from datetime import datetime, timedelta
from importlib import reload
import string
import sys

import numpy as np
import pytest

from pandas._libs.tslibs import iNaT

from pandas.core.dtypes.dtypes import CategoricalDtype

import pandas as pd
from pandas import (
    Categorical,
    DataFrame,
    Index,
    Series,
    Timedelta,
    Timestamp,
    date_range,
)
import pandas._testing as tm


class TestSeriesDtypes:
    def test_dt64_series_astype_object(self):
        dt64ser = Series(date_range("20130101", periods=3))
        result = dt64ser.astype(object)
        assert isinstance(result.iloc[0], datetime)
        assert result.dtype == np.object_

    def test_td64_series_astype_object(self):
        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="timedelta64[ns]")
        result = tdser.astype(object)
        assert isinstance(result.iloc[0], timedelta)
        assert result.dtype == np.object_

    @pytest.mark.parametrize("dtype", ["float32", "float64", "int64", "int32"])
    def test_astype(self, dtype):
        s = Series(np.random.randn(5), name="foo")
        as_typed = s.astype(dtype)

        assert as_typed.dtype == dtype
        assert as_typed.name == s.name

    def test_dtype(self, datetime_series):

        assert datetime_series.dtype == np.dtype("float64")
        assert datetime_series.dtypes == np.dtype("float64")

    @pytest.mark.parametrize("value", [np.nan, np.inf])
    @pytest.mark.parametrize("dtype", [np.int32, np.int64])
    def test_astype_cast_nan_inf_int(self, dtype, value):
        # gh-14265: check NaN and inf raise error when converting to int
        msg = "Cannot convert non-finite values \\(NA or inf\\) to integer"
        s = Series([value])

        with pytest.raises(ValueError, match=msg):
            s.astype(dtype)

    @pytest.mark.parametrize("dtype", [int, np.int8, np.int64])
    def test_astype_cast_object_int_fail(self, dtype):
        arr = Series(["car", "house", "tree", "1"])
        msg = r"invalid literal for int\(\) with base 10: 'car'"
        with pytest.raises(ValueError, match=msg):
            arr.astype(dtype)

    def test_astype_cast_object_int(self):
        arr = Series(["1", "2", "3", "4"], dtype=object)
        result = arr.astype(int)

        tm.assert_series_equal(result, Series(np.arange(1, 5)))

    def test_astype_datetime(self):
        s = Series(iNaT, dtype="M8[ns]", index=range(5))

        s = s.astype("O")
        assert s.dtype == np.object_

        s = Series([datetime(2001, 1, 2, 0, 0)])

        s = s.astype("O")
        assert s.dtype == np.object_

        s = Series([datetime(2001, 1, 2, 0, 0) for i in range(3)])

        s[1] = np.nan
        assert s.dtype == "M8[ns]"

        s = s.astype("O")
        assert s.dtype == np.object_

    def test_astype_datetime64tz(self):
        s = Series(date_range("20130101", periods=3, tz="US/Eastern"))

        # astype
        result = s.astype(object)
        expected = Series(s.astype(object), dtype=object)
        tm.assert_series_equal(result, expected)

        result = Series(s.values).dt.tz_localize("UTC").dt.tz_convert(s.dt.tz)
        tm.assert_series_equal(result, s)

        # astype - object, preserves on construction
        result = Series(s.astype(object))
        expected = s.astype(object)
        tm.assert_series_equal(result, expected)

        # astype - datetime64[ns, tz]
        result = Series(s.values).astype("datetime64[ns, US/Eastern]")
        tm.assert_series_equal(result, s)

        result = Series(s.values).astype(s.dtype)
        tm.assert_series_equal(result, s)

        result = s.astype("datetime64[ns, CET]")
        expected = Series(date_range("20130101 06:00:00", periods=3, tz="CET"))
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("dtype", [str, np.str_])
    @pytest.mark.parametrize(
        "series",
        [
            Series([string.digits * 10, tm.rands(63), tm.rands(64), tm.rands(1000)]),
            Series([string.digits * 10, tm.rands(63), tm.rands(64), np.nan, 1.0]),
        ],
    )
    def test_astype_str_map(self, dtype, series):
        # see gh-4405
        result = series.astype(dtype)
        expected = series.map(str)
        tm.assert_series_equal(result, expected)

    def test_astype_str_cast_dt64(self):
        # see gh-9757
        ts = Series([Timestamp("2010-01-04 00:00:00")])
        s = ts.astype(str)

        expected = Series([str("2010-01-04")])
        tm.assert_series_equal(s, expected)

