1485 lines
50 KiB
Python
1485 lines
50 KiB
Python
""" test positional based indexing with iloc """
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from datetime import datetime
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import re
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import numpy as np
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import pytest
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from pandas.errors import IndexingError
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import pandas.util._test_decorators as td
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from pandas import (
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NA,
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Categorical,
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CategoricalDtype,
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DataFrame,
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Index,
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Interval,
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NaT,
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Series,
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Timestamp,
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array,
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concat,
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date_range,
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interval_range,
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isna,
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to_datetime,
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)
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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.tests.indexing.common import check_indexing_smoketest_or_raises
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# We pass through the error message from numpy
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_slice_iloc_msg = re.escape(
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"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) "
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"and integer or boolean arrays are valid indices"
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)
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class TestiLoc:
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@pytest.mark.parametrize("key", [2, -1, [0, 1, 2]])
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@pytest.mark.parametrize("kind", ["series", "frame"])
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@pytest.mark.parametrize(
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"col",
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["labels", "mixed", "ts", "floats", "empty"],
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)
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def test_iloc_getitem_int_and_list_int(self, key, kind, col, request):
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obj = request.getfixturevalue(f"{kind}_{col}")
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check_indexing_smoketest_or_raises(
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obj,
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"iloc",
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key,
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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 TestiLocBaseIndependent:
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"""Tests Independent Of Base Class"""
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@pytest.mark.parametrize(
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"key",
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[
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slice(None),
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slice(3),
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range(3),
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[0, 1, 2],
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Index(range(3)),
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np.asarray([0, 1, 2]),
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],
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)
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@pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
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def test_iloc_setitem_fullcol_categorical(self, indexer, key, using_array_manager):
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frame = DataFrame({0: range(3)}, dtype=object)
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cat = Categorical(["alpha", "beta", "gamma"])
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if not using_array_manager:
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assert frame._mgr.blocks[0]._can_hold_element(cat)
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df = frame.copy()
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orig_vals = df.values
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indexer(df)[key, 0] = cat
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expected = DataFrame({0: cat}).astype(object)
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if not using_array_manager:
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assert np.shares_memory(df[0].values, orig_vals)
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tm.assert_frame_equal(df, expected)
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# check we dont have a view on cat (may be undesired GH#39986)
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df.iloc[0, 0] = "gamma"
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assert cat[0] != "gamma"
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# pre-2.0 with mixed dataframe ("split" path) we always overwrote the
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# column. as of 2.0 we correctly write "into" the column, so
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# we retain the object dtype.
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frame = DataFrame({0: np.array([0, 1, 2], dtype=object), 1: range(3)})
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df = frame.copy()
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indexer(df)[key, 0] = cat
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expected = DataFrame({0: Series(cat.astype(object), dtype=object), 1: range(3)})
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tm.assert_frame_equal(df, expected)
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@pytest.mark.parametrize("box", [array, Series])
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def test_iloc_setitem_ea_inplace(self, frame_or_series, box, using_copy_on_write):
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# GH#38952 Case with not setting a full column
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# IntegerArray without NAs
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arr = array([1, 2, 3, 4])
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obj = frame_or_series(arr.to_numpy("i8"))
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if frame_or_series is Series:
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values = obj.values
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else:
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values = obj._mgr.arrays[0]
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if frame_or_series is Series:
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obj.iloc[:2] = box(arr[2:])
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else:
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obj.iloc[:2, 0] = box(arr[2:])
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expected = frame_or_series(np.array([3, 4, 3, 4], dtype="i8"))
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tm.assert_equal(obj, expected)
