import csv
from io import StringIO
import os

import numpy as np
import pytest

from pandas.errors import ParserError

import pandas as pd
from pandas import (
    DataFrame,
    Index,
    MultiIndex,
    NaT,
    Series,
    Timestamp,
    date_range,
    read_csv,
    to_datetime,
)
import pandas._testing as tm
import pandas.core.common as com

from pandas.io.common import get_handle

MIXED_FLOAT_DTYPES = ["float16", "float32", "float64"]
MIXED_INT_DTYPES = [
    "uint8",
    "uint16",
    "uint32",
    "uint64",
    "int8",
    "int16",
    "int32",
    "int64",
]


class TestDataFrameToCSV:
    def read_csv(self, path, **kwargs):
        params = {"index_col": 0, "parse_dates": True}
        params.update(**kwargs)

        return read_csv(path, **params)

    def test_to_csv_from_csv1(self, float_frame, datetime_frame):

        with tm.ensure_clean("__tmp_to_csv_from_csv1__") as path:
            float_frame["A"][:5] = np.nan

            float_frame.to_csv(path)
            float_frame.to_csv(path, columns=["A", "B"])
            float_frame.to_csv(path, header=False)
            float_frame.to_csv(path, index=False)

            # test roundtrip
            # freq does not roundtrip
            datetime_frame.index = datetime_frame.index._with_freq(None)
            datetime_frame.to_csv(path)
            recons = self.read_csv(path)
            tm.assert_frame_equal(datetime_frame, recons)

            datetime_frame.to_csv(path, index_label="index")
            recons = self.read_csv(path, index_col=None)

            assert len(recons.columns) == len(datetime_frame.columns) + 1

            # no index
            datetime_frame.to_csv(path, index=False)
            recons = self.read_csv(path, index_col=None)
            tm.assert_almost_equal(datetime_frame.values, recons.values)

            # corner case
            dm = DataFrame(
                {
                    "s1": Series(range(3), index=np.arange(3)),
                    "s2": Series(range(2), index=np.arange(2)),
                }
            )
            dm.to_csv(path)

            recons = self.read_csv(path)
            tm.assert_frame_equal(dm, recons)

    def test_to_csv_from_csv2(self, float_frame):

        with tm.ensure_clean("__tmp_to_csv_from_csv2__") as path:

            # duplicate index
            df = DataFrame(
                np.random.randn(3, 3), index=["a", "a", "b"], columns=["x", "y", "z"]
            )
            df.to_csv(path)
            result = self.read_csv(path)
            tm.assert_frame_equal(result, df)

            midx = MultiIndex.from_tuples([("A", 1, 2), ("A", 1, 2), ("B", 1, 2)])
            df = DataFrame(np.random.randn(3, 3), index=midx, columns=["x", "y", "z"])

            df.to_csv(path)
            result = self.read_csv(path, index_col=[0, 1, 2], parse_dates=False)
            tm.assert_frame_equal(result, df, check_names=False)

            # column aliases
            col_aliases = Index(["AA", "X", "Y", "Z"])
            float_frame.to_csv(path, header=col_aliases)

            rs = self.read_csv(path)
            xp = float_frame.copy()
            xp.columns = col_aliases
            tm.assert_frame_equal(xp, rs)

            msg = "Writing 4 cols but got 2 aliases"
            with pytest.raises(ValueError, match=msg):
                float_frame.to_csv(path, header=["AA", "X"])

    def test_to_csv_from_csv3(self):

        with tm.ensure_clean("__tmp_to_csv_from_csv3__") as path:
            df1 = DataFrame(np.random.randn(3, 1))
            df2 = DataFrame(np.random.randn(3, 1))

            df1.to_csv(path)
            df2.to_csv(path, mode="a", header=False)
            xp = pd.concat([df1, df2])
            rs = read_csv(path, index_col=0)
            rs.columns = [int(label) for label in rs.columns]
            xp.columns = [int(label) for label in xp.columns]
            tm.assert_frame_equal(xp, rs)

    def test_to_csv_from_csv4(self):

        with tm.ensure_clean("__tmp_to_csv_from_csv4__") as path:
            # GH 10833 (TimedeltaIndex formatting)
            dt = pd.Timedelta(seconds=1)
            df = DataFrame(
                {"dt_data": [i * dt for i in range(3)]},
                index=Index([i * dt for i in range(3)], name="dt_index"),
            )
            df.to_csv(path)

            result = read_csv(path, index_col="dt_index")
            result.index = pd.to_timedelta(result.index)
            result["dt_data"] = pd.to_timedelta(result["dt_data"])

            tm.assert_frame_equal(df, result, check_index_type=True)

    def test_to_csv_from_csv5(self, timezone_frame):

        # tz, 8260
        with tm.ensure_clean("__tmp_to_csv_from_csv5__") as path:

            timezone_frame.to_csv(path)
            result = read_csv(path, index_col=0, parse_dates=["A"])

            converter = (
                lambda c: to_datetime(result[c])
                .dt.tz_convert("UTC")
                .dt.tz_convert(timezone_frame[c].dt.tz)
            )
            result["B"] = converter("B")
            result["C"] = converter("C")
            tm.assert_frame_equal(result, timezone_frame)

    def test_to_csv_cols_reordering(self):
        # GH3454
        chunksize = 5
        N = int(chunksize * 2.5)

        df = tm.makeCustomDataframe(N, 3)
        cs = df.columns
        cols = [cs[2], cs[0]]

        with tm.ensure_clean() as path:
            df.to_csv(path, columns=cols, chunksize=chunksize)
            rs_c = read_csv(path, index_col=0)

        tm.assert_frame_equal(df[cols], rs_c, check_names=False)

    def test_to_csv_new_dupe_cols(self):
        def _check_df(df, cols=None):
            with tm.ensure_clean() as path:
                df.to_csv(path, columns=cols, chunksize=chunksize)
                rs_c = read_csv(path, index_col=0)

