<commit_before>from io import StringIO
import random
import string

import numpy as np

from pandas import Categorical, DataFrame, date_range, read_csv, to_datetime
import pandas.util.testing as tm

from ..pandas_vb_common import BaseIO


class ToCSV(BaseIO):

    fname = "__test__.csv"
    params = ["wide", "long", "mixed"]
    param_names = ["kind"]

    def setup(self, kind):
        wide_frame = DataFrame(np.random.randn(3000, 30))
        long_frame = DataFrame(
            {
                "A": np.arange(50000),
                "B": np.arange(50000) + 1.0,
                "C": np.arange(50000) + 2.0,
                "D": np.arange(50000) + 3.0,
            }
        )
        mixed_frame = DataFrame(
            {
                "float": np.random.randn(5000),
                "int": np.random.randn(5000).astype(int),
                "bool": (np.arange(5000) % 2) == 0,
                "datetime": date_range("2001", freq="s", periods=5000),
                "object": ["foo"] * 5000,
            }
        )
        mixed_frame.loc[30:500, "float"] = np.nan
        data = {"wide": wide_frame, "long": long_frame, "mixed": mixed_frame}
        self.df = data[kind]

    def time_frame(self, kind):
        self.df.to_csv(self.fname)


class ToCSVDatetime(BaseIO):

    fname = "__test__.csv"

    def setup(self):
        rng = date_range("1/1/2000", periods=1000)
        self.data = DataFrame(rng, index=rng)

    def time_frame_date_formatting(self):
        self.data.to_csv(self.fname, date_format="%Y%m%d")


class ToCSVDatetimeBig(BaseIO):

    fname = "__test__.csv"
    timeout = 1500
    params = [1000, 10000, 100000]
    param_names = ["obs"]

    def setup(self, obs):
        d = "2018-11-29"
        dt = "2018-11-26 11:18:27.0"
        self.data = DataFrame(
            {
                "dt": [np.datetime64(dt)] * obs,
                "d": [np.datetime64(d)] * obs,
                "r": [np.random.uniform()] * obs,
            }
        )

    def time_frame(self, obs):
        self.data.to_csv(self.fname)


class StringIORewind:
    def data(self, stringio_object):
        stringio_object.seek(0)
        return stringio_object


class ReadCSVDInferDatetimeFormat(StringIORewind):

    params = ([True, False], ["custom", "iso8601", "ymd"])
    param_names = ["infer_datetime_format", "format"]

    def setup(self, infer_datetime_format, format):
        rng = date_range("1/1/2000", periods=1000)
        formats = {
            "custom": "%m/%d/%Y %H:%M:%S.%f",
            "iso8601": "%Y-%m-%d %H:%M:%S",
            "ymd": "%Y%m%d",
        }
        dt_format = formats[format]
        self.StringIO_input = StringIO("\n".join(rng.strftime(dt_format).tolist()))

    def time_read_csv(self, infer_datetime_format, format):
        read_csv(
            self.data(self.StringIO_input),
            header=None,
            names=["foo"],
            parse_dates=["foo"],
            infer_datetime_format=infer_datetime_format,
        )


class ReadCSVConcatDatetime(StringIORewind):

    iso8601 = "%Y-%m-%d %H:%M:%S"

    def setup(self):
        rng = date_range("1/1/2000", periods=50000, freq="S")
        self.StringIO_input = StringIO("\n".join(rng.strftime(self.iso8601).tolist()))

    def time_read_csv(self):
        read_csv(
            self.data(self.StringIO_input),
            header=None,
            names=["foo"],
            parse_dates=["foo"],
            infer_datetime_format=False,
        )


class ReadCSVConcatDatetimeBadDateValue(StringIORewind):

    params = (["nan", "0", ""],)
    param_names = ["bad_date_value"]

    def setup(self, bad_date_value):
        self.StringIO_input = StringIO(("%s,\n" % bad_date_value) * 50000)

    def time_read_csv(self, bad_date_value):
        read_csv(
            self.data(self.StringIO_input),
            header=None,
            names=["foo", "bar"],
            parse_dates=["foo"],
            infer_datetime_format=False,
        )