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# Check that we are actually in-place
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if frame_or_series is Series:
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if using_copy_on_write:
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assert obj.values is not values
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assert np.shares_memory(obj.values, values)
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else:
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assert obj.values is values
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else:
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assert np.shares_memory(obj[0].values, values)
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def test_is_scalar_access(self):
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# GH#32085 index with duplicates doesn't matter for _is_scalar_access
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index = Index([1, 2, 1])
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ser = 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.default_rng(2).random((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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dfl = DataFrame(
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np.random.default_rng(2).standard_normal((5, 2)), columns=list("AB")
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)
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tm.assert_frame_equal(
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dfl.iloc[:, 2:3],
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DataFrame(index=dfl.index, columns=Index([], dtype=dfl.columns.dtype)),
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)
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tm.assert_frame_equal(dfl.iloc[:, 1:3], dfl.iloc[:, [1]])
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tm.assert_frame_equal(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.default_rng(2).standard_normal((len(index), len(columns))),
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index=index,
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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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def test_iloc_getitem_invalid_scalar(self, frame_or_series):
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# GH 21982
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obj = DataFrame(np.arange(100).reshape(10, 10))
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obj = tm.get_obj(obj, frame_or_series)
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with pytest.raises(TypeError, match="Cannot index by location index"):
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obj.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 = 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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df = DataFrame(
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[
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{"A": 1, "B": 2, "C": 3},
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{"A": 100, "B": 200, "C": 300},
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{"A": 1000, "B": 2000, "C": 3000},
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]
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)
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expected = DataFrame([{"A": 1, "B": 2, "C": 3}])
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tm.assert_frame_equal(df.iloc[[0]], expected)
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expected = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 100, "B": 200, "C": 300}])
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tm.assert_frame_equal(df.iloc[[0, 1]], expected)
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expected = DataFrame([{"B": 2, "C": 3}, {"B": 2000, "C": 3000}], index=[0, 2])
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result = df.iloc[[0, 2], [1, 2]]
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tm.assert_frame_equal(result, expected)
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def test_iloc_getitem_bool(self):
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df = DataFrame(
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[
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{"A": 1, "B": 2, "C": 3},
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{"A": 100, "B": 200, "C": 300},
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{"A": 1000, "B": 2000, "C": 3000},
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]
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)
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expected = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 100, "B": 200, "C": 300}])
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result = df.iloc[[True, True, False]]
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tm.assert_frame_equal(result, expected)
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expected = DataFrame(
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[{"A": 1, "B": 2, "C": 3}, {"A": 1000, "B": 2000, "C": 3000}], index=[0, 2]
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)
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result = df.iloc[lambda x: x.index % 2 == 0]
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tm.assert_frame_equal(result, expected)
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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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df = DataFrame(
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[
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{"A": 1, "B": 2, "C": 3},
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{"A": 100, "B": 200, "C": 300},
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{"A": 1000, "B": 2000, "C": 3000},
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]
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)
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expected = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 100, "B": 200, "C": 300}])
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result = df.iloc[:2]
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tm.assert_frame_equal(result, expected)
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expected = DataFrame([{"A": 100, "B": 200}], index=[1])
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result = df.iloc[1:2, 0:2]
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tm.assert_frame_equal(result, expected)
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expected = DataFrame(
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[{"A": 1, "C": 3}, {"A": 100, "C": 300}, {"A": 1000, "C": 3000}]
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)
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result = df.iloc[:, lambda df: [0, 2]]
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tm.assert_frame_equal(result, expected)
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def test_iloc_getitem_slice_dups(self):
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df1 = DataFrame(
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np.random.default_rng(2).standard_normal((10, 4)),
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columns=["A", "A", "B", "B"],
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)
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df2 = DataFrame(
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np.random.default_rng(2).integers(0, 10, size=20).reshape(10, 2),
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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, warn_copy_on_write):
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df = DataFrame(
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np.random.default_rng(2).standard_normal((4, 4)),
