467 lines
12 KiB
Python
467 lines
12 KiB
Python
import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_integer
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import pandas as pd
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from pandas import (
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Series,
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Timestamp,
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date_range,
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isna,
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)
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import pandas._testing as tm
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def test_where_unsafe_int(any_signed_int_numpy_dtype):
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s = Series(np.arange(10), dtype=any_signed_int_numpy_dtype)
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mask = s < 5
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s[mask] = range(2, 7)
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expected = Series(
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list(range(2, 7)) + list(range(5, 10)),
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dtype=any_signed_int_numpy_dtype,
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)
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tm.assert_series_equal(s, expected)
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def test_where_unsafe_float(float_numpy_dtype):
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s = Series(np.arange(10), dtype=float_numpy_dtype)
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mask = s < 5
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s[mask] = range(2, 7)
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data = list(range(2, 7)) + list(range(5, 10))
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expected = Series(data, dtype=float_numpy_dtype)
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tm.assert_series_equal(s, expected)
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@pytest.mark.parametrize(
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"dtype,expected_dtype",
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[
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(np.int8, np.float64),
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(np.int16, np.float64),
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(np.int32, np.float64),
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(np.int64, np.float64),
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(np.float32, np.float32),
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(np.float64, np.float64),
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],
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)
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def test_where_unsafe_upcast(dtype, expected_dtype):
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# see gh-9743
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s = Series(np.arange(10), dtype=dtype)
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values = [2.5, 3.5, 4.5, 5.5, 6.5]
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mask = s < 5
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expected = Series(values + list(range(5, 10)), dtype=expected_dtype)
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s[mask] = values
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tm.assert_series_equal(s, expected)
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def test_where_unsafe():
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# see gh-9731
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s = Series(np.arange(10), dtype="int64")
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values = [2.5, 3.5, 4.5, 5.5]
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mask = s > 5
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expected = Series(list(range(6)) + values, dtype="float64")
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s[mask] = values
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tm.assert_series_equal(s, expected)
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# see gh-3235
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s = Series(np.arange(10), dtype="int64")
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mask = s < 5
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s[mask] = range(2, 7)
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expected = Series(list(range(2, 7)) + list(range(5, 10)), dtype="int64")
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tm.assert_series_equal(s, expected)
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assert s.dtype == expected.dtype
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s = Series(np.arange(10), dtype="int64")
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mask = s > 5
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s[mask] = [0] * 4
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expected = Series([0, 1, 2, 3, 4, 5] + [0] * 4, dtype="int64")
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tm.assert_series_equal(s, expected)
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s = Series(np.arange(10))
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mask = s > 5
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msg = "cannot set using a list-like indexer with a different length than the value"
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with pytest.raises(ValueError, match=msg):
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s[mask] = [5, 4, 3, 2, 1]
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with pytest.raises(ValueError, match=msg):
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s[mask] = [0] * 5
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# dtype changes
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s = Series([1, 2, 3, 4])
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result = s.where(s > 2, np.nan)
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expected = Series([np.nan, np.nan, 3, 4])
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tm.assert_series_equal(result, expected)
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# GH 4667
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# setting with None changes dtype
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s = Series(range(10)).astype(float)
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s[8] = None
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result = s[8]
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assert isna(result)
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s = Series(range(10)).astype(float)
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s[s > 8] = None
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result = s[isna(s)]
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expected = Series(np.nan, index=[9])
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tm.assert_series_equal(result, expected)
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def test_where():
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s = Series(np.random.randn(5))
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cond = s > 0
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rs = s.where(cond).dropna()
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rs2 = s[cond]
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tm.assert_series_equal(rs, rs2)
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rs = s.where(cond, -s)
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tm.assert_series_equal(rs, s.abs())
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rs = s.where(cond)
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assert s.shape == rs.shape
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assert rs is not s
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# test alignment
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cond = Series([True, False, False, True, False], index=s.index)
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s2 = -(s.abs())
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expected = s2[cond].reindex(s2.index[:3]).reindex(s2.index)
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rs = s2.where(cond[:3])
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tm.assert_series_equal(rs, expected)
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expected = s2.abs()
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expected.iloc[0] = s2[0]
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rs = s2.where(cond[:3], -s2)
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tm.assert_series_equal(rs, expected)
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def test_where_error():
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s = Series(np.random.randn(5))
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cond = s > 0
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msg = "Array conditional must be same shape as self"
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with pytest.raises(ValueError, match=msg):
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s.where(1)
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with pytest.raises(ValueError, match=msg):
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s.where(cond[:3].values, -s)
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# GH 2745
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s = Series([1, 2])
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s[[True, False]] = [0, 1]
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expected = Series([0, 2])
