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755 lines
25 KiB
Python
755 lines
25 KiB
Python
""" test positional based indexing with iloc """
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from datetime import datetime
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from warnings import catch_warnings, simplefilter
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import DataFrame, Series, concat, date_range, isna
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import pandas._testing as tm
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from pandas.api.types import is_scalar
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from pandas.core.indexing import IndexingError
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from pandas.tests.indexing.common import Base
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class TestiLoc(Base):
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def test_iloc_getitem_int(self):
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# integer
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self.check_result(
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"iloc",
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2,
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typs=["labels", "mixed", "ts", "floats", "empty"],
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fails=IndexError,
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)
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def test_iloc_getitem_neg_int(self):
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# neg integer
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self.check_result(
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"iloc",
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-1,
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typs=["labels", "mixed", "ts", "floats", "empty"],
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fails=IndexError,
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)
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def test_iloc_getitem_list_int(self):
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self.check_result(
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"iloc",
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[0, 1, 2],
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typs=["labels", "mixed", "ts", "floats", "empty"],
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fails=IndexError,
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)
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# array of ints (GH5006), make sure that a single indexer is returning
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# the correct type
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class TestiLoc2:
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# TODO: better name, just separating out things that dont rely on base class
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def test_is_scalar_access(self):
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# GH#32085 index with duplicates doesnt matter for _is_scalar_access
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index = pd.Index([1, 2, 1])
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ser = pd.Series(range(3), index=index)
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assert ser.iloc._is_scalar_access((1,))
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df = ser.to_frame()
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assert df.iloc._is_scalar_access((1, 0,))
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def test_iloc_exceeds_bounds(self):
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# GH6296
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# iloc should allow indexers that exceed the bounds
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df = DataFrame(np.random.random_sample((20, 5)), columns=list("ABCDE"))
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# lists of positions should raise IndexError!
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msg = "positional indexers are out-of-bounds"
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with pytest.raises(IndexError, match=msg):
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df.iloc[:, [0, 1, 2, 3, 4, 5]]
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with pytest.raises(IndexError, match=msg):
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df.iloc[[1, 30]]
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with pytest.raises(IndexError, match=msg):
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df.iloc[[1, -30]]
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with pytest.raises(IndexError, match=msg):
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df.iloc[[100]]
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s = df["A"]
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with pytest.raises(IndexError, match=msg):
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s.iloc[[100]]
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with pytest.raises(IndexError, match=msg):
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s.iloc[[-100]]
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# still raise on a single indexer
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msg = "single positional indexer is out-of-bounds"
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with pytest.raises(IndexError, match=msg):
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df.iloc[30]
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with pytest.raises(IndexError, match=msg):
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df.iloc[-30]
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# GH10779
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# single positive/negative indexer exceeding Series bounds should raise
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# an IndexError
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with pytest.raises(IndexError, match=msg):
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s.iloc[30]
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with pytest.raises(IndexError, match=msg):
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s.iloc[-30]
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# slices are ok
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result = df.iloc[:, 4:10] # 0 < start < len < stop
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expected = df.iloc[:, 4:]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:, -4:-10] # stop < 0 < start < len
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expected = df.iloc[:, :0]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:, 10:4:-1] # 0 < stop < len < start (down)
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expected = df.iloc[:, :4:-1]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:, 4:-10:-1] # stop < 0 < start < len (down)
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expected = df.iloc[:, 4::-1]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:, -10:4] # start < 0 < stop < len
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expected = df.iloc[:, :4]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:, 10:4] # 0 < stop < len < start
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expected = df.iloc[:, :0]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:, -10:-11:-1] # stop < start < 0 < len (down)
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expected = df.iloc[:, :0]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:, 10:11] # 0 < len < start < stop
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expected = df.iloc[:, :0]
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tm.assert_frame_equal(result, expected)
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# slice bounds exceeding is ok
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result = s.iloc[18:30]
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expected = s.iloc[18:]
