inference.py

import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame, Series, to_numeric

from .pandas_vb_common import numeric_dtypes, lib


class NumericInferOps:
    # from GH 7332
    params = numeric_dtypes
    param_names = ['dtype']

    def setup(self, dtype):
        N = 5 * 10**5
        self.df = DataFrame({'A': np.arange(N).astype(dtype),
                             'B': np.arange(N).astype(dtype)})

    def time_add(self, dtype):
        self.df['A'] + self.df['B']

    def time_subtract(self, dtype):
        self.df['A'] - self.df['B']

    def time_multiply(self, dtype):
        self.df['A'] * self.df['B']

    def time_divide(self, dtype):
        self.df['A'] / self.df['B']

    def time_modulo(self, dtype):
        self.df['A'] % self.df['B']


class DateInferOps:
    # from GH 7332
    def setup_cache(self):
        N = 5 * 10**5
        df = DataFrame({'datetime64': np.arange(N).astype('datetime64[ms]')})
        df['timedelta'] = df['datetime64'] - df['datetime64']
        return df

    def time_subtract_datetimes(self, df):
        df['datetime64'] - df['datetime64']

    def time_timedelta_plus_datetime(self, df):
        df['timedelta'] + df['datetime64']

    def time_add_timedeltas(self, df):
        df['timedelta'] + df['timedelta']


class ToNumeric:

    params = ['ignore', 'coerce']
    param_names = ['errors']

    def setup(self, errors):
        N = 10000
        self.float = Series(np.random.randn(N))
        self.numstr = self.float.astype('str')
        self.str = Series(tm.makeStringIndex(N))

    def time_from_float(self, errors):
        to_numeric(self.float, errors=errors)

    def time_from_numeric_str(self, errors):
        to_numeric(self.numstr, errors=errors)

    def time_from_str(self, errors):
        to_numeric(self.str, errors=errors)


class ToNumericDowncast:

    param_names = ['dtype', 'downcast']
    params = [['string-float', 'string-int', 'string-nint', 'datetime64',
               'int-list', 'int32'],
              [None, 'integer', 'signed', 'unsigned', 'float']]

    N = 500000
    N2 = int(N / 2)

    data_dict = {'string-int': ['1'] * N2 + [2] * N2,
                 'string-nint': ['-1'] * N2 + [2] * N2,
                 'datetime64': np.repeat(np.array(['1970-01-01', '1970-01-02'],
                                                  dtype='datetime64[D]'), N),
                 'string-float': ['1.1'] * N2 + [2] * N2,
                 'int-list': [1] * N2 + [2] * N2,
                 'int32': np.repeat(np.int32(1), N)}

    def setup(self, dtype, downcast):
        self.data = self.data_dict[dtype]

    def time_downcast(self, dtype, downcast):
        to_numeric(self.data, downcast=downcast)


class MaybeConvertNumeric:

    def setup_cache(self):
        N = 10**6
        arr = np.repeat([2**63], N) + np.arange(N).astype('uint64')
        data = arr.astype(object)
        data[1::2] = arr[1::2].astype(str)
        data[-1] = -1
        return data

    def time_convert(self, data):
        lib.maybe_convert_numeric(data, set(), coerce_numeric=False)


from .pandas_vb_common import setup  # noqa: F401