490 lines
16 KiB
Python
490 lines
16 KiB
Python
import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.tests.extension.base.base import BaseExtensionTests
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class BaseGetitemTests(BaseExtensionTests):
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"""Tests for ExtensionArray.__getitem__."""
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def test_iloc_series(self, data):
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ser = pd.Series(data)
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result = ser.iloc[:4]
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expected = pd.Series(data[:4])
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self.assert_series_equal(result, expected)
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result = ser.iloc[[0, 1, 2, 3]]
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self.assert_series_equal(result, expected)
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def test_iloc_frame(self, data):
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df = pd.DataFrame({"A": data, "B": np.arange(len(data), dtype="int64")})
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expected = pd.DataFrame({"A": data[:4]})
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# slice -> frame
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result = df.iloc[:4, [0]]
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self.assert_frame_equal(result, expected)
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# sequence -> frame
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result = df.iloc[[0, 1, 2, 3], [0]]
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self.assert_frame_equal(result, expected)
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expected = pd.Series(data[:4], name="A")
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# slice -> series
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result = df.iloc[:4, 0]
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self.assert_series_equal(result, expected)
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# sequence -> series
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result = df.iloc[:4, 0]
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self.assert_series_equal(result, expected)
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# GH#32959 slice columns with step
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result = df.iloc[:, ::2]
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self.assert_frame_equal(result, df[["A"]])
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result = df[["B", "A"]].iloc[:, ::2]
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self.assert_frame_equal(result, df[["B"]])
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def test_iloc_frame_single_block(self, data):
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# GH#32959 null slice along index, slice along columns with single-block
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df = pd.DataFrame({"A": data})
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result = df.iloc[:, :]
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self.assert_frame_equal(result, df)
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result = df.iloc[:, :1]
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self.assert_frame_equal(result, df)
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result = df.iloc[:, :2]
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self.assert_frame_equal(result, df)
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result = df.iloc[:, ::2]
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self.assert_frame_equal(result, df)
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result = df.iloc[:, 1:2]
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self.assert_frame_equal(result, df.iloc[:, :0])
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result = df.iloc[:, -1:]
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self.assert_frame_equal(result, df)
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def test_loc_series(self, data):
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ser = pd.Series(data)
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result = ser.loc[:3]
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expected = pd.Series(data[:4])
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self.assert_series_equal(result, expected)
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result = ser.loc[[0, 1, 2, 3]]
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self.assert_series_equal(result, expected)
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def test_loc_frame(self, data):
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df = pd.DataFrame({"A": data, "B": np.arange(len(data), dtype="int64")})
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expected = pd.DataFrame({"A": data[:4]})
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# slice -> frame
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result = df.loc[:3, ["A"]]
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self.assert_frame_equal(result, expected)
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# sequence -> frame
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result = df.loc[[0, 1, 2, 3], ["A"]]
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self.assert_frame_equal(result, expected)
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expected = pd.Series(data[:4], name="A")
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# slice -> series
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result = df.loc[:3, "A"]
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self.assert_series_equal(result, expected)
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# sequence -> series
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result = df.loc[:3, "A"]
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self.assert_series_equal(result, expected)
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def test_loc_iloc_frame_single_dtype(self, data):
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# GH#27110 bug in ExtensionBlock.iget caused df.iloc[n] to incorrectly
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# return a scalar
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df = pd.DataFrame({"A": data})
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expected = pd.Series([data[2]], index=["A"], name=2, dtype=data.dtype)
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result = df.loc[2]
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self.assert_series_equal(result, expected)
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expected = pd.Series(
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[data[-1]], index=["A"], name=len(data) - 1, dtype=data.dtype
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)
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result = df.iloc[-1]
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self.assert_series_equal(result, expected)
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def test_getitem_scalar(self, data):
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result = data[0]
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assert isinstance(result, data.dtype.type)
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result = pd.Series(data)[0]
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assert isinstance(result, data.dtype.type)
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def test_getitem_invalid(self, data):
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# TODO: box over scalar, [scalar], (scalar,)?