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tm.assert_series_equal(result, expected)
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result = s.iloc[30:]
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expected = s.iloc[:0]
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tm.assert_series_equal(result, expected)
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result = s.iloc[30::-1]
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expected = s.iloc[::-1]
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tm.assert_series_equal(result, expected)
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# doc example
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def check(result, expected):
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str(result)
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result.dtypes
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tm.assert_frame_equal(result, expected)
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dfl = DataFrame(np.random.randn(5, 2), columns=list("AB"))
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check(dfl.iloc[:, 2:3], DataFrame(index=dfl.index))
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check(dfl.iloc[:, 1:3], dfl.iloc[:, [1]])
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check(dfl.iloc[4:6], dfl.iloc[[4]])
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msg = "positional indexers are out-of-bounds"
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with pytest.raises(IndexError, match=msg):
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dfl.iloc[[4, 5, 6]]
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msg = "single positional indexer is out-of-bounds"
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with pytest.raises(IndexError, match=msg):
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dfl.iloc[:, 4]
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@pytest.mark.parametrize("index,columns", [(np.arange(20), list("ABCDE"))])
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@pytest.mark.parametrize(
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"index_vals,column_vals",
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[
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([slice(None), ["A", "D"]]),
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(["1", "2"], slice(None)),
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([datetime(2019, 1, 1)], slice(None)),
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],
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)
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def test_iloc_non_integer_raises(self, index, columns, index_vals, column_vals):
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# GH 25753
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df = DataFrame(
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np.random.randn(len(index), len(columns)), index=index, columns=columns
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)
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msg = ".iloc requires numeric indexers, got"
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with pytest.raises(IndexError, match=msg):
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df.iloc[index_vals, column_vals]
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@pytest.mark.parametrize("dims", [1, 2])
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def test_iloc_getitem_invalid_scalar(self, dims):
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# GH 21982
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if dims == 1:
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s = Series(np.arange(10))
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else:
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s = DataFrame(np.arange(100).reshape(10, 10))
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with pytest.raises(TypeError, match="Cannot index by location index"):
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s.iloc["a"]
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def test_iloc_array_not_mutating_negative_indices(self):
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# GH 21867
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array_with_neg_numbers = np.array([1, 2, -1])
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array_copy = array_with_neg_numbers.copy()
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df = pd.DataFrame(
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{"A": [100, 101, 102], "B": [103, 104, 105], "C": [106, 107, 108]},
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index=[1, 2, 3],
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)
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df.iloc[array_with_neg_numbers]
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tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy)
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df.iloc[:, array_with_neg_numbers]
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tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy)
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def test_iloc_getitem_neg_int_can_reach_first_index(self):
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# GH10547 and GH10779
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# negative integers should be able to reach index 0
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df = DataFrame({"A": [2, 3, 5], "B": [7, 11, 13]})
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s = df["A"]
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expected = df.iloc[0]
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result = df.iloc[-3]
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tm.assert_series_equal(result, expected)
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expected = df.iloc[[0]]
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result = df.iloc[[-3]]
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tm.assert_frame_equal(result, expected)
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expected = s.iloc[0]
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result = s.iloc[-3]
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assert result == expected
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expected = s.iloc[[0]]
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result = s.iloc[[-3]]
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tm.assert_series_equal(result, expected)
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# check the length 1 Series case highlighted in GH10547
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expected = Series(["a"], index=["A"])
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result = expected.iloc[[-1]]
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tm.assert_series_equal(result, expected)
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def test_iloc_getitem_dups(self):
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# GH 6766
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df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
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df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
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df = concat([df1, df2], axis=1)
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# cross-sectional indexing
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result = df.iloc[0, 0]
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assert isna(result)
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result = df.iloc[0, :]
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expected = Series([np.nan, 1, 3, 3], index=["A", "B", "A", "B"], name=0)
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tm.assert_series_equal(result, expected)
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def test_iloc_getitem_array(self):
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# TODO: test something here?