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tm.assert_series_equal(s, expected)
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# failures
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msg = "cannot set using a list-like indexer with a different length than the value"
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with pytest.raises(ValueError, match=msg):
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s[[True, False]] = [0, 2, 3]
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with pytest.raises(ValueError, match=msg):
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s[[True, False]] = []
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@pytest.mark.parametrize("klass", [list, tuple, np.array, Series])
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def test_where_array_like(klass):
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# see gh-15414
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s = Series([1, 2, 3])
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cond = [False, True, True]
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expected = Series([np.nan, 2, 3])
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result = s.where(klass(cond))
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"cond",
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[
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[1, 0, 1],
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Series([2, 5, 7]),
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["True", "False", "True"],
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[Timestamp("2017-01-01"), pd.NaT, Timestamp("2017-01-02")],
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],
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)
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def test_where_invalid_input(cond):
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# see gh-15414: only boolean arrays accepted
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s = Series([1, 2, 3])
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msg = "Boolean array expected for the condition"
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with pytest.raises(ValueError, match=msg):
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s.where(cond)
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msg = "Array conditional must be same shape as self"
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with pytest.raises(ValueError, match=msg):
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s.where([True])
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def test_where_ndframe_align():
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msg = "Array conditional must be same shape as self"
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s = Series([1, 2, 3])
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cond = [True]
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with pytest.raises(ValueError, match=msg):
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s.where(cond)
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expected = Series([1, np.nan, np.nan])
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out = s.where(Series(cond))
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tm.assert_series_equal(out, expected)
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cond = np.array([False, True, False, True])
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with pytest.raises(ValueError, match=msg):
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s.where(cond)
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expected = Series([np.nan, 2, np.nan])
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out = s.where(Series(cond))
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tm.assert_series_equal(out, expected)
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def test_where_setitem_invalid():
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# GH 2702
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# make sure correct exceptions are raised on invalid list assignment
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msg = (
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lambda x: f"cannot set using a {x} indexer with a "
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"different length than the value"
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)
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# slice
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s = Series(list("abc"))
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with pytest.raises(ValueError, match=msg("slice")):
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s[0:3] = list(range(27))
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s[0:3] = list(range(3))
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expected = Series([0, 1, 2])
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tm.assert_series_equal(s.astype(np.int64), expected)
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# slice with step
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s = Series(list("abcdef"))
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with pytest.raises(ValueError, match=msg("slice")):
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s[0:4:2] = list(range(27))
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s = Series(list("abcdef"))
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s[0:4:2] = list(range(2))
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expected = Series([0, "b", 1, "d", "e", "f"])
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tm.assert_series_equal(s, expected)
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# neg slices
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s = Series(list("abcdef"))
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with pytest.raises(ValueError, match=msg("slice")):
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s[:-1] = list(range(27))
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s[-3:-1] = list(range(2))
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expected = Series(["a", "b", "c", 0, 1, "f"])
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tm.assert_series_equal(s, expected)
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# list
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s = Series(list("abc"))
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with pytest.raises(ValueError, match=msg("list-like")):
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s[[0, 1, 2]] = list(range(27))
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s = Series(list("abc"))
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with pytest.raises(ValueError, match=msg("list-like")):
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s[[0, 1, 2]] = list(range(2))
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# scalar
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s = Series(list("abc"))
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s[0] = list(range(10))
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expected = Series([list(range(10)), "b", "c"])
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tm.assert_series_equal(s, expected)
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@pytest.mark.parametrize("size", range(2, 6))
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@pytest.mark.parametrize(
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"mask", [[True, False, False, False, False], [True, False], [False]]
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)
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@pytest.mark.parametrize(
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"item", [2.0, np.nan, np.finfo(float).max, np.finfo(float).min]
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)
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# Test numpy arrays, lists and tuples as the input to be
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# broadcast
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@pytest.mark.parametrize(
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"box", [lambda x: np.array([x]), lambda x: [x], lambda x: (x,)]
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)
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def test_broadcast(size, mask, item, box):
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# GH#8801, GH#4195
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selection = np.resize(mask, size)
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data = np.arange(size, dtype=float)
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# Construct the expected series by taking the source
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# data or item based on the selection
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expected = Series(
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[item if use_item else data[i] for i, use_item in enumerate(selection)]
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)
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s = Series(data)
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s[selection] = item
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tm.assert_series_equal(s, expected)
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s = Series(data)
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result = s.where(~selection, box(item))
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tm.assert_series_equal(result, expected)
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s = Series(data)
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result = s.mask(selection, box(item))
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tm.assert_series_equal(result, expected)
