test_apply.py

from collections import Counter, defaultdict
from itertools import chain

import numpy as np
import pytest

import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, isna
import pandas._testing as tm
from pandas.core.base import SpecificationError


class TestSeriesApply:
    def test_apply(self, datetime_series):
        with np.errstate(all="ignore"):
            tm.assert_series_equal(
                datetime_series.apply(np.sqrt), np.sqrt(datetime_series)
            )

            # element-wise apply
            import math

            tm.assert_series_equal(
                datetime_series.apply(math.exp), np.exp(datetime_series)
            )

        # empty series
        s = Series(dtype=object, name="foo", index=pd.Index([], name="bar"))
        rs = s.apply(lambda x: x)
        tm.assert_series_equal(s, rs)

        # check all metadata (GH 9322)
        assert s is not rs
        assert s.index is rs.index
        assert s.dtype == rs.dtype
        assert s.name == rs.name

        # index but no data
        s = Series(index=[1, 2, 3], dtype=np.float64)
        rs = s.apply(lambda x: x)
        tm.assert_series_equal(s, rs)

    def test_apply_same_length_inference_bug(self):
        s = Series([1, 2])

        def f(x):
            return (x, x + 1)

        result = s.apply(f)
        expected = s.map(f)
        tm.assert_series_equal(result, expected)

        s = Series([1, 2, 3])
        result = s.apply(f)
        expected = s.map(f)
        tm.assert_series_equal(result, expected)

    def test_apply_dont_convert_dtype(self):
        s = Series(np.random.randn(10))

        def f(x):
            return x if x > 0 else np.nan

        result = s.apply(f, convert_dtype=False)
        assert result.dtype == object

    def test_with_string_args(self, datetime_series):

        for arg in ["sum", "mean", "min", "max", "std"]:
            result = datetime_series.apply(arg)
            expected = getattr(datetime_series, arg)()
            assert result == expected

    def test_apply_args(self):
        s = Series(["foo,bar"])

        result = s.apply(str.split, args=(",",))
        assert result[0] == ["foo", "bar"]
        assert isinstance(result[0], list)

    def test_series_map_box_timestamps(self):
        # GH#2689, GH#2627
        ser = Series(pd.date_range("1/1/2000", periods=10))

        def func(x):
            return (x.hour, x.day, x.month)

        # it works!
        ser.map(func)
        ser.apply(func)

    def test_apply_box(self):
        # ufunc will not be boxed. Same test cases as the test_map_box
        vals = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]
        s = pd.Series(vals)
        assert s.dtype == "datetime64[ns]"
        # boxed value must be Timestamp instance
        res = s.apply(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
        exp = pd.Series(["Timestamp_1_None", "Timestamp_2_None"])
        tm.assert_series_equal(res, exp)

        vals = [
            pd.Timestamp("2011-01-01", tz="US/Eastern"),
            pd.Timestamp("2011-01-02", tz="US/Eastern"),
        ]
        s = pd.Series(vals)
        assert s.dtype == "datetime64[ns, US/Eastern]"
        res = s.apply(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
        exp = pd.Series(["Timestamp_1_US/Eastern", "Timestamp_2_US/Eastern"])
        tm.assert_series_equal(res, exp)

        # timedelta
        vals = [pd.Timedelta("1 days"), pd.Timedelta("2 days")]
        s = pd.Series(vals)
        assert s.dtype == "timedelta64[ns]"
        res = s.apply(lambda x: f"{type(x).__name__}_{x.days}")
        exp = pd.Series(["Timedelta_1", "Timedelta_2"])
        tm.assert_series_equal(res, exp)

        # period
        vals = [pd.Period("2011-01-01", freq="M"), pd.Period("2011-01-02", freq="M")]
        s = pd.Series(vals)
        assert s.dtype == "Period[M]"
        res = s.apply(lambda x: f"{type(x).__name__}_{x.freqstr}")
        exp = pd.Series(["Period_M", "Period_M"])
        tm.assert_series_equal(res, exp)

    def test_apply_datetimetz(self):
        values = pd.date_range("2011-01-01", "2011-01-02", freq="H").tz_localize(
            "Asia/Tokyo"
        )
        s = pd.Series(values, name="XX")

        result = s.apply(lambda x: x + pd.offsets.Day())
        exp_values = pd.date_range("2011-01-02", "2011-01-03", freq="H").tz_localize(
            "Asia/Tokyo"
        )
        exp = pd.Series(exp_values, name="XX")
        tm.assert_series_equal(result, exp)

