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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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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@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())
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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())
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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)
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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):
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cat = Categorical(["A", "B", "B", "C", "A"])
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msg = (
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'For argument "inplace" expected type bool, '
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f"received type {type(value).__name__}"
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)
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with pytest.raises(ValueError, match=msg):
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cat.sort_values(inplace=value)
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def test_quantile_empty(self):
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# make sure we have correct itemsize on resulting codes
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cat = Categorical(["A", "B"])
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idx = Index([0.0, 0.5])
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result = cat[:0]._quantile(idx, interpolation="linear")
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assert result._codes.dtype == np.int8
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expected = cat.take([-1, -1], allow_fill=True)
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tm.assert_extension_array_equal(result, expected)
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