class ReadCSVSkipRows(BaseIO):

    fname = "__test__.csv"
    params = [None, 10000]
    param_names = ["skiprows"]

    def setup(self, skiprows):
        N = 20000
        index = tm.makeStringIndex(N)
        df = DataFrame(
            {
                "float1": np.random.randn(N),
                "float2": np.random.randn(N),
                "string1": ["foo"] * N,
                "bool1": [True] * N,
                "int1": np.random.randint(0, N, size=N),
            },
            index=index,
        )
        df.to_csv(self.fname)

    def time_skipprows(self, skiprows):
        read_csv(self.fname, skiprows=skiprows)


class ReadUint64Integers(StringIORewind):
    def setup(self):
        self.na_values = [2 ** 63 + 500]
        arr = np.arange(10000).astype("uint64") + 2 ** 63
        self.data1 = StringIO("\n".join(arr.astype(str).tolist()))
        arr = arr.astype(object)
        arr[500] = -1
        self.data2 = StringIO("\n".join(arr.astype(str).tolist()))

    def time_read_uint64(self):
        read_csv(self.data(self.data1), header=None, names=["foo"])

    def time_read_uint64_neg_values(self):
        read_csv(self.data(self.data2), header=None, names=["foo"])

    def time_read_uint64_na_values(self):
        read_csv(
            self.data(self.data1), header=None, names=["foo"], na_values=self.na_values
        )


class ReadCSVThousands(BaseIO):

    fname = "__test__.csv"
    params = ([",", "|"], [None, ","])
    param_names = ["sep", "thousands"]

    def setup(self, sep, thousands):
        N = 10000
        K = 8
        data = np.random.randn(N, K) * np.random.randint(100, 10000, (N, K))
        df = DataFrame(data)
        if thousands is not None:
            fmt = f":{thousands}"
            fmt = "{" + fmt + "}"
            df = df.applymap(lambda x: fmt.format(x))
        df.to_csv(self.fname, sep=sep)

    def time_thousands(self, sep, thousands):
        read_csv(self.fname, sep=sep, thousands=thousands)


class ReadCSVComment(StringIORewind):
    def setup(self):
        data = ["A,B,C"] + (["1,2,3 # comment"] * 100000)
        self.StringIO_input = StringIO("\n".join(data))

    def time_comment(self):
        read_csv(
            self.data(self.StringIO_input), comment="#", header=None, names=list("abc")
        )


class ReadCSVFloatPrecision(StringIORewind):

    params = ([",", ";"], [".", "_"], [None, "high", "round_trip"])
    param_names = ["sep", "decimal", "float_precision"]

    def setup(self, sep, decimal, float_precision):
        floats = [
            "".join(random.choice(string.digits) for _ in range(28)) for _ in range(15)
        ]
        rows = sep.join([f"0{decimal}" + "{}"] * 3) + "\n"
        data = rows * 5
        data = data.format(*floats) * 200  # 1000 x 3 strings csv
        self.StringIO_input = StringIO(data)

    def time_read_csv(self, sep, decimal, float_precision):
        read_csv(
            self.data(self.StringIO_input),
            sep=sep,
            header=None,
            names=list("abc"),
            float_precision=float_precision,
        )

    def time_read_csv_python_engine(self, sep, decimal, float_precision):
        read_csv(
            self.data(self.StringIO_input),
            sep=sep,
            header=None,
            engine="python",
            float_precision=None,
            names=list("abc"),
        )


class ReadCSVCategorical(BaseIO):

    fname = "__test__.csv"

    def setup(self):
        N = 100000
        group1 = ["aaaaaaaa", "bbbbbbb", "cccccccc", "dddddddd", "eeeeeeee"]
        df = DataFrame(np.random.choice(group1, (N, 3)), columns=list("abc"))
        df.to_csv(self.fname, index=False)

    def time_convert_post(self):
        read_csv(self.fname).apply(Categorical)

    def time_convert_direct(self):
        read_csv(self.fname, dtype="category")


