Updated script that can be controled by Nodejs web app
This commit is contained in:
mac OS
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import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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@pytest.mark.parametrize("ordered", [True, False])
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@pytest.mark.parametrize("categories", [["b", "a", "c"], ["a", "b", "c", "d"]])
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def test_factorize(categories, ordered):
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cat = pd.Categorical(
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["b", "b", "a", "c", None], categories=categories, ordered=ordered
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)
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codes, uniques = pd.factorize(cat)
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expected_codes = np.array([0, 0, 1, 2, -1], dtype=np.intp)
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expected_uniques = pd.Categorical(
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["b", "a", "c"], categories=categories, ordered=ordered
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)
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tm.assert_numpy_array_equal(codes, expected_codes)
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tm.assert_categorical_equal(uniques, expected_uniques)
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def test_factorized_sort():
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cat = pd.Categorical(["b", "b", None, "a"])
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codes, uniques = pd.factorize(cat, sort=True)
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expected_codes = np.array([1, 1, -1, 0], dtype=np.intp)
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expected_uniques = pd.Categorical(["a", "b"])
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tm.assert_numpy_array_equal(codes, expected_codes)
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tm.assert_categorical_equal(uniques, expected_uniques)
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def test_factorized_sort_ordered():
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cat = pd.Categorical(
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["b", "b", None, "a"], categories=["c", "b", "a"], ordered=True
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)
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codes, uniques = pd.factorize(cat, sort=True)
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expected_codes = np.array([0, 0, -1, 1], dtype=np.intp)
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expected_uniques = pd.Categorical(
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["b", "a"], categories=["c", "b", "a"], ordered=True
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)
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tm.assert_numpy_array_equal(codes, expected_codes)
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tm.assert_categorical_equal(uniques, expected_uniques)
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def test_isin_cats():
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# GH2003
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cat = pd.Categorical(["a", "b", np.nan])
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result = cat.isin(["a", np.nan])
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expected = np.array([True, False, True], dtype=bool)
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tm.assert_numpy_array_equal(expected, result)
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result = cat.isin(["a", "c"])
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expected = np.array([True, False, False], dtype=bool)
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tm.assert_numpy_array_equal(expected, result)
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@pytest.mark.parametrize("value", [[""], [None, ""], [pd.NaT, ""]])
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def test_isin_cats_corner_cases(value):
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# GH36550
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cat = pd.Categorical([""])
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result = cat.isin(value)
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expected = np.array([True], dtype=bool)
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tm.assert_numpy_array_equal(expected, result)
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@pytest.mark.parametrize("empty", [[], pd.Series(dtype=object), np.array([])])
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def test_isin_empty(empty):
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s = pd.Categorical(["a", "b"])
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expected = np.array([False, False], dtype=bool)
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result = s.isin(empty)
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tm.assert_numpy_array_equal(expected, result)
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def test_diff():
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ser = pd.Series([1, 2, 3], dtype="category")
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msg = "Convert to a suitable dtype"
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with pytest.raises(TypeError, match=msg):
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ser.diff()
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df = ser.to_frame(name="A")
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with pytest.raises(TypeError, match=msg):
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df.diff()
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@ -0,0 +1,349 @@
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import re
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import sys
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import numpy as np
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import pytest
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from pandas.compat import PYPY
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from pandas import (
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Categorical,
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CategoricalDtype,
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DataFrame,
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Index,
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NaT,
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Series,
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date_range,
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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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class TestCategoricalAnalytics:
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@pytest.mark.parametrize("aggregation", ["min", "max"])
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def test_min_max_not_ordered_raises(self, aggregation):
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# unordered cats have no min/max
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cat = Categorical(["a", "b", "c", "d"], ordered=False)
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msg = f"Categorical is not ordered for operation {aggregation}"
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agg_func = getattr(cat, aggregation)
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with pytest.raises(TypeError, match=msg):
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agg_func()
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ufunc = np.minimum if aggregation == "min" else np.maximum
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with pytest.raises(TypeError, match=msg):
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ufunc.reduce(cat)
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def test_min_max_ordered(self, index_or_series_or_array):
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cat = Categorical(["a", "b", "c", "d"], ordered=True)
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obj = index_or_series_or_array(cat)
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_min = obj.min()
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_max = obj.max()
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assert _min == "a"
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assert _max == "d"
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assert np.minimum.reduce(obj) == "a"
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assert np.maximum.reduce(obj) == "d"
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# TODO: raises if we pass axis=0 (on Index and Categorical, not Series)
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cat = Categorical(
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["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True
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)
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obj = index_or_series_or_array(cat)
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_min = obj.min()
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_max = obj.max()
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assert _min == "d"
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assert _max == "a"
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assert np.minimum.reduce(obj) == "d"
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assert np.maximum.reduce(obj) == "a"
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def test_min_max_reduce(self):
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# GH52788
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cat = Categorical(["a", "b", "c", "d"], ordered=True)
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df = DataFrame(cat)
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result_max = df.agg("max")
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expected_max = Series(Categorical(["d"], dtype=cat.dtype))
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tm.assert_series_equal(result_max, expected_max)
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result_min = df.agg("min")
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expected_min = Series(Categorical(["a"], dtype=cat.dtype))
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tm.assert_series_equal(result_min, expected_min)
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@pytest.mark.parametrize(
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"categories,expected",
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[
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(list("ABC"), np.nan),
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([1, 2, 3], np.nan),
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pytest.param(
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Series(date_range("2020-01-01", periods=3), dtype="category"),
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NaT,
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marks=pytest.mark.xfail(
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reason="https://github.com/pandas-dev/pandas/issues/29962"
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),
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),
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],
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)
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@pytest.mark.parametrize("aggregation", ["min", "max"])
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def test_min_max_ordered_empty(self, categories, expected, aggregation):
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# GH 30227
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cat = Categorical([], categories=categories, ordered=True)
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agg_func = getattr(cat, aggregation)
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result = agg_func()
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assert result is expected
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@pytest.mark.parametrize(
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"values, categories",
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[(["a", "b", "c", np.nan], list("cba")), ([1, 2, 3, np.nan], [3, 2, 1])],
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)
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@pytest.mark.parametrize("skipna", [True, False])
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@pytest.mark.parametrize("function", ["min", "max"])
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def test_min_max_with_nan(self, values, categories, function, skipna):
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# GH 25303
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cat = Categorical(values, categories=categories, ordered=True)
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result = getattr(cat, function)(skipna=skipna)
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if skipna is False:
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assert result is np.nan
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else:
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expected = categories[0] if function == "min" else categories[2]
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assert result == expected
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@pytest.mark.parametrize("function", ["min", "max"])
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@pytest.mark.parametrize("skipna", [True, False])
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def test_min_max_only_nan(self, function, skipna):
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# https://github.com/pandas-dev/pandas/issues/33450
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cat = Categorical([np.nan], categories=[1, 2], ordered=True)
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result = getattr(cat, function)(skipna=skipna)
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assert result is np.nan
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@pytest.mark.parametrize("method", ["min", "max"])
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def test_numeric_only_min_max_raises(self, method):
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# GH 25303
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cat = Categorical(
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[np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True
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)
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with pytest.raises(TypeError, match=".* got an unexpected keyword"):
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getattr(cat, method)(numeric_only=True)
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@pytest.mark.parametrize("method", ["min", "max"])
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def test_numpy_min_max_raises(self, method):
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cat = Categorical(["a", "b", "c", "b"], ordered=False)
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msg = (
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f"Categorical is not ordered for operation {method}\n"
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"you can use .as_ordered() to change the Categorical to an ordered one"
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)
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method = getattr(np, method)
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with pytest.raises(TypeError, match=re.escape(msg)):
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method(cat)
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@pytest.mark.parametrize("kwarg", ["axis", "out", "keepdims"])
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@pytest.mark.parametrize("method", ["min", "max"])
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def test_numpy_min_max_unsupported_kwargs_raises(self, method, kwarg):
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cat = Categorical(["a", "b", "c", "b"], ordered=True)
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msg = (
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f"the '{kwarg}' parameter is not supported in the pandas implementation "
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f"of {method}"
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)
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if kwarg == "axis":
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msg = r"`axis` must be fewer than the number of dimensions \(1\)"
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kwargs = {kwarg: 42}
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method = getattr(np, method)
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with pytest.raises(ValueError, match=msg):
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method(cat, **kwargs)
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@pytest.mark.parametrize("method, expected", [("min", "a"), ("max", "c")])
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def test_numpy_min_max_axis_equals_none(self, method, expected):
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cat = Categorical(["a", "b", "c", "b"], ordered=True)
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method = getattr(np, method)
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result = method(cat, axis=None)
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assert result == expected
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@pytest.mark.parametrize(
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"values,categories,exp_mode",
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[
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([1, 1, 2, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5]),
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([1, 1, 1, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5, 1]),
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([1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1]),
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([np.nan, np.nan, np.nan, 4, 5], [5, 4, 3, 2, 1], [5, 4]),
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([np.nan, np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]),
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([np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]),
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],
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)
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def test_mode(self, values, categories, exp_mode):
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cat = Categorical(values, categories=categories, ordered=True)
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res = Series(cat).mode()._values
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exp = Categorical(exp_mode, categories=categories, ordered=True)
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tm.assert_categorical_equal(res, exp)
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def test_searchsorted(self, ordered):
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# https://github.com/pandas-dev/pandas/issues/8420
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# https://github.com/pandas-dev/pandas/issues/14522
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cat = Categorical(
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["cheese", "milk", "apple", "bread", "bread"],
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categories=["cheese", "milk", "apple", "bread"],
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ordered=ordered,
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)
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ser = Series(cat)
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# Searching for single item argument, side='left' (default)
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res_cat = cat.searchsorted("apple")
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assert res_cat == 2
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assert is_scalar(res_cat)
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res_ser = ser.searchsorted("apple")
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assert res_ser == 2
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assert is_scalar(res_ser)
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# Searching for single item array, side='left' (default)
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res_cat = cat.searchsorted(["bread"])
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res_ser = ser.searchsorted(["bread"])
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exp = np.array([3], dtype=np.intp)
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tm.assert_numpy_array_equal(res_cat, exp)
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tm.assert_numpy_array_equal(res_ser, exp)
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# Searching for several items array, side='right'
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res_cat = cat.searchsorted(["apple", "bread"], side="right")
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res_ser = ser.searchsorted(["apple", "bread"], side="right")
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exp = np.array([3, 5], dtype=np.intp)
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tm.assert_numpy_array_equal(res_cat, exp)
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tm.assert_numpy_array_equal(res_ser, exp)
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# Searching for a single value that is not from the Categorical
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with pytest.raises(TypeError, match="cucumber"):
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cat.searchsorted("cucumber")
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with pytest.raises(TypeError, match="cucumber"):
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ser.searchsorted("cucumber")
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# Searching for multiple values one of each is not from the Categorical
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msg = (
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"Cannot setitem on a Categorical with a new category, "
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"set the categories first"
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)
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with pytest.raises(TypeError, match=msg):
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cat.searchsorted(["bread", "cucumber"])
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with pytest.raises(TypeError, match=msg):
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ser.searchsorted(["bread", "cucumber"])
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def test_unique(self, ordered):
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# GH38140
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dtype = CategoricalDtype(["a", "b", "c"], ordered=ordered)
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# categories are reordered based on value when ordered=False
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cat = Categorical(["a", "b", "c"], dtype=dtype)
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res = cat.unique()
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tm.assert_categorical_equal(res, cat)
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cat = Categorical(["a", "b", "a", "a"], dtype=dtype)
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res = cat.unique()
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tm.assert_categorical_equal(res, Categorical(["a", "b"], dtype=dtype))
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cat = Categorical(["c", "a", "b", "a", "a"], dtype=dtype)
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res = cat.unique()
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exp_cat = Categorical(["c", "a", "b"], dtype=dtype)
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tm.assert_categorical_equal(res, exp_cat)
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# nan must be removed
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cat = Categorical(["b", np.nan, "b", np.nan, "a"], dtype=dtype)
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res = cat.unique()
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exp_cat = Categorical(["b", np.nan, "a"], dtype=dtype)
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tm.assert_categorical_equal(res, exp_cat)
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def test_unique_index_series(self, ordered):
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# GH38140
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dtype = CategoricalDtype([3, 2, 1], ordered=ordered)
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c = Categorical([3, 1, 2, 2, 1], dtype=dtype)
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# Categorical.unique sorts categories by appearance order
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# if ordered=False
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exp = Categorical([3, 1, 2], dtype=dtype)
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tm.assert_categorical_equal(c.unique(), exp)
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tm.assert_index_equal(Index(c).unique(), Index(exp))
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tm.assert_categorical_equal(Series(c).unique(), exp)
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c = Categorical([1, 1, 2, 2], dtype=dtype)
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exp = Categorical([1, 2], dtype=dtype)
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tm.assert_categorical_equal(c.unique(), exp)
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tm.assert_index_equal(Index(c).unique(), Index(exp))
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tm.assert_categorical_equal(Series(c).unique(), exp)
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def test_shift(self):
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# GH 9416
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cat = Categorical(["a", "b", "c", "d", "a"])
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# shift forward
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sp1 = cat.shift(1)
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xp1 = Categorical([np.nan, "a", "b", "c", "d"])
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tm.assert_categorical_equal(sp1, xp1)
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tm.assert_categorical_equal(cat[:-1], sp1[1:])
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# shift back
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sn2 = cat.shift(-2)
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xp2 = Categorical(
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["c", "d", "a", np.nan, np.nan], categories=["a", "b", "c", "d"]
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)
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tm.assert_categorical_equal(sn2, xp2)
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tm.assert_categorical_equal(cat[2:], sn2[:-2])
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# shift by zero
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tm.assert_categorical_equal(cat, cat.shift(0))
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def test_nbytes(self):
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cat = Categorical([1, 2, 3])
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exp = 3 + 3 * 8 # 3 int8s for values + 3 int64s for categories
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assert cat.nbytes == exp
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def test_memory_usage(self):
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cat = Categorical([1, 2, 3])
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# .categories is an index, so we include the hashtable
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assert 0 < cat.nbytes <= cat.memory_usage()
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assert 0 < cat.nbytes <= cat.memory_usage(deep=True)
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cat = Categorical(["foo", "foo", "bar"])
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assert cat.memory_usage(deep=True) > cat.nbytes
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if not PYPY:
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# sys.getsizeof will call the .memory_usage with
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# deep=True, and add on some GC overhead
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diff = cat.memory_usage(deep=True) - sys.getsizeof(cat)
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assert abs(diff) < 100
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def test_map(self):
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c = Categorical(list("ABABC"), categories=list("CBA"), ordered=True)
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result = c.map(lambda x: x.lower(), na_action=None)
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exp = Categorical(list("ababc"), categories=list("cba"), ordered=True)
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tm.assert_categorical_equal(result, exp)
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c = Categorical(list("ABABC"), categories=list("ABC"), ordered=False)
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result = c.map(lambda x: x.lower(), na_action=None)
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exp = Categorical(list("ababc"), categories=list("abc"), ordered=False)
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tm.assert_categorical_equal(result, exp)
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result = c.map(lambda x: 1, na_action=None)
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# GH 12766: Return an index not an array
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tm.assert_index_equal(result, Index(np.array([1] * 5, dtype=np.int64)))
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@pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
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def test_validate_inplace_raises(self, value):
|
||||
cat = Categorical(["A", "B", "B", "C", "A"])
|
||||
msg = (
|
||||
'For argument "inplace" expected type bool, '
|
||||
f"received type {type(value).__name__}"
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
cat.sort_values(inplace=value)
|
||||
|
||||
def test_quantile_empty(self):
|
||||
# make sure we have correct itemsize on resulting codes
|
||||
cat = Categorical(["A", "B"])
|
||||
idx = Index([0.0, 0.5])
|
||||
result = cat[:0]._quantile(idx, interpolation="linear")
|
||||
assert result._codes.dtype == np.int8
|
||||
|
||||
expected = cat.take([-1, -1], allow_fill=True)
|
||||
tm.assert_extension_array_equal(result, expected)
|
||||
@ -0,0 +1,501 @@
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas.compat import PY311
|
||||
|
||||
from pandas import (
|
||||
Categorical,
|
||||
CategoricalIndex,
|
||||
DataFrame,
|
||||
Index,
|
||||
Series,
|
||||
StringDtype,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
from pandas.core.arrays.categorical import recode_for_categories
|
||||
|
||||
|
||||
class TestCategoricalAPI:
|
||||
def test_to_list_deprecated(self):
|
||||
# GH#51254
|
||||
cat1 = Categorical(list("acb"), ordered=False)
|
||||
msg = "Categorical.to_list is deprecated and will be removed"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
cat1.to_list()
|
||||
|
||||
def test_ordered_api(self):
|
||||
# GH 9347
|
||||
cat1 = Categorical(list("acb"), ordered=False)
|
||||
tm.assert_index_equal(cat1.categories, Index(["a", "b", "c"]))
|
||||
assert not cat1.ordered
|
||||
|
||||
cat2 = Categorical(list("acb"), categories=list("bca"), ordered=False)
|
||||
tm.assert_index_equal(cat2.categories, Index(["b", "c", "a"]))
|
||||
assert not cat2.ordered
|
||||
|
||||
cat3 = Categorical(list("acb"), ordered=True)
|
||||
tm.assert_index_equal(cat3.categories, Index(["a", "b", "c"]))
|
||||
assert cat3.ordered
|
||||
|
||||
cat4 = Categorical(list("acb"), categories=list("bca"), ordered=True)
|
||||
tm.assert_index_equal(cat4.categories, Index(["b", "c", "a"]))
|
||||
assert cat4.ordered
|
||||
|
||||
def test_set_ordered(self):
|
||||
cat = Categorical(["a", "b", "c", "a"], ordered=True)
|
||||
cat2 = cat.as_unordered()
|
||||
assert not cat2.ordered
|
||||
cat2 = cat.as_ordered()
|
||||
assert cat2.ordered
|
||||
|
||||
assert cat2.set_ordered(True).ordered
|
||||
assert not cat2.set_ordered(False).ordered
|
||||
|
||||
# removed in 0.19.0
|
||||
msg = (
|
||||
"property 'ordered' of 'Categorical' object has no setter"
|
||||
if PY311
|
||||
else "can't set attribute"
|
||||
)
|
||||
with pytest.raises(AttributeError, match=msg):
|
||||
cat.ordered = True
|
||||
with pytest.raises(AttributeError, match=msg):
|
||||
cat.ordered = False
|
||||
|
||||
def test_rename_categories(self):
|
||||
cat = Categorical(["a", "b", "c", "a"])
|
||||
|
||||
# inplace=False: the old one must not be changed
|
||||
res = cat.rename_categories([1, 2, 3])
|
||||
tm.assert_numpy_array_equal(
|
||||
res.__array__(), np.array([1, 2, 3, 1], dtype=np.int64)
|
||||
)
|
||||
tm.assert_index_equal(res.categories, Index([1, 2, 3]))
|
||||
|
||||
exp_cat = np.array(["a", "b", "c", "a"], dtype=np.object_)
|
||||
tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
|
||||
|
||||
exp_cat = Index(["a", "b", "c"])
|
||||
tm.assert_index_equal(cat.categories, exp_cat)
|
||||
|
||||
# GH18862 (let rename_categories take callables)
|
||||
result = cat.rename_categories(lambda x: x.upper())
|
||||
expected = Categorical(["A", "B", "C", "A"])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("new_categories", [[1, 2, 3, 4], [1, 2]])
|
||||
def test_rename_categories_wrong_length_raises(self, new_categories):
|
||||
cat = Categorical(["a", "b", "c", "a"])
|
||||
msg = (
|
||||
"new categories need to have the same number of items as the "
|
||||
"old categories!"