        ts = Series([Timestamp("2010-01-04 00:00:00", tz="US/Eastern")])
        s = ts.astype(str)

        expected = Series([str("2010-01-04 00:00:00-05:00")])
        tm.assert_series_equal(s, expected)

    def test_astype_str_cast_td64(self):
        # see gh-9757

        td = Series([Timedelta(1, unit="d")])
        ser = td.astype(str)

        expected = Series([str("1 days")])
        tm.assert_series_equal(ser, expected)

    def test_astype_unicode(self):
        # see gh-7758: A bit of magic is required to set
        # default encoding to utf-8
        digits = string.digits
        test_series = [
            Series([digits * 10, tm.rands(63), tm.rands(64), tm.rands(1000)]),
            Series(["データーサイエンス、お前はもう死んでいる"]),
        ]

        former_encoding = None

        if sys.getdefaultencoding() == "utf-8":
            test_series.append(Series(["野菜食べないとやばい".encode("utf-8")]))

        for s in test_series:
            res = s.astype("unicode")
            expec = s.map(str)
            tm.assert_series_equal(res, expec)

        # Restore the former encoding
        if former_encoding is not None and former_encoding != "utf-8":
            reload(sys)
            sys.setdefaultencoding(former_encoding)

    @pytest.mark.parametrize("dtype_class", [dict, Series])
    def test_astype_dict_like(self, dtype_class):
        # see gh-7271
        s = Series(range(0, 10, 2), name="abc")

        dt1 = dtype_class({"abc": str})
        result = s.astype(dt1)
        expected = Series(["0", "2", "4", "6", "8"], name="abc")
        tm.assert_series_equal(result, expected)

        dt2 = dtype_class({"abc": "float64"})
        result = s.astype(dt2)
        expected = Series([0.0, 2.0, 4.0, 6.0, 8.0], dtype="float64", name="abc")
        tm.assert_series_equal(result, expected)

        dt3 = dtype_class({"abc": str, "def": str})
        msg = (
            "Only the Series name can be used for the key in Series dtype "
            r"mappings\."
        )
        with pytest.raises(KeyError, match=msg):
            s.astype(dt3)

        dt4 = dtype_class({0: str})
        with pytest.raises(KeyError, match=msg):
            s.astype(dt4)

        # GH16717
        # if dtypes provided is empty, it should error
        if dtype_class is Series:
            dt5 = dtype_class({}, dtype=object)
        else:
            dt5 = dtype_class({})

        with pytest.raises(KeyError, match=msg):
            s.astype(dt5)

    def test_astype_categories_raises(self):
        # deprecated 17636, removed in GH-27141
        s = Series(["a", "b", "a"])
        with pytest.raises(TypeError, match="got an unexpected"):
            s.astype("category", categories=["a", "b"], ordered=True)

    def test_astype_from_categorical(self):
        items = ["a", "b", "c", "a"]
        s = Series(items)
        exp = Series(Categorical(items))
        res = s.astype("category")
        tm.assert_series_equal(res, exp)

        items = [1, 2, 3, 1]
        s = Series(items)
        exp = Series(Categorical(items))
        res = s.astype("category")
        tm.assert_series_equal(res, exp)

        df = DataFrame({"cats": [1, 2, 3, 4, 5, 6], "vals": [1, 2, 3, 4, 5, 6]})
        cats = Categorical([1, 2, 3, 4, 5, 6])
        exp_df = DataFrame({"cats": cats, "vals": [1, 2, 3, 4, 5, 6]})
        df["cats"] = df["cats"].astype("category")
        tm.assert_frame_equal(exp_df, df)

        df = DataFrame(
            {"cats": ["a", "b", "b", "a", "a", "d"], "vals": [1, 2, 3, 4, 5, 6]}
        )
        cats = Categorical(["a", "b", "b", "a", "a", "d"])
        exp_df = DataFrame({"cats": cats, "vals": [1, 2, 3, 4, 5, 6]})
        df["cats"] = df["cats"].astype("category")
        tm.assert_frame_equal(exp_df, df)