class ReadCSVParseDates(StringIORewind):
    def setup(self):
        data = """{},19:00:00,18:56:00,0.8100,2.8100,7.2000,0.0000,280.0000\n
                  {},20:00:00,19:56:00,0.0100,2.2100,7.2000,0.0000,260.0000\n
                  {},21:00:00,20:56:00,-0.5900,2.2100,5.7000,0.0000,280.0000\n
                  {},21:00:00,21:18:00,-0.9900,2.0100,3.6000,0.0000,270.0000\n
                  {},22:00:00,21:56:00,-0.5900,1.7100,5.1000,0.0000,290.0000\n
               """
        two_cols = ["KORD,19990127"] * 5
        data = data.format(*two_cols)
        self.StringIO_input = StringIO(data)

    def time_multiple_date(self):
        read_csv(
            self.data(self.StringIO_input),
            sep=",",
            header=None,
            names=list(string.digits[:9]),
            parse_dates=[[1, 2], [1, 3]],
        )

    def time_baseline(self):
        read_csv(
            self.data(self.StringIO_input),
            sep=",",
            header=None,
            parse_dates=[1],
            names=list(string.digits[:9]),
        )


class ReadCSVCachedParseDates(StringIORewind):
    params = ([True, False],)
    param_names = ["do_cache"]

    def setup(self, do_cache):
        data = ("\n".join(f"10/{year}" for year in range(2000, 2100)) + "\n") * 10
        self.StringIO_input = StringIO(data)

    def time_read_csv_cached(self, do_cache):
        try:
            read_csv(
                self.data(self.StringIO_input),
                header=None,
                parse_dates=[0],
                cache_dates=do_cache,
            )
        except TypeError:
            # cache_dates is a new keyword in 0.25
            pass


class ReadCSVMemoryGrowth(BaseIO):

    chunksize = 20
    num_rows = 1000
    fname = "__test__.csv"

    def setup(self):
        with open(self.fname, "w") as f:
            for i in range(self.num_rows):
                f.write(f"{i}\n")

    def mem_parser_chunks(self):
        # see gh-24805.
        result = read_csv(self.fname, chunksize=self.chunksize)

        for _ in result:
            pass


class ReadCSVParseSpecialDate(StringIORewind):
    params = (["mY", "mdY", "hm"],)
    param_names = ["value"]
    objects = {
        "mY": "01-2019\n10-2019\n02/2000\n",
        "mdY": "12/02/2010\n",
        "hm": "21:34\n",
    }

    def setup(self, value):
        count_elem = 10000
        data = self.objects[value] * count_elem
        self.StringIO_input = StringIO(data)

    def time_read_special_date(self, value):
        read_csv(
            self.data(self.StringIO_input),
            sep=",",
            header=None,
            names=["Date"],
            parse_dates=["Date"],
        )


class ParseDateComparison(StringIORewind):
    params = ([False, True],)
    param_names = ["cache_dates"]

    def setup(self, cache_dates):
        count_elem = 10000
        data = "12-02-2010\n" * count_elem
        self.StringIO_input = StringIO(data)

    def time_read_csv_dayfirst(self, cache_dates):
        try:
            read_csv(
                self.data(self.StringIO_input),
                sep=",",
                header=None,
                names=["Date"],
                parse_dates=["Date"],
                cache_dates=cache_dates,
                dayfirst=True,
            )
        except TypeError:
            # cache_dates is a new keyword in 0.25
            pass

    def time_to_datetime_dayfirst(self, cache_dates):
        df = read_csv(
            self.data(self.StringIO_input), dtype={"date": str}, names=["date"]
        )
        to_datetime(df["date"], cache=cache_dates, dayfirst=True)

    def time_to_datetime_format_DD_MM_YYYY(self, cache_dates):
        df = read_csv(
            self.data(self.StringIO_input), dtype={"date": str}, names=["date"]
        )
        to_datetime(df["date"], cache=cache_dates, format="%d-%m-%Y")


from ..pandas_vb_common import setup  # noqa: F401 isort:skip
<commit_msg>F-string (#29663)<commit_after>from io import StringIO
import random
import string

import numpy as np

from pandas import Categorical, DataFrame, date_range, read_csv, to_datetime
import pandas.util.testing as tm