|
||||
)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
cat.rename_categories(new_categories)
|
||||
|
||||
def test_rename_categories_series(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/17981
|
||||
c = Categorical(["a", "b"])
|
||||
result = c.rename_categories(Series([0, 1], index=["a", "b"]))
|
||||
expected = Categorical([0, 1])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_rename_categories_dict(self):
|
||||
# GH 17336
|
||||
cat = Categorical(["a", "b", "c", "d"])
|
||||
res = cat.rename_categories({"a": 4, "b": 3, "c": 2, "d": 1})
|
||||
expected = Index([4, 3, 2, 1])
|
||||
tm.assert_index_equal(res.categories, expected)
|
||||
|
||||
# Test for dicts of smaller length
|
||||
cat = Categorical(["a", "b", "c", "d"])
|
||||
res = cat.rename_categories({"a": 1, "c": 3})
|
||||
|
||||
expected = Index([1, "b", 3, "d"])
|
||||
tm.assert_index_equal(res.categories, expected)
|
||||
|
||||
# Test for dicts with bigger length
|
||||
cat = Categorical(["a", "b", "c", "d"])
|
||||
res = cat.rename_categories({"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6})
|
||||
expected = Index([1, 2, 3, 4])
|
||||
tm.assert_index_equal(res.categories, expected)
|
||||
|
||||
# Test for dicts with no items from old categories
|
||||
cat = Categorical(["a", "b", "c", "d"])
|
||||
res = cat.rename_categories({"f": 1, "g": 3})
|
||||
|
||||
expected = Index(["a", "b", "c", "d"])
|
||||
tm.assert_index_equal(res.categories, expected)
|
||||
|
||||
def test_reorder_categories(self):
|
||||
cat = Categorical(["a", "b", "c", "a"], ordered=True)
|
||||
old = cat.copy()
|
||||
new = Categorical(
|
||||
["a", "b", "c", "a"], categories=["c", "b", "a"], ordered=True
|
||||
)
|
||||
|
||||
res = cat.reorder_categories(["c", "b", "a"])
|
||||
# cat must be the same as before
|
||||
tm.assert_categorical_equal(cat, old)
|
||||
# only res is changed
|
||||
tm.assert_categorical_equal(res, new)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"new_categories",
|
||||
[
|
||||
["a"], # not all "old" included in "new"
|
||||
["a", "b", "d"], # still not all "old" in "new"
|
||||
["a", "b", "c", "d"], # all "old" included in "new", but too long
|
||||
],
|
||||
)
|
||||
def test_reorder_categories_raises(self, new_categories):
|
||||
cat = Categorical(["a", "b", "c", "a"], ordered=True)
|
||||
msg = "items in new_categories are not the same as in old categories"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
cat.reorder_categories(new_categories)
|
||||
|
||||
def test_add_categories(self):
|
||||
cat = Categorical(["a", "b", "c", "a"], ordered=True)
|
||||
old = cat.copy()
|
||||
new = Categorical(
|
||||
["a", "b", "c", "a"], categories=["a", "b", "c", "d"], ordered=True
|
||||
)
|
||||
|
||||
res = cat.add_categories("d")
|
||||
tm.assert_categorical_equal(cat, old)
|
||||
tm.assert_categorical_equal(res, new)
|
||||
|
||||
res = cat.add_categories(["d"])
|
||||
tm.assert_categorical_equal(cat, old)
|
||||
tm.assert_categorical_equal(res, new)
|
||||
|
||||
# GH 9927
|
||||
cat = Categorical(list("abc"), ordered=True)
|
||||
expected = Categorical(list("abc"), categories=list("abcde"), ordered=True)
|
||||
# test with Series, np.array, index, list
|
||||
res = cat.add_categories(Series(["d", "e"]))
|
||||
tm.assert_categorical_equal(res, expected)
|
||||
res = cat.add_categories(np.array(["d", "e"]))
|
||||
tm.assert_categorical_equal(res, expected)
|
||||
res = cat.add_categories(Index(["d", "e"]))
|
||||
tm.assert_categorical_equal(res, expected)
|
||||
res = cat.add_categories(["d", "e"])
|
||||
tm.assert_categorical_equal(res, expected)
|
||||
|
||||
def test_add_categories_existing_raises(self):
|
||||
# new is in old categories
|
||||
cat = Categorical(["a", "b", "c", "d"], ordered=True)
|
||||
msg = re.escape("new categories must not include old categories: {'d'}")
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
cat.add_categories(["d"])
|
||||
|
||||
def test_add_categories_losing_dtype_information(self):
|
||||
# GH#48812
|
||||
cat = Categorical(Series([1, 2], dtype="Int64"))
|
||||
ser = Series([4], dtype="Int64")
|
||||
result = cat.add_categories(ser)
|
||||
expected = Categorical(
|
||||
Series([1, 2], dtype="Int64"), categories=Series([1, 2, 4], dtype="Int64")
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
cat = Categorical(Series(["a", "b", "a"], dtype=StringDtype()))
|
||||
ser = Series(["d"], dtype=StringDtype())
|
||||
result = cat.add_categories(ser)
|
||||
expected = Categorical(
|
||||
Series(["a", "b", "a"], dtype=StringDtype()),
|
||||
categories=Series(["a", "b", "d"], dtype=StringDtype()),
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_set_categories(self):
|
||||
cat = Categorical(["a", "b", "c", "a"], ordered=True)
|
||||
exp_categories = Index(["c", "b", "a"])
|
||||
exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
|
||||
|
||||
cat = cat.set_categories(["c", "b", "a"])
|
||||
res = cat.set_categories(["a", "b", "c"])
|
||||
# cat must be the same as before
|
||||
tm.assert_index_equal(cat.categories, exp_categories)
|
||||
tm.assert_numpy_array_equal(cat.__array__(), exp_values)
|
||||
# only res is changed
|
||||
exp_categories_back = Index(["a", "b", "c"])
|
||||
tm.assert_index_equal(res.categories, exp_categories_back)
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp_values)
|
||||
|
||||
# not all "old" included in "new" -> all not included ones are now
|
||||
# np.nan
|
||||
cat = Categorical(["a", "b", "c", "a"], ordered=True)
|
||||
res = cat.set_categories(["a"])
|
||||
tm.assert_numpy_array_equal(res.codes, np.array([0, -1, -1, 0], dtype=np.int8))
|
||||
|
||||
# still not all "old" in "new"
|
||||
res = cat.set_categories(["a", "b", "d"])
|
||||
tm.assert_numpy_array_equal(res.codes, np.array([0, 1, -1, 0], dtype=np.int8))
|
||||
tm.assert_index_equal(res.categories, Index(["a", "b", "d"]))
|
||||
|
||||
# all "old" included in "new"
|
||||
cat = cat.set_categories(["a", "b", "c", "d"])
|
||||
exp_categories = Index(["a", "b", "c", "d"])
|
||||
tm.assert_index_equal(cat.categories, exp_categories)
|
||||
|
||||
# internals...
|
||||
c = Categorical([1, 2, 3, 4, 1], categories=[1, 2, 3, 4], ordered=True)
|
||||
tm.assert_numpy_array_equal(c._codes, np.array([0, 1, 2, 3, 0], dtype=np.int8))
|
||||
tm.assert_index_equal(c.categories, Index([1, 2, 3, 4]))
|
||||
|
||||
exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
|
||||
tm.assert_numpy_array_equal(np.asarray(c), exp)
|
||||
|
||||
# all "pointers" to '4' must be changed from 3 to 0,...
|
||||
c = c.set_categories([4, 3, 2, 1])
|
||||
|
||||
# positions are changed
|
||||
tm.assert_numpy_array_equal(c._codes, np.array([3, 2, 1, 0, 3], dtype=np.int8))
|
||||
|
||||
# categories are now in new order
|
||||
tm.assert_index_equal(c.categories, Index([4, 3, 2, 1]))
|
||||
|
||||
# output is the same
|
||||
exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
|
||||
tm.assert_numpy_array_equal(np.asarray(c), exp)
|
||||
assert c.min() == 4
|
||||
assert c.max() == 1
|
||||
|
||||
# set_categories should set the ordering if specified
|
||||
c2 = c.set_categories([4, 3, 2, 1], ordered=False)
|
||||
assert not c2.ordered
|
||||
|
||||
tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2))
|
||||
|
||||
# set_categories should pass thru the ordering
|
||||
c2 = c.set_ordered(False).set_categories([4, 3, 2, 1])
|
||||
assert not c2.ordered
|
||||
|
||||
tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2))
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"values, categories, new_categories",
|
||||
[
|
||||
# No NaNs, same cats, same order
|
||||
(["a", "b", "a"], ["a", "b"], ["a", "b"]),
|
||||
# No NaNs, same cats, different order
|
||||
(["a", "b", "a"], ["a", "b"], ["b", "a"]),
|
||||
# Same, unsorted
|
||||
(["b", "a", "a"], ["a", "b"], ["a", "b"]),
|
||||
# No NaNs, same cats, different order
|
||||
(["b", "a", "a"], ["a", "b"], ["b", "a"]),
|
||||
# NaNs
|
||||
(["a", "b", "c"], ["a", "b"], ["a", "b"]),
|
||||
(["a", "b", "c"], ["a", "b"], ["b", "a"]),
|
||||
(["b", "a", "c"], ["a", "b"], ["a", "b"]),
|
||||
(["b", "a", "c"], ["a", "b"], ["a", "b"]),
|
||||
# Introduce NaNs
|
||||
(["a", "b", "c"], ["a", "b"], ["a"]),
|
||||
(["a", "b", "c"], ["a", "b"], ["b"]),
|
||||
(["b", "a", "c"], ["a", "b"], ["a"]),
|
||||
(["b", "a", "c"], ["a", "b"], ["a"]),
|
||||
# No overlap
|
||||
(["a", "b", "c"], ["a", "b"], ["d", "e"]),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("ordered", [True, False])
|
||||
def test_set_categories_many(self, values, categories, new_categories, ordered):
|
||||
c = Categorical(values, categories)
|
||||
expected = Categorical(values, new_categories, ordered)
|
||||
result = c.set_categories(new_categories, ordered=ordered)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_set_categories_rename_less(self):
|
||||
# GH 24675
|
||||
cat = Categorical(["A", "B"])
|
||||
result = cat.set_categories(["A"], rename=True)
|
||||
expected = Categorical(["A", np.nan])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_set_categories_private(self):
|
||||
cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])
|
||||
cat._set_categories(["a", "c", "d", "e"])
|
||||
expected = Categorical(["a", "c", "d"], categories=list("acde"))
|
||||
tm.assert_categorical_equal(cat, expected)
|
||||
|
||||
# fastpath
|
||||
cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])
|
||||
cat._set_categories(["a", "c", "d", "e"], fastpath=True)
|
||||
expected = Categorical(["a", "c", "d"], categories=list("acde"))
|
||||
tm.assert_categorical_equal(cat, expected)
|
||||
|
||||
def test_remove_categories(self):
|
||||
cat = Categorical(["a", "b", "c", "a"], ordered=True)
|
||||
old = cat.copy()
|
||||
new = Categorical(["a", "b", np.nan, "a"], categories=["a", "b"], ordered=True)
|
||||
|
||||
res = cat.remove_categories("c")
|
||||
tm.assert_categorical_equal(cat, old)
|
||||
tm.assert_categorical_equal(res, new)
|
||||
|
||||
res = cat.remove_categories(["c"])
|
||||
tm.assert_categorical_equal(cat, old)
|
||||
tm.assert_categorical_equal(res, new)
|
||||
|
||||
@pytest.mark.parametrize("removals", [["c"], ["c", np.nan], "c", ["c", "c"]])
|
||||
def test_remove_categories_raises(self, removals):
|
||||
cat = Categorical(["a", "b", "a"])
|
||||
message = re.escape("removals must all be in old categories: {'c'}")
|
||||
|
||||
with pytest.raises(ValueError, match=message):
|
||||
cat.remove_categories(removals)
|
||||
|
||||
def test_remove_unused_categories(self):
|
||||
c = Categorical(["a", "b", "c", "d", "a"], categories=["a", "b", "c", "d", "e"])
|
||||
exp_categories_all = Index(["a", "b", "c", "d", "e"])
|
||||
exp_categories_dropped = Index(["a", "b", "c", "d"])
|
||||
|
||||
tm.assert_index_equal(c.categories, exp_categories_all)
|
||||
|
||||
res = c.remove_unused_categories()
|
||||
tm.assert_index_equal(res.categories, exp_categories_dropped)
|
||||
tm.assert_index_equal(c.categories, exp_categories_all)
|
||||
|
||||
# with NaN values (GH11599)
|
||||
c = Categorical(["a", "b", "c", np.nan], categories=["a", "b", "c", "d", "e"])
|
||||
res = c.remove_unused_categories()
|
||||
tm.assert_index_equal(res.categories, Index(np.array(["a", "b", "c"])))
|
||||
exp_codes = np.array([0, 1, 2, -1], dtype=np.int8)
|
||||
tm.assert_numpy_array_equal(res.codes, exp_codes)
|
||||
tm.assert_index_equal(c.categories, exp_categories_all)
|
||||
|
||||
val = ["F", np.nan, "D", "B", "D", "F", np.nan]
|
||||
cat = Categorical(values=val, categories=list("ABCDEFG"))
|
||||
out = cat.remove_unused_categories()
|
||||
tm.assert_index_equal(out.categories, Index(["B", "D", "F"]))
|
||||
exp_codes = np.array([2, -1, 1, 0, 1, 2, -1], dtype=np.int8)
|
||||
tm.assert_numpy_array_equal(out.codes, exp_codes)
|
||||
assert out.tolist() == val
|
||||
|
||||
alpha = list("abcdefghijklmnopqrstuvwxyz")
|
||||
val = np.random.default_rng(2).choice(alpha[::2], 10000).astype("object")
|
||||
val[np.random.default_rng(2).choice(len(val), 100)] = np.nan
|
||||
|
||||
cat = Categorical(values=val, categories=alpha)
|
||||
out = cat.remove_unused_categories()
|
||||
assert out.tolist() == val.tolist()
|
||||
|
||||
|
||||
class TestCategoricalAPIWithFactor:
|
||||
def test_describe(self):
|
||||
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
|
||||
# string type
|
||||
desc = factor.describe()
|
||||
assert factor.ordered
|
||||
exp_index = CategoricalIndex(
|
||||
["a", "b", "c"], name="categories", ordered=factor.ordered
|
||||
)
|
||||
expected = DataFrame(
|
||||
{"counts": [3, 2, 3], "freqs": [3 / 8.0, 2 / 8.0, 3 / 8.0]}, index=exp_index
|
||||
)
|
||||
tm.assert_frame_equal(desc, expected)
|
||||
|
||||
# check unused categories
|
||||
cat = factor.copy()
|
||||
cat = cat.set_categories(["a", "b", "c", "d"])
|
||||
desc = cat.describe()
|
||||
|
||||
exp_index = CategoricalIndex(
|
||||
list("abcd"), ordered=factor.ordered, name="categories"
|
||||
)
|
||||
expected = DataFrame(
|
||||
{"counts": [3, 2, 3, 0], "freqs": [3 / 8.0, 2 / 8.0, 3 / 8.0, 0]},
|
||||
index=exp_index,
|
||||
)
|
||||
tm.assert_frame_equal(desc, expected)
|
||||
|
||||
# check an integer one
|
||||
cat = Categorical([1, 2, 3, 1, 2, 3, 3, 2, 1, 1, 1])
|
||||
desc = cat.describe()
|
||||
exp_index = CategoricalIndex([1, 2, 3], ordered=cat.ordered, name="categories")
|
||||
expected = DataFrame(
|
||||
{"counts": [5, 3, 3], "freqs": [5 / 11.0, 3 / 11.0, 3 / 11.0]},
|
||||
index=exp_index,
|
||||
)
|
||||
tm.assert_frame_equal(desc, expected)
|
||||
|
||||
# https://github.com/pandas-dev/pandas/issues/3678
|
||||
# describe should work with NaN
|
||||
cat = Categorical([np.nan, 1, 2, 2])
|
||||
desc = cat.describe()
|
||||
expected = DataFrame(
|
||||
{"counts": [1, 2, 1], "freqs": [1 / 4.0, 2 / 4.0, 1 / 4.0]},
|
||||
index=CategoricalIndex(
|
||||
[1, 2, np.nan], categories=[1, 2], name="categories"
|
||||
),
|
||||
)
|
||||
tm.assert_frame_equal(desc, expected)
|
||||
|
||||
|
||||
class TestPrivateCategoricalAPI:
|
||||
def test_codes_immutable(self):
|
||||
# Codes should be read only
|
||||
c = Categorical(["a", "b", "c", "a", np.nan])
|
||||
exp = np.array([0, 1, 2, 0, -1], dtype="int8")
|
||||
tm.assert_numpy_array_equal(c.codes, exp)
|
||||
|
||||
# Assignments to codes should raise
|
||||
msg = (
|
||||
"property 'codes' of 'Categorical' object has no setter"
|
||||
if PY311
|
||||
else "can't set attribute"
|
||||
)
|
||||
with pytest.raises(AttributeError, match=msg):
|
||||
c.codes = np.array([0, 1, 2, 0, 1], dtype="int8")
|
||||
|
||||
# changes in the codes array should raise
|
||||
codes = c.codes
|
||||
|
||||
with pytest.raises(ValueError, match="assignment destination is read-only"):