                # we wrote them in a different order
                # so compare them in that order
                if cols is not None:

                    if df.columns.is_unique:
                        rs_c.columns = cols
                    else:
                        indexer, missing = df.columns.get_indexer_non_unique(cols)
                        rs_c.columns = df.columns.take(indexer)

                    for c in cols:
                        obj_df = df[c]
                        obj_rs = rs_c[c]
                        if isinstance(obj_df, Series):
                            tm.assert_series_equal(obj_df, obj_rs)
                        else:
                            tm.assert_frame_equal(obj_df, obj_rs, check_names=False)

                # wrote in the same order
                else:
                    rs_c.columns = df.columns
                    tm.assert_frame_equal(df, rs_c, check_names=False)

        chunksize = 5
        N = int(chunksize * 2.5)

        # dupe cols
        df = tm.makeCustomDataframe(N, 3)
        df.columns = ["a", "a", "b"]
        _check_df(df, None)

        # dupe cols with selection
        cols = ["b", "a"]
        _check_df(df, cols)

    @pytest.mark.slow
    def test_to_csv_dtnat(self):
        # GH3437
        def make_dtnat_arr(n, nnat=None):
            if nnat is None:
                nnat = int(n * 0.1)  # 10%
            s = list(date_range("2000", freq="5min", periods=n))
            if nnat:
                for i in np.random.randint(0, len(s), nnat):
                    s[i] = NaT
                i = np.random.randint(100)
                s[-i] = NaT
                s[i] = NaT
            return s

        chunksize = 1000
        # N=35000
        s1 = make_dtnat_arr(chunksize + 5)
        s2 = make_dtnat_arr(chunksize + 5, 0)

        # s3=make_dtnjat_arr(chunksize+5,0)
        with tm.ensure_clean("1.csv") as pth:
            df = DataFrame({"a": s1, "b": s2})
            df.to_csv(pth, chunksize=chunksize)

            recons = self.read_csv(pth).apply(to_datetime)
            tm.assert_frame_equal(df, recons, check_names=False)

    @pytest.mark.slow
    def test_to_csv_moar(self):
        def _do_test(
            df, r_dtype=None, c_dtype=None, rnlvl=None, cnlvl=None, dupe_col=False
        ):

            kwargs = {"parse_dates": False}
            if cnlvl:
                if rnlvl is not None:
                    kwargs["index_col"] = list(range(rnlvl))
                kwargs["header"] = list(range(cnlvl))

                with tm.ensure_clean("__tmp_to_csv_moar__") as path:
                    df.to_csv(path, encoding="utf8", chunksize=chunksize)
                    recons = self.read_csv(path, **kwargs)
            else:
                kwargs["header"] = 0

                with tm.ensure_clean("__tmp_to_csv_moar__") as path:
                    df.to_csv(path, encoding="utf8", chunksize=chunksize)
                    recons = self.read_csv(path, **kwargs)

            def _to_uni(x):
                if not isinstance(x, str):
                    return x.decode("utf8")
                return x

            if dupe_col:
                # read_Csv disambiguates the columns by
                # labeling them dupe.1,dupe.2, etc'. monkey patch columns
                recons.columns = df.columns
            if rnlvl and not cnlvl:
                delta_lvl = [recons.iloc[:, i].values for i in range(rnlvl - 1)]
                ix = MultiIndex.from_arrays([list(recons.index)] + delta_lvl)
                recons.index = ix
                recons = recons.iloc[:, rnlvl - 1 :]

            type_map = {"i": "i", "f": "f", "s": "O", "u": "O", "dt": "O", "p": "O"}
            if r_dtype:
                if r_dtype == "u":  # unicode
                    r_dtype = "O"
                    recons.index = np.array(
                        [_to_uni(label) for label in recons.index], dtype=r_dtype
                    )
                    df.index = np.array(
                        [_to_uni(label) for label in df.index], dtype=r_dtype
                    )
                elif r_dtype == "dt":  # unicode
                    r_dtype = "O"
                    recons.index = np.array(
                        [Timestamp(label) for label in recons.index], dtype=r_dtype
                    )
                    df.index = np.array(
                        [Timestamp(label) for label in df.index], dtype=r_dtype
                    )
                elif r_dtype == "p":
                    r_dtype = "O"
                    idx_list = to_datetime(recons.index)
                    recons.index = np.array(
                        [Timestamp(label) for label in idx_list], dtype=r_dtype
                    )
                    df.index = np.array(
                        list(map(Timestamp, df.index.to_timestamp())), dtype=r_dtype
                    )
                else:
                    r_dtype = type_map.get(r_dtype)
                    recons.index = np.array(recons.index, dtype=r_dtype)
                    df.index = np.array(df.index, dtype=r_dtype)
            if c_dtype:
                if c_dtype == "u":
                    c_dtype = "O"
                    recons.columns = np.array(
                        [_to_uni(label) for label in recons.columns], dtype=c_dtype
                    )
                    df.columns = np.array(
                        [_to_uni(label) for label in df.columns], dtype=c_dtype
                    )
                elif c_dtype == "dt":
                    c_dtype = "O"
                    recons.columns = np.array(
                        [Timestamp(label) for label in recons.columns], dtype=c_dtype
                    )
                    df.columns = np.array(
                        [Timestamp(label) for label in df.columns], dtype=c_dtype
                    )
                elif c_dtype == "p":
                    c_dtype = "O"
                    col_list = to_datetime(recons.columns)
                    recons.columns = np.array(
                        [Timestamp(label) for label in col_list], dtype=c_dtype
                    )
                    col_list = df.columns.to_timestamp()
                    df.columns = np.array(
                        [Timestamp(label) for label in col_list], dtype=c_dtype
                    )
                else:
                    c_dtype = type_map.get(c_dtype)
                    recons.columns = np.array(recons.columns, dtype=c_dtype)
                    df.columns = np.array(df.columns, dtype=c_dtype)

            tm.assert_frame_equal(df, recons, check_names=False)