from ..pandas_vb_common import BaseIO


class ToCSV(BaseIO):

    fname = "__test__.csv"
    params = ["wide", "long", "mixed"]
    param_names = ["kind"]

    def setup(self, kind):
        wide_frame = DataFrame(np.random.randn(3000, 30))
        long_frame = DataFrame(
            {
                "A": np.arange(50000),
                "B": np.arange(50000) + 1.0,
                "C": np.arange(50000) + 2.0,
                "D": np.arange(50000) + 3.0,
            }
        )
        mixed_frame = DataFrame(
            {
                "float": np.random.randn(5000),
                "int": np.random.randn(5000).astype(int),
                "bool": (np.arange(5000) % 2) == 0,
                "datetime": date_range("2001", freq="s", periods=5000),
                "object": ["foo"] * 5000,
            }
        )
        mixed_frame.loc[30:500, "float"] = np.nan
        data = {"wide": wide_frame, "long": long_frame, "mixed": mixed_frame}
        self.df = data[kind]

    def time_frame(self, kind):
        self.df.to_csv(self.fname)


class ToCSVDatetime(BaseIO):

    fname = "__test__.csv"

    def setup(self):
        rng = date_range("1/1/2000", periods=1000)
        self.data = DataFrame(rng, index=rng)

    def time_frame_date_formatting(self):
        self.data.to_csv(self.fname, date_format="%Y%m%d")


class ToCSVDatetimeBig(BaseIO):

    fname = "__test__.csv"
    timeout = 1500
    params = [1000, 10000, 100000]
    param_names = ["obs"]

    def setup(self, obs):
        d = "2018-11-29"
        dt = "2018-11-26 11:18:27.0"
        self.data = DataFrame(
            {
                "dt": [np.datetime64(dt)] * obs,
                "d": [np.datetime64(d)] * obs,
                "r": [np.random.uniform()] * obs,
            }
        )

    def time_frame(self, obs):
        self.data.to_csv(self.fname)


class StringIORewind:
    def data(self, stringio_object):
        stringio_object.seek(0)
        return stringio_object


class ReadCSVDInferDatetimeFormat(StringIORewind):

    params = ([True, False], ["custom", "iso8601", "ymd"])
    param_names = ["infer_datetime_format", "format"]

    def setup(self, infer_datetime_format, format):
        rng = date_range("1/1/2000", periods=1000)
        formats = {
            "custom": "%m/%d/%Y %H:%M:%S.%f",
            "iso8601": "%Y-%m-%d %H:%M:%S",
            "ymd": "%Y%m%d",
        }
        dt_format = formats[format]
        self.StringIO_input = StringIO("\n".join(rng.strftime(dt_format).tolist()))

    def time_read_csv(self, infer_datetime_format, format):
        read_csv(
            self.data(self.StringIO_input),
            header=None,
            names=["foo"],
            parse_dates=["foo"],
            infer_datetime_format=infer_datetime_format,
        )


class ReadCSVConcatDatetime(StringIORewind):

    iso8601 = "%Y-%m-%d %H:%M:%S"

    def setup(self):
        rng = date_range("1/1/2000", periods=50000, freq="S")
        self.StringIO_input = StringIO("\n".join(rng.strftime(self.iso8601).tolist()))

    def time_read_csv(self):
        read_csv(
            self.data(self.StringIO_input),
            header=None,
            names=["foo"],
            parse_dates=["foo"],
            infer_datetime_format=False,
        )


class ReadCSVConcatDatetimeBadDateValue(StringIORewind):

    params = (["nan", "0", ""],)
    param_names = ["bad_date_value"]

    def setup(self, bad_date_value):
        self.StringIO_input = StringIO((f"{bad_date_value},\n") * 50000)

    def time_read_csv(self, bad_date_value):
        read_csv(
            self.data(self.StringIO_input),
            header=None,
            names=["foo", "bar"],
            parse_dates=["foo"],
            infer_datetime_format=False,
        )


class ReadCSVSkipRows(BaseIO):

    fname = "__test__.csv"
    params = [None, 10000]
    param_names = ["skiprows"]