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index=np.arange(0, 8, 2),
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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_axis_argument(self):
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# GH45032
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df = DataFrame([[6, "c", 10], [7, "d", 11], [8, "e", 12]])
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df[1] = df[1].astype(object)
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expected = DataFrame([[6, "c", 10], [7, "d", 11], [5, 5, 5]])
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expected[1] = expected[1].astype(object)
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df.iloc(axis=0)[2] = 5
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tm.assert_frame_equal(df, expected)
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df = DataFrame([[6, "c", 10], [7, "d", 11], [8, "e", 12]])
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df[1] = df[1].astype(object)
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expected = DataFrame([[6, "c", 5], [7, "d", 5], [8, "e", 5]])
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expected[1] = expected[1].astype(object)
|
|
df.iloc(axis=1)[2] = 5
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_iloc_setitem_list(self):
|
|
# setitem with an iloc list
|
|
df = DataFrame(
|
|
np.arange(9).reshape((3, 3)), index=["A", "B", "C"], columns=["A", "B", "C"]
|
|
)
|
|
df.iloc[[0, 1], [1, 2]]
|
|
df.iloc[[0, 1], [1, 2]] += 100
|
|
|
|
expected = DataFrame(
|
|
np.array([0, 101, 102, 3, 104, 105, 6, 7, 8]).reshape((3, 3)),
|
|
index=["A", "B", "C"],
|
|
columns=["A", "B", "C"],
|
|
)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_iloc_setitem_pandas_object(self):
|
|
# GH 17193
|
|
s_orig = Series([0, 1, 2, 3])
|
|
expected = Series([0, -1, -2, 3])
|
|
|
|
s = s_orig.copy()
|
|
s.iloc[Series([1, 2])] = [-1, -2]
|
|
tm.assert_series_equal(s, expected)
|
|
|
|
s = s_orig.copy()
|
|
s.iloc[Index([1, 2])] = [-1, -2]
|
|
tm.assert_series_equal(s, expected)
|
|
|
|
def test_iloc_setitem_dups(self):
|
|
# GH 6766
|
|
# iloc with a mask aligning from another iloc
|
|
df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
|
|
df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
|
|
df = concat([df1, df2], axis=1)
|
|
|
|
expected = df.fillna(3)
|
|
inds = np.isnan(df.iloc[:, 0])
|
|
mask = inds[inds].index
|
|
df.iloc[mask, 0] = df.iloc[mask, 2]
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# del a dup column across blocks
|
|
expected = DataFrame({0: [1, 2], 1: [3, 4]})
|
|
expected.columns = ["B", "B"]
|
|
del df["A"]
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# assign back to self
|
|
df.iloc[[0, 1], [0, 1]] = df.iloc[[0, 1], [0, 1]]
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# reversed x 2
|
|
df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True)
|
|
df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_iloc_setitem_frame_duplicate_columns_multiple_blocks(
|
|
self, using_array_manager
|
|
):
|
|
# Same as the "assign back to self" check in test_iloc_setitem_dups
|
|
# but on a DataFrame with multiple blocks
|
|
df = DataFrame([[0, 1], [2, 3]], columns=["B", "B"])
|
|
|
|
# setting float values that can be held by existing integer arrays
|
|
# is inplace
|
|
df.iloc[:, 0] = df.iloc[:, 0].astype("f8")
|
|
if not using_array_manager:
|
|
assert len(df._mgr.blocks) == 1
|
|
|
|
# if the assigned values cannot be held by existing integer arrays,
|
|
# we cast
|
|
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
|
|
df.iloc[:, 0] = df.iloc[:, 0] + 0.5
|
|
if not using_array_manager:
|
|
assert len(df._mgr.blocks) == 2
|
|
|
|
expected = df.copy()
|
|
|
|
# assign back to self
|
|
df.iloc[[0, 1], [0, 1]] = df.iloc[[0, 1], [0, 1]]
|
|
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# TODO: GH#27620 this test used to compare iloc against ix; check if this
|
|
# is redundant with another test comparing iloc against loc
|
|
def test_iloc_getitem_frame(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((10, 4)),
|
|
index=range(0, 20, 2),
|
|
columns=range(0, 8, 2),
|
|
)
|
|
|
|
result = df.iloc[2]
|
|
exp = df.loc[4]
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
result = df.iloc[2, 2]
|
|
exp = df.loc[4, 4]
|
|
assert result == exp
|
|
|
|
# slice
|
|
result = df.iloc[4:8]
|
|
expected = df.loc[8:14]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.iloc[:, 2:3]
|
|
expected = df.loc[:, 4:5]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# list of integers
|
|
result = df.iloc[[0, 1, 3]]
|
|
expected = df.loc[[0, 2, 6]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.iloc[[0, 1, 3], [0, 1]]
|
|
expected = df.loc[[0, 2, 6], [0, 2]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# neg indices
|
|
result = df.iloc[[-1, 1, 3], [-1, 1]]
|
|
expected = df.loc[[18, 2, 6], [6, 2]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# dups indices
|
|
result = df.iloc[[-1, -1, 1, 3], [-1, 1]]
|
|
expected = df.loc[[18, 18, 2, 6], [6, 2]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# with index-like
|
|
s = Series(index=range(1, 5), dtype=object)
|
|
result = df.iloc[s.index]
|
|
expected = df.loc[[2, 4, 6, 8]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_iloc_getitem_labelled_frame(self):
|
|
# try with labelled frame
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((10, 4)),
|
|
index=list("abcdefghij"),
|
|
columns=list("ABCD"),
|
|
)
|
|
|
|
result = df.iloc[1, 1]
|
|
exp = df.loc["b", "B"]
|
|
assert result == exp
|
|
|
|
result = df.iloc[:, 2:3]
|
|
expected = df.loc[:, ["C"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# negative indexing
|
|
result = df.iloc[-1, -1]
|
|
exp = df.loc["j", "D"]
|
|
assert result == exp
|
|
|
|
# out-of-bounds exception
|
|
msg = "index 5 is out of bounds for axis 0 with size 4|index out of bounds"
|
|
with pytest.raises(IndexError, match=msg):
|
|
df.iloc[10, 5]
|
|
|
|
# trying to use a label
|
|
msg = (
|
|
r"Location based indexing can only have \[integer, integer "
|
|
r"slice \(START point is INCLUDED, END point is EXCLUDED\), "
|
|
r"listlike of integers, boolean array\] types"
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.iloc["j", "D"]
|
|
|
|
def test_iloc_getitem_doc_issue(self, using_array_manager):
|
|
# multi axis slicing issue with single block
|
|
# surfaced in GH 6059
|
|
|
|
arr = np.random.default_rng(2).standard_normal((6, 4))
|
|
index = date_range("20130101", periods=6)
|
|
columns = list("ABCD")
|
|
df = DataFrame(arr, index=index, columns=columns)
|
|
|
|
# defines ref_locs
|
|
df.describe()
|
|
|
|
result = df.iloc[3:5, 0:2]
|
|
|
|
expected = DataFrame(arr[3:5, 0:2], index=index[3:5], columns=columns[0:2])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# for dups
|
|
df.columns = list("aaaa")
|
|
result = df.iloc[3:5, 0:2]
|
|
|
|
expected = DataFrame(arr[3:5, 0:2], index=index[3:5], columns=list("aa"))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# related
|
|
arr = np.random.default_rng(2).standard_normal((6, 4))
|
|
index = list(range(0, 12, 2))
|
|
columns = list(range(0, 8, 2))
|
|
df = DataFrame(arr, index=index, columns=columns)
|
|
|
|
if not using_array_manager:
|
|
df._mgr.blocks[0].mgr_locs
|
|
result = df.iloc[1:5, 2:4]
|
|
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.default_rng(2).standard_normal((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.default_rng(2).standard_normal(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(
|
|
{"A": np.arange(5, dtype="int64"), "B": np.arange(5, 10, dtype="int64")}
|
|
)
|
|
df.iloc[2:4] = [[10, 11], [12, 13]]
|
|
expected = DataFrame({"A": [0, 1, 10, 12, 4], "B": [5, 6, 11, 13, 9]})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
df = DataFrame(
|
|
{"A": ["a", "b", "c", "d", "e"], "B": np.arange(5, 10, dtype="int64")}
|
|
)
|
|
df.iloc[2:4] = [["x", 11], ["y", 13]]
|
|
expected = DataFrame({"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'])".