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pass
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def test_iloc_getitem_bool(self):
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# TODO: test something here?
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pass
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@pytest.mark.parametrize("index", [[True, False], [True, False, True, False]])
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def test_iloc_getitem_bool_diff_len(self, index):
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# GH26658
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s = Series([1, 2, 3])
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msg = f"Boolean index has wrong length: {len(index)} instead of {len(s)}"
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with pytest.raises(IndexError, match=msg):
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_ = s.iloc[index]
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def test_iloc_getitem_slice(self):
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# TODO: test something here?
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pass
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def test_iloc_getitem_slice_dups(self):
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df1 = DataFrame(np.random.randn(10, 4), columns=["A", "A", "B", "B"])
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df2 = DataFrame(
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np.random.randint(0, 10, size=20).reshape(10, 2), columns=["A", "C"]
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)
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# axis=1
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df = concat([df1, df2], axis=1)
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tm.assert_frame_equal(df.iloc[:, :4], df1)
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tm.assert_frame_equal(df.iloc[:, 4:], df2)
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df = concat([df2, df1], axis=1)
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tm.assert_frame_equal(df.iloc[:, :2], df2)
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tm.assert_frame_equal(df.iloc[:, 2:], df1)
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exp = concat([df2, df1.iloc[:, [0]]], axis=1)
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tm.assert_frame_equal(df.iloc[:, 0:3], exp)
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# axis=0
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df = concat([df, df], axis=0)
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tm.assert_frame_equal(df.iloc[0:10, :2], df2)
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tm.assert_frame_equal(df.iloc[0:10, 2:], df1)
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tm.assert_frame_equal(df.iloc[10:, :2], df2)
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tm.assert_frame_equal(df.iloc[10:, 2:], df1)
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def test_iloc_setitem(self):
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df = DataFrame(
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np.random.randn(4, 4), index=np.arange(0, 8, 2), columns=np.arange(0, 12, 3)
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)
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df.iloc[1, 1] = 1
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result = df.iloc[1, 1]
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assert result == 1
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df.iloc[:, 2:3] = 0
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expected = df.iloc[:, 2:3]
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result = df.iloc[:, 2:3]
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tm.assert_frame_equal(result, expected)
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# GH5771
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s = Series(0, index=[4, 5, 6])
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s.iloc[1:2] += 1
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expected = Series([0, 1, 0], index=[4, 5, 6])
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tm.assert_series_equal(s, expected)
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def test_iloc_setitem_list(self):
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# setitem with an iloc list
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df = DataFrame(
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np.arange(9).reshape((3, 3)), index=["A", "B", "C"], columns=["A", "B", "C"]
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)
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df.iloc[[0, 1], [1, 2]]
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df.iloc[[0, 1], [1, 2]] += 100
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expected = DataFrame(
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np.array([0, 101, 102, 3, 104, 105, 6, 7, 8]).reshape((3, 3)),
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index=["A", "B", "C"],
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columns=["A", "B", "C"],
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)
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tm.assert_frame_equal(df, expected)
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def test_iloc_setitem_pandas_object(self):
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# GH 17193
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s_orig = Series([0, 1, 2, 3])
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expected = Series([0, -1, -2, 3])