        # change dtype
        # GH 14506 : Returned dtype changed from int32 to int64
        result = s.apply(lambda x: x.hour)
        exp = pd.Series(list(range(24)) + [0], name="XX", dtype=np.int64)
        tm.assert_series_equal(result, exp)

        # not vectorized
        def f(x):
            if not isinstance(x, pd.Timestamp):
                raise ValueError
            return str(x.tz)

        result = s.map(f)
        exp = pd.Series(["Asia/Tokyo"] * 25, name="XX")
        tm.assert_series_equal(result, exp)

    def test_apply_dict_depr(self):

        tsdf = pd.DataFrame(
            np.random.randn(10, 3),
            columns=["A", "B", "C"],
            index=pd.date_range("1/1/2000", periods=10),
        )
        msg = "nested renamer is not supported"
        with pytest.raises(SpecificationError, match=msg):
            tsdf.A.agg({"foo": ["sum", "mean"]})

    def test_apply_categorical(self):
        values = pd.Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True)
        ser = pd.Series(values, name="XX", index=list("abcdefg"))
        result = ser.apply(lambda x: x.lower())

        # should be categorical dtype when the number of categories are
        # the same
        values = pd.Categorical(list("abbabcd"), categories=list("dcba"), ordered=True)
        exp = pd.Series(values, name="XX", index=list("abcdefg"))
        tm.assert_series_equal(result, exp)
        tm.assert_categorical_equal(result.values, exp.values)

        result = ser.apply(lambda x: "A")
        exp = pd.Series(["A"] * 7, name="XX", index=list("abcdefg"))
        tm.assert_series_equal(result, exp)
        assert result.dtype == np.object

    @pytest.mark.parametrize("series", [["1-1", "1-1", np.NaN], ["1-1", "1-2", np.NaN]])
    def test_apply_categorical_with_nan_values(self, series):
        # GH 20714 bug fixed in: GH 24275
        s = pd.Series(series, dtype="category")
        result = s.apply(lambda x: x.split("-")[0])
        result = result.astype(object)
        expected = pd.Series(["1", "1", np.NaN], dtype="category")
        expected = expected.astype(object)
        tm.assert_series_equal(result, expected)

    def test_apply_empty_integer_series_with_datetime_index(self):
        # GH 21245
        s = pd.Series([], index=pd.date_range(start="2018-01-01", periods=0), dtype=int)
        result = s.apply(lambda x: x)
        tm.assert_series_equal(result, s)


class TestSeriesAggregate:
    def test_transform(self, string_series):
        # transforming functions

        with np.errstate(all="ignore"):

            f_sqrt = np.sqrt(string_series)
            f_abs = np.abs(string_series)

            # ufunc
            result = string_series.transform(np.sqrt)
            expected = f_sqrt.copy()
            tm.assert_series_equal(result, expected)

            result = string_series.apply(np.sqrt)
            tm.assert_series_equal(result, expected)

            # list-like
            result = string_series.transform([np.sqrt])
            expected = f_sqrt.to_frame().copy()
            expected.columns = ["sqrt"]
            tm.assert_frame_equal(result, expected)

            result = string_series.transform([np.sqrt])
            tm.assert_frame_equal(result, expected)

            result = string_series.transform(["sqrt"])
            tm.assert_frame_equal(result, expected)

            # multiple items in list
            # these are in the order as if we are applying both functions per
            # series and then concatting
            expected = pd.concat([f_sqrt, f_abs], axis=1)
            expected.columns = ["sqrt", "absolute"]
            result = string_series.apply([np.sqrt, np.abs])
            tm.assert_frame_equal(result, expected)

            result = string_series.transform(["sqrt", "abs"])
            expected.columns = ["sqrt", "abs"]
            tm.assert_frame_equal(result, expected)