|
|
|
|
# Set object type to avoid upcast when setting "Z"
|
|
df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]).astype({"A": object})
|
|
df.iloc[0, indexer] = value
|
|
result = df.iloc[0, 0]
|
|
|
|
assert is_scalar(result) and result == "Z"
|
|
|
|
@pytest.mark.filterwarnings("ignore::UserWarning")
|
|
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
|
|
for idx in [None, "index", "locs"]:
|
|
mask = (df.nums > 2).values
|
|
if idx:
|
|
mask_index = getattr(df, idx)[::-1]
|
|
mask = Series(mask, list(mask_index))
|
|
for method in ["", ".loc", ".iloc"]:
|
|
try:
|
|
if method:
|
|
accessor = getattr(df, method[1:])
|
|
else:
|
|
accessor = df
|
|
answer = str(bin(accessor[mask]["nums"].sum()))
|
|
except (ValueError, IndexingError, NotImplementedError) as err:
|
|
answer = str(err)
|
|
|
|
key = (
|
|
idx,
|
|
method,
|
|
)
|
|
r = expected.get(key)
|
|
if r != answer:
|
|
raise AssertionError(
|
|
f"[{key}] does not match [{answer}], 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="not in index"):
|
|
df2.loc[idx]
|
|
|
|
def test_iloc_empty_list_indexer_is_ok(self):
|
|
df = DataFrame(
|
|
np.ones((5, 2)),
|
|
index=Index([f"i-{i}" for i in range(5)], name="a"),
|
|
columns=Index([f"i-{i}" for i in range(2)], name="a"),
|
|
)
|
|
# 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, using_copy_on_write, warn_copy_on_write
|
|
):
|
|
# 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
|
|
assert np.shares_memory(original_df["a"], sliced_df["a"])
|
|
|
|
# Setting using .loc[:, "a"] sets inplace so alters both sliced and orig
|
|
# depending on CoW
|
|
with tm.assert_cow_warning(warn_copy_on_write):
|
|
original_df.loc[:, "a"] = [4, 4, 4]
|
|
if using_copy_on_write:
|
|
assert (sliced_df["a"] == [1, 2, 3]).all()
|
|
else:
|
|
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
|
|
with tm.assert_cow_warning(warn_copy_on_write):
|
|
original_series[:3] = [7, 8, 9]
|
|
if using_copy_on_write:
|
|
# shallow copy not updated (CoW)
|
|
assert all(sliced_series[:3] == [1, 2, 3])
|
|
else:
|
|
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 = 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
|
|
|
|
def test_iloc_setitem_categorical_updates_inplace(self):
|
|
# Mixed dtype ensures we go through take_split_path in setitem_with_indexer
|
|
cat = Categorical(["A", "B", "C"])
|
|
df = DataFrame({1: cat, 2: [1, 2, 3]}, copy=False)
|
|
|
|
assert tm.shares_memory(df[1], cat)
|
|
|
|
# With the enforcement of GH#45333 in 2.0, this modifies original
|
|
# values inplace
|
|
df.iloc[:, 0] = cat[::-1]
|
|
|
|
assert tm.shares_memory(df[1], cat)
|
|
expected = Categorical(["C", "B", "A"], categories=["A", "B", "C"])
|
|
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, 4.0], [8, 12.0], [4, 5.0], [6, np.nan]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_iloc_getitem_singlerow_slice_categoricaldtype_gives_series(self):
|
|
# GH#29521
|
|
df = DataFrame({"x": Categorical("a b c d e".split())})
|
|
result = df.iloc[0]
|
|
raw_cat = Categorical(["a"], categories=["a", "b", "c", "d", "e"])
|
|
expected = Series(raw_cat, index=["x"], name=0, dtype="category")
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_iloc_getitem_categorical_values(self):
|
|
# GH#14580
|
|
# test iloc() on Series with Categorical data
|
|
|
|
ser = Series([1, 2, 3]).astype("category")
|
|
|
|
# get slice
|
|
result = ser.iloc[0:2]
|
|
expected = Series([1, 2]).astype(CategoricalDtype([1, 2, 3]))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# get list of indexes
|
|
result = ser.iloc[[0, 1]]
|
|
expected = Series([1, 2]).astype(CategoricalDtype([1, 2, 3]))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# get boolean array
|
|
result = ser.iloc[[True, False, False]]
|
|
expected = Series([1]).astype(CategoricalDtype([1, 2, 3]))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("value", [None, NaT, np.nan])
|
|
def test_iloc_setitem_td64_values_cast_na(self, value):
|
|
# GH#18586
|
|
series = Series([0, 1, 2], dtype="timedelta64[ns]")
|
|
series.iloc[0] = value