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s = s_orig.copy()
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s.iloc[Series([1, 2])] = [-1, -2]
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tm.assert_series_equal(s, expected)
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s = s_orig.copy()
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s.iloc[pd.Index([1, 2])] = [-1, -2]
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tm.assert_series_equal(s, expected)
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def test_iloc_setitem_dups(self):
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# GH 6766
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# iloc with a mask aligning from another iloc
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df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
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df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
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df = concat([df1, df2], axis=1)
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expected = df.fillna(3)
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inds = np.isnan(df.iloc[:, 0])
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mask = inds[inds].index
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df.iloc[mask, 0] = df.iloc[mask, 2]
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tm.assert_frame_equal(df, expected)
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# del a dup column across blocks
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expected = DataFrame({0: [1, 2], 1: [3, 4]})
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expected.columns = ["B", "B"]
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del df["A"]
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tm.assert_frame_equal(df, expected)
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# assign back to self
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df.iloc[[0, 1], [0, 1]] = df.iloc[[0, 1], [0, 1]]
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tm.assert_frame_equal(df, expected)
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# reversed x 2
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df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True)
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df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True)
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tm.assert_frame_equal(df, expected)
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# TODO: GH#27620 this test used to compare iloc against ix; check if this
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# is redundant with another test comparing iloc against loc
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def test_iloc_getitem_frame(self):
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df = DataFrame(
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np.random.randn(10, 4), index=range(0, 20, 2), columns=range(0, 8, 2)
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)
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result = df.iloc[2]
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exp = df.loc[4]
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tm.assert_series_equal(result, exp)
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result = df.iloc[2, 2]
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exp = df.loc[4, 4]
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assert result == exp
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# slice
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result = df.iloc[4:8]
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expected = df.loc[8:14]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:, 2:3]
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expected = df.loc[:, 4:5]
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tm.assert_frame_equal(result, expected)
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# list of integers
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result = df.iloc[[0, 1, 3]]
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expected = df.loc[[0, 2, 6]]
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tm.assert_frame_equal(result, expected)
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result = df.iloc[[0, 1, 3], [0, 1]]
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expected = df.loc[[0, 2, 6], [0, 2]]
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tm.assert_frame_equal(result, expected)
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# neg indices
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result = df.iloc[[-1, 1, 3], [-1, 1]]
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expected = df.loc[[18, 2, 6], [6, 2]]
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tm.assert_frame_equal(result, expected)
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# dups indices
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result = df.iloc[[-1, -1, 1, 3], [-1, 1]]
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expected = df.loc[[18, 18, 2, 6], [6, 2]]
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tm.assert_frame_equal(result, expected)
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# with index-like
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s = Series(index=range(1, 5), dtype=object)
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result = df.iloc[s.index]
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expected = df.loc[[2, 4, 6, 8]]
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tm.assert_frame_equal(result, expected)