|
||||
codes[4] = 1
|
||||
|
||||
# But even after getting the codes, the original array should still be
|
||||
# writeable!
|
||||
c[4] = "a"
|
||||
exp = np.array([0, 1, 2, 0, 0], dtype="int8")
|
||||
tm.assert_numpy_array_equal(c.codes, exp)
|
||||
c._codes[4] = 2
|
||||
exp = np.array([0, 1, 2, 0, 2], dtype="int8")
|
||||
tm.assert_numpy_array_equal(c.codes, exp)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"codes, old, new, expected",
|
||||
[
|
||||
([0, 1], ["a", "b"], ["a", "b"], [0, 1]),
|
||||
([0, 1], ["b", "a"], ["b", "a"], [0, 1]),
|
||||
([0, 1], ["a", "b"], ["b", "a"], [1, 0]),
|
||||
([0, 1], ["b", "a"], ["a", "b"], [1, 0]),
|
||||
([0, 1, 0, 1], ["a", "b"], ["a", "b", "c"], [0, 1, 0, 1]),
|
||||
([0, 1, 2, 2], ["a", "b", "c"], ["a", "b"], [0, 1, -1, -1]),
|
||||
([0, 1, -1], ["a", "b", "c"], ["a", "b", "c"], [0, 1, -1]),
|
||||
([0, 1, -1], ["a", "b", "c"], ["b"], [-1, 0, -1]),
|
||||
([0, 1, -1], ["a", "b", "c"], ["d"], [-1, -1, -1]),
|
||||
([0, 1, -1], ["a", "b", "c"], [], [-1, -1, -1]),
|
||||
([-1, -1], [], ["a", "b"], [-1, -1]),
|
||||
([1, 0], ["b", "a"], ["a", "b"], [0, 1]),
|
||||
],
|
||||
)
|
||||
def test_recode_to_categories(self, codes, old, new, expected):
|
||||
codes = np.asanyarray(codes, dtype=np.int8)
|
||||
expected = np.asanyarray(expected, dtype=np.int8)
|
||||
old = Index(old)
|
||||
new = Index(new)
|
||||
result = recode_for_categories(codes, old, new)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
def test_recode_to_categories_large(self):
|
||||
N = 1000
|
||||
codes = np.arange(N)
|
||||
old = Index(codes)
|
||||
expected = np.arange(N - 1, -1, -1, dtype=np.int16)
|
||||
new = Index(expected)
|
||||
result = recode_for_categories(codes, old, new)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
@ -0,0 +1,155 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas import (
|
||||
Categorical,
|
||||
CategoricalDtype,
|
||||
CategoricalIndex,
|
||||
DatetimeIndex,
|
||||
Interval,
|
||||
NaT,
|
||||
Period,
|
||||
Timestamp,
|
||||
array,
|
||||
to_datetime,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestAstype:
|
||||
@pytest.mark.parametrize("cls", [Categorical, CategoricalIndex])
|
||||
@pytest.mark.parametrize("values", [[1, np.nan], [Timestamp("2000"), NaT]])
|
||||
def test_astype_nan_to_int(self, cls, values):
|
||||
# GH#28406
|
||||
obj = cls(values)
|
||||
|
||||
msg = "Cannot (cast|convert)"
|
||||
with pytest.raises((ValueError, TypeError), match=msg):
|
||||
obj.astype(int)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"expected",
|
||||
[
|
||||
array(["2019", "2020"], dtype="datetime64[ns, UTC]"),
|
||||
array([0, 0], dtype="timedelta64[ns]"),
|
||||
array([Period("2019"), Period("2020")], dtype="period[Y-DEC]"),
|
||||
array([Interval(0, 1), Interval(1, 2)], dtype="interval"),
|
||||
array([1, np.nan], dtype="Int64"),
|
||||
],
|
||||
)
|
||||
def test_astype_category_to_extension_dtype(self, expected):
|
||||
# GH#28668
|
||||
result = expected.astype("category").astype(expected.dtype)
|
||||
|
||||
tm.assert_extension_array_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"dtype, expected",
|
||||
[
|
||||
(
|
||||
"datetime64[ns]",
|
||||
np.array(["2015-01-01T00:00:00.000000000"], dtype="datetime64[ns]"),
|
||||
),
|
||||
(
|
||||
"datetime64[ns, MET]",
|
||||
DatetimeIndex([Timestamp("2015-01-01 00:00:00+0100", tz="MET")]).array,
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_astype_to_datetime64(self, dtype, expected):
|
||||
# GH#28448
|
||||
result = Categorical(["2015-01-01"]).astype(dtype)
|
||||
assert result == expected
|
||||
|
||||
def test_astype_str_int_categories_to_nullable_int(self):
|
||||
# GH#39616
|
||||
dtype = CategoricalDtype([str(i) for i in range(5)])
|
||||
codes = np.random.default_rng(2).integers(5, size=20)
|
||||
arr = Categorical.from_codes(codes, dtype=dtype)
|
||||
|
||||
res = arr.astype("Int64")
|
||||
expected = array(codes, dtype="Int64")
|
||||
tm.assert_extension_array_equal(res, expected)
|
||||
|
||||
def test_astype_str_int_categories_to_nullable_float(self):
|
||||
# GH#39616
|
||||
dtype = CategoricalDtype([str(i / 2) for i in range(5)])
|
||||
codes = np.random.default_rng(2).integers(5, size=20)
|
||||
arr = Categorical.from_codes(codes, dtype=dtype)
|
||||
|
||||
res = arr.astype("Float64")
|
||||
expected = array(codes, dtype="Float64") / 2
|
||||
tm.assert_extension_array_equal(res, expected)
|
||||
|
||||
@pytest.mark.parametrize("ordered", [True, False])
|
||||
def test_astype(self, ordered):
|
||||
# string
|
||||
cat = Categorical(list("abbaaccc"), ordered=ordered)
|
||||
result = cat.astype(object)
|
||||
expected = np.array(cat)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
msg = r"Cannot cast object|string dtype to float64"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
cat.astype(float)
|
||||
|
||||
# numeric
|
||||
cat = Categorical([0, 1, 2, 2, 1, 0, 1, 0, 2], ordered=ordered)
|
||||
result = cat.astype(object)
|
||||
expected = np.array(cat, dtype=object)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
result = cat.astype(int)
|
||||
expected = np.array(cat, dtype="int")
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
result = cat.astype(float)
|
||||
expected = np.array(cat, dtype=float)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("dtype_ordered", [True, False])
|
||||
@pytest.mark.parametrize("cat_ordered", [True, False])
|
||||
def test_astype_category(self, dtype_ordered, cat_ordered):
|
||||
# GH#10696/GH#18593
|
||||
data = list("abcaacbab")
|
||||
cat = Categorical(data, categories=list("bac"), ordered=cat_ordered)
|
||||
|
||||
# standard categories
|
||||
dtype = CategoricalDtype(ordered=dtype_ordered)
|
||||
result = cat.astype(dtype)
|
||||
expected = Categorical(data, categories=cat.categories, ordered=dtype_ordered)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
# non-standard categories
|
||||
dtype = CategoricalDtype(list("adc"), dtype_ordered)
|
||||
result = cat.astype(dtype)
|
||||
expected = Categorical(data, dtype=dtype)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
if dtype_ordered is False:
|
||||
# dtype='category' can't specify ordered, so only test once
|
||||
result = cat.astype("category")
|
||||
expected = cat
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_astype_object_datetime_categories(self):
|
||||
# GH#40754
|
||||
cat = Categorical(to_datetime(["2021-03-27", NaT]))
|
||||
result = cat.astype(object)
|
||||
expected = np.array([Timestamp("2021-03-27 00:00:00"), NaT], dtype="object")
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
def test_astype_object_timestamp_categories(self):
|
||||
# GH#18024
|
||||
cat = Categorical([Timestamp("2014-01-01")])
|
||||
result = cat.astype(object)
|
||||
expected = np.array([Timestamp("2014-01-01 00:00:00")], dtype="object")
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
def test_astype_category_readonly_mask_values(self):
|
||||
# GH#53658
|
||||
arr = array([0, 1, 2], dtype="Int64")
|
||||
arr._mask.flags["WRITEABLE"] = False
|
||||
result = arr.astype("category")
|
||||
expected = array([0, 1, 2], dtype="Int64").astype("category")
|
||||
tm.assert_extension_array_equal(result, expected)
|
||||
@ -0,0 +1,783 @@
|
||||
from datetime import (
|
||||
date,
|
||||
datetime,
|
||||
)
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas._config import using_pyarrow_string_dtype
|
||||
|
||||
from pandas.core.dtypes.common import (
|
||||
is_float_dtype,
|
||||
is_integer_dtype,
|
||||
)
|
||||
from pandas.core.dtypes.dtypes import CategoricalDtype
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
Categorical,
|
||||
CategoricalIndex,
|
||||
DatetimeIndex,
|
||||
Index,
|
||||
Interval,
|
||||
IntervalIndex,
|
||||
MultiIndex,
|
||||
NaT,
|
||||
Series,
|
||||
Timestamp,
|
||||
date_range,
|
||||
period_range,
|
||||
timedelta_range,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestCategoricalConstructors:
|
||||
def test_fastpath_deprecated(self):
|
||||
codes = np.array([1, 2, 3])
|
||||
dtype = CategoricalDtype(categories=["a", "b", "c", "d"], ordered=False)
|
||||
msg = "The 'fastpath' keyword in Categorical is deprecated"
|
||||
with tm.assert_produces_warning(DeprecationWarning, match=msg):
|
||||
Categorical(codes, dtype=dtype, fastpath=True)
|
||||
|
||||
def test_categorical_from_cat_and_dtype_str_preserve_ordered(self):
|
||||
# GH#49309 we should preserve orderedness in `res`
|
||||
cat = Categorical([3, 1], categories=[3, 2, 1], ordered=True)
|
||||
|
||||
res = Categorical(cat, dtype="category")
|
||||
assert res.dtype.ordered
|
||||
|
||||
def test_categorical_disallows_scalar(self):
|
||||
# GH#38433
|
||||
with pytest.raises(TypeError, match="Categorical input must be list-like"):
|
||||
Categorical("A", categories=["A", "B"])
|
||||
|
||||
def test_categorical_1d_only(self):
|
||||
# ndim > 1
|
||||
msg = "> 1 ndim Categorical are not supported at this time"
|
||||
with pytest.raises(NotImplementedError, match=msg):
|
||||
Categorical(np.array([list("abcd")]))
|
||||
|
||||
def test_validate_ordered(self):
|
||||
# see gh-14058
|
||||
exp_msg = "'ordered' must either be 'True' or 'False'"
|
||||
exp_err = TypeError
|
||||
|
||||
# This should be a boolean.
|
||||
ordered = np.array([0, 1, 2])
|
||||
|
||||
with pytest.raises(exp_err, match=exp_msg):
|
||||
Categorical([1, 2, 3], ordered=ordered)
|
||||
|
||||
with pytest.raises(exp_err, match=exp_msg):
|
||||
Categorical.from_codes(
|
||||
[0, 0, 1], categories=["a", "b", "c"], ordered=ordered
|
||||
)
|
||||
|
||||
def test_constructor_empty(self):
|
||||
# GH 17248
|
||||
c = Categorical([])
|
||||
expected = Index([])
|
||||
tm.assert_index_equal(c.categories, expected)
|
||||
|
||||
c = Categorical([], categories=[1, 2, 3])
|
||||
expected = Index([1, 2, 3], dtype=np.int64)
|
||||
tm.assert_index_equal(c.categories, expected)
|
||||
|
||||
def test_constructor_empty_boolean(self):
|
||||
# see gh-22702
|
||||
cat = Categorical([], categories=[True, False])
|
||||
categories = sorted(cat.categories.tolist())
|
||||
assert categories == [False, True]
|
||||
|
||||
def test_constructor_tuples(self):
|
||||
values = np.array([(1,), (1, 2), (1,), (1, 2)], dtype=object)
|
||||
result = Categorical(values)
|
||||
expected = Index([(1,), (1, 2)], tupleize_cols=False)
|
||||
tm.assert_index_equal(result.categories, expected)
|
||||
assert result.ordered is False
|
||||
|
||||
def test_constructor_tuples_datetimes(self):
|
||||
# numpy will auto reshape when all of the tuples are the
|
||||
# same len, so add an extra one with 2 items and slice it off
|
||||
values = np.array(
|
||||
[
|
||||
(Timestamp("2010-01-01"),),
|
||||
(Timestamp("2010-01-02"),),
|
||||
(Timestamp("2010-01-01"),),
|
||||
(Timestamp("2010-01-02"),),
|
||||
("a", "b"),
|
||||
],
|
||||
dtype=object,
|
||||
)[:-1]
|
||||
result = Categorical(values)
|
||||
expected = Index(
|
||||
[(Timestamp("2010-01-01"),), (Timestamp("2010-01-02"),)],
|
||||
tupleize_cols=False,
|
||||
)
|
||||
tm.assert_index_equal(result.categories, expected)
|
||||
|
||||
def test_constructor_unsortable(self):
|
||||
# it works!
|
||||
arr = np.array([1, 2, 3, datetime.now()], dtype="O")
|
||||
factor = Categorical(arr, ordered=False)
|
||||
assert not factor.ordered
|
||||
|
||||
# this however will raise as cannot be sorted
|
||||
msg = (
|
||||
"'values' is not ordered, please explicitly specify the "
|
||||
"categories order by passing in a categories argument."
|
||||
)
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
Categorical(arr, ordered=True)
|
||||
|
||||
def test_constructor_interval(self):
|
||||
result = Categorical(
|
||||
[Interval(1, 2), Interval(2, 3), Interval(3, 6)], ordered=True
|
||||
)
|
||||
ii = IntervalIndex([Interval(1, 2), Interval(2, 3), Interval(3, 6)])
|
||||
exp = Categorical(ii, ordered=True)
|
||||
tm.assert_categorical_equal(result, exp)
|
||||
tm.assert_index_equal(result.categories, ii)
|
||||
|
||||
def test_constructor(self):
|
||||
exp_arr = np.array(["a", "b", "c", "a", "b", "c"], dtype=np.object_)
|
||||
c1 = Categorical(exp_arr)
|
||||
tm.assert_numpy_array_equal(c1.__array__(), exp_arr)
|
||||
c2 = Categorical(exp_arr, categories=["a", "b", "c"])
|
||||
tm.assert_numpy_array_equal(c2.__array__(), exp_arr)
|
||||
c2 = Categorical(exp_arr, categories=["c", "b", "a"])
|
||||
tm.assert_numpy_array_equal(c2.__array__(), exp_arr)
|
||||
|
||||
# categories must be unique
|
||||
msg = "Categorical categories must be unique"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical([1, 2], [1, 2, 2])
|
||||
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical(["a", "b"], ["a", "b", "b"])
|
||||
|
||||
# The default should be unordered
|
||||
c1 = Categorical(["a", "b", "c", "a"])
|
||||
assert not c1.ordered
|
||||
|
||||
# Categorical as input
|
||||
c1 = Categorical(["a", "b", "c", "a"])
|
||||
c2 = Categorical(c1)
|
||||
tm.assert_categorical_equal(c1, c2)
|
||||
|
||||
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
|
||||
c2 = Categorical(c1)
|
||||
tm.assert_categorical_equal(c1, c2)
|
||||
|
||||
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
|
||||
c2 = Categorical(c1)
|
||||
tm.assert_categorical_equal(c1, c2)
|
||||
|
||||
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
|
||||
c2 = Categorical(c1, categories=["a", "b", "c"])
|
||||
tm.assert_numpy_array_equal(c1.__array__(), c2.__array__())
|
||||
tm.assert_index_equal(c2.categories, Index(["a", "b", "c"]))
|
||||
|
||||
# Series of dtype category
|
||||
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
|
||||
c2 = Categorical(Series(c1))
|
||||
tm.assert_categorical_equal(c1, c2)
|
||||
|
||||
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
|
||||
c2 = Categorical(Series(c1))
|
||||
tm.assert_categorical_equal(c1, c2)
|
||||
|
||||
# Series
|
||||
c1 = Categorical(["a", "b", "c", "a"])
|
||||
c2 = Categorical(Series(["a", "b", "c", "a"]))
|
||||
tm.assert_categorical_equal(c1, c2)
|
||||
|
||||
c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
|
||||
c2 = Categorical(Series(["a", "b", "c", "a"]), categories=["a", "b", "c", "d"])
|
||||
tm.assert_categorical_equal(c1, c2)
|
||||
|
||||
# This should result in integer categories, not float!
|
||||
cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3])
|
||||
assert is_integer_dtype(cat.categories)
|
||||
|
||||
# https://github.com/pandas-dev/pandas/issues/3678
|
||||
cat = Categorical([np.nan, 1, 2, 3])
|
||||
assert is_integer_dtype(cat.categories)
|
||||
|
||||
# this should result in floats
|
||||
cat = Categorical([np.nan, 1, 2.0, 3])
|
||||
assert is_float_dtype(cat.categories)
|
||||
|
||||
cat = Categorical([np.nan, 1.0, 2.0, 3.0])
|
||||
assert is_float_dtype(cat.categories)
|
||||
|
||||
# This doesn't work -> this would probably need some kind of "remember
|
||||
# the original type" feature to try to cast the array interface result
|
||||
# to...
|
||||
|
||||
# vals = np.asarray(cat[cat.notna()])
|
||||
# assert is_integer_dtype(vals)
|
||||
|
||||
# corner cases
|
||||
cat = Categorical([1])
|
||||
assert len(cat.categories) == 1
|
||||
assert cat.categories[0] == 1
|
||||
assert len(cat.codes) == 1
|
||||
assert cat.codes[0] == 0
|
||||
|
||||
cat = Categorical(["a"])
|
||||
assert len(cat.categories) == 1
|
||||
assert cat.categories[0] == "a"
|
||||
assert len(cat.codes) == 1
|
||||
assert cat.codes[0] == 0
|
||||
|
||||
# two arrays
|
||||
# - when the first is an integer dtype and the second is not
|
||||
# - when the resulting codes are all -1/NaN
|
||||
with tm.assert_produces_warning(None):
|
||||
Categorical([0, 1, 2, 0, 1, 2], categories=["a", "b", "c"])
|
||||
|
||||
with tm.assert_produces_warning(None):
|
||||
Categorical([0, 1, 2, 0, 1, 2], categories=[3, 4, 5])
|
||||
|
||||
# the next one are from the old docs
|
||||
with tm.assert_produces_warning(None):
|
||||
Categorical([0, 1, 2, 0, 1, 2], [1, 2, 3])
|
||||
cat = Categorical([1, 2], categories=[1, 2, 3])
|
||||
|
||||
# this is a legitimate constructor
|
||||
with tm.assert_produces_warning(None):
|
||||
Categorical(np.array([], dtype="int64"), categories=[3, 2, 1], ordered=True)
|
||||
|
||||
def test_constructor_with_existing_categories(self):
|
||||
# GH25318: constructing with pd.Series used to bogusly skip recoding
|
||||
# categories
|
||||
c0 = Categorical(["a", "b", "c", "a"])
|
||||
c1 = Categorical(["a", "b", "c", "a"], categories=["b", "c"])
|
||||
|
||||
c2 = Categorical(c0, categories=c1.categories)
|
||||
tm.assert_categorical_equal(c1, c2)
|
||||
|
||||
c3 = Categorical(Series(c0), categories=c1.categories)
|
||||
tm.assert_categorical_equal(c1, c3)
|
||||
|
||||
def test_constructor_not_sequence(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/16022
|
||||
msg = r"^Parameter 'categories' must be list-like, was"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
Categorical(["a", "b"], categories="a")
|
||||
|
||||
def test_constructor_with_null(self):
|
||||
# Cannot have NaN in categories
|
||||
msg = "Categorical categories cannot be null"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical([np.nan, "a", "b", "c"], categories=[np.nan, "a", "b", "c"])
|
||||
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical([None, "a", "b", "c"], categories=[None, "a", "b", "c"])
|
||||
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical(
|
||||
DatetimeIndex(["nat", "20160101"]),
|
||||
categories=[NaT, Timestamp("20160101")],
|
||||
)
|
||||
|
||||
def test_constructor_with_index(self):
|
||||
ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
|
||||
tm.assert_categorical_equal(ci.values, Categorical(ci))
|
||||
|
||||
ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
|
||||
tm.assert_categorical_equal(
|
||||
ci.values, Categorical(ci.astype(object), categories=ci.categories)
|
||||
)
|
||||
|
||||
def test_constructor_with_generator(self):
|
||||
# This was raising an Error in isna(single_val).any() because isna
|
||||
# returned a scalar for a generator
|
||||
|
||||
exp = Categorical([0, 1, 2])
|
||||
cat = Categorical(x for x in [0, 1, 2])
|
||||
tm.assert_categorical_equal(cat, exp)
|
||||
cat = Categorical(range(3))
|
||||
tm.assert_categorical_equal(cat, exp)
|
||||
|
||||
MultiIndex.from_product([range(5), ["a", "b", "c"]])
|
||||
|
||||
# check that categories accept generators and sequences
|
||||
cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
|
||||
tm.assert_categorical_equal(cat, exp)
|
||||
cat = Categorical([0, 1, 2], categories=range(3))
|
||||
tm.assert_categorical_equal(cat, exp)
|
||||
|
||||
def test_constructor_with_rangeindex(self):
|
||||
# RangeIndex is preserved in Categories
|
||||
rng = Index(range(3))
|
||||
|
||||
cat = Categorical(rng)
|
||||
tm.assert_index_equal(cat.categories, rng, exact=True)
|
||||
|
||||
cat = Categorical([1, 2, 0], categories=rng)
|
||||
tm.assert_index_equal(cat.categories, rng, exact=True)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"dtl",
|
||||
[
|
||||
date_range("1995-01-01 00:00:00", periods=5, freq="s"),
|
||||
date_range("1995-01-01 00:00:00", periods=5, freq="s", tz="US/Eastern"),
|
||||
timedelta_range("1 day", periods=5, freq="s"),
|
||||
],
|
||||
)
|
||||
def test_constructor_with_datetimelike(self, dtl):
|
||||
# see gh-12077
|
||||
# constructor with a datetimelike and NaT
|
||||
|
||||
s = Series(dtl)
|
||||
c = Categorical(s)
|
||||
|
||||
expected = type(dtl)(s)
|
||||
expected._data.freq = None
|
||||
|
||||
tm.assert_index_equal(c.categories, expected)
|
||||
tm.assert_numpy_array_equal(c.codes, np.arange(5, dtype="int8"))
|
||||
|
||||
# with NaT
|
||||
s2 = s.copy()
|
||||
s2.iloc[-1] = NaT
|
||||
c = Categorical(s2)
|
||||
|
||||
expected = type(dtl)(s2.dropna())
|
||||
expected._data.freq = None
|
||||
|
||||
tm.assert_index_equal(c.categories, expected)
|
||||
|
||||
exp = np.array([0, 1, 2, 3, -1], dtype=np.int8)
|
||||
tm.assert_numpy_array_equal(c.codes, exp)
|
||||
|
||||
result = repr(c)
|
||||
assert "NaT" in result
|
||||
|
||||
def test_constructor_from_index_series_datetimetz(self):
|
||||
idx = date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern")
|
||||
idx = idx._with_freq(None) # freq not preserved in result.categories
|
||||
result = Categorical(idx)
|
||||
tm.assert_index_equal(result.categories, idx)
|
||||
|
||||
result = Categorical(Series(idx))
|
||||
tm.assert_index_equal(result.categories, idx)
|
||||
|
||||
def test_constructor_date_objects(self):
|
||||
# we dont cast date objects to timestamps, matching Index constructor
|
||||
v = date.today()
|
||||
|
||||
cat = Categorical([v, v])
|
||||
assert cat.categories.dtype == object
|
||||
assert type(cat.categories[0]) is date
|
||||
|
||||
def test_constructor_from_index_series_timedelta(self):
|
||||
idx = timedelta_range("1 days", freq="D", periods=3)
|
||||
idx = idx._with_freq(None) # freq not preserved in result.categories
|
||||
result = Categorical(idx)
|
||||
tm.assert_index_equal(result.categories, idx)
|
||||
|
||||
result = Categorical(Series(idx))
|
||||
tm.assert_index_equal(result.categories, idx)
|
||||
|
||||
def test_constructor_from_index_series_period(self):
|
||||
idx = period_range("2015-01-01", freq="D", periods=3)
|
||||
result = Categorical(idx)
|
||||
tm.assert_index_equal(result.categories, idx)
|
||||
|
||||
result = Categorical(Series(idx))
|
||||
tm.assert_index_equal(result.categories, idx)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"values",
|
||||
[
|
||||
np.array([1.0, 1.2, 1.8, np.nan]),
|
||||
np.array([1, 2, 3], dtype="int64"),
|
||||
["a", "b", "c", np.nan],
|
||||
[pd.Period("2014-01"), pd.Period("2014-02"), NaT],
|
||||
[Timestamp("2014-01-01"), Timestamp("2014-01-02"), NaT],
|
||||
[
|
||||
Timestamp("2014-01-01", tz="US/Eastern"),
|
||||
Timestamp("2014-01-02", tz="US/Eastern"),
|
||||
NaT,
|
||||
],
|
||||
],
|
||||
)
|
||||
def test_constructor_invariant(self, values):
|
||||
# GH 14190
|
||||
c = Categorical(values)
|
||||
c2 = Categorical(c)
|
||||
tm.assert_categorical_equal(c, c2)
|
||||
|
||||
@pytest.mark.parametrize("ordered", [True, False])
|
||||
def test_constructor_with_dtype(self, ordered):
|
||||
categories = ["b", "a", "c"]
|
||||
dtype = CategoricalDtype(categories, ordered=ordered)
|
||||
result = Categorical(["a", "b", "a", "c"], dtype=dtype)
|
||||
expected = Categorical(
|
||||
["a", "b", "a", "c"], categories=categories, ordered=ordered
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
assert result.ordered is ordered
|
||||
|
||||
def test_constructor_dtype_and_others_raises(self):
|
||||
dtype = CategoricalDtype(["a", "b"], ordered=True)
|
||||
msg = "Cannot specify `categories` or `ordered` together with `dtype`."
