array", "numpy-array"],
|
|
)
|
|
def test_getitem_integer_array(self, data, idx):
|
|
result = data[idx]
|
|
assert len(result) == 3
|
|
assert isinstance(result, type(data))
|
|
expected = data.take([0, 1, 2])
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
expected = pd.Series(expected)
|
|
result = pd.Series(data)[idx]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"idx",
|
|
[[0, 1, 2, pd.NA], pd.array([0, 1, 2, pd.NA], dtype="Int64")],
|
|
ids=["list", "integer-array"],
|
|
)
|
|
def test_getitem_integer_with_missing_raises(self, data, idx):
|
|
msg = "Cannot index with an integer indexer containing NA values"
|
|
with pytest.raises(ValueError, match=msg):
|
|
data[idx]
|
|
|
|
@pytest.mark.xfail(
|
|
reason="Tries label-based and raises KeyError; "
|
|
"in some cases raises when calling np.asarray"
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"idx",
|
|
[[0, 1, 2, pd.NA], pd.array([0, 1, 2, pd.NA], dtype="Int64")],
|
|
ids=["list", "integer-array"],
|
|
)
|
|
def test_getitem_series_integer_with_missing_raises(self, data, idx):
|
|
msg = "Cannot index with an integer indexer containing NA values"
|
|
# TODO: this raises KeyError about labels not found (it tries label-based)
|
|
|
|
ser = pd.Series(data, index=[chr(100 + i) for i in range(len(data))])
|
|
with pytest.raises(ValueError, match=msg):
|
|
ser[idx]
|
|
|
|
def test_getitem_slice(self, data):
|
|
# getitem[slice] should return an array
|
|
result = data[slice(0)] # empty
|
|
assert isinstance(result, type(data))
|
|
|
|
result = data[slice(1)] # scalar
|
|
assert isinstance(result, type(data))
|
|
|
|
def test_getitem_ellipsis_and_slice(self, data):
|
|
# GH#40353 this is called from slice_block_rows
|
|
result = data[..., :]
|
|
tm.assert_extension_array_equal(result, data)
|
|
|
|
result = data[:, ...]
|
|
tm.assert_extension_array_equal(result, data)
|
|
|
|
result = data[..., :3]
|
|
tm.assert_extension_array_equal(result, data[:3])
|
|
|
|
result = data[:3, ...]
|
|
tm.assert_extension_array_equal(result, data[:3])
|
|
|
|
result = data[..., ::2]
|
|
tm.assert_extension_array_equal(result, data[::2])
|
|
|
|
result = data[::2, ...]
|
|
tm.assert_extension_array_equal(result, data[::2])
|
|
|
|
def test_get(self, data):
|
|
# GH 20882
|
|
s = pd.Series(data, index=[2 * i for i in range(len(data))])
|
|
assert s.get(4) == s.iloc[2]
|
|
|
|
result = s.get([4, 6])
|
|
expected = s.iloc[[2, 3]]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.get(slice(2))
|
|
expected = s.iloc[[0, 1]]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
assert s.get(-1) is None
|
|
assert s.get(s.index.max() + 1) is None
|
|
|
|
s = pd.Series(data[:6], index=list("abcdef"))
|
|
assert s.get("c") == s.iloc[2]
|
|
|
|
result = s.get(slice("b", "d"))
|
|
expected = s.iloc[[1, 2, 3]]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.get("Z")
|
|
assert result is None
|
|
|
|
msg = "Series.__getitem__ treating keys as positions is deprecated"
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
assert s.get(4) == s.iloc[4]
|
|
assert s.get(-1) == s.iloc[-1]
|
|
assert s.get(len(s)) is None
|
|
|
|
# GH 21257
|
|
s = pd.Series(data)
|
|
with tm.assert_produces_warning(None):
|
|
# GH#45324 make sure we aren't giving a spurious FutureWarning
|
|
s2 = s[::2]
|
|
assert s2.get(1) is None
|
|
|
|
def test_take_sequence(self, data):
|
|
result = pd.Series(data)[[0, 1, 3]]
|
|
assert result.iloc[0] == data[0]
|
|
assert result.iloc[1] == data[1]
|
|
assert result.iloc[2] == data[3]
|
|
|
|
def test_take(self, data, na_value, na_cmp):
|
|
result = data.take([0, -1])
|
|
assert result.dtype == data.dtype
|
|
assert result[0] == data[0]
|
|
assert result[1] == data[-1]