            # dict, provide renaming
            expected = pd.concat([f_sqrt, f_abs], axis=1)
            expected.columns = ["foo", "bar"]
            expected = expected.unstack().rename("series")

            result = string_series.apply({"foo": np.sqrt, "bar": np.abs})
            tm.assert_series_equal(result.reindex_like(expected), expected)

    def test_transform_and_agg_error(self, string_series):
        # we are trying to transform with an aggregator
        msg = "transforms cannot produce aggregated results"
        with pytest.raises(ValueError, match=msg):
            string_series.transform(["min", "max"])

        msg = "cannot combine transform and aggregation"
        with pytest.raises(ValueError, match=msg):
            with np.errstate(all="ignore"):
                string_series.agg(["sqrt", "max"])

        with pytest.raises(ValueError, match=msg):
            with np.errstate(all="ignore"):
                string_series.transform(["sqrt", "max"])

        msg = "cannot perform both aggregation and transformation"
        with pytest.raises(ValueError, match=msg):
            with np.errstate(all="ignore"):
                string_series.agg({"foo": np.sqrt, "bar": "sum"})

    def test_demo(self):
        # demonstration tests
        s = Series(range(6), dtype="int64", name="series")

        result = s.agg(["min", "max"])
        expected = Series([0, 5], index=["min", "max"], name="series")
        tm.assert_series_equal(result, expected)

        result = s.agg({"foo": "min"})
        expected = Series([0], index=["foo"], name="series")
        tm.assert_series_equal(result, expected)

        # nested renaming
        msg = "nested renamer is not supported"
        with pytest.raises(SpecificationError, match=msg):
            s.agg({"foo": ["min", "max"]})

    def test_multiple_aggregators_with_dict_api(self):

        s = Series(range(6), dtype="int64", name="series")
        # nested renaming
        msg = "nested renamer is not supported"
        with pytest.raises(SpecificationError, match=msg):
            s.agg({"foo": ["min", "max"], "bar": ["sum", "mean"]})

    def test_agg_apply_evaluate_lambdas_the_same(self, string_series):
        # test that we are evaluating row-by-row first
        # before vectorized evaluation
        result = string_series.apply(lambda x: str(x))
        expected = string_series.agg(lambda x: str(x))
        tm.assert_series_equal(result, expected)

        result = string_series.apply(str)
        expected = string_series.agg(str)
        tm.assert_series_equal(result, expected)

    def test_with_nested_series(self, datetime_series):
        # GH 2316
        # .agg with a reducer and a transform, what to do
        result = datetime_series.apply(
            lambda x: Series([x, x ** 2], index=["x", "x^2"])
        )
        expected = DataFrame({"x": datetime_series, "x^2": datetime_series ** 2})
        tm.assert_frame_equal(result, expected)

        result = datetime_series.agg(lambda x: Series([x, x ** 2], index=["x", "x^2"]))
        tm.assert_frame_equal(result, expected)

    def test_replicate_describe(self, string_series):
        # this also tests a result set that is all scalars
        expected = string_series.describe()
        result = string_series.apply(
            {
                "count": "count",
                "mean": "mean",
                "std": "std",
                "min": "min",
                "25%": lambda x: x.quantile(0.25),
                "50%": "median",
                "75%": lambda x: x.quantile(0.75),
                "max": "max",
            }
        )
        tm.assert_series_equal(result, expected)

    def test_reduce(self, string_series):
        # reductions with named functions
        result = string_series.agg(["sum", "mean"])
        expected = Series(
            [string_series.sum(), string_series.mean()],
            ["sum", "mean"],
            name=string_series.name,
        )
        tm.assert_series_equal(result, expected)

    def test_non_callable_aggregates(self):
        # test agg using non-callable series attributes
        s = Series([1, 2, None])

        # Calling agg w/ just a string arg same as calling s.arg
        result = s.agg("size")
        expected = s.size
        assert result == expected

        # test when mixed w/ callable reducers
        result = s.agg(["size", "count", "mean"])
        expected = Series({"size": 3.0, "count": 2.0, "mean": 1.5})
        tm.assert_series_equal(result[expected.index], expected)