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical(["a", "b"], categories=["a", "b"], dtype=dtype)
|
||||
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical(["a", "b"], ordered=True, dtype=dtype)
|
||||
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical(["a", "b"], ordered=False, dtype=dtype)
|
||||
|
||||
@pytest.mark.parametrize("categories", [None, ["a", "b"], ["a", "c"]])
|
||||
@pytest.mark.parametrize("ordered", [True, False])
|
||||
def test_constructor_str_category(self, categories, ordered):
|
||||
result = Categorical(
|
||||
["a", "b"], categories=categories, ordered=ordered, dtype="category"
|
||||
)
|
||||
expected = Categorical(["a", "b"], categories=categories, ordered=ordered)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_constructor_str_unknown(self):
|
||||
with pytest.raises(ValueError, match="Unknown dtype"):
|
||||
Categorical([1, 2], dtype="foo")
|
||||
|
||||
@pytest.mark.xfail(using_pyarrow_string_dtype(), reason="Can't be NumPy strings")
|
||||
def test_constructor_np_strs(self):
|
||||
# GH#31499 Hashtable.map_locations needs to work on np.str_ objects
|
||||
cat = Categorical(["1", "0", "1"], [np.str_("0"), np.str_("1")])
|
||||
assert all(isinstance(x, np.str_) for x in cat.categories)
|
||||
|
||||
def test_constructor_from_categorical_with_dtype(self):
|
||||
dtype = CategoricalDtype(["a", "b", "c"], ordered=True)
|
||||
values = Categorical(["a", "b", "d"])
|
||||
result = Categorical(values, dtype=dtype)
|
||||
# We use dtype.categories, not values.categories
|
||||
expected = Categorical(
|
||||
["a", "b", "d"], categories=["a", "b", "c"], ordered=True
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_constructor_from_categorical_with_unknown_dtype(self):
|
||||
dtype = CategoricalDtype(None, ordered=True)
|
||||
values = Categorical(["a", "b", "d"])
|
||||
result = Categorical(values, dtype=dtype)
|
||||
# We use values.categories, not dtype.categories
|
||||
expected = Categorical(
|
||||
["a", "b", "d"], categories=["a", "b", "d"], ordered=True
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_constructor_from_categorical_string(self):
|
||||
values = Categorical(["a", "b", "d"])
|
||||
# use categories, ordered
|
||||
result = Categorical(
|
||||
values, categories=["a", "b", "c"], ordered=True, dtype="category"
|
||||
)
|
||||
expected = Categorical(
|
||||
["a", "b", "d"], categories=["a", "b", "c"], ordered=True
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
# No string
|
||||
result = Categorical(values, categories=["a", "b", "c"], ordered=True)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_constructor_with_categorical_categories(self):
|
||||
# GH17884
|
||||
expected = Categorical(["a", "b"], categories=["a", "b", "c"])
|
||||
|
||||
result = Categorical(["a", "b"], categories=Categorical(["a", "b", "c"]))
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
result = Categorical(["a", "b"], categories=CategoricalIndex(["a", "b", "c"]))
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("klass", [lambda x: np.array(x, dtype=object), list])
|
||||
def test_construction_with_null(self, klass, nulls_fixture):
|
||||
# https://github.com/pandas-dev/pandas/issues/31927
|
||||
values = klass(["a", nulls_fixture, "b"])
|
||||
result = Categorical(values)
|
||||
|
||||
dtype = CategoricalDtype(["a", "b"])
|
||||
codes = [0, -1, 1]
|
||||
expected = Categorical.from_codes(codes=codes, dtype=dtype)
|
||||
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("validate", [True, False])
|
||||
def test_from_codes_nullable_int_categories(self, any_numeric_ea_dtype, validate):
|
||||
# GH#39649
|
||||
cats = pd.array(range(5), dtype=any_numeric_ea_dtype)
|
||||
codes = np.random.default_rng(2).integers(5, size=3)
|
||||
dtype = CategoricalDtype(cats)
|
||||
arr = Categorical.from_codes(codes, dtype=dtype, validate=validate)
|
||||
assert arr.categories.dtype == cats.dtype
|
||||
tm.assert_index_equal(arr.categories, Index(cats))
|
||||
|
||||
def test_from_codes_empty(self):
|
||||
cat = ["a", "b", "c"]
|
||||
result = Categorical.from_codes([], categories=cat)
|
||||
expected = Categorical([], categories=cat)
|
||||
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("validate", [True, False])
|
||||
def test_from_codes_validate(self, validate):
|
||||
# GH53122
|
||||
dtype = CategoricalDtype(["a", "b"])
|
||||
if validate:
|
||||
with pytest.raises(ValueError, match="codes need to be between "):
|
||||
Categorical.from_codes([4, 5], dtype=dtype, validate=validate)
|
||||
else:
|
||||
# passes, though has incorrect codes, but that's the user responsibility
|
||||
Categorical.from_codes([4, 5], dtype=dtype, validate=validate)
|
||||
|
||||
def test_from_codes_too_few_categories(self):
|
||||
dtype = CategoricalDtype(categories=[1, 2])
|
||||
msg = "codes need to be between "
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes([1, 2], categories=dtype.categories)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes([1, 2], dtype=dtype)
|
||||
|
||||
def test_from_codes_non_int_codes(self):
|
||||
dtype = CategoricalDtype(categories=[1, 2])
|
||||
msg = "codes need to be array-like integers"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes(["a"], categories=dtype.categories)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes(["a"], dtype=dtype)
|
||||
|
||||
def test_from_codes_non_unique_categories(self):
|
||||
with pytest.raises(ValueError, match="Categorical categories must be unique"):
|
||||
Categorical.from_codes([0, 1, 2], categories=["a", "a", "b"])
|
||||
|
||||
def test_from_codes_nan_cat_included(self):
|
||||
with pytest.raises(ValueError, match="Categorical categories cannot be null"):
|
||||
Categorical.from_codes([0, 1, 2], categories=["a", "b", np.nan])
|
||||
|
||||
def test_from_codes_too_negative(self):
|
||||
dtype = CategoricalDtype(categories=["a", "b", "c"])
|
||||
msg = r"codes need to be between -1 and len\(categories\)-1"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes([-2, 1, 2], categories=dtype.categories)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes([-2, 1, 2], dtype=dtype)
|
||||
|
||||
def test_from_codes(self):
|
||||
dtype = CategoricalDtype(categories=["a", "b", "c"])
|
||||
exp = Categorical(["a", "b", "c"], ordered=False)
|
||||
res = Categorical.from_codes([0, 1, 2], categories=dtype.categories)
|
||||
tm.assert_categorical_equal(exp, res)
|
||||
|
||||
res = Categorical.from_codes([0, 1, 2], dtype=dtype)
|
||||
tm.assert_categorical_equal(exp, res)
|
||||
|
||||
@pytest.mark.parametrize("klass", [Categorical, CategoricalIndex])
|
||||
def test_from_codes_with_categorical_categories(self, klass):
|
||||
# GH17884
|
||||
expected = Categorical(["a", "b"], categories=["a", "b", "c"])
|
||||
|
||||
result = Categorical.from_codes([0, 1], categories=klass(["a", "b", "c"]))
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("klass", [Categorical, CategoricalIndex])
|
||||
def test_from_codes_with_non_unique_categorical_categories(self, klass):
|
||||
with pytest.raises(ValueError, match="Categorical categories must be unique"):
|
||||
Categorical.from_codes([0, 1], klass(["a", "b", "a"]))
|
||||
|
||||
def test_from_codes_with_nan_code(self):
|
||||
# GH21767
|
||||
codes = [1, 2, np.nan]
|
||||
dtype = CategoricalDtype(categories=["a", "b", "c"])
|
||||
with pytest.raises(ValueError, match="codes need to be array-like integers"):
|
||||
Categorical.from_codes(codes, categories=dtype.categories)
|
||||
with pytest.raises(ValueError, match="codes need to be array-like integers"):
|
||||
Categorical.from_codes(codes, dtype=dtype)
|
||||
|
||||
@pytest.mark.parametrize("codes", [[1.0, 2.0, 0], [1.1, 2.0, 0]])
|
||||
def test_from_codes_with_float(self, codes):
|
||||
# GH21767
|
||||
# float codes should raise even if values are equal to integers
|
||||
dtype = CategoricalDtype(categories=["a", "b", "c"])
|
||||
|
||||
msg = "codes need to be array-like integers"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes(codes, dtype.categories)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes(codes, dtype=dtype)
|
||||
|
||||
def test_from_codes_with_dtype_raises(self):
|
||||
msg = "Cannot specify"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes(
|
||||
[0, 1], categories=["a", "b"], dtype=CategoricalDtype(["a", "b"])
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes(
|
||||
[0, 1], ordered=True, dtype=CategoricalDtype(["a", "b"])
|
||||
)
|
||||
|
||||
def test_from_codes_neither(self):
|
||||
msg = "Both were None"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes([0, 1])
|
||||
|
||||
def test_from_codes_with_nullable_int(self):
|
||||
codes = pd.array([0, 1], dtype="Int64")
|
||||
categories = ["a", "b"]
|
||||
|
||||
result = Categorical.from_codes(codes, categories=categories)
|
||||
expected = Categorical.from_codes(codes.to_numpy(int), categories=categories)
|
||||
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_from_codes_with_nullable_int_na_raises(self):
|
||||
codes = pd.array([0, None], dtype="Int64")
|
||||
categories = ["a", "b"]
|
||||
|
||||
msg = "codes cannot contain NA values"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
Categorical.from_codes(codes, categories=categories)
|
||||
|
||||
@pytest.mark.parametrize("dtype", [None, "category"])
|
||||
def test_from_inferred_categories(self, dtype):
|
||||
cats = ["a", "b"]
|
||||
codes = np.array([0, 0, 1, 1], dtype="i8")
|
||||
result = Categorical._from_inferred_categories(cats, codes, dtype)
|
||||
expected = Categorical.from_codes(codes, cats)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("dtype", [None, "category"])
|
||||
def test_from_inferred_categories_sorts(self, dtype):
|
||||
cats = ["b", "a"]
|
||||
codes = np.array([0, 1, 1, 1], dtype="i8")
|
||||
result = Categorical._from_inferred_categories(cats, codes, dtype)
|
||||
expected = Categorical.from_codes([1, 0, 0, 0], ["a", "b"])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_from_inferred_categories_dtype(self):
|
||||
cats = ["a", "b", "d"]
|
||||
codes = np.array([0, 1, 0, 2], dtype="i8")
|
||||
dtype = CategoricalDtype(["c", "b", "a"], ordered=True)
|
||||
result = Categorical._from_inferred_categories(cats, codes, dtype)
|
||||
expected = Categorical(
|
||||
["a", "b", "a", "d"], categories=["c", "b", "a"], ordered=True
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_from_inferred_categories_coerces(self):
|
||||
cats = ["1", "2", "bad"]
|
||||
codes = np.array([0, 0, 1, 2], dtype="i8")
|
||||
dtype = CategoricalDtype([1, 2])
|
||||
result = Categorical._from_inferred_categories(cats, codes, dtype)
|
||||
expected = Categorical([1, 1, 2, np.nan])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("ordered", [None, True, False])
|
||||
def test_construction_with_ordered(self, ordered):
|
||||
# GH 9347, 9190
|
||||
cat = Categorical([0, 1, 2], ordered=ordered)
|
||||
assert cat.ordered == bool(ordered)
|
||||
|
||||
def test_constructor_imaginary(self):
|
||||
values = [1, 2, 3 + 1j]
|
||||
c1 = Categorical(values)
|
||||
tm.assert_index_equal(c1.categories, Index(values))
|
||||
tm.assert_numpy_array_equal(np.array(c1), np.array(values))
|
||||
|
||||
def test_constructor_string_and_tuples(self):
|
||||
# GH 21416
|
||||
c = Categorical(np.array(["c", ("a", "b"), ("b", "a"), "c"], dtype=object))
|
||||
expected_index = Index([("a", "b"), ("b", "a"), "c"])
|
||||
assert c.categories.equals(expected_index)
|
||||
|
||||
def test_interval(self):
|
||||
idx = pd.interval_range(0, 10, periods=10)
|
||||
cat = Categorical(idx, categories=idx)
|
||||
expected_codes = np.arange(10, dtype="int8")
|
||||
tm.assert_numpy_array_equal(cat.codes, expected_codes)
|
||||
tm.assert_index_equal(cat.categories, idx)
|
||||
|
||||
# infer categories
|
||||
cat = Categorical(idx)
|
||||
tm.assert_numpy_array_equal(cat.codes, expected_codes)
|
||||
tm.assert_index_equal(cat.categories, idx)
|
||||
|
||||
# list values
|
||||
cat = Categorical(list(idx))
|
||||
tm.assert_numpy_array_equal(cat.codes, expected_codes)
|
||||
tm.assert_index_equal(cat.categories, idx)
|
||||
|
||||
# list values, categories
|
||||
cat = Categorical(list(idx), categories=list(idx))
|
||||
tm.assert_numpy_array_equal(cat.codes, expected_codes)
|
||||
tm.assert_index_equal(cat.categories, idx)
|
||||
|
||||
# shuffled
|
||||
values = idx.take([1, 2, 0])
|
||||
cat = Categorical(values, categories=idx)
|
||||
tm.assert_numpy_array_equal(cat.codes, np.array([1, 2, 0], dtype="int8"))
|
||||
tm.assert_index_equal(cat.categories, idx)
|
||||
|
||||
# extra
|
||||
values = pd.interval_range(8, 11, periods=3)
|
||||
cat = Categorical(values, categories=idx)
|
||||
expected_codes = np.array([8, 9, -1], dtype="int8")
|
||||
tm.assert_numpy_array_equal(cat.codes, expected_codes)
|
||||
tm.assert_index_equal(cat.categories, idx)
|
||||
|
||||
# overlapping
|
||||
idx = IntervalIndex([Interval(0, 2), Interval(0, 1)])
|
||||
cat = Categorical(idx, categories=idx)
|
||||
expected_codes = np.array([0, 1], dtype="int8")
|
||||
tm.assert_numpy_array_equal(cat.codes, expected_codes)
|
||||
tm.assert_index_equal(cat.categories, idx)
|
||||
|
||||
def test_categorical_extension_array_nullable(self, nulls_fixture):
|
||||
# GH:
|
||||
arr = pd.arrays.StringArray._from_sequence(
|
||||
[nulls_fixture] * 2, dtype=pd.StringDtype()
|
||||
)
|
||||
result = Categorical(arr)
|
||||
assert arr.dtype == result.categories.dtype
|
||||
expected = Categorical(Series([pd.NA, pd.NA], dtype=arr.dtype))
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_from_sequence_copy(self):
|
||||
cat = Categorical(np.arange(5).repeat(2))
|
||||
result = Categorical._from_sequence(cat, dtype=cat.dtype, copy=False)
|
||||
|
||||
# more generally, we'd be OK with a view
|
||||
assert result._codes is cat._codes
|
||||
|
||||
result = Categorical._from_sequence(cat, dtype=cat.dtype, copy=True)
|
||||
|
||||
assert not tm.shares_memory(result, cat)
|
||||
|
||||
def test_constructor_datetime64_non_nano(self):
|
||||
categories = np.arange(10).view("M8[D]")
|
||||
values = categories[::2].copy()
|
||||
|
||||
cat = Categorical(values, categories=categories)
|
||||
assert (cat == values).all()
|
||||
|
||||
def test_constructor_preserves_freq(self):
|
||||
# GH33830 freq retention in categorical
|
||||
dti = date_range("2016-01-01", periods=5)
|
||||
|
||||
expected = dti.freq
|
||||
|
||||
cat = Categorical(dti)
|
||||
result = cat.categories.freq
|
||||
|
||||
assert expected == result
|
||||
@ -0,0 +1,139 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas.core.dtypes.dtypes import CategoricalDtype
|
||||
|
||||
from pandas import (
|
||||
Categorical,
|
||||
CategoricalIndex,
|
||||
Index,
|
||||
IntervalIndex,
|
||||
Series,
|
||||
Timestamp,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestCategoricalDtypes:
|
||||
def test_categories_match_up_to_permutation(self):
|
||||
# test dtype comparisons between cats
|
||||
|
||||
c1 = Categorical(list("aabca"), categories=list("abc"), ordered=False)
|
||||
c2 = Categorical(list("aabca"), categories=list("cab"), ordered=False)
|
||||
c3 = Categorical(list("aabca"), categories=list("cab"), ordered=True)
|
||||
assert c1._categories_match_up_to_permutation(c1)
|
||||
assert c2._categories_match_up_to_permutation(c2)
|
||||
assert c3._categories_match_up_to_permutation(c3)
|
||||
assert c1._categories_match_up_to_permutation(c2)
|
||||
assert not c1._categories_match_up_to_permutation(c3)
|
||||
assert not c1._categories_match_up_to_permutation(Index(list("aabca")))
|
||||
assert not c1._categories_match_up_to_permutation(c1.astype(object))
|
||||
assert c1._categories_match_up_to_permutation(CategoricalIndex(c1))
|
||||
assert c1._categories_match_up_to_permutation(
|
||||
CategoricalIndex(c1, categories=list("cab"))
|
||||
)
|
||||
assert not c1._categories_match_up_to_permutation(
|
||||
CategoricalIndex(c1, ordered=True)
|
||||
)
|
||||
|
||||
# GH 16659
|
||||
s1 = Series(c1)
|
||||
s2 = Series(c2)
|
||||
s3 = Series(c3)
|
||||
assert c1._categories_match_up_to_permutation(s1)
|
||||
assert c2._categories_match_up_to_permutation(s2)
|
||||
assert c3._categories_match_up_to_permutation(s3)
|
||||
assert c1._categories_match_up_to_permutation(s2)
|
||||
assert not c1._categories_match_up_to_permutation(s3)
|
||||