|
|
expected = Series([NaT, 1, 2], dtype="timedelta64[ns]")
|
|
tm.assert_series_equal(series, expected)
|
|
|
|
@pytest.mark.parametrize("not_na", [Interval(0, 1), "a", 1.0])
|
|
def test_setitem_mix_of_nan_and_interval(self, not_na, nulls_fixture):
|
|
# GH#27937
|
|
dtype = CategoricalDtype(categories=[not_na])
|
|
ser = Series(
|
|
[nulls_fixture, nulls_fixture, nulls_fixture, nulls_fixture], dtype=dtype
|
|
)
|
|
ser.iloc[:3] = [nulls_fixture, not_na, nulls_fixture]
|
|
exp = Series([nulls_fixture, not_na, nulls_fixture, nulls_fixture], dtype=dtype)
|
|
tm.assert_series_equal(ser, exp)
|
|
|
|
def test_iloc_setitem_empty_frame_raises_with_3d_ndarray(self):
|
|
idx = Index([])
|
|
obj = DataFrame(
|
|
np.random.default_rng(2).standard_normal((len(idx), len(idx))),
|
|
index=idx,
|
|
columns=idx,
|
|
)
|
|
nd3 = np.random.default_rng(2).integers(5, size=(2, 2, 2))
|
|
|
|
msg = f"Cannot set values with ndim > {obj.ndim}"
|
|
with pytest.raises(ValueError, match=msg):
|
|
obj.iloc[nd3] = 0
|
|
|
|
@pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
|
|
def test_iloc_getitem_read_only_values(self, indexer):
|
|
# GH#10043 this is fundamentally a test for iloc, but test loc while
|
|
# we're here
|
|
rw_array = np.eye(10)
|
|
rw_df = DataFrame(rw_array)
|
|
|
|
ro_array = np.eye(10)
|
|
ro_array.setflags(write=False)
|
|
ro_df = DataFrame(ro_array)
|
|
|
|
tm.assert_frame_equal(indexer(rw_df)[[1, 2, 3]], indexer(ro_df)[[1, 2, 3]])
|
|
tm.assert_frame_equal(indexer(rw_df)[[1]], indexer(ro_df)[[1]])
|
|
tm.assert_series_equal(indexer(rw_df)[1], indexer(ro_df)[1])
|
|
tm.assert_frame_equal(indexer(rw_df)[1:3], indexer(ro_df)[1:3])
|
|
|
|
def test_iloc_getitem_readonly_key(self):
|
|
# GH#17192 iloc with read-only array raising TypeError
|
|
df = DataFrame({"data": np.ones(100, dtype="float64")})
|
|
indices = np.array([1, 3, 6])
|
|
indices.flags.writeable = False
|
|
|
|
result = df.iloc[indices]
|
|
expected = df.loc[[1, 3, 6]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df["data"].iloc[indices]
|
|
expected = df["data"].loc[[1, 3, 6]]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_iloc_assign_series_to_df_cell(self):
|
|
# GH 37593
|
|
df = DataFrame(columns=["a"], index=[0])
|
|
df.iloc[0, 0] = Series([1, 2, 3])
|
|
expected = DataFrame({"a": [Series([1, 2, 3])]}, columns=["a"], index=[0])
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
@pytest.mark.parametrize("klass", [list, np.array])
|
|
def test_iloc_setitem_bool_indexer(self, klass):
|
|
# GH#36741
|
|
df = DataFrame({"flag": ["x", "y", "z"], "value": [1, 3, 4]})
|
|
indexer = klass([True, False, False])
|
|
df.iloc[indexer, 1] = df.iloc[indexer, 1] * 2
|
|
expected = DataFrame({"flag": ["x", "y", "z"], "value": [2, 3, 4]})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
@pytest.mark.parametrize("indexer", [[1], slice(1, 2)])
|
|
def test_iloc_setitem_pure_position_based(self, indexer):
|
|
# GH#22046
|
|
df1 = DataFrame({"a2": [11, 12, 13], "b2": [14, 15, 16]})
|
|
df2 = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
|
|
df2.iloc[:, indexer] = df1.iloc[:, [0]]
|
|
expected = DataFrame({"a": [1, 2, 3], "b": [11, 12, 13], "c": [7, 8, 9]})
|
|
tm.assert_frame_equal(df2, expected)
|
|
|
|
def test_iloc_setitem_dictionary_value(self):
|
|
# GH#37728
|
|
df = DataFrame({"x": [1, 2], "y": [2, 2]})
|
|
rhs = {"x": 9, "y": 99}
|
|
df.iloc[1] = rhs
|
|
expected = DataFrame({"x": [1, 9], "y": [2, 99]})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# GH#38335 same thing, mixed dtypes
|
|
df = DataFrame({"x": [1, 2], "y": [2.0, 2.0]})
|
|
df.iloc[1] = rhs
|
|
expected = DataFrame({"x": [1, 9], "y": [2.0, 99.0]})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_iloc_getitem_float_duplicates(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((3, 3)),
|
|
index=[0.1, 0.2, 0.2],
|
|
columns=list("abc"),
|
|
)
|
|
expect = df.iloc[1:]
|
|
tm.assert_frame_equal(df.loc[0.2], expect)
|
|
|
|
expect = df.iloc[1:, 0]
|
|
tm.assert_series_equal(df.loc[0.2, "a"], expect)
|
|
|
|
