categoricals.py

import warnings

import numpy as np
import pandas as pd
import pandas.util.testing as tm
try:
    from pandas.api.types import union_categoricals
except ImportError:
    try:
        from pandas.types.concat import union_categoricals
    except ImportError:
        pass

from .pandas_vb_common import setup # noqa


class Concat(object):

    goal_time = 0.2

    def setup(self):
        N = 10**5
        self.s = pd.Series(list('aabbcd') * N).astype('category')

        self.a = pd.Categorical(list('aabbcd') * N)
        self.b = pd.Categorical(list('bbcdjk') * N)

    def time_concat(self):
        pd.concat([self.s, self.s])

    def time_union(self):
        union_categoricals([self.a, self.b])


class Constructor(object):

    goal_time = 0.2

    def setup(self):
        N = 10**5
        self.categories = list('abcde')
        self.cat_idx = pd.Index(self.categories)
        self.values = np.tile(self.categories, N)
        self.codes = np.tile(range(len(self.categories)), N)

        self.datetimes = pd.Series(pd.date_range('1995-01-01 00:00:00',
                                                 periods=N / 10,
                                                 freq='s'))
        self.datetimes_with_nat = self.datetimes.copy()
        self.datetimes_with_nat.iloc[-1] = pd.NaT

        self.values_some_nan = list(np.tile(self.categories + [np.nan], N))
        self.values_all_nan = [np.nan] * len(self.values)
        self.values_all_int8 = np.ones(N, 'int8')

    def time_regular(self):
        pd.Categorical(self.values, self.categories)

    def time_fastpath(self):
        pd.Categorical(self.codes, self.cat_idx, fastpath=True)

    def time_datetimes(self):
        pd.Categorical(self.datetimes)

    def time_datetimes_with_nat(self):
        pd.Categorical(self.datetimes_with_nat)

    def time_with_nan(self):
        pd.Categorical(self.values_some_nan)

    def time_all_nan(self):
        pd.Categorical(self.values_all_nan)

    def time_from_codes_all_int8(self):
        pd.Categorical.from_codes(self.values_all_int8, self.categories)


class ValueCounts(object):

    goal_time = 0.2

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

    def setup(self, dropna):
        n = 5 * 10**5
        arr = ['s%04d' % i for i in np.random.randint(0, n // 10, size=n)]
        self.ts = pd.Series(arr).astype('category')

    def time_value_counts(self, dropna):
        self.ts.value_counts(dropna=dropna)


class Repr(object):

    goal_time = 0.2

    def setup(self):
        self.sel = pd.Series(['s1234']).astype('category')

    def time_rendering(self):
        str(self.sel)


class SetCategories(object):

    goal_time = 0.2

    def setup(self):
        n = 5 * 10**5
        arr = ['s%04d' % i for i in np.random.randint(0, n // 10, size=n)]
        self.ts = pd.Series(arr).astype('category')

    def time_set_categories(self):
        self.ts.cat.set_categories(self.ts.cat.categories[::2])


class Rank(object):

    goal_time = 0.2

    def setup(self):
        N = 10**5
        ncats = 100

        self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
        self.s_str_cat = self.s_str.astype('category')
        with warnings.catch_warnings(record=True):
            self.s_str_cat_ordered = self.s_str.astype('category',
                                                       ordered=True)

        self.s_int = pd.Series(np.random.randint(0, ncats, size=N))
        self.s_int_cat = self.s_int.astype('category')
        with warnings.catch_warnings(record=True):
            self.s_int_cat_ordered = self.s_int.astype('category',
                                                       ordered=True)

    def time_rank_string(self):
        self.s_str.rank()

    def time_rank_string_cat(self):
        self.s_str_cat.rank()

    def time_rank_string_cat_ordered(self):
        self.s_str_cat_ordered.rank()

    def time_rank_int(self):
        self.s_int.rank()

    def time_rank_int_cat(self):
        self.s_int_cat.rank()

    def time_rank_int_cat_ordered(self):
        self.s_int_cat_ordered.rank()


class Isin(object):

    goal_time = 0.2

    params = ['object', 'int64']
    param_names = ['dtype']

    def setup(self, dtype):
        np.random.seed(1234)
        n = 5 * 10**5
        sample_size = 100
        arr = [i for i in np.random.randint(0, n // 10, size=n)]
        if dtype == 'object':
            arr = ['s%04d' % i for i in arr]
        self.sample = np.random.choice(arr, sample_size)
        self.series = pd.Series(arr).astype('category')

    def time_isin_categorical(self, dtype):
        self.series.isin(self.sample)


class IsMonotonic(object):

    def setup(self):
        N = 1000
        self.c = pd.CategoricalIndex(list('a' * N + 'b' * N + 'c' * N))
        self.s = pd.Series(self.c)

    def time_categorical_index_is_monotonic_increasing(self):
        self.c.is_monotonic_increasing

    def time_categorical_index_is_monotonic_decreasing(self):
        self.c.is_monotonic_decreasing

    def time_categorical_series_is_monotonic_increasing(self):
        self.s.is_monotonic_increasing

    def time_categorical_series_is_monotonic_decreasing(self):
        self.s.is_monotonic_decreasing


class Contains(object):

    params = ([
        "b",  # in array
        "d",  # in categories but not in codes
        "z",  # nowhere
        np.nan,
    ],
        [True, False],
    )
    param_names = ["value", "has_nan"]

    def setup(self, value, has_nan):
        n = 1 * 10 ** 4
        obj_values = list("a" * n + "b" * n + "c" * n)
        if has_nan:
            obj_values = [np.nan] + obj_values[:-2] + [np.nan]

        self.ci = pd.CategoricalIndex(obj_values, categories=list("abcd"))
        self.cat = pd.Categorical(obj_values, categories=list("abcd"))

    def time_contains_index(self, value, has_nan):
        value in self.ci

    def time_cat_isin(self, value, has_nan):
        value in self.cat


class Indexing(object):

    params = (["a", "c"], [True, False])
    param_names = ["value", "has_nan"]

    def setup(self, value, has_nan):
        n = 1 * 10 ** 4
        obj_values = list("a" * n + "b" * n + "c" * n)
        if has_nan:
            obj_values = [np.nan] + obj_values[:-2] + [np.nan]

        ci = pd.CategoricalIndex(obj_values, categories=list("abcd"))
        self.df = pd.DataFrame(dict(A=range(n * 3)), index=ci)
        self.ser = pd.Series(range(n * 3), index=ci)

    def time_loc_df(self, value, has_nan):
        self.df.loc[value]

    def time_loc_ser(self, value, has_nan):
        self.ser.loc[value]