assert not c1._categories_match_up_to_permutation(s1.astype(object))
|
||||
|
||||
def test_set_dtype_same(self):
|
||||
c = Categorical(["a", "b", "c"])
|
||||
result = c._set_dtype(CategoricalDtype(["a", "b", "c"]))
|
||||
tm.assert_categorical_equal(result, c)
|
||||
|
||||
def test_set_dtype_new_categories(self):
|
||||
c = Categorical(["a", "b", "c"])
|
||||
result = c._set_dtype(CategoricalDtype(list("abcd")))
|
||||
tm.assert_numpy_array_equal(result.codes, c.codes)
|
||||
tm.assert_index_equal(result.dtype.categories, Index(list("abcd")))
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"values, categories, new_categories",
|
||||
[
|
||||
# No NaNs, same cats, same order
|
||||
(["a", "b", "a"], ["a", "b"], ["a", "b"]),
|
||||
# No NaNs, same cats, different order
|
||||
(["a", "b", "a"], ["a", "b"], ["b", "a"]),
|
||||
# Same, unsorted
|
||||
(["b", "a", "a"], ["a", "b"], ["a", "b"]),
|
||||
# No NaNs, same cats, different order
|
||||
(["b", "a", "a"], ["a", "b"], ["b", "a"]),
|
||||
# NaNs
|
||||
(["a", "b", "c"], ["a", "b"], ["a", "b"]),
|
||||
(["a", "b", "c"], ["a", "b"], ["b", "a"]),
|
||||
(["b", "a", "c"], ["a", "b"], ["a", "b"]),
|
||||
(["b", "a", "c"], ["a", "b"], ["a", "b"]),
|
||||
# Introduce NaNs
|
||||
(["a", "b", "c"], ["a", "b"], ["a"]),
|
||||
(["a", "b", "c"], ["a", "b"], ["b"]),
|
||||
(["b", "a", "c"], ["a", "b"], ["a"]),
|
||||
(["b", "a", "c"], ["a", "b"], ["a"]),
|
||||
# No overlap
|
||||
(["a", "b", "c"], ["a", "b"], ["d", "e"]),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("ordered", [True, False])
|
||||
def test_set_dtype_many(self, values, categories, new_categories, ordered):
|
||||
c = Categorical(values, categories)
|
||||
expected = Categorical(values, new_categories, ordered)
|
||||
result = c._set_dtype(expected.dtype)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_set_dtype_no_overlap(self):
|
||||
c = Categorical(["a", "b", "c"], ["d", "e"])
|
||||
result = c._set_dtype(CategoricalDtype(["a", "b"]))
|
||||
expected = Categorical([None, None, None], categories=["a", "b"])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_codes_dtypes(self):
|
||||
# GH 8453
|
||||
result = Categorical(["foo", "bar", "baz"])
|
||||
assert result.codes.dtype == "int8"
|
||||
|
||||
result = Categorical([f"foo{i:05d}" for i in range(400)])
|
||||
assert result.codes.dtype == "int16"
|
||||
|
||||
result = Categorical([f"foo{i:05d}" for i in range(40000)])
|
||||
assert result.codes.dtype == "int32"
|
||||
|
||||
# adding cats
|
||||
result = Categorical(["foo", "bar", "baz"])
|
||||
assert result.codes.dtype == "int8"
|
||||
result = result.add_categories([f"foo{i:05d}" for i in range(400)])
|
||||
assert result.codes.dtype == "int16"
|
||||
|
||||
# removing cats
|
||||
result = result.remove_categories([f"foo{i:05d}" for i in range(300)])
|
||||
assert result.codes.dtype == "int8"
|
||||
|
||||
def test_iter_python_types(self):
|
||||
# GH-19909
|
||||
cat = Categorical([1, 2])
|
||||
assert isinstance(next(iter(cat)), int)
|
||||
assert isinstance(cat.tolist()[0], int)
|
||||
|
||||
def test_iter_python_types_datetime(self):
|
||||
cat = Categorical([Timestamp("2017-01-01"), Timestamp("2017-01-02")])
|
||||
assert isinstance(next(iter(cat)), Timestamp)
|
||||
assert isinstance(cat.tolist()[0], Timestamp)
|
||||
|
||||
def test_interval_index_category(self):
|
||||
# GH 38316
|
||||
index = IntervalIndex.from_breaks(np.arange(3, dtype="uint64"))
|
||||
|
||||
result = CategoricalIndex(index).dtype.categories
|
||||
expected = IntervalIndex.from_arrays(
|
||||
[0, 1], [1, 2], dtype="interval[uint64, right]"
|
||||
)
|
||||
tm.assert_index_equal(result, expected)
|
||||
@ -0,0 +1,388 @@
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas import (
|
||||
NA,
|
||||
Categorical,
|
||||
CategoricalIndex,
|
||||
Index,
|
||||
Interval,
|
||||
IntervalIndex,
|
||||
NaT,
|
||||
PeriodIndex,
|
||||
Series,
|
||||
Timedelta,
|
||||
Timestamp,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
import pandas.core.common as com
|
||||
|
||||
|
||||
class TestCategoricalIndexingWithFactor:
|
||||
def test_getitem(self):
|
||||
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
|
||||
assert factor[0] == "a"
|
||||
assert factor[-1] == "c"
|
||||
|
||||
subf = factor[[0, 1, 2]]
|
||||
tm.assert_numpy_array_equal(subf._codes, np.array([0, 1, 1], dtype=np.int8))
|
||||
|
||||
subf = factor[np.asarray(factor) == "c"]
|
||||
tm.assert_numpy_array_equal(subf._codes, np.array([2, 2, 2], dtype=np.int8))
|
||||
|
||||
def test_setitem(self):
|
||||
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
|
||||
# int/positional
|
||||
c = factor.copy()
|
||||
c[0] = "b"
|
||||
assert c[0] == "b"
|
||||
c[-1] = "a"
|
||||
assert c[-1] == "a"
|
||||
|
||||
# boolean
|
||||
c = factor.copy()
|
||||
indexer = np.zeros(len(c), dtype="bool")
|
||||
indexer[0] = True
|
||||
indexer[-1] = True
|
||||
c[indexer] = "c"
|
||||
expected = Categorical(["c", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
|
||||
|
||||
tm.assert_categorical_equal(c, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"other",
|
||||
[Categorical(["b", "a"]), Categorical(["b", "a"], categories=["b", "a"])],
|
||||
)
|
||||
def test_setitem_same_but_unordered(self, other):
|
||||
# GH-24142
|
||||
target = Categorical(["a", "b"], categories=["a", "b"])
|
||||
mask = np.array([True, False])
|
||||
target[mask] = other[mask]
|
||||
expected = Categorical(["b", "b"], categories=["a", "b"])
|
||||
tm.assert_categorical_equal(target, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"other",
|
||||
[
|
||||
Categorical(["b", "a"], categories=["b", "a", "c"]),
|
||||
Categorical(["b", "a"], categories=["a", "b", "c"]),
|
||||
Categorical(["a", "a"], categories=["a"]),
|
||||
Categorical(["b", "b"], categories=["b"]),
|
||||
],
|
||||
)
|
||||
def test_setitem_different_unordered_raises(self, other):
|
||||
# GH-24142
|
||||
target = Categorical(["a", "b"], categories=["a", "b"])
|
||||
mask = np.array([True, False])
|
||||
msg = "Cannot set a Categorical with another, without identical categories"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
target[mask] = other[mask]
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"other",
|
||||
[
|
||||
Categorical(["b", "a"]),
|
||||
Categorical(["b", "a"], categories=["b", "a"], ordered=True),
|
||||
Categorical(["b", "a"], categories=["a", "b", "c"], ordered=True),
|
||||
],
|
||||
)
|
||||
def test_setitem_same_ordered_raises(self, other):
|
||||
# Gh-24142
|
||||
target = Categorical(["a", "b"], categories=["a", "b"], ordered=True)
|
||||
mask = np.array([True, False])
|
||||
msg = "Cannot set a Categorical with another, without identical categories"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
target[mask] = other[mask]
|
||||
|
||||
def test_setitem_tuple(self):
|
||||
# GH#20439
|
||||
cat = Categorical([(0, 1), (0, 2), (0, 1)])
|
||||
|
||||
# This should not raise
|
||||
cat[1] = cat[0]
|
||||
assert cat[1] == (0, 1)
|
||||
|
||||
def test_setitem_listlike(self):
|
||||
# GH#9469
|
||||
# properly coerce the input indexers
|
||||
|
||||
cat = Categorical(
|
||||
np.random.default_rng(2).integers(0, 5, size=150000).astype(np.int8)
|
||||
).add_categories([-1000])
|
||||
indexer = np.array([100000]).astype(np.int64)
|
||||
cat[indexer] = -1000
|
||||
|
||||
# we are asserting the code result here
|
||||
# which maps to the -1000 category
|
||||
result = cat.codes[np.array([100000]).astype(np.int64)]
|
||||
tm.assert_numpy_array_equal(result, np.array([5], dtype="int8"))
|
||||
|
||||
|
||||
class TestCategoricalIndexing:
|
||||
def test_getitem_slice(self):
|
||||
cat = Categorical(["a", "b", "c", "d", "a", "b", "c"])
|
||||
sliced = cat[3]
|
||||
assert sliced == "d"
|
||||
|
||||
sliced = cat[3:5]
|
||||
expected = Categorical(["d", "a"], categories=["a", "b", "c", "d"])
|
||||
tm.assert_categorical_equal(sliced, expected)
|
||||
|
||||
def test_getitem_listlike(self):
|
||||
# GH 9469
|
||||
# properly coerce the input indexers
|
||||
|
||||
c = Categorical(
|
||||
np.random.default_rng(2).integers(0, 5, size=150000).astype(np.int8)
|
||||
)
|
||||
result = c.codes[np.array([100000]).astype(np.int64)]
|
||||
expected = c[np.array([100000]).astype(np.int64)].codes
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
def test_periodindex(self):
|
||||
idx1 = PeriodIndex(
|
||||
["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"],
|
||||
freq="M",
|
||||
)
|
||||
|
||||
cat1 = Categorical(idx1)
|
||||
str(cat1)
|
||||
exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.int8)
|
||||
exp_idx = PeriodIndex(["2014-01", "2014-02", "2014-03"], freq="M")
|
||||
tm.assert_numpy_array_equal(cat1._codes, exp_arr)
|
||||
tm.assert_index_equal(cat1.categories, exp_idx)
|
||||
|
||||
idx2 = PeriodIndex(
|
||||
["2014-03", "2014-03", "2014-02", "2014-01", "2014-03", "2014-01"],
|
||||
freq="M",
|
||||
)
|
||||
cat2 = Categorical(idx2, ordered=True)
|
||||
str(cat2)
|
||||
exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.int8)
|
||||
exp_idx2 = PeriodIndex(["2014-01", "2014-02", "2014-03"], freq="M")
|
||||
tm.assert_numpy_array_equal(cat2._codes, exp_arr)
|
||||
tm.assert_index_equal(cat2.categories, exp_idx2)
|
||||
|
||||
idx3 = PeriodIndex(
|
||||
[
|
||||
"2013-12",
|
||||
"2013-11",
|
||||
"2013-10",
|
||||
"2013-09",
|
||||
"2013-08",
|
||||
"2013-07",
|
||||
"2013-05",
|
||||
],
|
||||
freq="M",
|
||||
)
|
||||
cat3 = Categorical(idx3, ordered=True)
|
||||
exp_arr = np.array([6, 5, 4, 3, 2, 1, 0], dtype=np.int8)
|
||||
exp_idx = PeriodIndex(
|
||||
[
|
||||
"2013-05",
|
||||
"2013-07",
|
||||
"2013-08",
|
||||
"2013-09",
|
||||
"2013-10",
|
||||
"2013-11",
|
||||
"2013-12",
|
||||
],
|
||||
freq="M",
|
||||
)
|
||||
tm.assert_numpy_array_equal(cat3._codes, exp_arr)
|
||||
tm.assert_index_equal(cat3.categories, exp_idx)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"null_val",
|
||||
[None, np.nan, NaT, NA, math.nan, "NaT", "nat", "NAT", "nan", "NaN", "NAN"],
|
||||
)
|
||||
def test_periodindex_on_null_types(self, null_val):
|
||||
# GH 46673
|
||||
result = PeriodIndex(["2022-04-06", "2022-04-07", null_val], freq="D")
|
||||
expected = PeriodIndex(["2022-04-06", "2022-04-07", "NaT"], dtype="period[D]")
|
||||
assert result[2] is NaT
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("new_categories", [[1, 2, 3, 4], [1, 2]])
|
||||
def test_categories_assignments_wrong_length_raises(self, new_categories):
|
||||
cat = Categorical(["a", "b", "c", "a"])
|
||||
msg = (
|
||||
"new categories need to have the same number of items "
|
||||
"as the old categories!"
|
||||
)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
cat.rename_categories(new_categories)
|
||||
|
||||
# Combinations of sorted/unique:
|
||||
@pytest.mark.parametrize(
|
||||
"idx_values", [[1, 2, 3, 4], [1, 3, 2, 4], [1, 3, 3, 4], [1, 2, 2, 4]]
|
||||
)
|
||||
# Combinations of missing/unique
|
||||
@pytest.mark.parametrize("key_values", [[1, 2], [1, 5], [1, 1], [5, 5]])
|
||||
@pytest.mark.parametrize("key_class", [Categorical, CategoricalIndex])
|
||||
@pytest.mark.parametrize("dtype", [None, "category", "key"])
|
||||
def test_get_indexer_non_unique(self, idx_values, key_values, key_class, dtype):
|
||||
# GH 21448
|
||||
key = key_class(key_values, categories=range(1, 5))
|
||||
|
||||
if dtype == "key":
|
||||
dtype = key.dtype
|
||||
|
||||
# Test for flat index and CategoricalIndex with same/different cats:
|
||||
idx = Index(idx_values, dtype=dtype)
|
||||
expected, exp_miss = idx.get_indexer_non_unique(key_values)
|
||||
result, res_miss = idx.get_indexer_non_unique(key)
|
||||
|
||||
tm.assert_numpy_array_equal(expected, result)
|
||||
tm.assert_numpy_array_equal(exp_miss, res_miss)
|
||||
|
||||
exp_unique = idx.unique().get_indexer(key_values)
|
||||
res_unique = idx.unique().get_indexer(key)
|
||||
tm.assert_numpy_array_equal(res_unique, exp_unique)
|
||||
|
||||
def test_where_unobserved_nan(self):
|
||||
ser = Series(Categorical(["a", "b"]))
|
||||
result = ser.where([True, False])
|
||||
expected = Series(Categorical(["a", None], categories=["a", "b"]))
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
# all NA
|
||||
ser = Series(Categorical(["a", "b"]))
|
||||
result = ser.where([False, False])
|
||||
expected = Series(Categorical([None, None], categories=["a", "b"]))
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_where_unobserved_categories(self):
|
||||
ser = Series(Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"]))
|
||||
result = ser.where([True, True, False], other="b")
|
||||
expected = Series(Categorical(["a", "b", "b"], categories=ser.cat.categories))
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_where_other_categorical(self):
|
||||
ser = Series(Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"]))
|
||||
other = Categorical(["b", "c", "a"], categories=["a", "c", "b", "d"])
|
||||
result = ser.where([True, False, True], other)
|
||||
expected = Series(Categorical(["a", "c", "c"], dtype=ser.dtype))
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_where_new_category_raises(self):
|
||||
ser = Series(Categorical(["a", "b", "c"]))
|
||||
msg = "Cannot setitem on a Categorical with a new category"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
ser.where([True, False, True], "d")
|
||||
|
||||
def test_where_ordered_differs_rasies(self):
|
||||
ser = Series(
|
||||
Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"], ordered=True)
|
||||
)
|
||||
other = Categorical(
|
||||
["b", "c", "a"], categories=["a", "c", "b", "d"], ordered=True
|
||||
)
|
||||
with pytest.raises(TypeError, match="without identical categories"):
|
||||
ser.where([True, False, True], other)
|
||||
|
||||
|
||||
class TestContains:
|
||||
def test_contains(self):
|
||||
# GH#21508
|
||||
cat = Categorical(list("aabbca"), categories=list("cab"))
|
||||
|
||||
assert "b" in cat
|
||||
assert "z" not in cat
|
||||
assert np.nan not in cat
|
||||
with pytest.raises(TypeError, match="unhashable type: 'list'"):
|
||||
assert [1] in cat
|
||||
|
||||
# assert codes NOT in index
|
||||
assert 0 not in cat
|
||||
assert 1 not in cat
|
||||
|
||||
cat = Categorical(list("aabbca") + [np.nan], categories=list("cab"))
|
||||
assert np.nan in cat
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"item, expected",
|
||||
[
|
||||
(Interval(0, 1), True),
|
||||
(1.5, True),
|
||||
(Interval(0.5, 1.5), False),
|
||||
("a", False),
|
||||
(Timestamp(1), False),
|
||||
(Timedelta(1), False),
|
||||
],
|
||||
ids=str,
|
||||
)
|
||||
def test_contains_interval(self, item, expected):
|
||||
# GH#23705
|
||||
cat = Categorical(IntervalIndex.from_breaks(range(3)))
|
||||
result = item in cat
|
||||
assert result is expected
|
||||
|
||||
def test_contains_list(self):
|
||||
# GH#21729
|
||||
cat = Categorical([1, 2, 3])
|
||||
|
||||
assert "a" not in cat
|
||||
|
||||
with pytest.raises(TypeError, match="unhashable type"):
|
||||
["a"] in cat
|
||||
|
||||
with pytest.raises(TypeError, match="unhashable type"):
|
||||
["a", "b"] in cat
|
||||
|
||||
|
||||
@pytest.mark.parametrize("index", [True, False])
|
||||
def test_mask_with_boolean(index):
|
||||
ser = Series(range(3))
|
||||
idx = Categorical([True, False, True])
|
||||
if index:
|
||||
idx = CategoricalIndex(idx)
|
||||
|
||||
assert com.is_bool_indexer(idx)
|
||||
result = ser[idx]
|
||||
expected = ser[idx.astype("object")]
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("index", [True, False])
|
||||
def test_mask_with_boolean_na_treated_as_false(index):
|
||||
# https://github.com/pandas-dev/pandas/issues/31503
|
||||
ser = Series(range(3))
|
||||
idx = Categorical([True, False, None])
|
||||
if index:
|
||||
idx = CategoricalIndex(idx)
|
||||
|
||||
result = ser[idx]
|
||||
expected = ser[idx.fillna(False)]
|
||||
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def non_coercible_categorical(monkeypatch):
|
||||
"""
|
||||
Monkeypatch Categorical.__array__ to ensure no implicit conversion.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
When Categorical.__array__ is called.
|
||||
"""
|
||||
|
||||
# TODO(Categorical): identify other places where this may be
|
||||
# useful and move to a conftest.py
|
||||
def array(self, dtype=None):
|
||||
raise ValueError("I cannot be converted.")