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def test_iloc_getitem_labelled_frame(self):
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# try with labelled frame
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df = DataFrame(
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np.random.randn(10, 4), index=list("abcdefghij"), columns=list("ABCD")
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)
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result = df.iloc[1, 1]
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exp = df.loc["b", "B"]
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assert result == exp
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result = df.iloc[:, 2:3]
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expected = df.loc[:, ["C"]]
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tm.assert_frame_equal(result, expected)
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# negative indexing
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result = df.iloc[-1, -1]
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exp = df.loc["j", "D"]
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assert result == exp
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# out-of-bounds exception
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msg = "single positional indexer is out-of-bounds"
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with pytest.raises(IndexError, match=msg):
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df.iloc[10, 5]
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# trying to use a label
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msg = (
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r"Location based indexing can only have \[integer, integer "
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r"slice \(START point is INCLUDED, END point is EXCLUDED\), "
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r"listlike of integers, boolean array\] types"
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)
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with pytest.raises(ValueError, match=msg):
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df.iloc["j", "D"]
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def test_iloc_getitem_doc_issue(self):
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# multi axis slicing issue with single block
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# surfaced in GH 6059
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arr = np.random.randn(6, 4)
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index = date_range("20130101", periods=6)
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columns = list("ABCD")
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df = DataFrame(arr, index=index, columns=columns)
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# defines ref_locs
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df.describe()
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result = df.iloc[3:5, 0:2]
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str(result)
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result.dtypes
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expected = DataFrame(arr[3:5, 0:2], index=index[3:5], columns=columns[0:2])
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tm.assert_frame_equal(result, expected)
|
|
|
|
# for dups
|
|
df.columns = list("aaaa")
|
|
result = df.iloc[3:5, 0:2]
|
|
str(result)
|
|
result.dtypes
|
|
|
|
expected = DataFrame(arr[3:5, 0:2], index=index[3:5], columns=list("aa"))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# related
|
|
arr = np.random.randn(6, 4)
|
|
index = list(range(0, 12, 2))
|
|
columns = list(range(0, 8, 2))
|
|
df = DataFrame(arr, index=index, columns=columns)
|
|
|
|
df._mgr.blocks[0].mgr_locs
|
|
result = df.iloc[1:5, 2:4]
|
|
str(result)
|
|
result.dtypes
|
|
expected = DataFrame(arr[1:5, 2:4], index=index[1:5], columns=columns[2:4])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_iloc_setitem_series(self):
|
|
df = DataFrame(
|
|
np.random.randn(10, 4), index=list("abcdefghij"), columns=list("ABCD")
|
|
)
|
|
|
|
df.iloc[1, 1] = 1
|
|
result = df.iloc[1, 1]
|
|
assert result == 1
|
|
|
|
df.iloc[:, 2:3] = 0
|
|
expected = df.iloc[:, 2:3]
|
|
result = df.iloc[:, 2:3]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
s = Series(np.random.randn(10), index=range(0, 20, 2))
|
|
|
|
s.iloc[1] = 1
|
|
result = s.iloc[1]
|
|
assert result == 1
|
|
|
|
s.iloc[:4] = 0
|
|
expected = s.iloc[:4]
|
|
result = s.iloc[:4]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
s = Series([-1] * 6)
|
|
s.iloc[0::2] = [0, 2, 4]
|
|
s.iloc[1::2] = [1, 3, 5]
|
|
result = s
|
|
expected = Series([0, 1, 2, 3, 4, 5])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_iloc_setitem_list_of_lists(self):
|
|
|
|
# GH 7551
|
|
# list-of-list is set incorrectly in mixed vs. single dtyped frames
|
|
df = DataFrame(
|
|
dict(A=np.arange(5, dtype="int64"), B=np.arange(5, 10, dtype="int64"))
|
|
)
|
|
df.iloc[2:4] = [[10, 11], [12, 13]]
|
|
expected = DataFrame(dict(A=[0, 1, 10, 12, 4], B=[5, 6, 11, 13, 9]))
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
df = DataFrame(dict(A=list("abcde"), B=np.arange(5, 10, dtype="int64")))
|
|
df.iloc[2:4] = [["x", 11], ["y", 13]]
|
|
expected = DataFrame(dict(A=["a", "b", "x", "y", "e"], B=[5, 6, 11, 13, 9]))
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
@pytest.mark.parametrize("indexer", [[0], slice(None, 1, None), np.array([0])])
|
|
@pytest.mark.parametrize("value", [["Z"], np.array(["Z"])])
|
|
def test_iloc_setitem_with_scalar_index(self, indexer, value):
