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| 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)
|
|