|
||||
|
||||
with monkeypatch.context() as m:
|
||||
m.setattr(Categorical, "__array__", array)
|
||||
yield
|
||||
|
||||
|
||||
def test_series_at():
|
||||
arr = Categorical(["a", "b", "c"])
|
||||
ser = Series(arr)
|
||||
result = ser.at[0]
|
||||
assert result == "a"
|
||||
@ -0,0 +1,154 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
Categorical,
|
||||
Index,
|
||||
Series,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
@pytest.fixture(params=[None, "ignore"])
|
||||
def na_action(request):
|
||||
return request.param
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"data, categories",
|
||||
[
|
||||
(list("abcbca"), list("cab")),
|
||||
(pd.interval_range(0, 3).repeat(3), pd.interval_range(0, 3)),
|
||||
],
|
||||
ids=["string", "interval"],
|
||||
)
|
||||
def test_map_str(data, categories, ordered, na_action):
|
||||
# GH 31202 - override base class since we want to maintain categorical/ordered
|
||||
cat = Categorical(data, categories=categories, ordered=ordered)
|
||||
result = cat.map(str, na_action=na_action)
|
||||
expected = Categorical(
|
||||
map(str, data), categories=map(str, categories), ordered=ordered
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
|
||||
def test_map(na_action):
|
||||
cat = Categorical(list("ABABC"), categories=list("CBA"), ordered=True)
|
||||
result = cat.map(lambda x: x.lower(), na_action=na_action)
|
||||
exp = Categorical(list("ababc"), categories=list("cba"), ordered=True)
|
||||
tm.assert_categorical_equal(result, exp)
|
||||
|
||||
cat = Categorical(list("ABABC"), categories=list("BAC"), ordered=False)
|
||||
result = cat.map(lambda x: x.lower(), na_action=na_action)
|
||||
exp = Categorical(list("ababc"), categories=list("bac"), ordered=False)
|
||||
tm.assert_categorical_equal(result, exp)
|
||||
|
||||
# GH 12766: Return an index not an array
|
||||
result = cat.map(lambda x: 1, na_action=na_action)
|
||||
exp = Index(np.array([1] * 5, dtype=np.int64))
|
||||
tm.assert_index_equal(result, exp)
|
||||
|
||||
# change categories dtype
|
||||
cat = Categorical(list("ABABC"), categories=list("BAC"), ordered=False)
|
||||
|
||||
def f(x):
|
||||
return {"A": 10, "B": 20, "C": 30}.get(x)
|
||||
|
||||
result = cat.map(f, na_action=na_action)
|
||||
exp = Categorical([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False)
|
||||
tm.assert_categorical_equal(result, exp)
|
||||
|
||||
mapper = Series([10, 20, 30], index=["A", "B", "C"])
|
||||
result = cat.map(mapper, na_action=na_action)
|
||||
tm.assert_categorical_equal(result, exp)
|
||||
|
||||
result = cat.map({"A": 10, "B": 20, "C": 30}, na_action=na_action)
|
||||
tm.assert_categorical_equal(result, exp)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("data", "f", "expected"),
|
||||
(
|
||||
([1, 1, np.nan], pd.isna, Index([False, False, True])),
|
||||
([1, 2, np.nan], pd.isna, Index([False, False, True])),
|
||||
([1, 1, np.nan], {1: False}, Categorical([False, False, np.nan])),
|
||||
([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
|
||||
(
|
||||
[1, 1, np.nan],
|
||||
Series([False, False]),
|
||||
Categorical([False, False, np.nan]),
|
||||
),
|
||||
(
|
||||
[1, 2, np.nan],
|
||||
Series([False] * 3),
|
||||
Index([False, False, np.nan]),
|
||||
),
|
||||
),
|
||||
)
|
||||
def test_map_with_nan_none(data, f, expected): # GH 24241
|
||||
values = Categorical(data)
|
||||
result = values.map(f, na_action=None)
|
||||
if isinstance(expected, Categorical):
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
else:
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("data", "f", "expected"),
|
||||
(
|
||||
([1, 1, np.nan], pd.isna, Categorical([False, False, np.nan])),
|
||||
([1, 2, np.nan], pd.isna, Index([False, False, np.nan])),
|
||||
([1, 1, np.nan], {1: False}, Categorical([False, False, np.nan])),
|
||||
([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
|
||||
(
|
||||
[1, 1, np.nan],
|
||||
Series([False, False]),
|
||||
Categorical([False, False, np.nan]),
|
||||
),
|
||||
(
|
||||
[1, 2, np.nan],
|
||||
Series([False, False, False]),
|
||||
Index([False, False, np.nan]),
|
||||
),
|
||||
),
|
||||
)
|
||||
def test_map_with_nan_ignore(data, f, expected): # GH 24241
|
||||
values = Categorical(data)
|
||||
result = values.map(f, na_action="ignore")
|
||||
if data[1] == 1:
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
else:
|
||||
tm.assert_index_equal(result, expected)
|
||||
|
||||
|
||||
def test_map_with_dict_or_series(na_action):
|
||||
orig_values = ["a", "B", 1, "a"]
|
||||
new_values = ["one", 2, 3.0, "one"]
|
||||
cat = Categorical(orig_values)
|
||||
|
||||
mapper = Series(new_values[:-1], index=orig_values[:-1])
|
||||
result = cat.map(mapper, na_action=na_action)
|
||||
|
||||
# Order of categories in result can be different
|
||||
expected = Categorical(new_values, categories=[3.0, 2, "one"])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
mapper = dict(zip(orig_values[:-1], new_values[:-1]))
|
||||
result = cat.map(mapper, na_action=na_action)
|
||||
# Order of categories in result can be different
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
|
||||
def test_map_na_action_no_default_deprecated():
|
||||
# GH51645
|
||||
cat = Categorical(["a", "b", "c"])
|
||||
msg = (
|
||||
"The default value of 'ignore' for the `na_action` parameter in "
|
||||
"pandas.Categorical.map is deprecated and will be "
|
||||
"changed to 'None' in a future version. Please set na_action to the "
|
||||
"desired value to avoid seeing this warning"
|
||||
)
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
cat.map(lambda x: x)
|
||||
@ -0,0 +1,216 @@
|
||||
import collections
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas.core.dtypes.dtypes import CategoricalDtype
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
Categorical,
|
||||
DataFrame,
|
||||
Index,
|
||||
Series,
|
||||
isna,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestCategoricalMissing:
|
||||
def test_isna(self):
|
||||
exp = np.array([False, False, True])
|
||||
cat = Categorical(["a", "b", np.nan])
|
||||
res = cat.isna()
|
||||
|
||||
tm.assert_numpy_array_equal(res, exp)
|
||||
|
||||
def test_na_flags_int_categories(self):
|
||||
# #1457
|
||||
|
||||
categories = list(range(10))
|
||||
labels = np.random.default_rng(2).integers(0, 10, 20)
|
||||
labels[::5] = -1
|
||||
|
||||
cat = Categorical(labels, categories)
|
||||
repr(cat)
|
||||
|
||||
tm.assert_numpy_array_equal(isna(cat), labels == -1)
|
||||
|
||||
def test_nan_handling(self):
|
||||
# Nans are represented as -1 in codes
|
||||
c = Categorical(["a", "b", np.nan, "a"])
|
||||
tm.assert_index_equal(c.categories, Index(["a", "b"]))
|
||||
tm.assert_numpy_array_equal(c._codes, np.array([0, 1, -1, 0], dtype=np.int8))
|
||||
c[1] = np.nan
|
||||
tm.assert_index_equal(c.categories, Index(["a", "b"]))
|
||||
tm.assert_numpy_array_equal(c._codes, np.array([0, -1, -1, 0], dtype=np.int8))
|
||||
|
||||
# Adding nan to categories should make assigned nan point to the
|
||||
# category!
|
||||
c = Categorical(["a", "b", np.nan, "a"])
|
||||
tm.assert_index_equal(c.categories, Index(["a", "b"]))
|
||||
tm.assert_numpy_array_equal(c._codes, np.array([0, 1, -1, 0], dtype=np.int8))
|
||||
|
||||
def test_set_dtype_nans(self):
|
||||
c = Categorical(["a", "b", np.nan])
|
||||
result = c._set_dtype(CategoricalDtype(["a", "c"]))
|
||||
tm.assert_numpy_array_equal(result.codes, np.array([0, -1, -1], dtype="int8"))
|
||||
|
||||
def test_set_item_nan(self):
|
||||
cat = Categorical([1, 2, 3])
|
||||
cat[1] = np.nan
|
||||
|
||||
exp = Categorical([1, np.nan, 3], categories=[1, 2, 3])
|
||||
tm.assert_categorical_equal(cat, exp)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"fillna_kwargs, msg",
|
||||
[
|
||||
(
|
||||
{"value": 1, "method": "ffill"},
|
||||
"Cannot specify both 'value' and 'method'.",
|
||||
),
|
||||
({}, "Must specify a fill 'value' or 'method'."),
|
||||
({"method": "bad"}, "Invalid fill method. Expecting .* bad"),
|
||||
(
|
||||
{"value": Series([1, 2, 3, 4, "a"])},
|
||||
"Cannot setitem on a Categorical with a new category",
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_fillna_raises(self, fillna_kwargs, msg):
|
||||
# https://github.com/pandas-dev/pandas/issues/19682
|
||||
# https://github.com/pandas-dev/pandas/issues/13628
|
||||
cat = Categorical([1, 2, 3, None, None])
|
||||
|
||||
if len(fillna_kwargs) == 1 and "value" in fillna_kwargs:
|
||||
err = TypeError
|
||||
else:
|
||||
err = ValueError
|
||||
|
||||
with pytest.raises(err, match=msg):
|
||||
cat.fillna(**fillna_kwargs)
|
||||
|
||||
@pytest.mark.parametrize("named", [True, False])
|
||||
def test_fillna_iterable_category(self, named):
|
||||
# https://github.com/pandas-dev/pandas/issues/21097
|
||||
if named:
|
||||
Point = collections.namedtuple("Point", "x y")
|
||||
else:
|
||||
Point = lambda *args: args # tuple
|
||||
cat = Categorical(np.array([Point(0, 0), Point(0, 1), None], dtype=object))
|
||||
result = cat.fillna(Point(0, 0))
|
||||
expected = Categorical([Point(0, 0), Point(0, 1), Point(0, 0)])
|
||||
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
# Case where the Point is not among our categories; we want ValueError,
|
||||
# not NotImplementedError GH#41914
|
||||
cat = Categorical(np.array([Point(1, 0), Point(0, 1), None], dtype=object))
|
||||
msg = "Cannot setitem on a Categorical with a new category"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat.fillna(Point(0, 0))
|
||||
|
||||
def test_fillna_array(self):
|
||||
# accept Categorical or ndarray value if it holds appropriate values
|
||||
cat = Categorical(["A", "B", "C", None, None])
|
||||
|
||||
other = cat.fillna("C")
|
||||
result = cat.fillna(other)
|
||||
tm.assert_categorical_equal(result, other)
|
||||
assert isna(cat[-1]) # didn't modify original inplace
|
||||
|
||||
other = np.array(["A", "B", "C", "B", "A"])
|
||||
result = cat.fillna(other)
|
||||
expected = Categorical(["A", "B", "C", "B", "A"], dtype=cat.dtype)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
assert isna(cat[-1]) # didn't modify original inplace
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"values, expected",
|
||||
[
|
||||
([1, 2, 3], np.array([False, False, False])),
|
||||
([1, 2, np.nan], np.array([False, False, True])),
|
||||
([1, 2, np.inf], np.array([False, False, True])),
|
||||
([1, 2, pd.NA], np.array([False, False, True])),
|
||||
],
|
||||
)
|
||||
def test_use_inf_as_na(self, values, expected):
|
||||
# https://github.com/pandas-dev/pandas/issues/33594
|
||||
msg = "use_inf_as_na option is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
with pd.option_context("mode.use_inf_as_na", True):
|
||||
cat = Categorical(values)
|
||||
result = cat.isna()
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
result = Series(cat).isna()
|
||||
expected = Series(expected)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
result = DataFrame(cat).isna()
|
||||
expected = DataFrame(expected)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"values, expected",
|
||||
[
|
||||
([1, 2, 3], np.array([False, False, False])),
|
||||
([1, 2, np.nan], np.array([False, False, True])),
|
||||
([1, 2, np.inf], np.array([False, False, True])),
|
||||
([1, 2, pd.NA], np.array([False, False, True])),
|
||||
],
|
||||
)
|
||||
def test_use_inf_as_na_outside_context(self, values, expected):
|
||||
# https://github.com/pandas-dev/pandas/issues/33594
|
||||
# Using isna directly for Categorical will fail in general here
|
||||
cat = Categorical(values)
|
||||
|
||||
msg = "use_inf_as_na option is deprecated"
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
with pd.option_context("mode.use_inf_as_na", True):
|
||||
result = isna(cat)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
result = isna(Series(cat))
|
||||
expected = Series(expected)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
result = isna(DataFrame(cat))
|
||||
expected = DataFrame(expected)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"a1, a2, categories",
|
||||
[
|
||||
(["a", "b", "c"], [np.nan, "a", "b"], ["a", "b", "c"]),
|
||||
([1, 2, 3], [np.nan, 1, 2], [1, 2, 3]),
|
||||
],
|
||||
)
|
||||
def test_compare_categorical_with_missing(self, a1, a2, categories):
|
||||
# GH 28384
|
||||
cat_type = CategoricalDtype(categories)
|
||||
|
||||
# !=
|
||||
result = Series(a1, dtype=cat_type) != Series(a2, dtype=cat_type)
|
||||
expected = Series(a1) != Series(a2)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
# ==
|
||||
result = Series(a1, dtype=cat_type) == Series(a2, dtype=cat_type)
|
||||
expected = Series(a1) == Series(a2)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"na_value, dtype",
|
||||
[
|
||||
(pd.NaT, "datetime64[ns]"),
|
||||
(None, "float64"),
|
||||
(np.nan, "float64"),
|
||||
(pd.NA, "float64"),
|
||||
],
|
||||
)
|
||||
def test_categorical_only_missing_values_no_cast(self, na_value, dtype):
|
||||
# GH#44900
|
||||
result = Categorical([na_value, na_value])
|
||||
tm.assert_index_equal(result.categories, Index([], dtype=dtype))
|
||||
@ -0,0 +1,414 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
Categorical,
|
||||
DataFrame,
|
||||
Series,
|
||||
Timestamp,
|
||||
date_range,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestCategoricalOpsWithFactor:
|
||||
def test_categories_none_comparisons(self):
|
||||
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
|
||||
tm.assert_categorical_equal(factor, factor)
|
||||
|
||||
def test_comparisons(self):
|
||||
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
|
||||
result = factor[factor == "a"]
|
||||
expected = factor[np.asarray(factor) == "a"]
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
result = factor[factor != "a"]
|
||||
expected = factor[np.asarray(factor) != "a"]
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
result = factor[factor < "c"]
|
||||
expected = factor[np.asarray(factor) < "c"]
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
result = factor[factor > "a"]
|
||||
expected = factor[np.asarray(factor) > "a"]
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
result = factor[factor >= "b"]
|
||||
expected = factor[np.asarray(factor) >= "b"]
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
result = factor[factor <= "b"]
|
||||
expected = factor[np.asarray(factor) <= "b"]
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
n = len(factor)
|
||||
|
||||
other = factor[np.random.default_rng(2).permutation(n)]
|
||||
result = factor == other
|
||||
expected = np.asarray(factor) == np.asarray(other)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
result = factor == "d"
|
||||
expected = np.zeros(len(factor), dtype=bool)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
# comparisons with categoricals
|
||||
cat_rev = Categorical(["a", "b", "c"], categories=["c", "b", "a"], ordered=True)
|
||||
cat_rev_base = Categorical(
|
||||
["b", "b", "b"], categories=["c", "b", "a"], ordered=True
|
||||
)
|
||||
cat = Categorical(["a", "b", "c"], ordered=True)
|
||||
cat_base = Categorical(["b", "b", "b"], categories=cat.categories, ordered=True)
|
||||
|
||||
# comparisons need to take categories ordering into account
|
||||
res_rev = cat_rev > cat_rev_base
|
||||
exp_rev = np.array([True, False, False])
|
||||
tm.assert_numpy_array_equal(res_rev, exp_rev)
|
||||
|
||||
res_rev = cat_rev < cat_rev_base
|
||||
exp_rev = np.array([False, False, True])
|
||||
tm.assert_numpy_array_equal(res_rev, exp_rev)
|
||||
|
||||
res = cat > cat_base
|
||||
exp = np.array([False, False, True])
|
||||
tm.assert_numpy_array_equal(res, exp)
|
||||
|
||||
# Only categories with same categories can be compared
|
||||
msg = "Categoricals can only be compared if 'categories' are the same"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat > cat_rev
|
||||
|
||||
cat_rev_base2 = Categorical(["b", "b", "b"], categories=["c", "b", "a", "d"])
|
||||
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat_rev > cat_rev_base2
|
||||
|
||||
# Only categories with same ordering information can be compared
|
||||
cat_unordered = cat.set_ordered(False)
|
||||
assert not (cat > cat).any()
|
||||
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat > cat_unordered
|
||||
|
||||
# comparison (in both directions) with Series will raise
|
||||
s = Series(["b", "b", "b"], dtype=object)
|
||||
msg = (
|
||||
"Cannot compare a Categorical for op __gt__ with type "
|
||||
r"<class 'numpy\.ndarray'>"
|
||||
)
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat > s
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat_rev > s
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
s < cat
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
s < cat_rev
|
||||
|
||||
# comparison with numpy.array will raise in both direction, but only on
|
||||
# newer numpy versions
|
||||
a = np.array(["b", "b", "b"], dtype=object)
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat > a
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat_rev > a
|
||||
|
||||
# Make sure that unequal comparison take the categories order in
|
||||
# account
|
||||
cat_rev = Categorical(list("abc"), categories=list("cba"), ordered=True)
|
||||
exp = np.array([True, False, False])
|
||||
res = cat_rev > "b"
|
||||
tm.assert_numpy_array_equal(res, exp)
|
||||
|
||||
# check that zero-dim array gets unboxed
|
||||
res = cat_rev > np.array("b")
|
||||
tm.assert_numpy_array_equal(res, exp)
|
||||
|
||||
|
||||
class TestCategoricalOps:
|
||||
@pytest.mark.parametrize(
|
||||
"categories",
|
||||
[["a", "b"], [0, 1], [Timestamp("2019"), Timestamp("2020")]],
|
||||
)
|
||||
def test_not_equal_with_na(self, categories):
|
||||
# https://github.com/pandas-dev/pandas/issues/32276
|
||||
c1 = Categorical.from_codes([-1, 0], categories=categories)
|
||||
c2 = Categorical.from_codes([0, 1], categories=categories)
|
||||
|
||||
result = c1 != c2
|
||||
|
||||
assert result.all()
|
||||
|
||||
def test_compare_frame(self):
|
||||
# GH#24282 check that Categorical.__cmp__(DataFrame) defers to frame
|
||||
data = ["a", "b", 2, "a"]
|
||||
cat = Categorical(data)
|
||||
|
||||
df = DataFrame(cat)
|
||||
|
||||
result = cat == df.T
|
||||
expected = DataFrame([[True, True, True, True]])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
result = cat[::-1] != df.T
|
||||
expected = DataFrame([[False, True, True, False]])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_compare_frame_raises(self, comparison_op):
|
||||
# alignment raises unless we transpose
|
||||
op = comparison_op
|
||||
cat = Categorical(["a", "b", 2, "a"])
|
||||
df = DataFrame(cat)
|
||||
msg = "Unable to coerce to Series, length must be 1: given 4"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
op(cat, df)
|
||||
|
||||
def test_datetime_categorical_comparison(self):
|
||||
dt_cat = Categorical(date_range("2014-01-01", periods=3), ordered=True)
|
||||
tm.assert_numpy_array_equal(dt_cat > dt_cat[0], np.array([False, True, True]))
|
||||
tm.assert_numpy_array_equal(dt_cat[0] < dt_cat, np.array([False, True, True]))
|
||||
|
||||
def test_reflected_comparison_with_scalars(self):
|
||||
# GH8658
|
||||
cat = Categorical([1, 2, 3], ordered=True)
|
||||
tm.assert_numpy_array_equal(cat > cat[0], np.array([False, True, True]))
|
||||
tm.assert_numpy_array_equal(cat[0] < cat, np.array([False, True, True]))
|
||||
|
||||
def test_comparison_with_unknown_scalars(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/9836#issuecomment-92123057
|
||||
# and following comparisons with scalars not in categories should raise
|
||||
# for unequal comps, but not for equal/not equal
|
||||
cat = Categorical([1, 2, 3], ordered=True)
|
||||
|
||||
msg = "Invalid comparison between dtype=category and int"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat < 4
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat > 4
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
4 < cat
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
4 > cat
|
||||
|
||||
tm.assert_numpy_array_equal(cat == 4, np.array([False, False, False]))
|
||||
tm.assert_numpy_array_equal(cat != 4, np.array([True, True, True]))
|
||||
|
||||
def test_comparison_with_tuple(self):
|
||||
cat = Categorical(np.array(["foo", (0, 1), 3, (0, 1)], dtype=object))
|
||||
|
||||
result = cat == "foo"
|
||||
expected = np.array([True, False, False, False], dtype=bool)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
result = cat == (0, 1)
|
||||
expected = np.array([False, True, False, True], dtype=bool)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
result = cat != (0, 1)
|
||||
tm.assert_numpy_array_equal(result, ~expected)
|
||||
|
||||
@pytest.mark.filterwarnings("ignore::RuntimeWarning")
|
||||
def test_comparison_of_ordered_categorical_with_nan_to_scalar(
|
||||
self, compare_operators_no_eq_ne
|
||||
):
|
||||
# https://github.com/pandas-dev/pandas/issues/26504
|
||||
# BUG: fix ordered categorical comparison with missing values (#26504 )
|
||||
# and following comparisons with scalars in categories with missing
|
||||
# values should be evaluated as False
|
||||
|
||||
cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
|
||||
scalar = 2
|
||||
expected = getattr(np.array(cat), compare_operators_no_eq_ne)(scalar)
|
||||
actual = getattr(cat, compare_operators_no_eq_ne)(scalar)
|
||||
tm.assert_numpy_array_equal(actual, expected)
|
||||
|
||||
@pytest.mark.filterwarnings("ignore::RuntimeWarning")
|
||||
def test_comparison_of_ordered_categorical_with_nan_to_listlike(
|
||||
self, compare_operators_no_eq_ne
|
||||
):
|
||||
# https://github.com/pandas-dev/pandas/issues/26504
|
||||
# and following comparisons of missing values in ordered Categorical
|
||||
# with listlike should be evaluated as False
|
||||
|
||||
cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
|
||||
other = Categorical([2, 2, 2, 2], categories=[1, 2, 3], ordered=True)
|
||||
expected = getattr(np.array(cat), compare_operators_no_eq_ne)(2)
|
||||
actual = getattr(cat, compare_operators_no_eq_ne)(other)
|
||||
tm.assert_numpy_array_equal(actual, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"data,reverse,base",
|
||||
[(list("abc"), list("cba"), list("bbb")), ([1, 2, 3], [3, 2, 1], [2, 2, 2])],
|
||||
)
|
||||
def test_comparisons(self, data, reverse, base):
|
||||
cat_rev = Series(Categorical(data, categories=reverse, ordered=True))
|
||||
cat_rev_base = Series(Categorical(base, categories=reverse, ordered=True))
|
||||
cat = Series(Categorical(data, ordered=True))
|
||||
cat_base = Series(
|
||||
Categorical(base, categories=cat.cat.categories, ordered=True)
|
||||
)
|
||||
s = Series(base, dtype=object if base == list("bbb") else None)
|
||||
a = np.array(base)
|
||||
|
||||
# comparisons need to take categories ordering into account
|
||||
res_rev = cat_rev > cat_rev_base
|
||||
exp_rev = Series([True, False, False])
|
||||
tm.assert_series_equal(res_rev, exp_rev)
|
||||
|
||||
res_rev = cat_rev < cat_rev_base
|
||||
exp_rev = Series([False, False, True])
|
||||
tm.assert_series_equal(res_rev, exp_rev)
|
||||
|
||||
res = cat > cat_base
|
||||
exp = Series([False, False, True])
|
||||
tm.assert_series_equal(res, exp)
|
||||
|
||||
scalar = base[1]
|
||||
res = cat > scalar
|
||||
exp = Series([False, False, True])
|
||||
exp2 = cat.values > scalar
|
||||
tm.assert_series_equal(res, exp)
|
||||
tm.assert_numpy_array_equal(res.values, exp2)
|
||||
res_rev = cat_rev > scalar
|
||||
exp_rev = Series([True, False, False])
|
||||
exp_rev2 = cat_rev.values > scalar
|
||||
tm.assert_series_equal(res_rev, exp_rev)
|
||||
tm.assert_numpy_array_equal(res_rev.values, exp_rev2)
|
||||
|
||||
# Only categories with same categories can be compared
|
||||
msg = "Categoricals can only be compared if 'categories' are the same"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat > cat_rev
|
||||
|
||||
# categorical cannot be compared to Series or numpy array, and also
|
||||
# not the other way around
|
||||
msg = (
|
||||
"Cannot compare a Categorical for op __gt__ with type "
|
||||
r"<class 'numpy\.ndarray'>"
|
||||
)
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat > s
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat_rev > s
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat > a
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
cat_rev > a
|
||||
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
s < cat
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
s < cat_rev
|
||||
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
a < cat
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
a < cat_rev
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"ctor",
|
||||
[
|
||||
lambda *args, **kwargs: Categorical(*args, **kwargs),
|
||||
lambda *args, **kwargs: Series(Categorical(*args, **kwargs)),
|
||||
],
|
||||
)
|
||||
def test_unordered_different_order_equal(self, ctor):
|
||||
# https://github.com/pandas-dev/pandas/issues/16014
|
||||
c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)
|
||||
c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)
|
||||
assert (c1 == c2).all()
|
||||
|
||||
c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)
|
||||
c2 = ctor(["b", "a"], categories=["b", "a"], ordered=False)
|
||||
assert (c1 != c2).all()
|
||||
|
||||
c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)
|
||||
c2 = ctor(["b", "b"], categories=["b", "a"], ordered=False)
|
||||
assert (c1 != c2).all()
|
||||
|
||||
c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)
|
||||
c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)
|
||||
result = c1 == c2
|
||||
tm.assert_numpy_array_equal(np.array(result), np.array([True, False]))
|
||||
|
||||
def test_unordered_different_categories_raises(self):
|
||||
c1 = Categorical(["a", "b"], categories=["a", "b"], ordered=False)
|
||||
c2 = Categorical(["a", "c"], categories=["c", "a"], ordered=False)
|
||||
|
||||
with pytest.raises(TypeError, match=("Categoricals can only be compared")):
|
||||
c1 == c2
|
||||
|
||||
def test_compare_different_lengths(self):
|
||||
c1 = Categorical([], categories=["a", "b"])
|
||||
c2 = Categorical([], categories=["a"])
|
||||
|
||||
msg = "Categoricals can only be compared if 'categories' are the same."
