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| import numpy as np
| import pytest
|
| import pandas as pd
| from pandas import (
| CategoricalIndex,
| Index,
| Series,
| )
| import pandas._testing as tm
|
|
| @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):
| # GH 31202 - override base class since we want to maintain categorical/ordered
| index = CategoricalIndex(data, categories=categories, ordered=ordered)
| result = index.map(str)
| expected = CategoricalIndex(
| map(str, data), categories=map(str, categories), ordered=ordered
| )
| tm.assert_index_equal(result, expected)
|
|
| def test_map():
| ci = CategoricalIndex(list("ABABC"), categories=list("CBA"), ordered=True)
| result = ci.map(lambda x: x.lower())
| exp = CategoricalIndex(list("ababc"), categories=list("cba"), ordered=True)
| tm.assert_index_equal(result, exp)
|
| ci = CategoricalIndex(
| list("ABABC"), categories=list("BAC"), ordered=False, name="XXX"
| )
| result = ci.map(lambda x: x.lower())
| exp = CategoricalIndex(
| list("ababc"), categories=list("bac"), ordered=False, name="XXX"
| )
| tm.assert_index_equal(result, exp)
|
| # GH 12766: Return an index not an array
| tm.assert_index_equal(
| ci.map(lambda x: 1), Index(np.array([1] * 5, dtype=np.int64), name="XXX")
| )
|
| # change categories dtype
| ci = CategoricalIndex(list("ABABC"), categories=list("BAC"), ordered=False)
|
| def f(x):
| return {"A": 10, "B": 20, "C": 30}.get(x)
|
| result = ci.map(f)
| exp = CategoricalIndex([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False)
| tm.assert_index_equal(result, exp)
|
| result = ci.map(Series([10, 20, 30], index=["A", "B", "C"]))
| tm.assert_index_equal(result, exp)
|
| result = ci.map({"A": 10, "B": 20, "C": 30})
| tm.assert_index_equal(result, exp)
|
|
| def test_map_with_categorical_series():
| # GH 12756
| a = Index([1, 2, 3, 4])
| b = Series(["even", "odd", "even", "odd"], dtype="category")
| c = Series(["even", "odd", "even", "odd"])
|
| exp = CategoricalIndex(["odd", "even", "odd", np.nan])
| tm.assert_index_equal(a.map(b), exp)
| exp = Index(["odd", "even", "odd", np.nan])
| tm.assert_index_equal(a.map(c), exp)
|
|
| @pytest.mark.parametrize(
| ("data", "f", "expected"),
| (
| ([1, 1, np.nan], pd.isna, CategoricalIndex([False, False, np.nan])),
| ([1, 2, np.nan], pd.isna, Index([False, False, np.nan])),
| ([1, 1, np.nan], {1: False}, CategoricalIndex([False, False, np.nan])),
| ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
| (
| [1, 1, np.nan],
| Series([False, False]),
| CategoricalIndex([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 = CategoricalIndex(data)
| result = values.map(f, na_action="ignore")
| tm.assert_index_equal(result, expected)
|
|
| @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}, CategoricalIndex([False, False, np.nan])),
| ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
| (
| [1, 1, np.nan],
| Series([False, False]),
| CategoricalIndex([False, False, np.nan]),
| ),
| (
| [1, 2, np.nan],
| Series([False, False, False]),
| Index([False, False, np.nan]),
| ),
| ),
| )
| def test_map_with_nan_none(data, f, expected): # GH 24241
| values = CategoricalIndex(data)
| result = values.map(f, na_action=None)
| tm.assert_index_equal(result, expected)
|
|
| def test_map_with_dict_or_series():
| orig_values = ["a", "B", 1, "a"]
| new_values = ["one", 2, 3.0, "one"]
| cur_index = CategoricalIndex(orig_values, name="XXX")
| expected = CategoricalIndex(new_values, name="XXX", categories=[3.0, 2, "one"])
|
| mapper = Series(new_values[:-1], index=orig_values[:-1])
| result = cur_index.map(mapper)
| # Order of categories in result can be different
| tm.assert_index_equal(result, expected)
|
| mapper = dict(zip(orig_values[:-1], new_values[:-1]))
| result = cur_index.map(mapper)
| # Order of categories in result can be different
| tm.assert_index_equal(result, expected)
|
|