    @pytest.mark.parametrize(
        "series, func, expected",
        chain(
            tm.get_cython_table_params(
                Series(dtype=np.float64),
                [
                    ("sum", 0),
                    ("max", np.nan),
                    ("min", np.nan),
                    ("all", True),
                    ("any", False),
                    ("mean", np.nan),
                    ("prod", 1),
                    ("std", np.nan),
                    ("var", np.nan),
                    ("median", np.nan),
                ],
            ),
            tm.get_cython_table_params(
                Series([np.nan, 1, 2, 3]),
                [
                    ("sum", 6),
                    ("max", 3),
                    ("min", 1),
                    ("all", True),
                    ("any", True),
                    ("mean", 2),
                    ("prod", 6),
                    ("std", 1),
                    ("var", 1),
                    ("median", 2),
                ],
            ),
            tm.get_cython_table_params(
                Series("a b c".split()),
                [
                    ("sum", "abc"),
                    ("max", "c"),
                    ("min", "a"),
                    ("all", "c"),  # see GH12863
                    ("any", "a"),
                ],
            ),
        ),
    )
    def test_agg_cython_table(self, series, func, expected):
        # GH21224
        # test reducing functions in
        # pandas.core.base.SelectionMixin._cython_table
        result = series.agg(func)
        if tm.is_number(expected):
            assert np.isclose(result, expected, equal_nan=True)
        else:
            assert result == expected

    @pytest.mark.parametrize(
        "series, func, expected",
        chain(
            tm.get_cython_table_params(
                Series(dtype=np.float64),
                [
                    ("cumprod", Series([], Index([]), dtype=np.float64)),
                    ("cumsum", Series([], Index([]), dtype=np.float64)),
                ],
            ),
            tm.get_cython_table_params(
                Series([np.nan, 1, 2, 3]),
                [
                    ("cumprod", Series([np.nan, 1, 2, 6])),
                    ("cumsum", Series([np.nan, 1, 3, 6])),
                ],
            ),
            tm.get_cython_table_params(
                Series("a b c".split()), [("cumsum", Series(["a", "ab", "abc"]))]
            ),
        ),
    )
    def test_agg_cython_table_transform(self, series, func, expected):
        # GH21224
        # test transforming functions in
        # pandas.core.base.SelectionMixin._cython_table (cumprod, cumsum)
        result = series.agg(func)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "series, func, expected",
        chain(
            tm.get_cython_table_params(
                Series("a b c".split()),
                [
                    ("mean", TypeError),  # mean raises TypeError
                    ("prod", TypeError),
                    ("std", TypeError),
                    ("var", TypeError),
                    ("median", TypeError),
                    ("cumprod", TypeError),
                ],
            )
        ),
    )
    def test_agg_cython_table_raises(self, series, func, expected):
        # GH21224
        with pytest.raises(expected):
            # e.g. Series('a b'.split()).cumprod() will raise
            series.agg(func)

    def test_transform_none_to_type(self):
        # GH34377
        df = pd.DataFrame({"a": [None]})

        msg = "DataFrame constructor called with incompatible data and dtype"
        with pytest.raises(TypeError, match=msg):
            df.transform({"a": int})


class TestSeriesMap:
    def test_map(self, datetime_series):
        index, data = tm.getMixedTypeDict()

        source = Series(data["B"], index=data["C"])
        target = Series(data["C"][:4], index=data["D"][:4])

        merged = target.map(source)

        for k, v in merged.items():
            assert v == source[target[k]]

        # input could be a dict
        merged = target.map(source.to_dict())

        for k, v in merged.items():
            assert v == source[target[k]]

        # function
        result = datetime_series.map(lambda x: x * 2)
        tm.assert_series_equal(result, datetime_series * 2)

        # GH 10324
        a = Series([1, 2, 3, 4])
        b = Series(["even", "odd", "even", "odd"], dtype="category")
        c = Series(["even", "odd", "even", "odd"])

        exp = Series(["odd", "even", "odd", np.nan], dtype="category")
        tm.assert_series_equal(a.map(b), exp)
        exp = Series(["odd", "even", "odd", np.nan])
        tm.assert_series_equal(a.map(c), exp)

        a = Series(["a", "b", "c", "d"])
        b = Series([1, 2, 3, 4], index=pd.CategoricalIndex(["b", "c", "d", "e"]))
        c = Series([1, 2, 3, 4], index=Index(["b", "c", "d", "e"]))