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
c1 == c2
|
||||
|
||||
def test_compare_unordered_different_order(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/16603#issuecomment-
|
||||
# 349290078
|
||||
a = Categorical(["a"], categories=["a", "b"])
|
||||
b = Categorical(["b"], categories=["b", "a"])
|
||||
assert not a.equals(b)
|
||||
|
||||
def test_numeric_like_ops(self):
|
||||
df = DataFrame({"value": np.random.default_rng(2).integers(0, 10000, 100)})
|
||||
labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
|
||||
cat_labels = Categorical(labels, labels)
|
||||
|
||||
df = df.sort_values(by=["value"], ascending=True)
|
||||
df["value_group"] = pd.cut(
|
||||
df.value, range(0, 10500, 500), right=False, labels=cat_labels
|
||||
)
|
||||
|
||||
# numeric ops should not succeed
|
||||
for op, str_rep in [
|
||||
("__add__", r"\+"),
|
||||
("__sub__", "-"),
|
||||
("__mul__", r"\*"),
|
||||
("__truediv__", "/"),
|
||||
]:
|
||||
msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
getattr(df, op)(df)
|
||||
|
||||
# reduction ops should not succeed (unless specifically defined, e.g.
|
||||
# min/max)
|
||||
s = df["value_group"]
|
||||
for op in ["kurt", "skew", "var", "std", "mean", "sum", "median"]:
|
||||
msg = f"does not support reduction '{op}'"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
getattr(s, op)(numeric_only=False)
|
||||
|
||||
def test_numeric_like_ops_series(self):
|
||||
# numpy ops
|
||||
s = Series(Categorical([1, 2, 3, 4]))
|
||||
with pytest.raises(TypeError, match="does not support reduction 'sum'"):
|
||||
np.sum(s)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"op, str_rep",
|
||||
[
|
||||
("__add__", r"\+"),
|
||||
("__sub__", "-"),
|
||||
("__mul__", r"\*"),
|
||||
("__truediv__", "/"),
|
||||
],
|
||||
)
|
||||
def test_numeric_like_ops_series_arith(self, op, str_rep):
|
||||
# numeric ops on a Series
|
||||
s = Series(Categorical([1, 2, 3, 4]))
|
||||
msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
getattr(s, op)(2)
|
||||
|
||||
def test_numeric_like_ops_series_invalid(self):
|
||||
# invalid ufunc
|
||||
s = Series(Categorical([1, 2, 3, 4]))
|
||||
msg = "Object with dtype category cannot perform the numpy op log"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
np.log(s)
|
||||
@ -0,0 +1,111 @@
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
from pandas import Categorical
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"to_replace,value,expected,flip_categories",
|
||||
[
|
||||
# one-to-one
|
||||
(1, 2, [2, 2, 3], False),
|
||||
(1, 4, [4, 2, 3], False),
|
||||
(4, 1, [1, 2, 3], False),
|
||||
(5, 6, [1, 2, 3], False),
|
||||
# many-to-one
|
||||
([1], 2, [2, 2, 3], False),
|
||||
([1, 2], 3, [3, 3, 3], False),
|
||||
([1, 2], 4, [4, 4, 3], False),
|
||||
((1, 2, 4), 5, [5, 5, 3], False),
|
||||
((5, 6), 2, [1, 2, 3], False),
|
||||
([1], [2], [2, 2, 3], False),
|
||||
([1, 4], [5, 2], [5, 2, 3], False),
|
||||
# GH49404: overlap between to_replace and value
|
||||
([1, 2, 3], [2, 3, 4], [2, 3, 4], False),
|
||||
# GH50872, GH46884: replace with null
|
||||
(1, None, [None, 2, 3], False),
|
||||
(1, pd.NA, [None, 2, 3], False),
|
||||
# check_categorical sorts categories, which crashes on mixed dtypes
|
||||
(3, "4", [1, 2, "4"], False),
|
||||
([1, 2, "3"], "5", ["5", "5", 3], True),
|
||||
],
|
||||
)
|
||||
@pytest.mark.filterwarnings(
|
||||
"ignore:.*with CategoricalDtype is deprecated:FutureWarning"
|
||||
)
|
||||
def test_replace_categorical_series(to_replace, value, expected, flip_categories):
|
||||
# GH 31720
|
||||
|
||||
ser = pd.Series([1, 2, 3], dtype="category")
|
||||
result = ser.replace(to_replace, value)
|
||||
expected = pd.Series(expected, dtype="category")
|
||||
ser.replace(to_replace, value, inplace=True)
|
||||
|
||||
if flip_categories:
|
||||
expected = expected.cat.set_categories(expected.cat.categories[::-1])
|
||||
|
||||
tm.assert_series_equal(expected, result, check_category_order=False)
|
||||
tm.assert_series_equal(expected, ser, check_category_order=False)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"to_replace, value, result, expected_error_msg",
|
||||
[
|
||||
("b", "c", ["a", "c"], "Categorical.categories are different"),
|
||||
("c", "d", ["a", "b"], None),
|
||||
# https://github.com/pandas-dev/pandas/issues/33288
|
||||
("a", "a", ["a", "b"], None),
|
||||
("b", None, ["a", None], "Categorical.categories length are different"),
|
||||
],
|
||||
)
|
||||
def test_replace_categorical(to_replace, value, result, expected_error_msg):
|
||||
# GH#26988
|
||||
cat = Categorical(["a", "b"])
|
||||
expected = Categorical(result)
|
||||
msg = (
|
||||
r"The behavior of Series\.replace \(and DataFrame.replace\) "
|
||||
"with CategoricalDtype"
|
||||
)
|
||||
warn = FutureWarning if expected_error_msg is not None else None
|
||||
with tm.assert_produces_warning(warn, match=msg):
|
||||
result = pd.Series(cat, copy=False).replace(to_replace, value)._values
|
||||
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
if to_replace == "b": # the "c" test is supposed to be unchanged
|
||||
with pytest.raises(AssertionError, match=expected_error_msg):
|
||||
# ensure non-inplace call does not affect original
|
||||
tm.assert_categorical_equal(cat, expected)
|
||||
|
||||
ser = pd.Series(cat, copy=False)
|
||||
with tm.assert_produces_warning(warn, match=msg):
|
||||
ser.replace(to_replace, value, inplace=True)
|
||||
tm.assert_categorical_equal(cat, expected)
|
||||
|
||||
|
||||
def test_replace_categorical_ea_dtype():
|
||||
# GH49404
|
||||
cat = Categorical(pd.array(["a", "b"], dtype="string"))
|
||||
msg = (
|
||||
r"The behavior of Series\.replace \(and DataFrame.replace\) "
|
||||
"with CategoricalDtype"
|
||||
)
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = pd.Series(cat).replace(["a", "b"], ["c", pd.NA])._values
|
||||
expected = Categorical(pd.array(["c", pd.NA], dtype="string"))
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
|
||||
def test_replace_maintain_ordering():
|
||||
# GH51016
|
||||
dtype = pd.CategoricalDtype([0, 1, 2], ordered=True)
|
||||
ser = pd.Series([0, 1, 2], dtype=dtype)
|
||||
msg = (
|
||||
r"The behavior of Series\.replace \(and DataFrame.replace\) "
|
||||
"with CategoricalDtype"
|
||||
)
|
||||
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||||
result = ser.replace(0, 2)
|
||||
expected_dtype = pd.CategoricalDtype([1, 2], ordered=True)
|
||||
expected = pd.Series([2, 1, 2], dtype=expected_dtype)
|
||||
tm.assert_series_equal(expected, result, check_category_order=True)
|
||||
@ -0,0 +1,550 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas._config import using_pyarrow_string_dtype
|
||||
|
||||
from pandas import (
|
||||
Categorical,
|
||||
CategoricalDtype,
|
||||
CategoricalIndex,
|
||||
Index,
|
||||
Series,
|
||||
date_range,
|
||||
option_context,
|
||||
period_range,
|
||||
timedelta_range,
|
||||
)
|
||||
|
||||
|
||||
class TestCategoricalReprWithFactor:
|
||||
def test_print(self, using_infer_string):
|
||||
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
|
||||
if using_infer_string:
|
||||
expected = [
|
||||
"['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c']",
|
||||
"Categories (3, string): [a < b < c]",
|
||||
]
|
||||
else:
|
||||
expected = [
|
||||
"['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c']",
|
||||
"Categories (3, object): ['a' < 'b' < 'c']",
|
||||
]
|
||||
expected = "\n".join(expected)
|
||||
actual = repr(factor)
|
||||
assert actual == expected
|
||||
|
||||
|
||||
class TestCategoricalRepr:
|
||||
def test_big_print(self):
|
||||
codes = np.array([0, 1, 2, 0, 1, 2] * 100)
|
||||
dtype = CategoricalDtype(categories=Index(["a", "b", "c"], dtype=object))
|
||||
factor = Categorical.from_codes(codes, dtype=dtype)
|
||||
expected = [
|
||||
"['a', 'b', 'c', 'a', 'b', ..., 'b', 'c', 'a', 'b', 'c']",
|
||||
"Length: 600",
|
||||
"Categories (3, object): ['a', 'b', 'c']",
|
||||
]
|
||||
expected = "\n".join(expected)
|
||||
|
||||
actual = repr(factor)
|
||||
|
||||
assert actual == expected
|
||||
|
||||
def test_empty_print(self):
|
||||
factor = Categorical([], Index(["a", "b", "c"], dtype=object))
|
||||
expected = "[], Categories (3, object): ['a', 'b', 'c']"
|
||||
actual = repr(factor)
|
||||
assert actual == expected
|
||||
|
||||
assert expected == actual
|
||||
factor = Categorical([], Index(["a", "b", "c"], dtype=object), ordered=True)
|
||||
expected = "[], Categories (3, object): ['a' < 'b' < 'c']"
|
||||
actual = repr(factor)
|
||||
assert expected == actual
|
||||
|
||||
factor = Categorical([], [])
|
||||
expected = "[], Categories (0, object): []"
|
||||
assert expected == repr(factor)
|
||||
|
||||
def test_print_none_width(self):
|
||||
# GH10087
|
||||
a = Series(Categorical([1, 2, 3, 4]))
|
||||
exp = (
|
||||
"0 1\n1 2\n2 3\n3 4\n"
|
||||
"dtype: category\nCategories (4, int64): [1, 2, 3, 4]"
|
||||
)
|
||||
|
||||
with option_context("display.width", None):
|
||||
assert exp == repr(a)
|
||||
|
||||
@pytest.mark.skipif(
|
||||
using_pyarrow_string_dtype(),
|
||||
reason="Change once infer_string is set to True by default",
|
||||
)
|
||||
def test_unicode_print(self):
|
||||
c = Categorical(["aaaaa", "bb", "cccc"] * 20)
|
||||
expected = """\
|
||||
['aaaaa', 'bb', 'cccc', 'aaaaa', 'bb', ..., 'bb', 'cccc', 'aaaaa', 'bb', 'cccc']
|
||||
Length: 60
|
||||
Categories (3, object): ['aaaaa', 'bb', 'cccc']"""
|
||||
|
||||
assert repr(c) == expected
|
||||
|
||||
c = Categorical(["ああああ", "いいいいい", "ううううううう"] * 20)
|
||||
expected = """\
|
||||
['ああああ', 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', ..., 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', 'ううううううう']
|
||||
Length: 60
|
||||
Categories (3, object): ['ああああ', 'いいいいい', 'ううううううう']""" # noqa: E501
|
||||
|
||||
assert repr(c) == expected
|
||||
|
||||
# unicode option should not affect to Categorical, as it doesn't care
|
||||
# the repr width
|
||||
with option_context("display.unicode.east_asian_width", True):
|
||||
c = Categorical(["ああああ", "いいいいい", "ううううううう"] * 20)
|
||||
expected = """['ああああ', 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', ..., 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', 'ううううううう']
|
||||
Length: 60
|
||||
Categories (3, object): ['ああああ', 'いいいいい', 'ううううううう']""" # noqa: E501
|
||||
|
||||
assert repr(c) == expected
|
||||
|
||||
def test_categorical_repr(self):
|
||||
c = Categorical([1, 2, 3])
|
||||
exp = """[1, 2, 3]
|
||||
Categories (3, int64): [1, 2, 3]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical([1, 2, 3, 1, 2, 3], categories=[1, 2, 3])
|
||||
exp = """[1, 2, 3, 1, 2, 3]
|
||||
Categories (3, int64): [1, 2, 3]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical([1, 2, 3, 4, 5] * 10)
|
||||
exp = """[1, 2, 3, 4, 5, ..., 1, 2, 3, 4, 5]
|
||||
Length: 50
|
||||
Categories (5, int64): [1, 2, 3, 4, 5]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(np.arange(20, dtype=np.int64))
|
||||
exp = """[0, 1, 2, 3, 4, ..., 15, 16, 17, 18, 19]
|
||||
Length: 20
|
||||
Categories (20, int64): [0, 1, 2, 3, ..., 16, 17, 18, 19]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
def test_categorical_repr_ordered(self):
|
||||
c = Categorical([1, 2, 3], ordered=True)
|
||||
exp = """[1, 2, 3]
|
||||
Categories (3, int64): [1 < 2 < 3]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical([1, 2, 3, 1, 2, 3], categories=[1, 2, 3], ordered=True)
|
||||
exp = """[1, 2, 3, 1, 2, 3]
|
||||
Categories (3, int64): [1 < 2 < 3]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical([1, 2, 3, 4, 5] * 10, ordered=True)
|
||||
exp = """[1, 2, 3, 4, 5, ..., 1, 2, 3, 4, 5]
|
||||
Length: 50
|
||||
Categories (5, int64): [1 < 2 < 3 < 4 < 5]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(np.arange(20, dtype=np.int64), ordered=True)
|
||||
exp = """[0, 1, 2, 3, 4, ..., 15, 16, 17, 18, 19]
|
||||
Length: 20
|
||||
Categories (20, int64): [0 < 1 < 2 < 3 ... 16 < 17 < 18 < 19]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
def test_categorical_repr_datetime(self):
|
||||
idx = date_range("2011-01-01 09:00", freq="h", periods=5)
|
||||
c = Categorical(idx)
|
||||
|
||||
exp = (
|
||||
"[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, "
|
||||
"2011-01-01 12:00:00, 2011-01-01 13:00:00]\n"
|
||||
"Categories (5, datetime64[ns]): [2011-01-01 09:00:00, "
|
||||
"2011-01-01 10:00:00, 2011-01-01 11:00:00,\n"
|
||||
" 2011-01-01 12:00:00, "
|
||||
"2011-01-01 13:00:00]"
|
||||
""
|
||||
)
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx)
|
||||
exp = (
|
||||
"[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, "
|
||||
"2011-01-01 12:00:00, 2011-01-01 13:00:00, 2011-01-01 09:00:00, "
|
||||
"2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, "
|
||||
"2011-01-01 13:00:00]\n"
|
||||
"Categories (5, datetime64[ns]): [2011-01-01 09:00:00, "
|
||||
"2011-01-01 10:00:00, 2011-01-01 11:00:00,\n"
|
||||
" 2011-01-01 12:00:00, "
|
||||
"2011-01-01 13:00:00]"
|
||||
)
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
|
||||
c = Categorical(idx)
|
||||
exp = (
|
||||
"[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, "
|
||||
"2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, "
|
||||
"2011-01-01 13:00:00-05:00]\n"
|
||||
"Categories (5, datetime64[ns, US/Eastern]): "
|
||||
"[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00,\n"
|
||||
" "
|
||||
"2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00,\n"
|
||||
" "
|
||||
"2011-01-01 13:00:00-05:00]"
|
||||
)
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx)
|
||||
exp = (
|
||||
"[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, "
|
||||
"2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, "
|
||||
"2011-01-01 13:00:00-05:00, 2011-01-01 09:00:00-05:00, "
|
||||
"2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, "
|
||||
"2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]\n"
|
||||
"Categories (5, datetime64[ns, US/Eastern]): "
|
||||
"[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00,\n"
|
||||
" "
|
||||
"2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00,\n"
|
||||
" "
|
||||
"2011-01-01 13:00:00-05:00]"
|
||||
)
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
def test_categorical_repr_datetime_ordered(self):
|
||||
idx = date_range("2011-01-01 09:00", freq="h", periods=5)
|
||||
c = Categorical(idx, ordered=True)
|
||||
exp = """[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00]
|
||||
Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
|
||||
2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx, ordered=True)
|
||||
exp = """[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00, 2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00]
|
||||
Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
|
||||
2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
|
||||
c = Categorical(idx, ordered=True)
|
||||
exp = """[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]
|
||||
Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
|
||||
2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
|
||||
2011-01-01 13:00:00-05:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx, ordered=True)
|
||||
exp = """[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00, 2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]
|
||||
Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
|
||||
2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
|
||||
2011-01-01 13:00:00-05:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
def test_categorical_repr_int_with_nan(self):
|
||||
c = Categorical([1, 2, np.nan])
|
||||
c_exp = """[1, 2, NaN]\nCategories (2, int64): [1, 2]"""
|
||||
assert repr(c) == c_exp
|
||||
|
||||
s = Series([1, 2, np.nan], dtype="object").astype("category")
|
||||
s_exp = """0 1\n1 2\n2 NaN
|
||||
dtype: category
|
||||
Categories (2, int64): [1, 2]"""
|
||||
assert repr(s) == s_exp
|
||||
|
||||
def test_categorical_repr_period(self):
|
||||
idx = period_range("2011-01-01 09:00", freq="h", periods=5)
|
||||
c = Categorical(idx)
|
||||
exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]
|
||||
Categories (5, period[h]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00,
|
||||
2011-01-01 13:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx)
|
||||
exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00, 2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]
|
||||
Categories (5, period[h]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00,
|
||||
2011-01-01 13:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
idx = period_range("2011-01", freq="M", periods=5)
|
||||
c = Categorical(idx)
|
||||
exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05]
|
||||
Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx)
|
||||
exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05, 2011-01, 2011-02, 2011-03, 2011-04, 2011-05]
|
||||
Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
def test_categorical_repr_period_ordered(self):
|
||||
idx = period_range("2011-01-01 09:00", freq="h", periods=5)
|
||||
c = Categorical(idx, ordered=True)
|
||||
exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]
|
||||
Categories (5, period[h]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 <
|
||||
2011-01-01 13:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx, ordered=True)
|
||||
exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00, 2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]
|
||||
Categories (5, period[h]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 <
|
||||
2011-01-01 13:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
idx = period_range("2011-01", freq="M", periods=5)
|
||||
c = Categorical(idx, ordered=True)
|
||||
exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05]
|
||||
Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx, ordered=True)
|
||||
exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05, 2011-01, 2011-02, 2011-03, 2011-04, 2011-05]
|
||||
Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
def test_categorical_repr_timedelta(self):
|
||||
idx = timedelta_range("1 days", periods=5)
|
||||
c = Categorical(idx)
|
||||
exp = """[1 days, 2 days, 3 days, 4 days, 5 days]
|
||||
Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx)
|
||||
exp = """[1 days, 2 days, 3 days, 4 days, 5 days, 1 days, 2 days, 3 days, 4 days, 5 days]
|
||||
Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
idx = timedelta_range("1 hours", periods=20)
|
||||
c = Categorical(idx)
|
||||
exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00]
|
||||
Length: 20
|
||||
Categories (20, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00,
|
||||
3 days 01:00:00, ..., 16 days 01:00:00, 17 days 01:00:00,
|
||||
18 days 01:00:00, 19 days 01:00:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx)
|
||||
exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00]
|
||||
Length: 40
|
||||