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msg = (
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r"only integers, slices \(`:`\), ellipsis \(`...`\), numpy.newaxis "
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r"\(`None`\) and integer or boolean arrays are valid indices"
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)
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with pytest.raises(IndexError, match=msg):
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data["foo"]
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with pytest.raises(IndexError, match=msg):
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data[2.5]
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ub = len(data)
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msg = "|".join(
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[
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"list index out of range", # json
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"index out of bounds", # pyarrow
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"Out of bounds access", # Sparse
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f"loc must be an integer between -{ub} and {ub}", # Sparse
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f"index {ub+1} is out of bounds for axis 0 with size {ub}",
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f"index -{ub+1} is out of bounds for axis 0 with size {ub}",
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]
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)
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with pytest.raises(IndexError, match=msg):
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data[ub + 1]
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with pytest.raises(IndexError, match=msg):
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data[-ub - 1]
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def test_getitem_scalar_na(self, data_missing, na_cmp, na_value):
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result = data_missing[0]
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assert na_cmp(result, na_value)
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def test_getitem_empty(self, data):
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# Indexing with empty list
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result = data[[]]
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assert len(result) == 0
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assert isinstance(result, type(data))
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expected = data[np.array([], dtype="int64")]
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self.assert_extension_array_equal(result, expected)
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def test_getitem_mask(self, data):
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# Empty mask, raw array
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mask = np.zeros(len(data), dtype=bool)
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result = data[mask]
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assert len(result) == 0
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assert isinstance(result, type(data))
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# Empty mask, in series
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mask = np.zeros(len(data), dtype=bool)
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result = pd.Series(data)[mask]
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assert len(result) == 0
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assert result.dtype == data.dtype
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# non-empty mask, raw array
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mask[0] = True
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result = data[mask]
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assert len(result) == 1
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assert isinstance(result, type(data))
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# non-empty mask, in series
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result = pd.Series(data)[mask]
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assert len(result) == 1
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assert result.dtype == data.dtype
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def test_getitem_mask_raises(self, data):
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mask = np.array([True, False])
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msg = f"Boolean index has wrong length: 2 instead of {len(data)}"
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with pytest.raises(IndexError, match=msg):
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data[mask]
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mask = pd.array(mask, dtype="boolean")
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with pytest.raises(IndexError, match=msg):
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data[mask]
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def test_getitem_boolean_array_mask(self, data):
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mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
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result = data[mask]
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assert len(result) == 0
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assert isinstance(result, type(data))
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result = pd.Series(data)[mask]
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assert len(result) == 0
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assert result.dtype == data.dtype
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mask[:5] = True
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expected = data.take([0, 1, 2, 3, 4])
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result = data[mask]
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self.assert_extension_array_equal(result, expected)
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expected = pd.Series(expected)
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result = pd.Series(data)[mask]
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self.assert_series_equal(result, expected)
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def test_getitem_boolean_na_treated_as_false(self, data):
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# https://github.com/pandas-dev/pandas/issues/31503
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mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
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mask[:2] = pd.NA
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mask[2:4] = True
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result = data[mask]
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expected = data[mask.fillna(False)]
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self.assert_extension_array_equal(result, expected)
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s = pd.Series(data)
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result = s[mask]
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expected = s[mask.fillna(False)]
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self.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"idx",
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[[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
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ids=["list", "integer-array", "numpy-array"],
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)
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def test_getitem_integer_array(self, data, idx):
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result = data[idx]
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assert len(result) == 3
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assert isinstance(result, type(data))
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expected = data.take([0, 1, 2])
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self.assert_extension_array_equal(result, expected)
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expected = pd.Series(expected)
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result = pd.Series(data)[idx]
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self.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"idx",
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[[0, 1, 2, pd.NA], pd.array([0, 1, 2, pd.NA], dtype="Int64")],
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ids=["list", "integer-array"],
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)
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def test_getitem_integer_with_missing_raises(self, data, idx):
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msg = "Cannot index with an integer indexer containing NA values"
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with pytest.raises(ValueError, match=msg):
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data[idx]
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@pytest.mark.xfail(
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reason="Tries label-based and raises KeyError; "
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"in some cases raises when calling np.asarray"
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)
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@pytest.mark.parametrize(
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"idx",
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[[0, 1, 2, pd.NA], pd.array([0, 1, 2, pd.NA], dtype="Int64")],
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ids=["list", "integer-array"],
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)
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def test_getitem_series_integer_with_missing_raises(self, data, idx):
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msg = "Cannot index with an integer indexer containing NA values"
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# TODO: this raises KeyError about labels not found (it tries label-based)
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ser = pd.Series(data, index=[tm.rands(4) for _ in range(len(data))])
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with pytest.raises(ValueError, match=msg):
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ser[idx]
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def test_getitem_slice(self, data):
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# getitem[slice] should return an array
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result = data[slice(0)] # empty
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assert isinstance(result, type(data))
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result = data[slice(1)] # scalar
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assert isinstance(result, type(data))
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def test_getitem_ellipsis_and_slice(self, data):
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# GH#40353 this is called from getitem_block_index
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result = data[..., :]
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self.assert_extension_array_equal(result, data)
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result = data[:, ...]
