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.
428 lines
14 KiB
428 lines
14 KiB
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.core.dtypes.dtypes import (
|
|
DatetimeTZDtype,
|
|
IntervalDtype,
|
|
PandasDtype,
|
|
PeriodDtype,
|
|
)
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
from pandas.tests.extension.base.base import BaseExtensionTests
|
|
|
|
|
|
class BaseSetitemTests(BaseExtensionTests):
|
|
@pytest.fixture(
|
|
params=[
|
|
lambda x: x.index,
|
|
lambda x: list(x.index),
|
|
lambda x: slice(None),
|
|
lambda x: slice(0, len(x)),
|
|
lambda x: range(len(x)),
|
|
lambda x: list(range(len(x))),
|
|
lambda x: np.ones(len(x), dtype=bool),
|
|
],
|
|
ids=[
|
|
"index",
|
|
"list[index]",
|
|
"null_slice",
|
|
"full_slice",
|
|
"range",
|
|
"list(range)",
|
|
"mask",
|
|
],
|
|
)
|
|
def full_indexer(self, request):
|
|
"""
|
|
Fixture for an indexer to pass to obj.loc to get/set the full length of the
|
|
object.
|
|
|
|
In some cases, assumes that obj.index is the default RangeIndex.
|
|
"""
|
|
return request.param
|
|
|
|
def test_setitem_scalar_series(self, data, box_in_series):
|
|
if box_in_series:
|
|
data = pd.Series(data)
|
|
data[0] = data[1]
|
|
assert data[0] == data[1]
|
|
|
|
def test_setitem_sequence(self, data, box_in_series):
|
|
if box_in_series:
|
|
data = pd.Series(data)
|
|
original = data.copy()
|
|
|
|
data[[0, 1]] = [data[1], data[0]]
|
|
assert data[0] == original[1]
|
|
assert data[1] == original[0]
|
|
|
|
def test_setitem_sequence_mismatched_length_raises(self, data, as_array):
|
|
ser = pd.Series(data)
|
|
original = ser.copy()
|
|
value = [data[0]]
|
|
if as_array:
|
|
value = data._from_sequence(value)
|
|
|
|
xpr = "cannot set using a {} indexer with a different length"
|
|
with pytest.raises(ValueError, match=xpr.format("list-like")):
|
|
ser[[0, 1]] = value
|
|
# Ensure no modifications made before the exception
|
|
self.assert_series_equal(ser, original)
|
|
|
|
with pytest.raises(ValueError, match=xpr.format("slice")):
|
|
ser[slice(3)] = value
|
|
self.assert_series_equal(ser, original)
|
|
|
|
def test_setitem_empty_indexer(self, data, box_in_series):
|
|
if box_in_series:
|
|
data = pd.Series(data)
|
|
original = data.copy()
|
|
data[np.array([], dtype=int)] = []
|
|
self.assert_equal(data, original)
|
|
|
|
def test_setitem_sequence_broadcasts(self, data, box_in_series):
|
|
if box_in_series:
|
|
data = pd.Series(data)
|
|
data[[0, 1]] = data[2]
|
|
assert data[0] == data[2]
|
|
assert data[1] == data[2]
|
|
|
|
@pytest.mark.parametrize("setter", ["loc", "iloc"])
|
|
def test_setitem_scalar(self, data, setter):
|
|
arr = pd.Series(data)
|
|
setter = getattr(arr, setter)
|
|
setter[0] = data[1]
|
|
assert arr[0] == data[1]
|
|
|
|
def test_setitem_loc_scalar_mixed(self, data):
|
|
df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
|
|
df.loc[0, "B"] = data[1]
|
|
assert df.loc[0, "B"] == data[1]
|
|
|
|
def test_setitem_loc_scalar_single(self, data):
|
|
df = pd.DataFrame({"B": data})
|
|
df.loc[10, "B"] = data[1]
|
|
assert df.loc[10, "B"] == data[1]
|
|
|
|
def test_setitem_loc_scalar_multiple_homogoneous(self, data):
|
|
df = pd.DataFrame({"A": data, "B": data})
|
|
df.loc[10, "B"] = data[1]
|
|