df.index = [1, 0.2, 0.2]
|
|
expect = df.iloc[1:]
|
|
tm.assert_frame_equal(df.loc[0.2], expect)
|
|
|
|
expect = df.iloc[1:, 0]
|
|
tm.assert_series_equal(df.loc[0.2, "a"], expect)
|
|
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((4, 3)),
|
|
index=[1, 0.2, 0.2, 1],
|
|
columns=list("abc"),
|
|
)
|
|
expect = df.iloc[1:-1]
|
|
tm.assert_frame_equal(df.loc[0.2], expect)
|
|
|
|
expect = df.iloc[1:-1, 0]
|
|
tm.assert_series_equal(df.loc[0.2, "a"], expect)
|
|
|
|
df.index = [0.1, 0.2, 2, 0.2]
|
|
expect = df.iloc[[1, -1]]
|
|
tm.assert_frame_equal(df.loc[0.2], expect)
|
|
|
|
expect = df.iloc[[1, -1], 0]
|
|
tm.assert_series_equal(df.loc[0.2, "a"], expect)
|
|
|
|
def test_iloc_setitem_custom_object(self):
|
|
# iloc with an object
|
|
class TO:
|
|
def __init__(self, value) -> None:
|
|
self.value = value
|
|
|
|
def __str__(self) -> str:
|
|
return f"[{self.value}]"
|
|
|
|
__repr__ = __str__
|
|
|
|
def __eq__(self, other) -> bool:
|
|
return self.value == other.value
|
|
|
|
def view(self):
|
|
return self
|
|
|
|
df = DataFrame(index=[0, 1], columns=[0])
|
|
df.iloc[1, 0] = TO(1)
|
|
df.iloc[1, 0] = TO(2)
|
|
|
|
result = DataFrame(index=[0, 1], columns=[0])
|
|
result.iloc[1, 0] = TO(2)
|
|
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
# remains object dtype even after setting it back
|
|
df = DataFrame(index=[0, 1], columns=[0])
|
|
df.iloc[1, 0] = TO(1)
|
|
df.iloc[1, 0] = np.nan
|
|
result = DataFrame(index=[0, 1], columns=[0])
|
|
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
def test_iloc_getitem_with_duplicates(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).random((3, 3)),
|
|
columns=list("ABC"),
|
|
index=list("aab"),
|
|
)
|
|
|
|
result = df.iloc[0]
|
|
assert isinstance(result, Series)
|
|
tm.assert_almost_equal(result.values, df.values[0])
|
|
|
|
result = df.T.iloc[:, 0]
|
|
assert isinstance(result, Series)
|
|
tm.assert_almost_equal(result.values, df.values[0])
|
|
|
|
def test_iloc_getitem_with_duplicates2(self):
|
|
# GH#2259
|
|
df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1, 1, 2])
|
|
result = df.iloc[:, [0]]
|
|
expected = df.take([0], axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_iloc_interval(self):
|
|
# GH#17130
|
|
df = DataFrame({Interval(1, 2): [1, 2]})
|
|
|
|
result = df.iloc[0]
|
|
expected = Series({Interval(1, 2): 1}, name=0)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.iloc[:, 0]
|
|
expected = Series([1, 2], name=Interval(1, 2))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.copy()
|
|
result.iloc[:, 0] += 1
|
|
expected = DataFrame({Interval(1, 2): [2, 3]})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("indexing_func", [list, np.array])
|
|
@pytest.mark.parametrize("rhs_func", [list, np.array])
|
|
def test_loc_setitem_boolean_list(self, rhs_func, indexing_func):
|
|
# GH#20438 testing specifically list key, not arraylike
|
|
ser = Series([0, 1, 2])
|
|
ser.iloc[indexing_func([True, False, True])] = rhs_func([5, 10])
|
|
expected = Series([5, 1, 10])
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
df = DataFrame({"a": [0, 1, 2]})
|
|
df.iloc[indexing_func([True, False, True])] = rhs_func([[5], [10]])
|
|
expected = DataFrame({"a": [5, 1, 10]})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_iloc_getitem_slice_negative_step_ea_block(self):
|
|
# GH#44551
|
|
df = DataFrame({"A": [1, 2, 3]}, dtype="Int64")
|
|
|
|
res = df.iloc[:, ::-1]
|
|
tm.assert_frame_equal(res, df)
|
|
|
|
df["B"] = "foo"
|
|
res = df.iloc[:, ::-1]
|
|
expected = DataFrame({"B": df["B"], "A": df["A"]})
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
def test_iloc_setitem_2d_ndarray_into_ea_block(self):
|
|
# GH#44703
|
|
df = DataFrame({"status": ["a", "b", "c"]}, dtype="category")
|
|
df.iloc[np.array([0, 1]), np.array([0])] = np.array([["a"], ["a"]])
|
|
|
|
expected = DataFrame({"status": ["a", "a", "c"]}, dtype=df["status"].dtype)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
@td.skip_array_manager_not_yet_implemented
|
|
def test_iloc_getitem_int_single_ea_block_view(self):