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self.assert_extension_array_equal(result, data)
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result = data[..., :3]
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self.assert_extension_array_equal(result, data[:3])
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result = data[:3, ...]
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self.assert_extension_array_equal(result, data[:3])
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result = data[..., ::2]
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self.assert_extension_array_equal(result, data[::2])
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result = data[::2, ...]
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self.assert_extension_array_equal(result, data[::2])
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def test_get(self, data):
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# GH 20882
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s = pd.Series(data, index=[2 * i for i in range(len(data))])
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assert s.get(4) == s.iloc[2]
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result = s.get([4, 6])
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expected = s.iloc[[2, 3]]
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self.assert_series_equal(result, expected)
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with tm.assert_produces_warning(FutureWarning, match="label-based"):
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result = s.get(slice(2))
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expected = s.iloc[[0, 1]]
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self.assert_series_equal(result, expected)
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assert s.get(-1) is None
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assert s.get(s.index.max() + 1) is None
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s = pd.Series(data[:6], index=list("abcdef"))
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assert s.get("c") == s.iloc[2]
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result = s.get(slice("b", "d"))
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expected = s.iloc[[1, 2, 3]]
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self.assert_series_equal(result, expected)
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result = s.get("Z")
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assert result is None
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assert s.get(4) == s.iloc[4]
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assert s.get(-1) == s.iloc[-1]
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assert s.get(len(s)) is None
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# GH 21257
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s = pd.Series(data)
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with tm.assert_produces_warning(None):
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# GH#45324 make sure we aren't giving a spurious FutureWarning
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s2 = s[::2]
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assert s2.get(1) is None
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def test_take_sequence(self, data):
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result = pd.Series(data)[[0, 1, 3]]
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assert result.iloc[0] == data[0]
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assert result.iloc[1] == data[1]
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assert result.iloc[2] == data[3]
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def test_take(self, data, na_value, na_cmp):
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result = data.take([0, -1])
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assert result.dtype == data.dtype
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assert result[0] == data[0]
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assert result[1] == data[-1]
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result = data.take([0, -1], allow_fill=True, fill_value=na_value)
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assert result[0] == data[0]
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assert na_cmp(result[1], na_value)
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with pytest.raises(IndexError, match="out of bounds"):
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data.take([len(data) + 1])
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def test_take_empty(self, data, na_value, na_cmp):
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empty = data[:0]
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result = empty.take([-1], allow_fill=True)
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assert na_cmp(result[0], na_value)
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msg = "cannot do a non-empty take from an empty axes|out of bounds"
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with pytest.raises(IndexError, match=msg):
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empty.take([-1])
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with pytest.raises(IndexError, match="cannot do a non-empty take"):
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empty.take([0, 1])
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def test_take_negative(self, data):
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# https://github.com/pandas-dev/pandas/issues/20640
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n = len(data)
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result = data.take([0, -n, n - 1, -1])
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expected = data.take([0, 0, n - 1, n - 1])
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self.assert_extension_array_equal(result, expected)
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def test_take_non_na_fill_value(self, data_missing):
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fill_value = data_missing[1] # valid
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na = data_missing[0]
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arr = data_missing._from_sequence(
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[na, fill_value, na], dtype=data_missing.dtype
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)
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result = arr.take([-1, 1], fill_value=fill_value, allow_fill=True)
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expected = arr.take([1, 1])
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self.assert_extension_array_equal(result, expected)
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def test_take_pandas_style_negative_raises(self, data, na_value):
