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


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)

    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)


class ValueCounts(object):

    goal_time = 0.2

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

    def setup(self, dropna):
        n = 5 * 10**5
        np.random.seed(2718281)
        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
        np.random.seed(2718281)
        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
        np.random.seed(1234)

        self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
        self.s_str_cat = self.s_str.astype('category')
        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')
        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()