assert df.loc[10, "B"] == data[1]
|
|
|
|
def test_setitem_iloc_scalar_mixed(self, data):
|
|
df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
|
|
df.iloc[0, 1] = data[1]
|
|
assert df.loc[0, "B"] == data[1]
|
|
|
|
def test_setitem_iloc_scalar_single(self, data):
|
|
df = pd.DataFrame({"B": data})
|
|
df.iloc[10, 0] = data[1]
|
|
assert df.loc[10, "B"] == data[1]
|
|
|
|
def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
|
|
df = pd.DataFrame({"A": data, "B": data})
|
|
df.iloc[10, 1] = data[1]
|
|
assert df.loc[10, "B"] == data[1]
|
|
|
|
@pytest.mark.parametrize(
|
|
"mask",
|
|
[
|
|
np.array([True, True, True, False, False]),
|
|
pd.array([True, True, True, False, False], dtype="boolean"),
|
|
pd.array([True, True, True, pd.NA, pd.NA], dtype="boolean"),
|
|
],
|
|
ids=["numpy-array", "boolean-array", "boolean-array-na"],
|
|
)
|
|
def test_setitem_mask(self, data, mask, box_in_series):
|
|
arr = data[:5].copy()
|
|
expected = arr.take([0, 0, 0, 3, 4])
|
|
if box_in_series:
|
|
arr = pd.Series(arr)
|
|
expected = pd.Series(expected)
|
|
arr[mask] = data[0]
|
|
self.assert_equal(expected, arr)
|
|
|
|
def test_setitem_mask_raises(self, data, box_in_series):
|
|
# wrong length
|
|
mask = np.array([True, False])
|
|
|
|
if box_in_series:
|
|
data = pd.Series(data)
|
|
|
|
with pytest.raises(IndexError, match="wrong length"):
|
|
data[mask] = data[0]
|
|
|
|
mask = pd.array(mask, dtype="boolean")
|
|
with pytest.raises(IndexError, match="wrong length"):
|
|
data[mask] = data[0]
|
|
|
|
def test_setitem_mask_boolean_array_with_na(self, data, box_in_series):
|
|
mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
|
|
mask[:3] = True
|
|
mask[3:5] = pd.NA
|
|
|
|
if box_in_series:
|
|
data = pd.Series(data)
|
|
|
|
data[mask] = data[0]
|
|
|
|
assert (data[:3] == data[0]).all()
|
|
|
|
@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_setitem_integer_array(self, data, idx, box_in_series):
|
|
arr = data[:5].copy()
|
|
expected = data.take([0, 0, 0, 3, 4])
|
|
|
|
if box_in_series:
|
|
arr = pd.Series(arr)
|
|
expected = pd.Series(expected)
|
|
|
|
arr[idx] = arr[0]
|
|
self.assert_equal(arr, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"idx, box_in_series",
|
|
[
|
|
([0, 1, 2, pd.NA], False),
|
|
pytest.param(
|
|
[0, 1, 2, pd.NA], True, marks=pytest.mark.xfail(reason="GH-31948")
|
|
),
|
|
(pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
|
|
(pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
|
|
],
|
|
ids=["list-False", "list-True", "integer-array-False", "integer-array-True"],
|
|
)
|
|
def test_setitem_integer_with_missing_raises(self, data, idx, box_in_series):
|
|
arr = data.copy()
|
|
|
|
# TODO(xfail) this raises KeyError about labels not found (it tries label-based)
|
|
# for list of labels with Series
|
|
if box_in_series:
|
|
arr = pd.Series(data, index=[tm.rands(4) for _ in range(len(data))])
|
|
|
|
msg = "Cannot index with an integer indexer containing NA values"
|
|
with pytest.raises(ValueError, match=msg):
|
|
arr[idx] = arr[0]
|
|
|
|
@pytest.mark.parametrize("as_callable", [True, False])
|
|
@pytest.mark.parametrize("setter", ["loc", None])
|
|
def test_setitem_mask_aligned(self, data, as_callable, setter):
|
|
