strings.py

import warnings

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


class Methods(object):

    def setup(self):
        self.s = Series(tm.makeStringIndex(10**5))

    def time_center(self):
        self.s.str.center(100)

    def time_count(self):
        self.s.str.count('A')

    def time_endswith(self):
        self.s.str.endswith('A')

    def time_extract(self):
        with warnings.catch_warnings(record=True):
            self.s.str.extract('(\\w*)A(\\w*)')

    def time_findall(self):
        self.s.str.findall('[A-Z]+')

    def time_get(self):
        self.s.str.get(0)

    def time_len(self):
        self.s.str.len()

    def time_match(self):
        self.s.str.match('A')

    def time_pad(self):
        self.s.str.pad(100, side='both')

    def time_replace(self):
        self.s.str.replace('A', '\x01\x01')

    def time_slice(self):
        self.s.str.slice(5, 15, 2)

    def time_startswith(self):
        self.s.str.startswith('A')

    def time_strip(self):
        self.s.str.strip('A')

    def time_rstrip(self):
        self.s.str.rstrip('A')

    def time_lstrip(self):
        self.s.str.lstrip('A')

    def time_title(self):
        self.s.str.title()

    def time_upper(self):
        self.s.str.upper()

    def time_lower(self):
        self.s.str.lower()


class Repeat(object):

    params = ['int', 'array']
    param_names = ['repeats']

    def setup(self, repeats):
        N = 10**5
        self.s = Series(tm.makeStringIndex(N))
        repeat = {'int': 1, 'array': np.random.randint(1, 3, N)}
        self.repeat = repeat[repeats]

    def time_repeat(self, repeats):
        self.s.str.repeat(self.repeat)


class Cat(object):

    params = ([0, 3], [None, ','], [None, '-'], [0.0, 0.001, 0.15])
    param_names = ['other_cols', 'sep', 'na_rep', 'na_frac']

    def setup(self, other_cols, sep, na_rep, na_frac):
        N = 10 ** 5
        mask_gen = lambda: np.random.choice([True, False], N,
                                            p=[1 - na_frac, na_frac])
        self.s = Series(tm.makeStringIndex(N)).where(mask_gen())
        if other_cols == 0:
            # str.cat self-concatenates only for others=None
            self.others = None
        else:
            self.others = DataFrame({i: tm.makeStringIndex(N).where(mask_gen())
                                     for i in range(other_cols)})

    def time_cat(self, other_cols, sep, na_rep, na_frac):
        # before the concatenation (one caller + other_cols columns), the total
        # expected fraction of rows containing any NaN is:
        # reduce(lambda t, _: t + (1 - t) * na_frac, range(other_cols + 1), 0)
        # for other_cols=3 and na_frac=0.15, this works out to ~48%
        self.s.str.cat(others=self.others, sep=sep, na_rep=na_rep)


class Contains(object):

    params = [True, False]
    param_names = ['regex']

    def setup(self, regex):
        self.s = Series(tm.makeStringIndex(10**5))

    def time_contains(self, regex):
        self.s.str.contains('A', regex=regex)


class Split(object):

    params = [True, False]
    param_names = ['expand']

    def setup(self, expand):
        self.s = Series(tm.makeStringIndex(10**5)).str.join('--')

    def time_split(self, expand):
        self.s.str.split('--', expand=expand)


class Dummies(object):

    def setup(self):
        self.s = Series(tm.makeStringIndex(10**5)).str.join('|')

    def time_get_dummies(self):
        self.s.str.get_dummies('|')


class Encode(object):

    def setup(self):
        self.ser = Series(tm.makeUnicodeIndex())

    def time_encode_decode(self):
        self.ser.str.encode('utf-8').str.decode('utf-8')


class Slice(object):

    def setup(self):
        self.s = Series(['abcdefg', np.nan] * 500000)

    def time_vector_slice(self):
        # GH 2602
        self.s.str[:5]