        exp = Series([np.nan, 1, 2, 3])
        tm.assert_series_equal(a.map(b), exp)
        exp = Series([np.nan, 1, 2, 3])
        tm.assert_series_equal(a.map(c), exp)

        a = Series(["a", "b", "c", "d"])
        b = Series(
            ["B", "C", "D", "E"],
            dtype="category",
            index=pd.CategoricalIndex(["b", "c", "d", "e"]),
        )
        c = Series(["B", "C", "D", "E"], index=Index(["b", "c", "d", "e"]))

        exp = Series(
            pd.Categorical([np.nan, "B", "C", "D"], categories=["B", "C", "D", "E"])
        )
        tm.assert_series_equal(a.map(b), exp)
        exp = Series([np.nan, "B", "C", "D"])
        tm.assert_series_equal(a.map(c), exp)

    def test_map_empty(self, indices):
        if isinstance(indices, MultiIndex):
            pytest.skip("Initializing a Series from a MultiIndex is not supported")

        s = Series(indices)
        result = s.map({})

        expected = pd.Series(np.nan, index=s.index)
        tm.assert_series_equal(result, expected)

    def test_map_compat(self):
        # related GH 8024
        s = Series([True, True, False], index=[1, 2, 3])
        result = s.map({True: "foo", False: "bar"})
        expected = Series(["foo", "foo", "bar"], index=[1, 2, 3])
        tm.assert_series_equal(result, expected)

    def test_map_int(self):
        left = Series({"a": 1.0, "b": 2.0, "c": 3.0, "d": 4})
        right = Series({1: 11, 2: 22, 3: 33})

        assert left.dtype == np.float_
        assert issubclass(right.dtype.type, np.integer)

        merged = left.map(right)
        assert merged.dtype == np.float_
        assert isna(merged["d"])
        assert not isna(merged["c"])

    def test_map_type_inference(self):
        s = Series(range(3))
        s2 = s.map(lambda x: np.where(x == 0, 0, 1))
        assert issubclass(s2.dtype.type, np.integer)

    def test_map_decimal(self, string_series):
        from decimal import Decimal

        result = string_series.map(lambda x: Decimal(str(x)))
        assert result.dtype == np.object_
        assert isinstance(result[0], Decimal)

    def test_map_na_exclusion(self):
        s = Series([1.5, np.nan, 3, np.nan, 5])

        result = s.map(lambda x: x * 2, na_action="ignore")
        exp = s * 2
        tm.assert_series_equal(result, exp)

    def test_map_dict_with_tuple_keys(self):
        """
        Due to new MultiIndex-ing behaviour in v0.14.0,
        dicts with tuple keys passed to map were being
        converted to a multi-index, preventing tuple values
        from being mapped properly.
        """
        # GH 18496
        df = pd.DataFrame({"a": [(1,), (2,), (3, 4), (5, 6)]})
        label_mappings = {(1,): "A", (2,): "B", (3, 4): "A", (5, 6): "B"}

        df["labels"] = df["a"].map(label_mappings)
        df["expected_labels"] = pd.Series(["A", "B", "A", "B"], index=df.index)
        # All labels should be filled now
        tm.assert_series_equal(df["labels"], df["expected_labels"], check_names=False)

    def test_map_counter(self):
        s = Series(["a", "b", "c"], index=[1, 2, 3])
        counter = Counter()
        counter["b"] = 5
        counter["c"] += 1
        result = s.map(counter)
        expected = Series([0, 5, 1], index=[1, 2, 3])
        tm.assert_series_equal(result, expected)

    def test_map_defaultdict(self):
        s = Series([1, 2, 3], index=["a", "b", "c"])
        default_dict = defaultdict(lambda: "blank")
        default_dict[1] = "stuff"
        result = s.map(default_dict)
        expected = Series(["stuff", "blank", "blank"], index=["a", "b", "c"])
        tm.assert_series_equal(result, expected)

    def test_map_dict_na_key(self):
        # https://github.com/pandas-dev/pandas/issues/17648
        # Checks that np.nan key is appropriately mapped
        s = Series([1, 2, np.nan])
        expected = Series(["a", "b", "c"])
        result = s.map({1: "a", 2: "b", np.nan: "c"})
        tm.assert_series_equal(result, expected)