ser = pd.Series(data)
|
|
mask = np.zeros(len(data), dtype=bool)
|
|
mask[:2] = True
|
|
|
|
if as_callable:
|
|
mask2 = lambda x: mask
|
|
else:
|
|
mask2 = mask
|
|
|
|
if setter:
|
|
# loc
|
|
target = getattr(ser, setter)
|
|
else:
|
|
# Series.__setitem__
|
|
target = ser
|
|
|
|
target[mask2] = data[5:7]
|
|
|
|
ser[mask2] = data[5:7]
|
|
assert ser[0] == data[5]
|
|
assert ser[1] == data[6]
|
|
|
|
@pytest.mark.parametrize("setter", ["loc", None])
|
|
def test_setitem_mask_broadcast(self, data, setter):
|
|
ser = pd.Series(data)
|
|
mask = np.zeros(len(data), dtype=bool)
|
|
mask[:2] = True
|
|
|
|
if setter: # loc
|
|
target = getattr(ser, setter)
|
|
else: # __setitem__
|
|
target = ser
|
|
|
|
target[mask] = data[10]
|
|
assert ser[0] == data[10]
|
|
assert ser[1] == data[10]
|
|
|
|
def test_setitem_expand_columns(self, data):
|
|
df = pd.DataFrame({"A": data})
|
|
result = df.copy()
|
|
result["B"] = 1
|
|
expected = pd.DataFrame({"A": data, "B": [1] * len(data)})
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
result = df.copy()
|
|
result.loc[:, "B"] = 1
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
# overwrite with new type
|
|
result["B"] = data
|
|
expected = pd.DataFrame({"A": data, "B": data})
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
def test_setitem_expand_with_extension(self, data):
|
|
df = pd.DataFrame({"A": [1] * len(data)})
|
|
result = df.copy()
|
|
result["B"] = data
|
|
expected = pd.DataFrame({"A": [1] * len(data), "B": data})
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
result = df.copy()
|
|
result.loc[:, "B"] = data
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
def test_setitem_frame_invalid_length(self, data):
|
|
df = pd.DataFrame({"A": [1] * len(data)})
|
|
xpr = (
|
|
rf"Length of values \({len(data[:5])}\) "
|
|
rf"does not match length of index \({len(df)}\)"
|
|
)
|
|
with pytest.raises(ValueError, match=xpr):
|
|
df["B"] = data[:5]
|
|
|
|
def test_setitem_tuple_index(self, data):
|
|
ser = pd.Series(data[:2], index=[(0, 0), (0, 1)])
|
|
expected = pd.Series(data.take([1, 1]), index=ser.index)
|
|
ser[(0, 0)] = data[1]
|
|
self.assert_series_equal(ser, expected)
|
|
|
|
def test_setitem_slice(self, data, box_in_series):
|
|
arr = data[:5].copy()
|
|
expected = data.take([0, 0, 0, 3, 4])
|
|
if box_in_series:
|
|
arr = pd.Series(arr)
|
|
expected = pd.Series(expected)
|
|
|
|
arr[:3] = data[0]
|
|
self.assert_equal(arr, expected)
|
|
|
|
def test_setitem_loc_iloc_slice(self, data):
|
|
arr = data[:5].copy()
|
|
s = pd.Series(arr, index=["a", "b", "c", "d", "e"])
|
|
expected = pd.Series(data.take([0, 0, 0, 3, 4]), index=s.index)
|
|
|
|
result = s.copy()
|
|
result.iloc[:3] = data[0]
|
|
self.assert_equal(result, expected)
|
|
|
|
result = s.copy()
|
|
result.loc[:"c"] = data[0]
|
|
self.assert_equal(result, expected)
|
|
|
|
def test_setitem_slice_mismatch_length_raises(self, data):
|
|
arr = data[:5]
|
|
with pytest.raises(ValueError):
|
|
arr[:1] = arr[:2]
|
|
|
|
def test_setitem_slice_array(self, data):
|
|
arr = data[:5].copy()
|
|
arr[:5] = data[-5:]
|
|
self.assert_extension_array_equal(arr, data[-5:])
|
|
|
|
def test_setitem_scalar_key_sequence_raise(self, data):
|
|
arr = data[:5].copy()
