112 lines
4.0 KiB
Python
112 lines
4.0 KiB
Python
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import CategoricalIndex, Index, Series
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import pandas._testing as tm
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class TestMap:
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@pytest.mark.parametrize(
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"data, categories",
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[
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(list("abcbca"), list("cab")),
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(pd.interval_range(0, 3).repeat(3), pd.interval_range(0, 3)),
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],
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ids=["string", "interval"],
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)
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def test_map_str(self, data, categories, ordered):
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# GH 31202 - override base class since we want to maintain categorical/ordered
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index = CategoricalIndex(data, categories=categories, ordered=ordered)
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result = index.map(str)
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expected = CategoricalIndex(
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map(str, data), categories=map(str, categories), ordered=ordered
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)
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tm.assert_index_equal(result, expected)
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def test_map(self):
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ci = CategoricalIndex(list("ABABC"), categories=list("CBA"), ordered=True)
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result = ci.map(lambda x: x.lower())
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exp = CategoricalIndex(list("ababc"), categories=list("cba"), ordered=True)
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tm.assert_index_equal(result, exp)
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ci = CategoricalIndex(
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list("ABABC"), categories=list("BAC"), ordered=False, name="XXX"
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)
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result = ci.map(lambda x: x.lower())
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exp = CategoricalIndex(
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list("ababc"), categories=list("bac"), ordered=False, name="XXX"
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)
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tm.assert_index_equal(result, exp)
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# GH 12766: Return an index not an array
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tm.assert_index_equal(
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ci.map(lambda x: 1), Index(np.array([1] * 5, dtype=np.int64), name="XXX")
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)
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# change categories dtype
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ci = CategoricalIndex(list("ABABC"), categories=list("BAC"), ordered=False)
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def f(x):
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return {"A": 10, "B": 20, "C": 30}.get(x)
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result = ci.map(f)
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exp = CategoricalIndex(
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[10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False
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)
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tm.assert_index_equal(result, exp)
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result = ci.map(Series([10, 20, 30], index=["A", "B", "C"]))
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tm.assert_index_equal(result, exp)
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result = ci.map({"A": 10, "B": 20, "C": 30})
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tm.assert_index_equal(result, exp)
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def test_map_with_categorical_series(self):
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# GH 12756
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a = Index([1, 2, 3, 4])
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b = Series(["even", "odd", "even", "odd"], dtype="category")
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c = Series(["even", "odd", "even", "odd"])
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exp = CategoricalIndex(["odd", "even", "odd", np.nan])
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tm.assert_index_equal(a.map(b), exp)
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exp = Index(["odd", "even", "odd", np.nan])
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tm.assert_index_equal(a.map(c), exp)
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@pytest.mark.parametrize(
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("data", "f"),
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(
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([1, 1, np.nan], pd.isna),
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([1, 2, np.nan], pd.isna),
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([1, 1, np.nan], {1: False}),
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([1, 2, np.nan], {1: False, 2: False}),
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([1, 1, np.nan], Series([False, False])),
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([1, 2, np.nan], Series([False, False, False])),
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),
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)
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def test_map_with_nan(self, data, f): # GH 24241
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values = pd.Categorical(data)
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result = values.map(f)
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if data[1] == 1:
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expected = pd.Categorical([False, False, np.nan])
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tm.assert_categorical_equal(result, expected)
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else:
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expected = Index([False, False, np.nan])
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tm.assert_index_equal(result, expected)
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def test_map_with_dict_or_series(self):
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orig_values = ["a", "B", 1, "a"]
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new_values = ["one", 2, 3.0, "one"]
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cur_index = CategoricalIndex(orig_values, name="XXX")
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expected = CategoricalIndex(new_values, name="XXX", categories=[3.0, 2, "one"])
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mapper = Series(new_values[:-1], index=orig_values[:-1])
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result = cur_index.map(mapper)
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# Order of categories in result can be different
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tm.assert_index_equal(result, expected)
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mapper = {o: n for o, n in zip(orig_values[:-1], new_values[:-1])}
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result = cur_index.map(mapper)
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# Order of categories in result can be different
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tm.assert_index_equal(result, expected)
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