|
|
|
|
result = data.take([0, -1], allow_fill=True, fill_value=na_value)
|
|
assert result[0] == data[0]
|
|
assert na_cmp(result[1], na_value)
|
|
|
|
with pytest.raises(IndexError, match="out of bounds"):
|
|
data.take([len(data) + 1])
|
|
|
|
def test_take_empty(self, data, na_value, na_cmp):
|
|
empty = data[:0]
|
|
|
|
result = empty.take([-1], allow_fill=True)
|
|
assert na_cmp(result[0], na_value)
|
|
|
|
msg = "cannot do a non-empty take from an empty axes|out of bounds"
|
|
|
|
with pytest.raises(IndexError, match=msg):
|
|
empty.take([-1])
|
|
|
|
with pytest.raises(IndexError, match="cannot do a non-empty take"):
|
|
empty.take([0, 1])
|
|
|
|
def test_take_negative(self, data):
|
|
# https://github.com/pandas-dev/pandas/issues/20640
|
|
n = len(data)
|
|
result = data.take([0, -n, n - 1, -1])
|
|
expected = data.take([0, 0, n - 1, n - 1])
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_take_non_na_fill_value(self, data_missing):
|
|
fill_value = data_missing[1] # valid
|
|
na = data_missing[0]
|
|
|
|
arr = data_missing._from_sequence(
|
|
[na, fill_value, na], dtype=data_missing.dtype
|
|
)
|
|
result = arr.take([-1, 1], fill_value=fill_value, allow_fill=True)
|
|
expected = arr.take([1, 1])
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_take_pandas_style_negative_raises(self, data, na_value):
|
|
with pytest.raises(ValueError, match=""):
|
|
data.take([0, -2], fill_value=na_value, allow_fill=True)
|
|
|
|
@pytest.mark.parametrize("allow_fill", [True, False])
|
|
def test_take_out_of_bounds_raises(self, data, allow_fill):
|
|
arr = data[:3]
|
|
|
|
with pytest.raises(IndexError, match="out of bounds|out-of-bounds"):
|
|
arr.take(np.asarray([0, 3]), allow_fill=allow_fill)
|
|
|
|
def test_take_series(self, data):
|
|
s = pd.Series(data)
|
|
result = s.take([0, -1])
|
|
expected = pd.Series(
|
|
data._from_sequence([data[0], data[len(data) - 1]], dtype=s.dtype),
|
|
index=[0, len(data) - 1],
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_reindex(self, data, na_value):
|
|
s = pd.Series(data)
|
|
result = s.reindex([0, 1, 3])
|
|
expected = pd.Series(data.take([0, 1, 3]), index=[0, 1, 3])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
n = len(data)
|
|
result = s.reindex([-1, 0, n])
|
|
expected = pd.Series(
|
|
data._from_sequence([na_value, data[0], na_value], dtype=s.dtype),
|
|
index=[-1, 0, n],
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.reindex([n, n + 1])
|
|
expected = pd.Series(
|
|
data._from_sequence([na_value, na_value], dtype=s.dtype), index=[n, n + 1]
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_reindex_non_na_fill_value(self, data_missing):
|
|
valid = data_missing[1]
|
|
na = data_missing[0]
|
|
|
|
arr = data_missing._from_sequence([na, valid], dtype=data_missing.dtype)
|
|
ser = pd.Series(arr)
|
|
result = ser.reindex([0, 1, 2], fill_value=valid)
|
|
expected = pd.Series(
|
|
data_missing._from_sequence([na, valid, valid], dtype=data_missing.dtype)
|
|
)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_len1(self, data):
|
|
# see GH-27785 take_nd with indexer of len 1 resulting in wrong ndim
|
|
df = pd.DataFrame({"A": data})
|
|
res = df.loc[[0], "A"]
|
|
assert res.ndim == 1
|
|
assert res._mgr.arrays[0].ndim == 1
|
|
if hasattr(res._mgr, "blocks"):
|
|
assert res._mgr._block.ndim == 1
|
|
|
|
def test_item(self, data):
|
|
# https://github.com/pandas-dev/pandas/pull/30175
|
|
s = pd.Series(data)
|
|
result = s[:1].item()
|
|
assert result == data[0]
|
|
|
|
msg = "can only convert an array of size 1 to a Python scalar"
|
|
with pytest.raises(ValueError, match=msg):
|
|
s[:0].item()
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
s.item()
|