        N = 100
        chunksize = 1000
        ncols = 4
        base = chunksize // ncols
        for nrows in [
            2,
            10,
            N - 1,
            N,
            N + 1,
            N + 2,
            2 * N - 2,
            2 * N - 1,
            2 * N,
            2 * N + 1,
            2 * N + 2,
            base - 1,
            base,
            base + 1,
        ]:
            _do_test(
                tm.makeCustomDataframe(nrows, ncols, r_idx_type="dt", c_idx_type="s"),
                "dt",
                "s",
            )

        for r_idx_type, c_idx_type in [("i", "i"), ("s", "s"), ("u", "dt"), ("p", "p")]:
            for ncols in [1, 2, 3, 4]:
                base = chunksize // ncols
                for nrows in [
                    2,
                    10,
                    N - 1,
                    N,
                    N + 1,
                    N + 2,
                    2 * N - 2,
                    2 * N - 1,
                    2 * N,
                    2 * N + 1,
                    2 * N + 2,
                    base - 1,
                    base,
                    base + 1,
                ]:
                    _do_test(
                        tm.makeCustomDataframe(
                            nrows, ncols, r_idx_type=r_idx_type, c_idx_type=c_idx_type
                        ),
                        r_idx_type,
                        c_idx_type,
                    )

        for ncols in [1, 2, 3, 4]:
            base = chunksize // ncols
            for nrows in [
                10,
                N - 2,
                N - 1,
                N,
                N + 1,
                N + 2,
                2 * N - 2,
                2 * N - 1,
                2 * N,
                2 * N + 1,
                2 * N + 2,
                base - 1,
                base,
                base + 1,
            ]:
                _do_test(tm.makeCustomDataframe(nrows, ncols))

        for nrows in [10, N - 2, N - 1, N, N + 1, N + 2]:
            df = tm.makeCustomDataframe(nrows, 3)
            cols = list(df.columns)
            cols[:2] = ["dupe", "dupe"]
            cols[-2:] = ["dupe", "dupe"]
            ix = list(df.index)
            ix[:2] = ["rdupe", "rdupe"]
            ix[-2:] = ["rdupe", "rdupe"]
            df.index = ix
            df.columns = cols
            _do_test(df, dupe_col=True)

        _do_test(DataFrame(index=np.arange(10)))
        _do_test(
            tm.makeCustomDataframe(chunksize // 2 + 1, 2, r_idx_nlevels=2), rnlvl=2
        )
        for ncols in [2, 3, 4]:
            base = int(chunksize // ncols)
            for nrows in [
                10,
                N - 2,
                N - 1,
                N,
                N + 1,
                N + 2,
                2 * N - 2,
                2 * N - 1,
                2 * N,
                2 * N + 1,
                2 * N + 2,
                base - 1,
                base,
                base + 1,
            ]:
                _do_test(tm.makeCustomDataframe(nrows, ncols, r_idx_nlevels=2), rnlvl=2)
                _do_test(tm.makeCustomDataframe(nrows, ncols, c_idx_nlevels=2), cnlvl=2)
                _do_test(
                    tm.makeCustomDataframe(
                        nrows, ncols, r_idx_nlevels=2, c_idx_nlevels=2
                    ),
                    rnlvl=2,
                    cnlvl=2,
                )

    def test_to_csv_from_csv_w_some_infs(self, float_frame):

        # test roundtrip with inf, -inf, nan, as full columns and mix
        float_frame["G"] = np.nan
        f = lambda x: [np.inf, np.nan][np.random.rand() < 0.5]
        float_frame["H"] = float_frame.index.map(f)

        with tm.ensure_clean() as path:
            float_frame.to_csv(path)
            recons = self.read_csv(path)

            tm.assert_frame_equal(float_frame, recons)
            tm.assert_frame_equal(np.isinf(float_frame), np.isinf(recons))

    def test_to_csv_from_csv_w_all_infs(self, float_frame):

        # test roundtrip with inf, -inf, nan, as full columns and mix
        float_frame["E"] = np.inf
        float_frame["F"] = -np.inf

        with tm.ensure_clean() as path:
            float_frame.to_csv(path)
            recons = self.read_csv(path)

            tm.assert_frame_equal(float_frame, recons)
            tm.assert_frame_equal(np.isinf(float_frame), np.isinf(recons))

    def test_to_csv_no_index(self):
        # GH 3624, after appending columns, to_csv fails
        with tm.ensure_clean("__tmp_to_csv_no_index__") as path:
            df = DataFrame({"c1": [1, 2, 3], "c2": [4, 5, 6]})
            df.to_csv(path, index=False)
            result = read_csv(path)
            tm.assert_frame_equal(df, result)
            df["c3"] = Series([7, 8, 9], dtype="int64")
            df.to_csv(path, index=False)
            result = read_csv(path)
            tm.assert_frame_equal(df, result)

    def test_to_csv_with_mix_columns(self):
        # gh-11637: incorrect output when a mix of integer and string column
        # names passed as columns parameter in to_csv

        df = DataFrame({0: ["a", "b", "c"], 1: ["aa", "bb", "cc"]})
        df["test"] = "txt"
        assert df.to_csv() == df.to_csv(columns=[0, 1, "test"])