    def setup(self, skiprows):
        N = 20000
        index = tm.makeStringIndex(N)
        df = DataFrame(
            {
                "float1": np.random.randn(N),
                "float2": np.random.randn(N),
                "string1": ["foo"] * N,
                "bool1": [True] * N,
                "int1": np.random.randint(0, N, size=N),
            },
            index=index,
        )
        df.to_csv(self.fname)

    def time_skipprows(self, skiprows):
        read_csv(self.fname, skiprows=skiprows)


class ReadUint64Integers(StringIORewind):
    def setup(self):
        self.na_values = [2 ** 63 + 500]
        arr = np.arange(10000).astype("uint64") + 2 ** 63
        self.data1 = StringIO("\n".join(arr.astype(str).tolist()))
        arr = arr.astype(object)
        arr[500] = -1
        self.data2 = StringIO("\n".join(arr.astype(str).tolist()))

    def time_read_uint64(self):
        read_csv(self.data(self.data1), header=None, names=["foo"])

    def time_read_uint64_neg_values(self):
        read_csv(self.data(self.data2), header=None, names=["foo"])

    def time_read_uint64_na_values(self):
        read_csv(
            self.data(self.data1), header=None, names=["foo"], na_values=self.na_values
        )


class ReadCSVThousands(BaseIO):

    fname = "__test__.csv"
    params = ([",", "|"], [None, ","])
    param_names = ["sep", "thousands"]

    def setup(self, sep, thousands):
        N = 10000
        K = 8
        data = np.random.randn(N, K) * np.random.randint(100, 10000, (N, K))
        df = DataFrame(data)
        if thousands is not None:
            fmt = f":{thousands}"
            fmt = "{" + fmt + "}"
            df = df.applymap(lambda x: fmt.format(x))
        df.to_csv(self.fname, sep=sep)

    def time_thousands(self, sep, thousands):
        read_csv(self.fname, sep=sep, thousands=thousands)


class ReadCSVComment(StringIORewind):
    def setup(self):
        data = ["A,B,C"] + (["1,2,3 # comment"] * 100000)
        self.StringIO_input = StringIO("\n".join(data))

    def time_comment(self):
        read_csv(
            self.data(self.StringIO_input), comment="#", header=None, names=list("abc")
        )


class ReadCSVFloatPrecision(StringIORewind):

    params = ([",", ";"], [".", "_"], [None, "high", "round_trip"])
    param_names = ["sep", "decimal", "float_precision"]

    def setup(self, sep, decimal, float_precision):
        floats = [
            "".join(random.choice(string.digits) for _ in range(28)) for _ in range(15)
        ]
        rows = sep.join([f"0{decimal}" + "{}"] * 3) + "\n"
        data = rows * 5
        data = data.format(*floats) * 200  # 1000 x 3 strings csv
        self.StringIO_input = StringIO(data)

    def time_read_csv(self, sep, decimal, float_precision):
        read_csv(
            self.data(self.StringIO_input),
            sep=sep,
            header=None,
            names=list("abc"),
            float_precision=float_precision,
        )

    def time_read_csv_python_engine(self, sep, decimal, float_precision):
        read_csv(
            self.data(self.StringIO_input),
            sep=sep,
            header=None,
            engine="python",
            float_precision=None,
            names=list("abc"),
        )


class ReadCSVCategorical(BaseIO):

    fname = "__test__.csv"

    def setup(self):
        N = 100000
        group1 = ["aaaaaaaa", "bbbbbbb", "cccccccc", "dddddddd", "eeeeeeee"]
        df = DataFrame(np.random.choice(group1, (N, 3)), columns=list("abc"))
        df.to_csv(self.fname, index=False)

    def time_convert_post(self):
        read_csv(self.fname).apply(Categorical)

    def time_convert_direct(self):
        read_csv(self.fname, dtype="category")