|
|
# GH #19474
|
|
# assigning like "df.iloc[0, [0]] = ['Z']" should be evaluated
|
|
# elementwisely, not using "setter('A', ['Z'])".
|
|
|
|
df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
|
|
df.iloc[0, indexer] = value
|
|
result = df.iloc[0, 0]
|
|
|
|
assert is_scalar(result) and result == "Z"
|
|
|
|
def test_iloc_mask(self):
|
|
|
|
# GH 3631, iloc with a mask (of a series) should raise
|
|
df = DataFrame(list(range(5)), index=list("ABCDE"), columns=["a"])
|
|
mask = df.a % 2 == 0
|
|
msg = "iLocation based boolean indexing cannot use an indexable as a mask"
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.iloc[mask]
|
|
mask.index = range(len(mask))
|
|
msg = "iLocation based boolean indexing on an integer type is not available"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
df.iloc[mask]
|
|
|
|
# ndarray ok
|
|
result = df.iloc[np.array([True] * len(mask), dtype=bool)]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
# the possibilities
|
|
locs = np.arange(4)
|
|
nums = 2 ** locs
|
|
reps = [bin(num) for num in nums]
|
|
df = DataFrame({"locs": locs, "nums": nums}, reps)
|
|
|
|
expected = {
|
|
(None, ""): "0b1100",
|
|
(None, ".loc"): "0b1100",
|
|
(None, ".iloc"): "0b1100",
|
|
("index", ""): "0b11",
|
|
("index", ".loc"): "0b11",
|
|
("index", ".iloc"): (
|
|
"iLocation based boolean indexing cannot use an indexable as a mask"
|
|
),
|
|
("locs", ""): "Unalignable boolean Series provided as indexer "
|
|
"(index of the boolean Series and of the indexed "
|
|
"object do not match).",
|
|
("locs", ".loc"): "Unalignable boolean Series provided as indexer "
|
|
"(index of the boolean Series and of the "
|
|
"indexed object do not match).",
|
|
("locs", ".iloc"): (
|
|
"iLocation based boolean indexing on an "
|
|
"integer type is not available"
|
|
),
|
|
}
|
|
|
|
# UserWarnings from reindex of a boolean mask
|
|
with catch_warnings(record=True):
|
|
simplefilter("ignore", UserWarning)
|
|
result = dict()
|
|
for idx in [None, "index", "locs"]:
|
|
mask = (df.nums > 2).values
|
|
if idx:
|
|
mask = Series(mask, list(reversed(getattr(df, idx))))
|
|
for method in ["", ".loc", ".iloc"]:
|
|
try:
|
|
if method:
|
|
accessor = getattr(df, method[1:])
|
|
else:
|
|
accessor = df
|
|
ans = str(bin(accessor[mask]["nums"].sum()))
|
|
except (ValueError, IndexingError, NotImplementedError) as e:
|
|
ans = str(e)
|
|
|
|
key = tuple([idx, method])
|
|
r = expected.get(key)
|
|
if r != ans:
|
|
raise AssertionError(
|
|
f"[{key}] does not match [{ans}], received [{r}]"
|
|
)
|
|
|
|
def test_iloc_non_unique_indexing(self):
|
|
|
|
# GH 4017, non-unique indexing (on the axis)
|
|
df = DataFrame({"A": [0.1] * 3000, "B": [1] * 3000})
|
|
idx = np.arange(30) * 99
|
|
expected = df.iloc[idx]
|
|
|
|
df3 = concat([df, 2 * df, 3 * df])
|
|
result = df3.iloc[idx]
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
df2 = DataFrame({"A": [0.1] * 1000, "B": [1] * 1000})
|
|
df2 = concat([df2, 2 * df2, 3 * df2])
|
|
|
|
with pytest.raises(KeyError, match="with any missing labels"):
|
|
df2.loc[idx]
|
|
|
|
def test_iloc_empty_list_indexer_is_ok(self):
|
|
|
|
df = tm.makeCustomDataframe(5, 2)
|
|
# vertical empty
|
|
tm.assert_frame_equal(
|
|
df.iloc[:, []],
|
|
df.iloc[:, :0],
|
|
check_index_type=True,
|
|
check_column_type=True,
|
|
)
|
|
# horizontal empty
|
|
tm.assert_frame_equal(
|
|
df.iloc[[], :],
|
|
df.iloc[:0, :],
|
|