    def test_to_csv_headers(self):
        # GH6186, the presence or absence of `index` incorrectly
        # causes to_csv to have different header semantics.
        from_df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
        to_df = DataFrame([[1, 2], [3, 4]], columns=["X", "Y"])
        with tm.ensure_clean("__tmp_to_csv_headers__") as path:
            from_df.to_csv(path, header=["X", "Y"])
            recons = self.read_csv(path)

            tm.assert_frame_equal(to_df, recons)

            from_df.to_csv(path, index=False, header=["X", "Y"])
            recons = self.read_csv(path)

            return_value = recons.reset_index(inplace=True)
            assert return_value is None
            tm.assert_frame_equal(to_df, recons)

    def test_to_csv_multiindex(self, float_frame, datetime_frame):

        frame = float_frame
        old_index = frame.index
        arrays = np.arange(len(old_index) * 2).reshape(2, -1)
        new_index = MultiIndex.from_arrays(arrays, names=["first", "second"])
        frame.index = new_index

        with tm.ensure_clean("__tmp_to_csv_multiindex__") as path:

            frame.to_csv(path, header=False)
            frame.to_csv(path, columns=["A", "B"])

            # round trip
            frame.to_csv(path)

            df = self.read_csv(path, index_col=[0, 1], parse_dates=False)

            # TODO to_csv drops column name
            tm.assert_frame_equal(frame, df, check_names=False)
            assert frame.index.names == df.index.names

            # needed if setUp becomes a class method
            float_frame.index = old_index

            # try multiindex with dates
            tsframe = datetime_frame
            old_index = tsframe.index
            new_index = [old_index, np.arange(len(old_index))]
            tsframe.index = MultiIndex.from_arrays(new_index)

            tsframe.to_csv(path, index_label=["time", "foo"])
            recons = self.read_csv(path, index_col=[0, 1])

            # TODO to_csv drops column name
            tm.assert_frame_equal(tsframe, recons, check_names=False)

            # do not load index
            tsframe.to_csv(path)
            recons = self.read_csv(path, index_col=None)
            assert len(recons.columns) == len(tsframe.columns) + 2

            # no index
            tsframe.to_csv(path, index=False)
            recons = self.read_csv(path, index_col=None)
            tm.assert_almost_equal(recons.values, datetime_frame.values)

            # needed if setUp becomes class method
            datetime_frame.index = old_index

        with tm.ensure_clean("__tmp_to_csv_multiindex__") as path:
            # GH3571, GH1651, GH3141

            def _make_frame(names=None):
                if names is True:
                    names = ["first", "second"]
                return DataFrame(
                    np.random.randint(0, 10, size=(3, 3)),
                    columns=MultiIndex.from_tuples(
                        [("bah", "foo"), ("bah", "bar"), ("ban", "baz")], names=names
                    ),
                    dtype="int64",
                )

            # column & index are multi-index
            df = tm.makeCustomDataframe(5, 3, r_idx_nlevels=2, c_idx_nlevels=4)
            df.to_csv(path)
            result = read_csv(path, header=[0, 1, 2, 3], index_col=[0, 1])
            tm.assert_frame_equal(df, result)

            # column is mi
            df = tm.makeCustomDataframe(5, 3, r_idx_nlevels=1, c_idx_nlevels=4)
            df.to_csv(path)
            result = read_csv(path, header=[0, 1, 2, 3], index_col=0)
            tm.assert_frame_equal(df, result)

            # dup column names?
            df = tm.makeCustomDataframe(5, 3, r_idx_nlevels=3, c_idx_nlevels=4)
            df.to_csv(path)
            result = read_csv(path, header=[0, 1, 2, 3], index_col=[0, 1, 2])
            tm.assert_frame_equal(df, result)

            # writing with no index
            df = _make_frame()
            df.to_csv(path, index=False)
            result = read_csv(path, header=[0, 1])
            tm.assert_frame_equal(df, result)

            # we lose the names here
            df = _make_frame(True)
            df.to_csv(path, index=False)
            result = read_csv(path, header=[0, 1])
            assert com.all_none(*result.columns.names)
            result.columns.names = df.columns.names
            tm.assert_frame_equal(df, result)

            # whatsnew example
            df = _make_frame()
            df.to_csv(path)
            result = read_csv(path, header=[0, 1], index_col=[0])
            tm.assert_frame_equal(df, result)

            df = _make_frame(True)
            df.to_csv(path)
            result = read_csv(path, header=[0, 1], index_col=[0])
            tm.assert_frame_equal(df, result)

            # invalid options
            df = _make_frame(True)
            df.to_csv(path)

            for i in [6, 7]:
                msg = f"len of {i}, but only 5 lines in file"
                with pytest.raises(ParserError, match=msg):
                    read_csv(path, header=list(range(i)), index_col=0)

            # write with cols
            msg = "cannot specify cols with a MultiIndex"
            with pytest.raises(TypeError, match=msg):
                df.to_csv(path, columns=["foo", "bar"])

        with tm.ensure_clean("__tmp_to_csv_multiindex__") as path:
            # empty
            tsframe[:0].to_csv(path)
            recons = self.read_csv(path)

            exp = tsframe[:0]
            exp.index = []

            tm.assert_index_equal(recons.columns, exp.columns)
            assert len(recons) == 0

    def test_to_csv_interval_index(self):
        # GH 28210
        df = DataFrame({"A": list("abc"), "B": range(3)}, index=pd.interval_range(0, 3))

        with tm.ensure_clean("__tmp_to_csv_interval_index__.csv") as path:
            df.to_csv(path)
            result = self.read_csv(path, index_col=0)