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with pytest.raises(ValueError, match=""):
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data.take([0, -2], fill_value=na_value, allow_fill=True)
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@pytest.mark.parametrize("allow_fill", [True, False])
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def test_take_out_of_bounds_raises(self, data, allow_fill):
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arr = data[:3]
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with pytest.raises(IndexError, match="out of bounds|out-of-bounds"):
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arr.take(np.asarray([0, 3]), allow_fill=allow_fill)
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def test_take_series(self, data):
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s = pd.Series(data)
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result = s.take([0, -1])
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expected = pd.Series(
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data._from_sequence([data[0], data[len(data) - 1]], dtype=s.dtype),
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index=[0, len(data) - 1],
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)
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self.assert_series_equal(result, expected)
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def test_reindex(self, data, na_value):
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s = pd.Series(data)
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result = s.reindex([0, 1, 3])
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expected = pd.Series(data.take([0, 1, 3]), index=[0, 1, 3])
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self.assert_series_equal(result, expected)
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n = len(data)
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result = s.reindex([-1, 0, n])
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expected = pd.Series(
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data._from_sequence([na_value, data[0], na_value], dtype=s.dtype),
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index=[-1, 0, n],
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)
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self.assert_series_equal(result, expected)
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result = s.reindex([n, n + 1])
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expected = pd.Series(
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data._from_sequence([na_value, na_value], dtype=s.dtype), index=[n, n + 1]
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)
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self.assert_series_equal(result, expected)
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def test_reindex_non_na_fill_value(self, data_missing):
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valid = data_missing[1]
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na = data_missing[0]
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arr = data_missing._from_sequence([na, valid], dtype=data_missing.dtype)
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ser = pd.Series(arr)
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result = ser.reindex([0, 1, 2], fill_value=valid)
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expected = pd.Series(
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data_missing._from_sequence([na, valid, valid], dtype=data_missing.dtype)
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)
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self.assert_series_equal(result, expected)
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def test_loc_len1(self, data):
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# see GH-27785 take_nd with indexer of len 1 resulting in wrong ndim
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df = pd.DataFrame({"A": data})
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res = df.loc[[0], "A"]
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assert res.ndim == 1
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assert res._mgr.arrays[0].ndim == 1
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if hasattr(res._mgr, "blocks"):
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assert res._mgr._block.ndim == 1
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def test_item(self, data):
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# https://github.com/pandas-dev/pandas/pull/30175
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s = pd.Series(data)
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result = s[:1].item()
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assert result == data[0]
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msg = "can only convert an array of size 1 to a Python scalar"
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with pytest.raises(ValueError, match=msg):
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s[:0].item()
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with pytest.raises(ValueError, match=msg):
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s.item()
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def test_ellipsis_index(self):
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# GH42430 1D slices over extension types turn into N-dimensional slices over
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# ExtensionArrays
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class CapturingStringArray(pd.arrays.StringArray):
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"""Extend StringArray to capture arguments to __getitem__"""
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def __getitem__(self, item):
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self.last_item_arg = item
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return super().__getitem__(item)
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df = pd.DataFrame(
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{"col1": CapturingStringArray(np.array(["hello", "world"], dtype=object))}
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)
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_ = df.iloc[:1]
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# String comparison because there's no native way to compare slices.
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# Before the fix for GH42430, last_item_arg would get set to the 2D slice
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# (Ellipsis, slice(None, 1, None))
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self.assert_equal(str(df["col1"].array.last_item_arg), "slice(None, 1, None)")
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