|
|
with pytest.raises(ValueError):
|
|
arr[0] = arr[[0, 1]]
|
|
|
|
def test_setitem_preserves_views(self, data):
|
|
# GH#28150 setitem shouldn't swap the underlying data
|
|
view1 = data.view()
|
|
view2 = data[:]
|
|
|
|
data[0] = data[1]
|
|
assert view1[0] == data[1]
|
|
assert view2[0] == data[1]
|
|
|
|
def test_setitem_with_expansion_dataframe_column(self, data, full_indexer):
|
|
# https://github.com/pandas-dev/pandas/issues/32395
|
|
df = expected = pd.DataFrame({"data": pd.Series(data)})
|
|
result = pd.DataFrame(index=df.index)
|
|
|
|
key = full_indexer(df)
|
|
result.loc[key, "data"] = df["data"]
|
|
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
def test_setitem_with_expansion_row(self, data, na_value):
|
|
df = pd.DataFrame({"data": data[:1]})
|
|
|
|
df.loc[1, "data"] = data[1]
|
|
expected = pd.DataFrame({"data": data[:2]})
|
|
self.assert_frame_equal(df, expected)
|
|
|
|
# https://github.com/pandas-dev/pandas/issues/47284
|
|
df.loc[2, "data"] = na_value
|
|
expected = pd.DataFrame(
|
|
{"data": pd.Series([data[0], data[1], na_value], dtype=data.dtype)}
|
|
)
|
|
self.assert_frame_equal(df, expected)
|
|
|
|
def test_setitem_series(self, data, full_indexer):
|
|
# https://github.com/pandas-dev/pandas/issues/32395
|
|
ser = pd.Series(data, name="data")
|
|
result = pd.Series(index=ser.index, dtype=object, name="data")
|
|
|
|
# because result has object dtype, the attempt to do setting inplace
|
|
# is successful, and object dtype is retained
|
|
key = full_indexer(ser)
|
|
result.loc[key] = ser
|
|
|
|
expected = pd.Series(
|
|
data.astype(object), index=ser.index, name="data", dtype=object
|
|
)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_setitem_frame_2d_values(self, data, request):
|
|
# GH#44514
|
|
df = pd.DataFrame({"A": data})
|
|
|
|
# Avoiding using_array_manager fixture
|
|
# https://github.com/pandas-dev/pandas/pull/44514#discussion_r754002410
|
|
using_array_manager = isinstance(df._mgr, pd.core.internals.ArrayManager)
|
|
if using_array_manager:
|
|
if not isinstance(
|
|
data.dtype, (PandasDtype, PeriodDtype, IntervalDtype, DatetimeTZDtype)
|
|
):
|
|
# These dtypes have non-broken implementations of _can_hold_element
|
|
mark = pytest.mark.xfail(reason="Goes through split path, loses dtype")
|
|
request.node.add_marker(mark)
|
|
|
|
df = pd.DataFrame({"A": data})
|
|
orig = df.copy()
|
|
|
|
df.iloc[:] = df
|
|
self.assert_frame_equal(df, orig)
|
|
|
|
df.iloc[:-1] = df.iloc[:-1]
|
|
self.assert_frame_equal(df, orig)
|
|
|
|
df.iloc[:] = df.values
|
|
self.assert_frame_equal(df, orig)
|
|
|
|
df.iloc[:-1] = df.values[:-1]
|
|
self.assert_frame_equal(df, orig)
|
|
|
|
def test_delitem_series(self, data):
|
|
# GH#40763
|
|
ser = pd.Series(data, name="data")
|
|
|
|
taker = np.arange(len(ser))
|
|
taker = np.delete(taker, 1)
|
|
|
|
expected = ser[taker]
|
|
del ser[1]
|
|
self.assert_series_equal(ser, expected)
|
|
|
|
def test_setitem_invalid(self, data, invalid_scalar):
|
|
msg = "" # messages vary by subclass, so we do not test it
|
|
with pytest.raises((ValueError, TypeError), match=msg):
|
|
data[0] = invalid_scalar
|
|
|
|
with pytest.raises((ValueError, TypeError), match=msg):
|
|
data[:] = invalid_scalar
|
|
|