            # can't roundtrip intervalindex via read_csv so check string repr (GH 23595)
            expected = df.copy()
            expected.index = expected.index.astype(str)

            tm.assert_frame_equal(result, expected)

    def test_to_csv_float32_nanrep(self):
        df = DataFrame(np.random.randn(1, 4).astype(np.float32))
        df[1] = np.nan

        with tm.ensure_clean("__tmp_to_csv_float32_nanrep__.csv") as path:
            df.to_csv(path, na_rep=999)

            with open(path) as f:
                lines = f.readlines()
                assert lines[1].split(",")[2] == "999"

    def test_to_csv_withcommas(self):

        # Commas inside fields should be correctly escaped when saving as CSV.
        df = DataFrame({"A": [1, 2, 3], "B": ["5,6", "7,8", "9,0"]})

        with tm.ensure_clean("__tmp_to_csv_withcommas__.csv") as path:
            df.to_csv(path)
            df2 = self.read_csv(path)
            tm.assert_frame_equal(df2, df)

    def test_to_csv_mixed(self):
        def create_cols(name):
            return [f"{name}{i:03d}" for i in range(5)]

        df_float = DataFrame(
            np.random.randn(100, 5), dtype="float64", columns=create_cols("float")
        )
        df_int = DataFrame(
            np.random.randn(100, 5).astype("int64"),
            dtype="int64",
            columns=create_cols("int"),
        )
        df_bool = DataFrame(True, index=df_float.index, columns=create_cols("bool"))
        df_object = DataFrame(
            "foo", index=df_float.index, columns=create_cols("object")
        )
        df_dt = DataFrame(
            Timestamp("20010101"), index=df_float.index, columns=create_cols("date")
        )

        # add in some nans
        df_float.iloc[30:50, 1:3] = np.nan

        # ## this is a bug in read_csv right now ####
        # df_dt.loc[30:50,1:3] = np.nan

        df = pd.concat([df_float, df_int, df_bool, df_object, df_dt], axis=1)

        # dtype
        dtypes = {}
        for n, dtype in [
            ("float", np.float64),
            ("int", np.int64),
            ("bool", np.bool_),
            ("object", object),
        ]:
            for c in create_cols(n):
                dtypes[c] = dtype

        with tm.ensure_clean() as filename:
            df.to_csv(filename)
            rs = read_csv(
                filename, index_col=0, dtype=dtypes, parse_dates=create_cols("date")
            )
            tm.assert_frame_equal(rs, df)

    def test_to_csv_dups_cols(self):

        df = DataFrame(
            np.random.randn(1000, 30),
            columns=list(range(15)) + list(range(15)),
            dtype="float64",
        )

        with tm.ensure_clean() as filename:
            df.to_csv(filename)  # single dtype, fine
            result = read_csv(filename, index_col=0)
            result.columns = df.columns
            tm.assert_frame_equal(result, df)

        df_float = DataFrame(np.random.randn(1000, 3), dtype="float64")
        df_int = DataFrame(np.random.randn(1000, 3)).astype("int64")
        df_bool = DataFrame(True, index=df_float.index, columns=range(3))
        df_object = DataFrame("foo", index=df_float.index, columns=range(3))
        df_dt = DataFrame(Timestamp("20010101"), index=df_float.index, columns=range(3))
        df = pd.concat(
            [df_float, df_int, df_bool, df_object, df_dt], axis=1, ignore_index=True
        )

        df.columns = [0, 1, 2] * 5

        with tm.ensure_clean() as filename:
            df.to_csv(filename)
            result = read_csv(filename, index_col=0)

            # date cols
            for i in ["0.4", "1.4", "2.4"]:
                result[i] = to_datetime(result[i])

            result.columns = df.columns
            tm.assert_frame_equal(result, df)

        # GH3457

        N = 10
        df = tm.makeCustomDataframe(N, 3)
        df.columns = ["a", "a", "b"]

        with tm.ensure_clean() as filename:
            df.to_csv(filename)

            # read_csv will rename the dups columns
            result = read_csv(filename, index_col=0)
            result = result.rename(columns={"a.1": "a"})
            tm.assert_frame_equal(result, df)

    def test_to_csv_chunking(self):

        aa = DataFrame({"A": range(100000)})
        aa["B"] = aa.A + 1.0
        aa["C"] = aa.A + 2.0
        aa["D"] = aa.A + 3.0

        for chunksize in [10000, 50000, 100000]:
            with tm.ensure_clean() as filename:
                aa.to_csv(filename, chunksize=chunksize)
                rs = read_csv(filename, index_col=0)
                tm.assert_frame_equal(rs, aa)

    @pytest.mark.slow
    def test_to_csv_wide_frame_formatting(self):
        # Issue #8621
        df = DataFrame(np.random.randn(1, 100010), columns=None, index=None)
        with tm.ensure_clean() as filename:
            df.to_csv(filename, header=False, index=False)
            rs = read_csv(filename, header=None)
            tm.assert_frame_equal(rs, df)

    def test_to_csv_bug(self):
        f1 = StringIO("a,1.0\nb,2.0")
        df = self.read_csv(f1, header=None)
        newdf = DataFrame({"t": df[df.columns[0]]})

        with tm.ensure_clean() as path:
            newdf.to_csv(path)

            recons = read_csv(path, index_col=0)
            # don't check_names as t != 1
            tm.assert_frame_equal(recons, newdf, check_names=False)

    def test_to_csv_unicode(self):

        df = DataFrame({"c/\u03c3": [1, 2, 3]})
        with tm.ensure_clean() as path:

            df.to_csv(path, encoding="UTF-8")
            df2 = read_csv(path, index_col=0, encoding="UTF-8")
            tm.assert_frame_equal(df, df2)

            df.to_csv(path, encoding="UTF-8", index=False)
            df2 = read_csv(path, index_col=None, encoding="UTF-8")
            tm.assert_frame_equal(df, df2)