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def test_where_inplace():
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s = Series(np.random.randn(5))
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cond = s > 0
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rs = s.copy()
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rs.where(cond, inplace=True)
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tm.assert_series_equal(rs.dropna(), s[cond])
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tm.assert_series_equal(rs, s.where(cond))
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rs = s.copy()
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rs.where(cond, -s, inplace=True)
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tm.assert_series_equal(rs, s.where(cond, -s))
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def test_where_dups():
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# GH 4550
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# where crashes with dups in index
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s1 = Series(list(range(3)))
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s2 = Series(list(range(3)))
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comb = pd.concat([s1, s2])
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result = comb.where(comb < 2)
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expected = Series([0, 1, np.nan, 0, 1, np.nan], index=[0, 1, 2, 0, 1, 2])
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tm.assert_series_equal(result, expected)
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# GH 4548
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# inplace updating not working with dups
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comb[comb < 1] = 5
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expected = Series([5, 1, 2, 5, 1, 2], index=[0, 1, 2, 0, 1, 2])
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tm.assert_series_equal(comb, expected)
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comb[comb < 2] += 10
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expected = Series([5, 11, 2, 5, 11, 2], index=[0, 1, 2, 0, 1, 2])
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tm.assert_series_equal(comb, expected)
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def test_where_numeric_with_string():
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# GH 9280
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s = Series([1, 2, 3])
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w = s.where(s > 1, "X")
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assert not is_integer(w[0])
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assert is_integer(w[1])
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assert is_integer(w[2])
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assert isinstance(w[0], str)
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assert w.dtype == "object"
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w = s.where(s > 1, ["X", "Y", "Z"])
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assert not is_integer(w[0])
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assert is_integer(w[1])
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assert is_integer(w[2])
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assert isinstance(w[0], str)
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assert w.dtype == "object"
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w = s.where(s > 1, np.array(["X", "Y", "Z"]))
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assert not is_integer(w[0])
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assert is_integer(w[1])
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assert is_integer(w[2])
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assert isinstance(w[0], str)
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assert w.dtype == "object"
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@pytest.mark.parametrize("dtype", ["timedelta64[ns]", "datetime64[ns]"])
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def test_where_datetimelike_coerce(dtype):
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ser = Series([1, 2], dtype=dtype)
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expected = Series([10, 10])
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mask = np.array([False, False])
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rs = ser.where(mask, [10, 10])
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tm.assert_series_equal(rs, expected)
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rs = ser.where(mask, 10)
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tm.assert_series_equal(rs, expected)
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rs = ser.where(mask, 10.0)
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tm.assert_series_equal(rs, expected)
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rs = ser.where(mask, [10.0, 10.0])
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tm.assert_series_equal(rs, expected)
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rs = ser.where(mask, [10.0, np.nan])
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expected = Series([10, None], dtype="object")
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tm.assert_series_equal(rs, expected)
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def test_where_datetimetz():
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# GH 15701
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timestamps = ["2016-12-31 12:00:04+00:00", "2016-12-31 12:00:04.010000+00:00"]
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ser = Series([Timestamp(t) for t in timestamps], dtype="datetime64[ns, UTC]")
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rs = ser.where(Series([False, True]))
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expected = Series([pd.NaT, ser[1]], dtype="datetime64[ns, UTC]")
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tm.assert_series_equal(rs, expected)
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def test_where_sparse():
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# GH#17198 make sure we dont get an AttributeError for sp_index
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ser = Series(pd.arrays.SparseArray([1, 2]))
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result = ser.where(ser >= 2, 0)
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expected = Series(pd.arrays.SparseArray([0, 2]))
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tm.assert_series_equal(result, expected)
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def test_where_empty_series_and_empty_cond_having_non_bool_dtypes():
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# https://github.com/pandas-dev/pandas/issues/34592
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ser = Series([], dtype=float)
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result = ser.where([])
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tm.assert_series_equal(result, ser)
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def test_where_categorical(frame_or_series):
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# https://github.com/pandas-dev/pandas/issues/18888
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exp = frame_or_series(
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pd.Categorical(["A", "A", "B", "B", np.nan], categories=["A", "B", "C"]),
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dtype="category",
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)
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df = frame_or_series(["A", "A", "B", "B", "C"], dtype="category")
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res = df.where(df != "C")
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tm.assert_equal(exp, res)
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def test_where_datetimelike_categorical(tz_naive_fixture):
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# GH#37682
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tz = tz_naive_fixture
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dr = date_range("2001-01-01", periods=3, tz=tz)._with_freq(None)
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lvals = pd.DatetimeIndex([dr[0], dr[1], pd.NaT])
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rvals = pd.Categorical([dr[0], pd.NaT, dr[2]])
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mask = np.array([True, True, False])
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# DatetimeIndex.where
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res = lvals.where(mask, rvals)
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tm.assert_index_equal(res, dr)
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# DatetimeArray.where
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res = lvals._data._where(mask, rvals)
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tm.assert_datetime_array_equal(res, dr._data)
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# Series.where
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res = Series(lvals).where(mask, rvals)
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tm.assert_series_equal(res, Series(dr))
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# DataFrame.where
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res = pd.DataFrame(lvals).where(mask[:, None], pd.DataFrame(rvals))
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tm.assert_frame_equal(res, pd.DataFrame(dr))
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