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 import (
    NA,
    Categorical,
    CategoricalDtype,
    Index,
    Interval,
    NaT,
    Series,
    Timedelta,
    Timestamp,
    cut,
    date_range,
)
import pandas._testing as tm


class TestAstypeAPI:
    def test_arg_for_errors_in_astype(self):
        # see GH#14878
        ser = 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):
            ser.astype(np.float64, errors=False)

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

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

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

        dt2 = dtype_class({"abc": "float64"})
        result = ser.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):
            ser.astype(dt3)

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

        # GH#16717
        # 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):
            ser.astype(dt5)


class TestAstype:
    @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)

    @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_float_to_period(self):
        result = Series([np.nan]).astype("period[D]")
        expected = Series([NaT], dtype="period[D]")
        tm.assert_series_equal(result, expected)

    def test_astype_no_pandas_dtype(self):
        # https://github.com/pandas-dev/pandas/pull/24866
        ser = Series([1, 2], dtype="int64")
        # Don't have PandasDtype in the public API, so we use `.array.dtype`,
        # which is a PandasDtype.
        result = ser.astype(ser.array.dtype)
        tm.assert_series_equal(result, ser)

    @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)

    def test_astype_dt64_to_str(self):
        # GH#10442 : testing astype(str) is correct for Series/DatetimeIndex
        dti = date_range("2012-01-01", periods=3)
        result = Series(dti).astype(str)
        expected = Series(["2012-01-01", "2012-01-02", "2012-01-03"], dtype=object)
        tm.assert_series_equal(result, expected)

    def test_astype_dt64tz_to_str(self):
        # GH#10442 : testing astype(str) is correct for Series/DatetimeIndex
        dti_tz = date_range("2012-01-01", periods=3, tz="US/Eastern")
        result = Series(dti_tz).astype(str)
        expected = Series(
            [
                "2012-01-01 00:00:00-05:00",
                "2012-01-02 00:00:00-05:00",
                "2012-01-03 00:00:00-05:00",
            ],
            dtype=object,
        )
        tm.assert_series_equal(result, expected)

    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)

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

    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(
        "values",
        [
            Series(["x", "y", "z"], dtype="string"),
            Series(["x", "y", "z"], dtype="category"),
            Series(3 * [Timestamp("2020-01-01", tz="UTC")]),
            Series(3 * [Interval(0, 1)]),
        ],
    )
    @pytest.mark.parametrize("errors", ["raise", "ignore"])
    def test_astype_ignores_errors_for_extension_dtypes(self, values, errors):
        # https://github.com/pandas-dev/pandas/issues/35471
        if errors == "ignore":
            expected = values
            result = values.astype(float, errors="ignore")
            tm.assert_series_equal(result, expected)
        else:
            msg = "(Cannot cast)|(could not convert)"
            with pytest.raises((ValueError, TypeError), match=msg):
                values.astype(float, errors=errors)

    @pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64])
    def test_astype_from_float_to_str(self, dtype):
        # https://github.com/pandas-dev/pandas/issues/36451
        s = Series([0.1], dtype=dtype)
        result = s.astype(str)
        expected = Series(["0.1"])
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "value, string_value",
        [
            (None, "None"),
            (np.nan, "nan"),
            (NA, "<NA>"),
        ],
    )
    def test_astype_to_str_preserves_na(self, value, string_value):
        # https://github.com/pandas-dev/pandas/issues/36904
        s = Series(["a", "b", value], dtype=object)
        result = s.astype(str)
        expected = Series(["a", "b", string_value], dtype=object)
        tm.assert_series_equal(result, expected)

    @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

    @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_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()]))

        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)


class TestAstypeCategorical:
    def test_astype_categorical_invalid_conversions(self):
        # invalid conversion (these are NOT a dtype)
        cat = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)])
        ser = Series(np.random.randint(0, 10000, 100)).sort_values()
        ser = cut(ser, range(0, 10500, 500), right=False, labels=cat)

        msg = (
            "dtype '<class 'pandas.core.arrays.categorical.Categorical'>' "
            "not understood"
        )
        with pytest.raises(TypeError, match=msg):
            ser.astype(Categorical)
        with pytest.raises(TypeError, match=msg):
            ser.astype("object").astype(Categorical)

    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("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, GH#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_categories_raises(self):
        # deprecated GH#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)