    def test_to_csv_unicode_index_col(self):
        buf = StringIO("")
        df = DataFrame(
            [["\u05d0", "d2", "d3", "d4"], ["a1", "a2", "a3", "a4"]],
            columns=["\u05d0", "\u05d1", "\u05d2", "\u05d3"],
            index=["\u05d0", "\u05d1"],
        )

        df.to_csv(buf, encoding="UTF-8")
        buf.seek(0)

        df2 = read_csv(buf, index_col=0, encoding="UTF-8")
        tm.assert_frame_equal(df, df2)

    def test_to_csv_stringio(self, float_frame):
        buf = StringIO()
        float_frame.to_csv(buf)
        buf.seek(0)
        recons = read_csv(buf, index_col=0)
        tm.assert_frame_equal(recons, float_frame)

    def test_to_csv_float_format(self):

        df = DataFrame(
            [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
            index=["A", "B"],
            columns=["X", "Y", "Z"],
        )

        with tm.ensure_clean() as filename:

            df.to_csv(filename, float_format="%.2f")

            rs = read_csv(filename, index_col=0)
            xp = DataFrame(
                [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]],
                index=["A", "B"],
                columns=["X", "Y", "Z"],
            )
            tm.assert_frame_equal(rs, xp)

    def test_to_csv_unicodewriter_quoting(self):
        df = DataFrame({"A": [1, 2, 3], "B": ["foo", "bar", "baz"]})

        buf = StringIO()
        df.to_csv(buf, index=False, quoting=csv.QUOTE_NONNUMERIC, encoding="utf-8")

        result = buf.getvalue()
        expected_rows = ['"A","B"', '1,"foo"', '2,"bar"', '3,"baz"']
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert result == expected

    def test_to_csv_quote_none(self):
        # GH4328
        df = DataFrame({"A": ["hello", '{"hello"}']})
        for encoding in (None, "utf-8"):
            buf = StringIO()
            df.to_csv(buf, quoting=csv.QUOTE_NONE, encoding=encoding, index=False)

            result = buf.getvalue()
            expected_rows = ["A", "hello", '{"hello"}']
            expected = tm.convert_rows_list_to_csv_str(expected_rows)
            assert result == expected

    def test_to_csv_index_no_leading_comma(self):
        df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["one", "two", "three"])

        buf = StringIO()
        df.to_csv(buf, index_label=False)

        expected_rows = ["A,B", "one,1,4", "two,2,5", "three,3,6"]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert buf.getvalue() == expected

    def test_to_csv_line_terminators(self):
        # see gh-20353
        df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["one", "two", "three"])

        with tm.ensure_clean() as path:
            # case 1: CRLF as line terminator
            df.to_csv(path, line_terminator="\r\n")
            expected = b",A,B\r\none,1,4\r\ntwo,2,5\r\nthree,3,6\r\n"

            with open(path, mode="rb") as f:
                assert f.read() == expected

        with tm.ensure_clean() as path:
            # case 2: LF as line terminator
            df.to_csv(path, line_terminator="\n")
            expected = b",A,B\none,1,4\ntwo,2,5\nthree,3,6\n"

            with open(path, mode="rb") as f:
                assert f.read() == expected

        with tm.ensure_clean() as path:
            # case 3: The default line terminator(=os.linesep)(gh-21406)
            df.to_csv(path)
            os_linesep = os.linesep.encode("utf-8")
            expected = (
                b",A,B"
                + os_linesep
                + b"one,1,4"
                + os_linesep
                + b"two,2,5"
                + os_linesep
                + b"three,3,6"
                + os_linesep
            )

            with open(path, mode="rb") as f:
                assert f.read() == expected

    def test_to_csv_from_csv_categorical(self):

        # CSV with categoricals should result in the same output
        # as when one would add a "normal" Series/DataFrame.
        s = Series(pd.Categorical(["a", "b", "b", "a", "a", "c", "c", "c"]))
        s2 = Series(["a", "b", "b", "a", "a", "c", "c", "c"])
        res = StringIO()

        s.to_csv(res, header=False)
        exp = StringIO()

        s2.to_csv(exp, header=False)
        assert res.getvalue() == exp.getvalue()

        df = DataFrame({"s": s})
        df2 = DataFrame({"s": s2})

        res = StringIO()
        df.to_csv(res)

        exp = StringIO()
        df2.to_csv(exp)

        assert res.getvalue() == exp.getvalue()

    def test_to_csv_path_is_none(self, float_frame):
        # GH 8215
        # Make sure we return string for consistency with
        # Series.to_csv()
        csv_str = float_frame.to_csv(path_or_buf=None)
        assert isinstance(csv_str, str)
        recons = read_csv(StringIO(csv_str), index_col=0)
        tm.assert_frame_equal(float_frame, recons)

    @pytest.mark.parametrize(
        "df,encoding",
        [
            (
                DataFrame(
                    [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
                    index=["A", "B"],
                    columns=["X", "Y", "Z"],
                ),
                None,
            ),
            # GH 21241, 21118
            (DataFrame([["abc", "def", "ghi"]], columns=["X", "Y", "Z"]), "ascii"),
            (DataFrame(5 * [[123, "你好", "世界"]], columns=["X", "Y", "Z"]), "gb2312"),
            (
                DataFrame(5 * [[123, "Γειά σου", "Κόσμε"]], columns=["X", "Y", "Z"]),
                "cp737",
            ),
        ],
    )
    def test_to_csv_compression(self, df, encoding, compression):

        with tm.ensure_clean() as filename:

            df.to_csv(filename, compression=compression, encoding=encoding)
            # test the round trip - to_csv -> read_csv
            result = read_csv(
                filename, compression=compression, index_col=0, encoding=encoding
            )
            tm.assert_frame_equal(df, result)