        # with keywords
        lst = ["a", "b", "c", "a"]
        s = Series(lst)
        exp = Series(Categorical(lst, ordered=True))
        res = s.astype(CategoricalDtype(None, ordered=True))
        tm.assert_series_equal(res, exp)

        exp = Series(Categorical(lst, categories=list("abcdef"), ordered=True))
        res = s.astype(CategoricalDtype(list("abcdef"), ordered=True))
        tm.assert_series_equal(res, exp)

    def test_astype_categorical_to_other(self):

        value = np.random.RandomState(0).randint(0, 10000, 100)
        df = DataFrame({"value": value})
        labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
        cat_labels = Categorical(labels, labels)

        df = df.sort_values(by=["value"], ascending=True)
        df["value_group"] = pd.cut(
            df.value, range(0, 10500, 500), right=False, labels=cat_labels
        )

        s = df["value_group"]
        expected = s
        tm.assert_series_equal(s.astype("category"), expected)
        tm.assert_series_equal(s.astype(CategoricalDtype()), expected)
        msg = r"could not convert string to float|invalid literal for float\(\)"
        with pytest.raises(ValueError, match=msg):
            s.astype("float64")

        cat = Series(Categorical(["a", "b", "b", "a", "a", "c", "c", "c"]))
        exp = Series(["a", "b", "b", "a", "a", "c", "c", "c"])
        tm.assert_series_equal(cat.astype("str"), exp)
        s2 = Series(Categorical(["1", "2", "3", "4"]))
        exp2 = Series([1, 2, 3, 4]).astype(int)
        tm.assert_series_equal(s2.astype("int"), exp2)

        # object don't sort correctly, so just compare that we have the same
        # values
        def cmp(a, b):
            tm.assert_almost_equal(np.sort(np.unique(a)), np.sort(np.unique(b)))

        expected = Series(np.array(s.values), name="value_group")
        cmp(s.astype("object"), expected)
        cmp(s.astype(np.object_), expected)

        # array conversion
        tm.assert_almost_equal(np.array(s), np.array(s.values))

        tm.assert_series_equal(s.astype("category"), s)
        tm.assert_series_equal(s.astype(CategoricalDtype()), s)

        roundtrip_expected = s.cat.set_categories(
            s.cat.categories.sort_values()
        ).cat.remove_unused_categories()
        tm.assert_series_equal(
            s.astype("object").astype("category"), roundtrip_expected
        )
        tm.assert_series_equal(
            s.astype("object").astype(CategoricalDtype()), roundtrip_expected
        )

        # invalid conversion (these are NOT a dtype)
        msg = (
            "dtype '<class 'pandas.core.arrays.categorical.Categorical'>' "
            "not understood"
        )

        for invalid in [
            lambda x: x.astype(Categorical),
            lambda x: x.astype("object").astype(Categorical),
        ]:
            with pytest.raises(TypeError, match=msg):
                invalid(s)

    @pytest.mark.parametrize("name", [None, "foo"])
    @pytest.mark.parametrize("dtype_ordered", [True, False])
    @pytest.mark.parametrize("series_ordered", [True, False])
    def test_astype_categorical_to_categorical(
        self, name, dtype_ordered, series_ordered
    ):
        # GH 10696/18593
        s_data = list("abcaacbab")
        s_dtype = CategoricalDtype(list("bac"), ordered=series_ordered)
        s = Series(s_data, dtype=s_dtype, name=name)

        # unspecified categories
        dtype = CategoricalDtype(ordered=dtype_ordered)
        result = s.astype(dtype)
        exp_dtype = CategoricalDtype(s_dtype.categories, dtype_ordered)
        expected = Series(s_data, name=name, dtype=exp_dtype)
        tm.assert_series_equal(result, expected)

        # different categories
        dtype = CategoricalDtype(list("adc"), dtype_ordered)
        result = s.astype(dtype)
        expected = Series(s_data, name=name, dtype=dtype)
        tm.assert_series_equal(result, expected)

        if dtype_ordered is False:
            # not specifying ordered, so only test once
            expected = s
            result = s.astype("category")
            tm.assert_series_equal(result, expected)

    def test_astype_bool_missing_to_categorical(self):
        # GH-19182
        s = Series([True, False, np.nan])
        assert s.dtypes == np.object_

        result = s.astype(CategoricalDtype(categories=[True, False]))
        expected = Series(Categorical([True, False, np.nan], categories=[True, False]))
        tm.assert_series_equal(result, expected)

    def test_astype_categoricaldtype(self):
        s = Series(["a", "b", "a"])
        result = s.astype(CategoricalDtype(["a", "b"], ordered=True))
        expected = Series(Categorical(["a", "b", "a"], ordered=True))
        tm.assert_series_equal(result, expected)

        result = s.astype(CategoricalDtype(["a", "b"], ordered=False))
        expected = Series(Categorical(["a", "b", "a"], ordered=False))
        tm.assert_series_equal(result, expected)

        result = s.astype(CategoricalDtype(["a", "b", "c"], ordered=False))
        expected = Series(
            Categorical(["a", "b", "a"], categories=["a", "b", "c"], ordered=False)
        )
        tm.assert_series_equal(result, expected)
        tm.assert_index_equal(result.cat.categories, Index(["a", "b", "c"]))