check_index_type=True,
|
|
check_column_type=True,
|
|
)
|
|
# horizontal empty
|
|
tm.assert_frame_equal(
|
|
df.iloc[[]], df.iloc[:0, :], check_index_type=True, check_column_type=True
|
|
)
|
|
|
|
def test_identity_slice_returns_new_object(self):
|
|
# GH13873
|
|
original_df = DataFrame({"a": [1, 2, 3]})
|
|
sliced_df = original_df.iloc[:]
|
|
assert sliced_df is not original_df
|
|
|
|
# should be a shallow copy
|
|
original_df["a"] = [4, 4, 4]
|
|
assert (sliced_df["a"] == 4).all()
|
|
|
|
original_series = Series([1, 2, 3, 4, 5, 6])
|
|
sliced_series = original_series.iloc[:]
|
|
assert sliced_series is not original_series
|
|
|
|
# should also be a shallow copy
|
|
original_series[:3] = [7, 8, 9]
|
|
assert all(sliced_series[:3] == [7, 8, 9])
|
|
|
|
def test_indexing_zerodim_np_array(self):
|
|
# GH24919
|
|
df = DataFrame([[1, 2], [3, 4]])
|
|
result = df.iloc[np.array(0)]
|
|
s = pd.Series([1, 2], name=0)
|
|
tm.assert_series_equal(result, s)
|
|
|
|
def test_series_indexing_zerodim_np_array(self):
|
|
# GH24919
|
|
s = Series([1, 2])
|
|
result = s.iloc[np.array(0)]
|
|
assert result == 1
|
|
|
|
@pytest.mark.xfail(reason="https://github.com/pandas-dev/pandas/issues/33457")
|
|
def test_iloc_setitem_categorical_updates_inplace(self):
|
|
# Mixed dtype ensures we go through take_split_path in setitem_with_indexer
|
|
cat = pd.Categorical(["A", "B", "C"])
|
|
df = pd.DataFrame({1: cat, 2: [1, 2, 3]})
|
|
|
|
# This should modify our original values in-place
|
|
df.iloc[:, 0] = cat[::-1]
|
|
|
|
expected = pd.Categorical(["C", "B", "A"])
|
|
tm.assert_categorical_equal(cat, expected)
|
|
|
|
def test_iloc_with_boolean_operation(self):
|
|
# GH 20627
|
|
result = DataFrame([[0, 1], [2, 3], [4, 5], [6, np.nan]])
|
|
result.iloc[result.index <= 2] *= 2
|
|
expected = DataFrame([[0, 2], [4, 6], [8, 10], [6, np.nan]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result.iloc[result.index > 2] *= 2
|
|
expected = DataFrame([[0, 2], [4, 6], [8, 10], [12, np.nan]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result.iloc[[True, True, False, False]] *= 2
|
|
expected = DataFrame([[0, 4], [8, 12], [8, 10], [12, np.nan]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result.iloc[[False, False, True, True]] /= 2
|
|
expected = DataFrame([[0.0, 4.0], [8.0, 12.0], [4.0, 5.0], [6.0, np.nan]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
class TestILocSetItemDuplicateColumns:
|
|
def test_iloc_setitem_scalar_duplicate_columns(self):
|
|
# GH#15686, duplicate columns and mixed dtype
|
|
df1 = pd.DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
|
|
df2 = pd.DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
|
|
df = pd.concat([df1, df2], axis=1)
|
|
df.iloc[0, 0] = -1
|
|
|
|
assert df.iloc[0, 0] == -1
|
|
assert df.iloc[0, 2] == 3
|
|
assert df.dtypes.iloc[2] == np.int64
|
|
|
|
def test_iloc_setitem_list_duplicate_columns(self):
|
|
# GH#22036 setting with same-sized list
|
|
df = pd.DataFrame([[0, "str", "str2"]], columns=["a", "b", "b"])
|
|
|
|
df.iloc[:, 2] = ["str3"]
|
|
|
|
expected = pd.DataFrame([[0, "str", "str3"]], columns=["a", "b", "b"])
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_iloc_setitem_series_duplicate_columns(self):
|
|
df = pd.DataFrame(
|
|
np.arange(8, dtype=np.int64).reshape(2, 4), columns=["A", "B", "A", "B"]
|
|
)
|
|
df.iloc[:, 0] = df.iloc[:, 0].astype(np.float64)
|
|
assert df.dtypes.iloc[2] == np.int64
|