            # test the round trip using file handle - to_csv -> read_csv
            with get_handle(
                filename, "w", compression=compression, encoding=encoding
            ) as handles:
                df.to_csv(handles.handle, encoding=encoding)
                assert not handles.handle.closed

            result = read_csv(
                filename,
                compression=compression,
                encoding=encoding,
                index_col=0,
            ).squeeze("columns")
            tm.assert_frame_equal(df, result)

            # explicitly make sure file is compressed
            with tm.decompress_file(filename, compression) as fh:
                text = fh.read().decode(encoding or "utf8")
                for col in df.columns:
                    assert col in text

            with tm.decompress_file(filename, compression) as fh:
                tm.assert_frame_equal(df, read_csv(fh, index_col=0, encoding=encoding))

    def test_to_csv_date_format(self, datetime_frame):
        with tm.ensure_clean("__tmp_to_csv_date_format__") as path:
            dt_index = datetime_frame.index
            datetime_frame = DataFrame(
                {"A": dt_index, "B": dt_index.shift(1)}, index=dt_index
            )
            datetime_frame.to_csv(path, date_format="%Y%m%d")

            # Check that the data was put in the specified format
            test = read_csv(path, index_col=0)

            datetime_frame_int = datetime_frame.applymap(
                lambda x: int(x.strftime("%Y%m%d"))
            )
            datetime_frame_int.index = datetime_frame_int.index.map(
                lambda x: int(x.strftime("%Y%m%d"))
            )

            tm.assert_frame_equal(test, datetime_frame_int)

            datetime_frame.to_csv(path, date_format="%Y-%m-%d")

            # Check that the data was put in the specified format
            test = read_csv(path, index_col=0)
            datetime_frame_str = datetime_frame.applymap(
                lambda x: x.strftime("%Y-%m-%d")
            )
            datetime_frame_str.index = datetime_frame_str.index.map(
                lambda x: x.strftime("%Y-%m-%d")
            )

            tm.assert_frame_equal(test, datetime_frame_str)

            # Check that columns get converted
            datetime_frame_columns = datetime_frame.T
            datetime_frame_columns.to_csv(path, date_format="%Y%m%d")

            test = read_csv(path, index_col=0)

            datetime_frame_columns = datetime_frame_columns.applymap(
                lambda x: int(x.strftime("%Y%m%d"))
            )
            # Columns don't get converted to ints by read_csv
            datetime_frame_columns.columns = datetime_frame_columns.columns.map(
                lambda x: x.strftime("%Y%m%d")
            )

            tm.assert_frame_equal(test, datetime_frame_columns)

            # test NaTs
            nat_index = to_datetime(
                ["NaT"] * 10 + ["2000-01-01", "1/1/2000", "1-1-2000"]
            )
            nat_frame = DataFrame({"A": nat_index}, index=nat_index)
            nat_frame.to_csv(path, date_format="%Y-%m-%d")

            test = read_csv(path, parse_dates=[0, 1], index_col=0)

            tm.assert_frame_equal(test, nat_frame)

    def test_to_csv_with_dst_transitions(self):

        with tm.ensure_clean("csv_date_format_with_dst") as path:
            # make sure we are not failing on transitions
            times = date_range(
                "2013-10-26 23:00",
                "2013-10-27 01:00",
                tz="Europe/London",
                freq="H",
                ambiguous="infer",
            )

            for i in [times, times + pd.Timedelta("10s")]:
                i = i._with_freq(None)  # freq is not preserved by read_csv
                time_range = np.array(range(len(i)), dtype="int64")
                df = DataFrame({"A": time_range}, index=i)
                df.to_csv(path, index=True)
                # we have to reconvert the index as we
                # don't parse the tz's
                result = read_csv(path, index_col=0)
                result.index = to_datetime(result.index, utc=True).tz_convert(
                    "Europe/London"
                )
                tm.assert_frame_equal(result, df)

        # GH11619
        idx = date_range("2015-01-01", "2015-12-31", freq="H", tz="Europe/Paris")
        idx = idx._with_freq(None)  # freq does not round-trip
        idx._data._freq = None  # otherwise there is trouble on unpickle
        df = DataFrame({"values": 1, "idx": idx}, index=idx)
        with tm.ensure_clean("csv_date_format_with_dst") as path:
            df.to_csv(path, index=True)
            result = read_csv(path, index_col=0)
            result.index = to_datetime(result.index, utc=True).tz_convert(
                "Europe/Paris"
            )
            result["idx"] = to_datetime(result["idx"], utc=True).astype(
                "datetime64[ns, Europe/Paris]"
            )
            tm.assert_frame_equal(result, df)

        # assert working
        df.astype(str)

        with tm.ensure_clean("csv_date_format_with_dst") as path:
            df.to_pickle(path)
            result = pd.read_pickle(path)
            tm.assert_frame_equal(result, df)

    def test_to_csv_quoting(self):
        df = DataFrame(
            {
                "c_bool": [True, False],
                "c_float": [1.0, 3.2],
                "c_int": [42, np.nan],
                "c_string": ["a", "b,c"],
            }
        )

        expected_rows = [
            ",c_bool,c_float,c_int,c_string",
            "0,True,1.0,42.0,a",
            '1,False,3.2,,"b,c"',
        ]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)

        result = df.to_csv()
        assert result == expected

        result = df.to_csv(quoting=None)
        assert result == expected

        expected_rows = [
            ",c_bool,c_float,c_int,c_string",
            "0,True,1.0,42.0,a",
            '1,False,3.2,,"b,c"',
        ]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)