    @pytest.mark.parametrize("dtype", [np.datetime64, np.timedelta64])
    def test_astype_generic_timestamp_no_frequency(self, dtype, request):
        # see gh-15524, gh-15987
        data = [1]
        s = Series(data)

        if np.dtype(dtype).name not in ["timedelta64", "datetime64"]:
            mark = pytest.mark.xfail(reason="GH#33890 Is assigned ns unit")
            request.node.add_marker(mark)

        msg = (
            fr"The '{dtype.__name__}' dtype has no unit\. "
            fr"Please pass in '{dtype.__name__}\[ns\]' instead."
        )
        with pytest.raises(ValueError, match=msg):
            s.astype(dtype)

    @pytest.mark.parametrize("dtype", np.typecodes["All"])
    def test_astype_empty_constructor_equality(self, dtype):
        # see gh-15524

        if dtype not in (
            "S",
            "V",  # poor support (if any) currently
            "M",
            "m",  # Generic timestamps raise a ValueError. Already tested.
        ):
            init_empty = Series([], dtype=dtype)
            with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
                as_type_empty = Series([]).astype(dtype)
            tm.assert_series_equal(init_empty, as_type_empty)

    def test_arg_for_errors_in_astype(self):
        # see gh-14878
        s = Series([1, 2, 3])

        msg = (
            r"Expected value of kwarg 'errors' to be one of \['raise', "
            r"'ignore'\]\. Supplied value is 'False'"
        )
        with pytest.raises(ValueError, match=msg):
            s.astype(np.float64, errors=False)

        s.astype(np.int8, errors="raise")

    def test_intercept_astype_object(self):
        series = Series(date_range("1/1/2000", periods=10))

        # This test no longer makes sense, as
        # Series is by default already M8[ns].
        expected = series.astype("object")

        df = DataFrame({"a": series, "b": np.random.randn(len(series))})
        exp_dtypes = Series(
            [np.dtype("datetime64[ns]"), np.dtype("float64")], index=["a", "b"]
        )
        tm.assert_series_equal(df.dtypes, exp_dtypes)

        result = df.values.squeeze()
        assert (result[:, 0] == expected.values).all()

        df = DataFrame({"a": series, "b": ["foo"] * len(series)})

        result = df.values.squeeze()
        assert (result[:, 0] == expected.values).all()

    def test_series_to_categorical(self):
        # see gh-16524: test conversion of Series to Categorical
        series = Series(["a", "b", "c"])

        result = Series(series, dtype="category")
        expected = Series(["a", "b", "c"], dtype="category")

        tm.assert_series_equal(result, expected)

    def test_infer_objects_series(self):
        # GH 11221
        actual = Series(np.array([1, 2, 3], dtype="O")).infer_objects()
        expected = Series([1, 2, 3])
        tm.assert_series_equal(actual, expected)

        actual = Series(np.array([1, 2, 3, None], dtype="O")).infer_objects()
        expected = Series([1.0, 2.0, 3.0, np.nan])
        tm.assert_series_equal(actual, expected)

        # only soft conversions, unconvertable pass thru unchanged
        actual = Series(np.array([1, 2, 3, None, "a"], dtype="O")).infer_objects()
        expected = Series([1, 2, 3, None, "a"])

        assert actual.dtype == "object"
        tm.assert_series_equal(actual, expected)

    @pytest.mark.parametrize(
        "data",
        [
            pd.period_range("2000", periods=4),
            pd.IntervalIndex.from_breaks([1, 2, 3, 4]),
        ],
    )
    def test_values_compatibility(self, data):
        # https://github.com/pandas-dev/pandas/issues/23995
        result = pd.Series(data).values
        expected = np.array(data.astype(object))
        tm.assert_numpy_array_equal(result, expected)

    def test_reindex_astype_order_consistency(self):
        # GH 17444
        s = Series([1, 2, 3], index=[2, 0, 1])
        new_index = [0, 1, 2]
        temp_dtype = "category"
        new_dtype = str
        s1 = s.reindex(new_index).astype(temp_dtype).astype(new_dtype)
        s2 = s.astype(temp_dtype).reindex(new_index).astype(new_dtype)
        tm.assert_series_equal(s1, s2)