    def test_map_dict_subclass_with_missing(self):
        """
        Test Series.map with a dictionary subclass that defines __missing__,
        i.e. sets a default value (GH #15999).
        """

        class DictWithMissing(dict):
            def __missing__(self, key):
                return "missing"

        s = Series([1, 2, 3])
        dictionary = DictWithMissing({3: "three"})
        result = s.map(dictionary)
        expected = Series(["missing", "missing", "three"])
        tm.assert_series_equal(result, expected)

    def test_map_dict_subclass_without_missing(self):
        class DictWithoutMissing(dict):
            pass

        s = Series([1, 2, 3])
        dictionary = DictWithoutMissing({3: "three"})
        result = s.map(dictionary)
        expected = Series([np.nan, np.nan, "three"])
        tm.assert_series_equal(result, expected)

    def test_map_abc_mapping(self, non_dict_mapping_subclass):
        # https://github.com/pandas-dev/pandas/issues/29733
        # Check collections.abc.Mapping support as mapper for Series.map
        s = Series([1, 2, 3])
        not_a_dictionary = non_dict_mapping_subclass({3: "three"})
        result = s.map(not_a_dictionary)
        expected = Series([np.nan, np.nan, "three"])
        tm.assert_series_equal(result, expected)

    def test_map_abc_mapping_with_missing(self, non_dict_mapping_subclass):
        # https://github.com/pandas-dev/pandas/issues/29733
        # Check collections.abc.Mapping support as mapper for Series.map
        class NonDictMappingWithMissing(non_dict_mapping_subclass):
            def __missing__(self, key):
                return "missing"

        s = Series([1, 2, 3])
        not_a_dictionary = NonDictMappingWithMissing({3: "three"})
        result = s.map(not_a_dictionary)
        # __missing__ is a dict concept, not a Mapping concept,
        # so it should not change the result!
        expected = Series([np.nan, np.nan, "three"])
        tm.assert_series_equal(result, expected)

    def test_map_box(self):
        vals = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]
        s = pd.Series(vals)
        assert s.dtype == "datetime64[ns]"
        # boxed value must be Timestamp instance
        res = s.apply(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
        exp = pd.Series(["Timestamp_1_None", "Timestamp_2_None"])
        tm.assert_series_equal(res, exp)

        vals = [
            pd.Timestamp("2011-01-01", tz="US/Eastern"),
            pd.Timestamp("2011-01-02", tz="US/Eastern"),
        ]
        s = pd.Series(vals)
        assert s.dtype == "datetime64[ns, US/Eastern]"
        res = s.apply(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
        exp = pd.Series(["Timestamp_1_US/Eastern", "Timestamp_2_US/Eastern"])
        tm.assert_series_equal(res, exp)

        # timedelta
        vals = [pd.Timedelta("1 days"), pd.Timedelta("2 days")]
        s = pd.Series(vals)
        assert s.dtype == "timedelta64[ns]"
        res = s.apply(lambda x: f"{type(x).__name__}_{x.days}")
        exp = pd.Series(["Timedelta_1", "Timedelta_2"])
        tm.assert_series_equal(res, exp)

        # period
        vals = [pd.Period("2011-01-01", freq="M"), pd.Period("2011-01-02", freq="M")]
        s = pd.Series(vals)
        assert s.dtype == "Period[M]"
        res = s.apply(lambda x: f"{type(x).__name__}_{x.freqstr}")
        exp = pd.Series(["Period_M", "Period_M"])
        tm.assert_series_equal(res, exp)

    def test_map_categorical(self):
        values = pd.Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True)
        s = pd.Series(values, name="XX", index=list("abcdefg"))

        result = s.map(lambda x: x.lower())
        exp_values = pd.Categorical(
            list("abbabcd"), categories=list("dcba"), ordered=True
        )
        exp = pd.Series(exp_values, name="XX", index=list("abcdefg"))
        tm.assert_series_equal(result, exp)
        tm.assert_categorical_equal(result.values, exp_values)

        result = s.map(lambda x: "A")
        exp = pd.Series(["A"] * 7, name="XX", index=list("abcdefg"))
        tm.assert_series_equal(result, exp)
        assert result.dtype == np.object

        with pytest.raises(NotImplementedError):
            s.map(lambda x: x, na_action="ignore")

    def test_map_datetimetz(self):
        values = pd.date_range("2011-01-01", "2011-01-02", freq="H").tz_localize(
            "Asia/Tokyo"
        )
        s = pd.Series(values, name="XX")