|
|
# GH#45241
|
|
# TODO: make an extension interface test for this?
|
|
arr = interval_range(1, 10.0)._values
|
|
df = DataFrame(arr)
|
|
|
|
# ser should be a *view* on the DataFrame data
|
|
ser = df.iloc[2]
|
|
|
|
# if we have a view, then changing arr[2] should also change ser[0]
|
|
assert arr[2] != arr[-1] # otherwise the rest isn't meaningful
|
|
arr[2] = arr[-1]
|
|
assert ser[0] == arr[-1]
|
|
|
|
def test_iloc_setitem_multicolumn_to_datetime(self, using_infer_string):
|
|
# GH#20511
|
|
df = DataFrame({"A": ["2022-01-01", "2022-01-02"], "B": ["2021", "2022"]})
|
|
|
|
if using_infer_string:
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Setting an item of incompatible dtype"
|
|
):
|
|
df.iloc[:, [0]] = DataFrame({"A": to_datetime(["2021", "2022"])})
|
|
else:
|
|
df.iloc[:, [0]] = DataFrame({"A": to_datetime(["2021", "2022"])})
|
|
expected = DataFrame(
|
|
{
|
|
"A": [
|
|
Timestamp("2021-01-01 00:00:00"),
|
|
Timestamp("2022-01-01 00:00:00"),
|
|
],
|
|
"B": ["2021", "2022"],
|
|
}
|
|
)
|
|
tm.assert_frame_equal(df, expected, check_dtype=False)
|
|
|
|
|
|
class TestILocErrors:
|
|
# NB: this test should work for _any_ Series we can pass as
|
|
# series_with_simple_index
|
|
def test_iloc_float_raises(
|
|
self, series_with_simple_index, frame_or_series, warn_copy_on_write
|
|
):
|
|
# GH#4892
|
|
# float_indexers should raise exceptions
|
|
# on appropriate Index types & accessors
|
|
# this duplicates the code below
|
|
# but is specifically testing for the error
|
|
# message
|
|
|
|
obj = series_with_simple_index
|
|
if frame_or_series is DataFrame:
|
|
obj = obj.to_frame()
|
|
|
|
msg = "Cannot index by location index with a non-integer key"
|
|
with pytest.raises(TypeError, match=msg):
|
|
obj.iloc[3.0]
|
|
|
|
with pytest.raises(IndexError, match=_slice_iloc_msg):
|
|
with tm.assert_cow_warning(
|
|
warn_copy_on_write and frame_or_series is DataFrame
|
|
):
|
|
obj.iloc[3.0] = 0
|
|
|
|
def test_iloc_getitem_setitem_fancy_exceptions(self, float_frame):
|
|
with pytest.raises(IndexingError, match="Too many indexers"):
|
|
float_frame.iloc[:, :, :]
|
|
|
|
with pytest.raises(IndexError, match="too many indices for array"):
|
|
# GH#32257 we let numpy do validation, get their exception
|
|
float_frame.iloc[:, :, :] = 1
|
|
|
|
def test_iloc_frame_indexer(self):
|
|
# GH#39004
|
|
df = DataFrame({"a": [1, 2, 3]})
|
|
indexer = DataFrame({"a": [True, False, True]})
|
|
msg = "DataFrame indexer for .iloc is not supported. Consider using .loc"
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.iloc[indexer] = 1
|
|
|
|
msg = (
|
|
"DataFrame indexer is not allowed for .iloc\n"
|
|
"Consider using .loc for automatic alignment."
|
|
)
|
|
with pytest.raises(IndexError, match=msg):
|
|
df.iloc[indexer]
|
|
|
|
|
|
class TestILocSetItemDuplicateColumns:
|
|
def test_iloc_setitem_scalar_duplicate_columns(self):
|
|
# GH#15686, duplicate columns and mixed dtype
|
|
df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}])
|
|
df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}])
|
|
df = 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 = DataFrame([[0, "str", "str2"]], columns=["a", "b", "b"])
|
|
|
|
df.iloc[:, 2] = ["str3"]
|
|
|
|
expected = DataFrame([[0, "str", "str3"]], columns=["a", "b", "b"])
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_iloc_setitem_series_duplicate_columns(self):
|
|
df = 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
|
|
|
|
@pytest.mark.parametrize(
|
|
["dtypes", "init_value", "expected_value"],
|
|
[("int64", "0", 0), ("float", "1.2", 1.2)],
|
|
)
|
|
def test_iloc_setitem_dtypes_duplicate_columns(
|
|
self, dtypes, init_value, expected_value
|
|
):
|
|
# GH#22035
|
|
df = DataFrame(
|
|
[[init_value, "str", "str2"]], columns=["a", "b", "b"], dtype=object
|
|
)
|
|
|
|
# with the enforcement of GH#45333 in 2.0, this sets values inplace,
|
|
# so we retain object dtype
|
|
df.iloc[:, 0] = df.iloc[:, 0].astype(dtypes)
|
|
|
|
expected_df = DataFrame(
|
|
[[expected_value, "str", "str2"]],
|
|
columns=["a", "b", "b"],
|
|
dtype=object,
|
|
)
|
|