Categories (20, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00,
|
||||
3 days 01:00:00, ..., 16 days 01:00:00, 17 days 01:00:00,
|
||||
18 days 01:00:00, 19 days 01:00:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
def test_categorical_repr_timedelta_ordered(self):
|
||||
idx = timedelta_range("1 days", periods=5)
|
||||
c = Categorical(idx, ordered=True)
|
||||
exp = """[1 days, 2 days, 3 days, 4 days, 5 days]
|
||||
Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]"""
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx, ordered=True)
|
||||
exp = """[1 days, 2 days, 3 days, 4 days, 5 days, 1 days, 2 days, 3 days, 4 days, 5 days]
|
||||
Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
idx = timedelta_range("1 hours", periods=20)
|
||||
c = Categorical(idx, ordered=True)
|
||||
exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00]
|
||||
Length: 20
|
||||
Categories (20, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 <
|
||||
3 days 01:00:00 ... 16 days 01:00:00 < 17 days 01:00:00 <
|
||||
18 days 01:00:00 < 19 days 01:00:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
c = Categorical(idx.append(idx), categories=idx, ordered=True)
|
||||
exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00]
|
||||
Length: 40
|
||||
Categories (20, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 <
|
||||
3 days 01:00:00 ... 16 days 01:00:00 < 17 days 01:00:00 <
|
||||
18 days 01:00:00 < 19 days 01:00:00]""" # noqa: E501
|
||||
|
||||
assert repr(c) == exp
|
||||
|
||||
def test_categorical_index_repr(self):
|
||||
idx = CategoricalIndex(Categorical([1, 2, 3]))
|
||||
exp = """CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category')""" # noqa: E501
|
||||
assert repr(idx) == exp
|
||||
|
||||
i = CategoricalIndex(Categorical(np.arange(10, dtype=np.int64)))
|
||||
exp = """CategoricalIndex([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], categories=[0, 1, 2, 3, ..., 6, 7, 8, 9], ordered=False, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
def test_categorical_index_repr_ordered(self):
|
||||
i = CategoricalIndex(Categorical([1, 2, 3], ordered=True))
|
||||
exp = """CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=True, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
i = CategoricalIndex(Categorical(np.arange(10, dtype=np.int64), ordered=True))
|
||||
exp = """CategoricalIndex([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], categories=[0, 1, 2, 3, ..., 6, 7, 8, 9], ordered=True, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
def test_categorical_index_repr_datetime(self):
|
||||
idx = date_range("2011-01-01 09:00", freq="h", periods=5)
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00:00', '2011-01-01 10:00:00',
|
||||
'2011-01-01 11:00:00', '2011-01-01 12:00:00',
|
||||
'2011-01-01 13:00:00'],
|
||||
categories=[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00], ordered=False, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00',
|
||||
'2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00',
|
||||
'2011-01-01 13:00:00-05:00'],
|
||||
categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=False, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
def test_categorical_index_repr_datetime_ordered(self):
|
||||
idx = date_range("2011-01-01 09:00", freq="h", periods=5)
|
||||
i = CategoricalIndex(Categorical(idx, ordered=True))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00:00', '2011-01-01 10:00:00',
|
||||
'2011-01-01 11:00:00', '2011-01-01 12:00:00',
|
||||
'2011-01-01 13:00:00'],
|
||||
categories=[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00], ordered=True, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
|
||||
i = CategoricalIndex(Categorical(idx, ordered=True))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00',
|
||||
'2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00',
|
||||
'2011-01-01 13:00:00-05:00'],
|
||||
categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=True, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
i = CategoricalIndex(Categorical(idx.append(idx), ordered=True))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00',
|
||||
'2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00',
|
||||
'2011-01-01 13:00:00-05:00', '2011-01-01 09:00:00-05:00',
|
||||
'2011-01-01 10:00:00-05:00', '2011-01-01 11:00:00-05:00',
|
||||
'2011-01-01 12:00:00-05:00', '2011-01-01 13:00:00-05:00'],
|
||||
categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=True, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
def test_categorical_index_repr_period(self):
|
||||
# test all length
|
||||
idx = period_range("2011-01-01 09:00", freq="h", periods=1)
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00'], categories=[2011-01-01 09:00], ordered=False, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = period_range("2011-01-01 09:00", freq="h", periods=2)
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00'], categories=[2011-01-01 09:00, 2011-01-01 10:00], ordered=False, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = period_range("2011-01-01 09:00", freq="h", periods=3)
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00'], categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00], ordered=False, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = period_range("2011-01-01 09:00", freq="h", periods=5)
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00',
|
||||
'2011-01-01 12:00', '2011-01-01 13:00'],
|
||||
categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=False, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
i = CategoricalIndex(Categorical(idx.append(idx)))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00',
|
||||
'2011-01-01 12:00', '2011-01-01 13:00', '2011-01-01 09:00',
|
||||
'2011-01-01 10:00', '2011-01-01 11:00', '2011-01-01 12:00',
|
||||
'2011-01-01 13:00'],
|
||||
categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=False, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = period_range("2011-01", freq="M", periods=5)
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05'], categories=[2011-01, 2011-02, 2011-03, 2011-04, 2011-05], ordered=False, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
def test_categorical_index_repr_period_ordered(self):
|
||||
idx = period_range("2011-01-01 09:00", freq="h", periods=5)
|
||||
i = CategoricalIndex(Categorical(idx, ordered=True))
|
||||
exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00',
|
||||
'2011-01-01 12:00', '2011-01-01 13:00'],
|
||||
categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=True, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = period_range("2011-01", freq="M", periods=5)
|
||||
i = CategoricalIndex(Categorical(idx, ordered=True))
|
||||
exp = """CategoricalIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05'], categories=[2011-01, 2011-02, 2011-03, 2011-04, 2011-05], ordered=True, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
def test_categorical_index_repr_timedelta(self):
|
||||
idx = timedelta_range("1 days", periods=5)
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], categories=[1 days, 2 days, 3 days, 4 days, 5 days], ordered=False, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = timedelta_range("1 hours", periods=10)
|
||||
i = CategoricalIndex(Categorical(idx))
|
||||
exp = """CategoricalIndex(['0 days 01:00:00', '1 days 01:00:00', '2 days 01:00:00',
|
||||
'3 days 01:00:00', '4 days 01:00:00', '5 days 01:00:00',
|
||||
'6 days 01:00:00', '7 days 01:00:00', '8 days 01:00:00',
|
||||
'9 days 01:00:00'],
|
||||
categories=[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00, 8 days 01:00:00, 9 days 01:00:00], ordered=False, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
def test_categorical_index_repr_timedelta_ordered(self):
|
||||
idx = timedelta_range("1 days", periods=5)
|
||||
i = CategoricalIndex(Categorical(idx, ordered=True))
|
||||
exp = """CategoricalIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], categories=[1 days, 2 days, 3 days, 4 days, 5 days], ordered=True, dtype='category')""" # noqa: E501
|
||||
assert repr(i) == exp
|
||||
|
||||
idx = timedelta_range("1 hours", periods=10)
|
||||
i = CategoricalIndex(Categorical(idx, ordered=True))
|
||||
exp = """CategoricalIndex(['0 days 01:00:00', '1 days 01:00:00', '2 days 01:00:00',
|
||||
'3 days 01:00:00', '4 days 01:00:00', '5 days 01:00:00',
|
||||
'6 days 01:00:00', '7 days 01:00:00', '8 days 01:00:00',
|
||||
'9 days 01:00:00'],
|
||||
categories=[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00, 8 days 01:00:00, 9 days 01:00:00], ordered=True, dtype='category')""" # noqa: E501
|
||||
|
||||
assert repr(i) == exp
|
||||
|
||||
def test_categorical_str_repr(self):
|
||||
# GH 33676
|
||||
result = repr(Categorical([1, "2", 3, 4]))
|
||||
expected = "[1, '2', 3, 4]\nCategories (4, object): [1, 3, 4, '2']"
|
||||
assert result == expected
|
||||
@ -0,0 +1,128 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas import (
|
||||
Categorical,
|
||||
Index,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestCategoricalSort:
|
||||
def test_argsort(self):
|
||||
c = Categorical([5, 3, 1, 4, 2], ordered=True)
|
||||
|
||||
expected = np.array([2, 4, 1, 3, 0])
|
||||
tm.assert_numpy_array_equal(
|
||||
c.argsort(ascending=True), expected, check_dtype=False
|
||||
)
|
||||
|
||||
expected = expected[::-1]
|
||||
tm.assert_numpy_array_equal(
|
||||
c.argsort(ascending=False), expected, check_dtype=False
|
||||
)
|
||||
|
||||
def test_numpy_argsort(self):
|
||||
c = Categorical([5, 3, 1, 4, 2], ordered=True)
|
||||
|
||||
expected = np.array([2, 4, 1, 3, 0])
|
||||
tm.assert_numpy_array_equal(np.argsort(c), expected, check_dtype=False)
|
||||
|
||||
tm.assert_numpy_array_equal(
|
||||
np.argsort(c, kind="mergesort"), expected, check_dtype=False
|
||||
)
|
||||
|
||||
msg = "the 'axis' parameter is not supported"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
np.argsort(c, axis=0)
|
||||
|
||||
msg = "the 'order' parameter is not supported"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
np.argsort(c, order="C")
|
||||
|
||||
def test_sort_values(self):
|
||||
# unordered cats are sortable
|
||||
cat = Categorical(["a", "b", "b", "a"], ordered=False)
|
||||
cat.sort_values()
|
||||
|
||||
cat = Categorical(["a", "c", "b", "d"], ordered=True)
|
||||
|
||||
# sort_values
|
||||
res = cat.sort_values()
|
||||
exp = np.array(["a", "b", "c", "d"], dtype=object)
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, cat.categories)
|
||||
|
||||
cat = Categorical(
|
||||
["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True
|
||||
)
|
||||
res = cat.sort_values()
|
||||
exp = np.array(["a", "b", "c", "d"], dtype=object)
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, cat.categories)
|
||||
|
||||
res = cat.sort_values(ascending=False)
|
||||
exp = np.array(["d", "c", "b", "a"], dtype=object)
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, cat.categories)
|
||||
|
||||
# sort (inplace order)
|
||||
cat1 = cat.copy()
|
||||
orig_codes = cat1._codes
|
||||
cat1.sort_values(inplace=True)
|
||||
assert cat1._codes is orig_codes
|
||||
exp = np.array(["a", "b", "c", "d"], dtype=object)
|
||||
tm.assert_numpy_array_equal(cat1.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, cat.categories)
|
||||
|
||||
# reverse
|
||||
cat = Categorical(["a", "c", "c", "b", "d"], ordered=True)
|
||||
res = cat.sort_values(ascending=False)
|
||||
exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object)
|
||||
exp_categories = Index(["a", "b", "c", "d"])
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp_val)
|
||||
tm.assert_index_equal(res.categories, exp_categories)
|
||||
|
||||
def test_sort_values_na_position(self):
|
||||
# see gh-12882
|
||||
cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True)
|
||||
exp_categories = Index([2, 5])
|
||||
|
||||
exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan])
|
||||
res = cat.sort_values() # default arguments
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, exp_categories)
|
||||
|
||||
exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0])
|
||||
res = cat.sort_values(ascending=True, na_position="first")
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, exp_categories)
|
||||
|
||||
exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0])
|
||||
res = cat.sort_values(ascending=False, na_position="first")
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, exp_categories)
|
||||
|
||||
exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan])
|
||||
res = cat.sort_values(ascending=True, na_position="last")
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, exp_categories)
|
||||
|
||||
exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan])
|
||||
res = cat.sort_values(ascending=False, na_position="last")
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp)
|
||||
tm.assert_index_equal(res.categories, exp_categories)
|
||||
|
||||
cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True)
|
||||
res = cat.sort_values(ascending=False, na_position="last")
|
||||
exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object)
|
||||
exp_categories = Index(["a", "b", "c", "d"])
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp_val)
|
||||
tm.assert_index_equal(res.categories, exp_categories)
|
||||
|
||||
cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True)
|
||||
res = cat.sort_values(ascending=False, na_position="first")
|
||||
exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object)
|
||||
exp_categories = Index(["a", "b", "c", "d"])
|
||||
tm.assert_numpy_array_equal(res.__array__(), exp_val)
|
||||
tm.assert_index_equal(res.categories, exp_categories)
|
||||
@ -0,0 +1,26 @@
|
||||
from pandas import Categorical
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class SubclassedCategorical(Categorical):
|
||||
pass
|
||||
|
||||
|
||||
class TestCategoricalSubclassing:
|
||||
def test_constructor(self):
|
||||
sc = SubclassedCategorical(["a", "b", "c"])
|
||||
assert isinstance(sc, SubclassedCategorical)
|
||||
tm.assert_categorical_equal(sc, Categorical(["a", "b", "c"]))
|
||||
|
||||
def test_from_codes(self):
|
||||
sc = SubclassedCategorical.from_codes([1, 0, 2], ["a", "b", "c"])
|
||||
assert isinstance(sc, SubclassedCategorical)
|
||||
exp = Categorical.from_codes([1, 0, 2], ["a", "b", "c"])
|
||||
tm.assert_categorical_equal(sc, exp)
|
||||
|
||||
def test_map(self):
|
||||
sc = SubclassedCategorical(["a", "b", "c"])
|
||||
res = sc.map(lambda x: x.upper(), na_action=None)
|
||||
assert isinstance(res, SubclassedCategorical)
|
||||
exp = Categorical(["A", "B", "C"])
|
||||
tm.assert_categorical_equal(res, exp)
|
||||
@ -0,0 +1,89 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas import Categorical
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
@pytest.fixture(params=[True, False])
|
||||
def allow_fill(request):
|
||||
"""Boolean 'allow_fill' parameter for Categorical.take"""
|
||||
return request.param
|
||||
|
||||
|
||||
class TestTake:
|
||||
# https://github.com/pandas-dev/pandas/issues/20664
|
||||
|
||||
def test_take_default_allow_fill(self):
|
||||
cat = Categorical(["a", "b"])
|
||||
with tm.assert_produces_warning(None):
|
||||
result = cat.take([0, -1])
|
||||
|
||||
assert result.equals(cat)
|
||||
|
||||
def test_take_positive_no_warning(self):
|
||||
cat = Categorical(["a", "b"])
|
||||
with tm.assert_produces_warning(None):
|
||||
cat.take([0, 0])
|
||||
|
||||
def test_take_bounds(self, allow_fill):
|
||||
# https://github.com/pandas-dev/pandas/issues/20664
|
||||
cat = Categorical(["a", "b", "a"])
|
||||
if allow_fill:
|
||||
msg = "indices are out-of-bounds"
|
||||
else:
|
||||
msg = "index 4 is out of bounds for( axis 0 with)? size 3"
|
||||
with pytest.raises(IndexError, match=msg):
|
||||
cat.take([4, 5], allow_fill=allow_fill)
|
||||
|
||||
def test_take_empty(self, allow_fill):
|
||||
# https://github.com/pandas-dev/pandas/issues/20664
|
||||
cat = Categorical([], categories=["a", "b"])
|
||||
if allow_fill:
|
||||
msg = "indices are out-of-bounds"
|
||||
else:
|
||||
msg = "cannot do a non-empty take from an empty axes"
|
||||
with pytest.raises(IndexError, match=msg):
|
||||
cat.take([0], allow_fill=allow_fill)
|
||||
|
||||
def test_positional_take(self, ordered):
|
||||
cat = Categorical(["a", "a", "b", "b"], categories=["b", "a"], ordered=ordered)
|
||||
result = cat.take([0, 1, 2], allow_fill=False)
|
||||
expected = Categorical(
|
||||
["a", "a", "b"], categories=cat.categories, ordered=ordered
|
||||
)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_positional_take_unobserved(self, ordered):
|
||||
cat = Categorical(["a", "b"], categories=["a", "b", "c"], ordered=ordered)
|
||||
result = cat.take([1, 0], allow_fill=False)
|
||||
expected = Categorical(["b", "a"], categories=cat.categories, ordered=ordered)
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_take_allow_fill(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/23296
|
||||
cat = Categorical(["a", "a", "b"])
|
||||
result = cat.take([0, -1, -1], allow_fill=True)
|
||||
expected = Categorical(["a", np.nan, np.nan], categories=["a", "b"])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_take_fill_with_negative_one(self):
|
||||
# -1 was a category
|
||||
cat = Categorical([-1, 0, 1])
|
||||
result = cat.take([0, -1, 1], allow_fill=True, fill_value=-1)
|
||||
expected = Categorical([-1, -1, 0], categories=[-1, 0, 1])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_take_fill_value(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/23296
|
||||
cat = Categorical(["a", "b", "c"])
|
||||
result = cat.take([0, 1, -1], fill_value="a", allow_fill=True)
|
||||
expected = Categorical(["a", "b", "a"], categories=["a", "b", "c"])
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
def test_take_fill_value_new_raises(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/23296
|
||||
cat = Categorical(["a", "b", "c"])
|
||||
xpr = r"Cannot setitem on a Categorical with a new category \(d\)"
|
||||
with pytest.raises(TypeError, match=xpr):
|
||||
cat.take([0, 1, -1], fill_value="d", allow_fill=True)
|
||||
@ -0,0 +1,19 @@
|
||||
import pytest
|
||||
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestCategoricalWarnings:
|
||||
def test_tab_complete_warning(self, ip):
|
||||
# https://github.com/pandas-dev/pandas/issues/16409
|
||||
pytest.importorskip("IPython", minversion="6.0.0")
|
||||
from IPython.core.completer import provisionalcompleter
|
||||
|
||||
code = "import pandas as pd; c = pd.Categorical([])"
|
||||
ip.run_cell(code)
|
||||
|
||||
# GH 31324 newer jedi version raises Deprecation warning;
|
||||
# appears resolved 2021-02-02
|
||||
with tm.assert_produces_warning(None, raise_on_extra_warnings=False):
|
||||
with provisionalcompleter("ignore"):
|
||||
list(ip.Completer.completions("c.", 1))
|
||||
Reference in New Issue
Block a user