        # keep tz
        result = s.map(lambda x: x + pd.offsets.Day())
        exp_values = pd.date_range("2011-01-02", "2011-01-03", freq="H").tz_localize(
            "Asia/Tokyo"
        )
        exp = pd.Series(exp_values, name="XX")
        tm.assert_series_equal(result, exp)

        # change dtype
        # GH 14506 : Returned dtype changed from int32 to int64
        result = s.map(lambda x: x.hour)
        exp = pd.Series(list(range(24)) + [0], name="XX", dtype=np.int64)
        tm.assert_series_equal(result, exp)

        with pytest.raises(NotImplementedError):
            s.map(lambda x: x, na_action="ignore")

        # not vectorized
        def f(x):
            if not isinstance(x, pd.Timestamp):
                raise ValueError
            return str(x.tz)

        result = s.map(f)
        exp = pd.Series(["Asia/Tokyo"] * 25, name="XX")
        tm.assert_series_equal(result, exp)

    @pytest.mark.parametrize(
        "vals,mapping,exp",
        [
            (list("abc"), {np.nan: "not NaN"}, [np.nan] * 3 + ["not NaN"]),
            (list("abc"), {"a": "a letter"}, ["a letter"] + [np.nan] * 3),
            (list(range(3)), {0: 42}, [42] + [np.nan] * 3),
        ],
    )
    def test_map_missing_mixed(self, vals, mapping, exp):
        # GH20495
        s = pd.Series(vals + [np.nan])
        result = s.map(mapping)

        tm.assert_series_equal(result, pd.Series(exp))

    @pytest.mark.parametrize(
        "dti,exp",
        [
            (
                Series([1, 2], index=pd.DatetimeIndex([0, 31536000000])),
                DataFrame(np.repeat([[1, 2]], 2, axis=0), dtype="int64"),
            ),
            (
                tm.makeTimeSeries(nper=30),
                DataFrame(np.repeat([[1, 2]], 30, axis=0), dtype="int64"),
            ),
        ],
    )
    def test_apply_series_on_date_time_index_aware_series(self, dti, exp):
        # GH 25959
        # Calling apply on a localized time series should not cause an error
        index = dti.tz_localize("UTC").index
        result = pd.Series(index).apply(lambda x: pd.Series([1, 2]))
        tm.assert_frame_equal(result, exp)

    def test_apply_scaler_on_date_time_index_aware_series(self):
        # GH 25959
        # Calling apply on a localized time series should not cause an error
        series = tm.makeTimeSeries(nper=30).tz_localize("UTC")
        result = pd.Series(series.index).apply(lambda x: 1)
        tm.assert_series_equal(result, pd.Series(np.ones(30), dtype="int64"))

    def test_map_float_to_string_precision(self):
        # GH 13228
        ser = pd.Series(1 / 3)
        result = ser.map(lambda val: str(val)).to_dict()
        expected = {0: "0.3333333333333333"}
        assert result == expected

    def test_map_with_invalid_na_action_raises(self):
        # https://github.com/pandas-dev/pandas/issues/32815
        s = pd.Series([1, 2, 3])
        msg = "na_action must either be 'ignore' or None"
        with pytest.raises(ValueError, match=msg):
            s.map(lambda x: x, na_action="____")

    def test_apply_to_timedelta(self):
        list_of_valid_strings = ["00:00:01", "00:00:02"]
        a = pd.to_timedelta(list_of_valid_strings)
        b = Series(list_of_valid_strings).apply(pd.to_timedelta)
        # FIXME: dont leave commented-out
        # Can't compare until apply on a Series gives the correct dtype
        # assert_series_equal(a, b)

        list_of_strings = ["00:00:01", np.nan, pd.NaT, pd.NaT]

        a = pd.to_timedelta(list_of_strings)  # noqa
        b = Series(list_of_strings).apply(pd.to_timedelta)  # noqa
        # Can't compare until apply on a Series gives the correct dtype
        # assert_series_equal(a, b)