tm.assert_frame_equal(df, expected_df)
|
|
|
|
|
|
class TestILocCallable:
|
|
def test_frame_iloc_getitem_callable(self):
|
|
# GH#11485
|
|
df = DataFrame({"X": [1, 2, 3, 4], "Y": list("aabb")}, index=list("ABCD"))
|
|
|
|
# return location
|
|
res = df.iloc[lambda x: [1, 3]]
|
|
tm.assert_frame_equal(res, df.iloc[[1, 3]])
|
|
|
|
res = df.iloc[lambda x: [1, 3], :]
|
|
tm.assert_frame_equal(res, df.iloc[[1, 3], :])
|
|
|
|
res = df.iloc[lambda x: [1, 3], lambda x: 0]
|
|
tm.assert_series_equal(res, df.iloc[[1, 3], 0])
|
|
|
|
res = df.iloc[lambda x: [1, 3], lambda x: [0]]
|
|
tm.assert_frame_equal(res, df.iloc[[1, 3], [0]])
|
|
|
|
# mixture
|
|
res = df.iloc[[1, 3], lambda x: 0]
|
|
tm.assert_series_equal(res, df.iloc[[1, 3], 0])
|
|
|
|
res = df.iloc[[1, 3], lambda x: [0]]
|
|
tm.assert_frame_equal(res, df.iloc[[1, 3], [0]])
|
|
|
|
res = df.iloc[lambda x: [1, 3], 0]
|
|
tm.assert_series_equal(res, df.iloc[[1, 3], 0])
|
|
|
|
res = df.iloc[lambda x: [1, 3], [0]]
|
|
tm.assert_frame_equal(res, df.iloc[[1, 3], [0]])
|
|
|
|
def test_frame_iloc_setitem_callable(self):
|
|
# GH#11485
|
|
df = DataFrame(
|
|
{"X": [1, 2, 3, 4], "Y": Series(list("aabb"), dtype=object)},
|
|
index=list("ABCD"),
|
|
)
|
|
|
|
# return location
|
|
res = df.copy()
|
|
res.iloc[lambda x: [1, 3]] = 0
|
|
exp = df.copy()
|
|
exp.iloc[[1, 3]] = 0
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.iloc[lambda x: [1, 3], :] = -1
|
|
exp = df.copy()
|
|
exp.iloc[[1, 3], :] = -1
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.iloc[lambda x: [1, 3], lambda x: 0] = 5
|
|
exp = df.copy()
|
|
exp.iloc[[1, 3], 0] = 5
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.iloc[lambda x: [1, 3], lambda x: [0]] = 25
|
|
exp = df.copy()
|
|
exp.iloc[[1, 3], [0]] = 25
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
# mixture
|
|
res = df.copy()
|
|
res.iloc[[1, 3], lambda x: 0] = -3
|
|
exp = df.copy()
|
|
exp.iloc[[1, 3], 0] = -3
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.iloc[[1, 3], lambda x: [0]] = -5
|
|
exp = df.copy()
|
|
exp.iloc[[1, 3], [0]] = -5
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.iloc[lambda x: [1, 3], 0] = 10
|
|
exp = df.copy()
|
|
exp.iloc[[1, 3], 0] = 10
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.iloc[lambda x: [1, 3], [0]] = [-5, -5]
|
|
exp = df.copy()
|
|
exp.iloc[[1, 3], [0]] = [-5, -5]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
|
|
class TestILocSeries:
|
|
def test_iloc(self, using_copy_on_write, warn_copy_on_write):
|
|
ser = Series(
|
|
np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2))
|
|
)
|
|
ser_original = ser.copy()
|
|
|
|
for i in range(len(ser)):
|
|
result = ser.iloc[i]
|
|
exp = ser[ser.index[i]]
|
|
tm.assert_almost_equal(result, exp)
|
|
|
|
# pass a slice
|
|
result = ser.iloc[slice(1, 3)]
|
|
expected = ser.loc[2:4]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# test slice is a view
|
|
with tm.assert_produces_warning(None):
|
|
# GH#45324 make sure we aren't giving a spurious FutureWarning
|
|
with tm.assert_cow_warning(warn_copy_on_write):
|
|
result[:] = 0
|
|
if using_copy_on_write:
|
|
tm.assert_series_equal(ser, ser_original)
|
|
else:
|
|
assert (ser.iloc[1:3] == 0).all()
|
|
|
|
# list of integers
|
|
result = ser.iloc[[0, 2, 3, 4, 5]]
|
|
expected = ser.reindex(ser.index[[0, 2, 3, 4, 5]])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_iloc_getitem_nonunique(self):
|
|
ser = Series([0, 1, 2], index=[0, 1, 0])
|
|
assert ser.iloc[2] == 2
|
|
|
|
def test_iloc_setitem_pure_position_based(self):
|
|
# GH#22046
|
|
ser1 = Series([1, 2, 3])
|
|
ser2 = Series([4, 5, 6], index=[1, 0, 2])
|
|
ser1.iloc[1:3] = ser2.iloc[1:3]
|
|
expected = Series([1, 5, 6])
|
|
tm.assert_series_equal(ser1, expected)
|
|
|
|
def test_iloc_nullable_int64_size_1_nan(self):
|
|
# GH 31861
|
|
result = DataFrame({"a": ["test"], "b": [np.nan]})
|
|
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
|
|
result.loc[:, "b"] = result.loc[:, "b"].astype("Int64")
|
|
expected = DataFrame({"a": ["test"], "b": array([NA], dtype="Int64")})
|
|
tm.assert_frame_equal(result, expected)
|