class ReadCSVParseDates(StringIORewind):
    def setup(self):
        data = """{},19:00:00,18:56:00,0.8100,2.8100,7.2000,0.0000,280.0000\n
                  {},20:00:00,19:56:00,0.0100,2.2100,7.2000,0.0000,260.0000\n
                  {},21:00:00,20:56:00,-0.5900,2.2100,5.7000,0.0000,280.0000\n
                  {},21:00:00,21:18:00,-0.9900,2.0100,3.6000,0.0000,270.0000\n
                  {},22:00:00,21:56:00,-0.5900,1.7100,5.1000,0.0000,290.0000\n
               """
        two_cols = ["KORD,19990127"] * 5
        data = data.format(*two_cols)
        self.StringIO_input = StringIO(data)

    def time_multiple_date(self):
        read_csv(
            self.data(self.StringIO_input),
            sep=",",
            header=None,
            names=list(string.digits[:9]),
            parse_dates=[[1, 2], [1, 3]],
        )

    def time_baseline(self):
        read_csv(
            self.data(self.StringIO_input),
            sep=",",
            header=None,
            parse_dates=[1],
            names=list(string.digits[:9]),
        )


class ReadCSVCachedParseDates(StringIORewind):
    params = ([True, False],)
    param_names = ["do_cache"]

    def setup(self, do_cache):
        data = ("\n".join(f"10/{year}" for year in range(2000, 2100)) + "\n") * 10
        self.StringIO_input = StringIO(data)

    def time_read_csv_cached(self, do_cache):
        try:
            read_csv(
                self.data(self.StringIO_input),
                header=None,
                parse_dates=[0],
                cache_dates=do_cache,
            )
        except TypeError:
            # cache_dates is a new keyword in 0.25
            pass


class ReadCSVMemoryGrowth(BaseIO):

    chunksize = 20
    num_rows = 1000
    fname = "__test__.csv"

    def setup(self):
        with open(self.fname, "w") as f:
            for i in range(self.num_rows):
                f.write(f"{i}\n")

    def mem_parser_chunks(self):
        # see gh-24805.
        result = read_csv(self.fname, chunksize=self.chunksize)

        for _ in result:
            pass


class ReadCSVParseSpecialDate(StringIORewind):
    params = (["mY", "mdY", "hm"],)
    param_names = ["value"]
    objects = {
        "mY": "01-2019\n10-2019\n02/2000\n",
        "mdY": "12/02/2010\n",
        "hm": "21:34\n",
    }

    def setup(self, value):
        count_elem = 10000
        data = self.objects[value] * count_elem
        self.StringIO_input = StringIO(data)

    def time_read_special_date(self, value):
        read_csv(
            self.data(self.StringIO_input),
            sep=",",
            header=None,
            names=["Date"],
            parse_dates=["Date"],
        )


class ParseDateComparison(StringIORewind):
    params = ([False, True],)
    param_names = ["cache_dates"]

    def setup(self, cache_dates):
        count_elem = 10000
        data = "12-02-2010\n" * count_elem
        self.StringIO_input = StringIO(data)

    def time_read_csv_dayfirst(self, cache_dates):
        try:
            read_csv(
                self.data(self.StringIO_input),
                sep=",",
                header=None,
                names=["Date"],
                parse_dates=["Date"],
                cache_dates=cache_dates,
                dayfirst=True,
            )
        except TypeError:
            # cache_dates is a new keyword in 0.25
            pass

    def time_to_datetime_dayfirst(self, cache_dates):
        df = read_csv(
            self.data(self.StringIO_input), dtype={"date": str}, names=["date"]
        )
        to_datetime(df["date"], cache=cache_dates, dayfirst=True)

    def time_to_datetime_format_DD_MM_YYYY(self, cache_dates):
        df = read_csv(
            self.data(self.StringIO_input), dtype={"date": str}, names=["date"]
        )
        to_datetime(df["date"], cache=cache_dates, format="%d-%m-%Y")


from ..pandas_vb_common import setup  # noqa: F401 isort:skip

{'fraction_non_alphanumeric': 0.14730661220321675, 'fraction_numerical': 0.04710237426601991, 'mean_word_length': 2.115043074884029, 'pattern_counts': {'":': 58, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 10, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4714011', 'n_tokens_mistral': 8786, 'n_tokens_neox': 7808, 'n_words': 1358}