        result = df.to_csv(quoting=csv.QUOTE_MINIMAL)
        assert result == expected

        expected_rows = [
            '"","c_bool","c_float","c_int","c_string"',
            '"0","True","1.0","42.0","a"',
            '"1","False","3.2","","b,c"',
        ]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)

        result = df.to_csv(quoting=csv.QUOTE_ALL)
        assert result == expected

        # see gh-12922, gh-13259: make sure changes to
        # the formatters do not break this behaviour
        expected_rows = [
            '"","c_bool","c_float","c_int","c_string"',
            '0,True,1.0,42.0,"a"',
            '1,False,3.2,"","b,c"',
        ]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        result = df.to_csv(quoting=csv.QUOTE_NONNUMERIC)
        assert result == expected

        msg = "need to escape, but no escapechar set"
        with pytest.raises(csv.Error, match=msg):
            df.to_csv(quoting=csv.QUOTE_NONE)

        with pytest.raises(csv.Error, match=msg):
            df.to_csv(quoting=csv.QUOTE_NONE, escapechar=None)

        expected_rows = [
            ",c_bool,c_float,c_int,c_string",
            "0,True,1.0,42.0,a",
            "1,False,3.2,,b!,c",
        ]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        result = df.to_csv(quoting=csv.QUOTE_NONE, escapechar="!")
        assert result == expected

        expected_rows = [
            ",c_bool,c_ffloat,c_int,c_string",
            "0,True,1.0,42.0,a",
            "1,False,3.2,,bf,c",
        ]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        result = df.to_csv(quoting=csv.QUOTE_NONE, escapechar="f")
        assert result == expected

        # see gh-3503: quoting Windows line terminators
        # presents with encoding?
        text_rows = ["a,b,c", '1,"test \r\n",3']
        text = tm.convert_rows_list_to_csv_str(text_rows)
        df = read_csv(StringIO(text))

        buf = StringIO()
        df.to_csv(buf, encoding="utf-8", index=False)
        assert buf.getvalue() == text

        # xref gh-7791: make sure the quoting parameter is passed through
        # with multi-indexes
        df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]})
        df = df.set_index(["a", "b"])

        expected_rows = ['"a","b","c"', '"1","3","5"', '"2","4","6"']
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert df.to_csv(quoting=csv.QUOTE_ALL) == expected

    def test_period_index_date_overflow(self):
        # see gh-15982

        dates = ["1990-01-01", "2000-01-01", "3005-01-01"]
        index = pd.PeriodIndex(dates, freq="D")

        df = DataFrame([4, 5, 6], index=index)
        result = df.to_csv()

        expected_rows = [",0", "1990-01-01,4", "2000-01-01,5", "3005-01-01,6"]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert result == expected

        date_format = "%m-%d-%Y"
        result = df.to_csv(date_format=date_format)

        expected_rows = [",0", "01-01-1990,4", "01-01-2000,5", "01-01-3005,6"]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert result == expected

        # Overflow with pd.NaT
        dates = ["1990-01-01", NaT, "3005-01-01"]
        index = pd.PeriodIndex(dates, freq="D")

        df = DataFrame([4, 5, 6], index=index)
        result = df.to_csv()

        expected_rows = [",0", "1990-01-01,4", ",5", "3005-01-01,6"]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert result == expected

    def test_multi_index_header(self):
        # see gh-5539
        columns = MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)])
        df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]])
        df.columns = columns

        header = ["a", "b", "c", "d"]
        result = df.to_csv(header=header)

        expected_rows = [",a,b,c,d", "0,1,2,3,4", "1,5,6,7,8"]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert result == expected

    def test_to_csv_single_level_multi_index(self):
        # see gh-26303
        index = Index([(1,), (2,), (3,)])
        df = DataFrame([[1, 2, 3]], columns=index)
        df = df.reindex(columns=[(1,), (3,)])
        expected = ",1,3\n0,1,3\n"
        result = df.to_csv(line_terminator="\n")
        tm.assert_almost_equal(result, expected)

    def test_gz_lineend(self):
        # GH 25311
        df = DataFrame({"a": [1, 2]})
        expected_rows = ["a", "1", "2"]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        with tm.ensure_clean("__test_gz_lineend.csv.gz") as path:
            df.to_csv(path, index=False)
            with tm.decompress_file(path, compression="gzip") as f:
                result = f.read().decode("utf-8")

        assert result == expected

    def test_to_csv_numpy_16_bug(self):
        frame = DataFrame({"a": date_range("1/1/2000", periods=10)})

        buf = StringIO()
        frame.to_csv(buf)

        result = buf.getvalue()
        assert "2000-01-01" in result

    def test_to_csv_na_quoting(self):
        # GH 15891
        # Normalize carriage return for Windows OS
        result = (
            DataFrame([None, None])
            .to_csv(None, header=False, index=False, na_rep="")
            .replace("\r\n", "\n")
        )
        expected = '""\n""\n'
        assert result == expected

    def test_to_csv_categorical_and_ea(self):
        # GH#46812
        df = DataFrame({"a": "x", "b": [1, pd.NA]})
        df["b"] = df["b"].astype("Int16")
        df["b"] = df["b"].astype("category")
        result = df.to_csv()
        expected_rows = [",a,b", "0,x,1", "1,x,"]
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert result == expected

    def test_to_csv_categorical_and_interval(self):
        # GH#46297
        df = DataFrame(
            {
                "a": [
                    pd.Interval(
                        Timestamp("2020-01-01"),
                        Timestamp("2020-01-02"),
                        closed="both",
                    )
                ]
            }
        )
        df["a"] = df["a"].astype("category")
        result = df.to_csv()
        expected_rows = [",a", '0,"[2020-01-01, 2020-01-02]"']
        expected = tm.convert_rows_list_to_csv_str(expected_rows)
        assert result == expected
