# coding=utf-8
# pylint: disable-msg=E1101,W0612

import pytest

from datetime import datetime

import numpy as np
import pandas as pd

from pandas import Index, Series
from pandas.core.index import MultiIndex, RangeIndex

from pandas.compat import lrange, range, zip
from pandas.util.testing import assert_series_equal, assert_frame_equal
import pandas.util.testing as tm

from .common import TestData


class TestSeriesAlterAxes(TestData):

    def test_setindex(self):
        # wrong type
        series = self.series.copy()
        pytest.raises(TypeError, setattr, series, 'index', None)

        # wrong length
        series = self.series.copy()
        pytest.raises(Exception, setattr, series, 'index',
                      np.arange(len(series) - 1))

        # works
        series = self.series.copy()
        series.index = np.arange(len(series))
        assert isinstance(series.index, Index)

    def test_rename(self):
        renamer = lambda x: x.strftime('%Y%m%d')
        renamed = self.ts.rename(renamer)
        assert renamed.index[0] == renamer(self.ts.index[0])

        # dict
        rename_dict = dict(zip(self.ts.index, renamed.index))
        renamed2 = self.ts.rename(rename_dict)
        assert_series_equal(renamed, renamed2)

        # partial dict
        s = Series(np.arange(4), index=['a', 'b', 'c', 'd'], dtype='int64')
        renamed = s.rename({'b': 'foo', 'd': 'bar'})
        tm.assert_index_equal(renamed.index, Index(['a', 'foo', 'c', 'bar']))

        # index with name
        renamer = Series(np.arange(4),
                         index=Index(['a', 'b', 'c', 'd'], name='name'),
                         dtype='int64')
        renamed = renamer.rename({})
        assert renamed.index.name == renamer.index.name

    def test_rename_by_series(self):
        s = Series(range(5), name='foo')
        renamer = Series({1: 10, 2: 20})
        result = s.rename(renamer)
        expected = Series(range(5), index=[0, 10, 20, 3, 4], name='foo')
        tm.assert_series_equal(result, expected)

    def test_rename_set_name(self):
        s = Series(range(4), index=list('abcd'))
        for name in ['foo', 123, 123., datetime(2001, 11, 11), ('foo',)]:
            result = s.rename(name)
            assert result.name == name
            tm.assert_numpy_array_equal(result.index.values, s.index.values)
            assert s.name is None

    def test_rename_set_name_inplace(self):
        s = Series(range(3), index=list('abc'))
        for name in ['foo', 123, 123., datetime(2001, 11, 11), ('foo',)]:
            s.rename(name, inplace=True)
            assert s.name == name

            exp = np.array(['a', 'b', 'c'], dtype=np.object_)
            tm.assert_numpy_array_equal(s.index.values, exp)

    def test_set_name_attribute(self):
        s = Series([1, 2, 3])
        s2 = Series([1, 2, 3], name='bar')
        for name in [7, 7., 'name', datetime(2001, 1, 1), (1,), u"\u05D0"]:
            s.name = name
            assert s.name == name
            s2.name = name
            assert s2.name == name

    def test_set_name(self):
        s = Series([1, 2, 3])
        s2 = s._set_name('foo')
        assert s2.name == 'foo'
        assert s.name is None
        assert s is not s2

    def test_rename_inplace(self):
        renamer = lambda x: x.strftime('%Y%m%d')
        expected = renamer(self.ts.index[0])

        self.ts.rename(renamer, inplace=True)
        assert self.ts.index[0] == expected

    def test_set_index_makes_timeseries(self):
        idx = tm.makeDateIndex(10)

        s = Series(lrange(10))
        s.index = idx
        assert s.index.is_all_dates

    def test_reset_index(self):
        df = tm.makeDataFrame()[:5]
        ser = df.stack()
        ser.index.names = ['hash', 'category']

        ser.name = 'value'
        df = ser.reset_index()
        assert 'value' in df

        df = ser.reset_index(name='value2')
        assert 'value2' in df

        # check inplace
        s = ser.reset_index(drop=True)
        s2 = ser
        s2.reset_index(drop=True, inplace=True)
        assert_series_equal(s, s2)

        # level
        index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
                           labels=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
                                   [0, 1, 0, 1, 0, 1]])
        s = Series(np.random.randn(6), index=index)
        rs = s.reset_index(level=1)
        assert len(rs.columns) == 2

        rs = s.reset_index(level=[0, 2], drop=True)
        tm.assert_index_equal(rs.index, Index(index.get_level_values(1)))
        assert isinstance(rs, Series)

    def test_reset_index_level(self):
        df = pd.DataFrame([[1, 2, 3], [4, 5, 6]],
                          columns=['A', 'B', 'C'])

        for levels in ['A', 'B'], [0, 1]:
            # With MultiIndex
            s = df.set_index(['A', 'B'])['C']

            result = s.reset_index(level=levels[0])
            tm.assert_frame_equal(result, df.set_index('B'))

            result = s.reset_index(level=levels[:1])
            tm.assert_frame_equal(result, df.set_index('B'))

            result = s.reset_index(level=levels)
            tm.assert_frame_equal(result, df)

            result = df.set_index(['A', 'B']).reset_index(level=levels,
                                                          drop=True)
            tm.assert_frame_equal(result, df[['C']])

            with tm.assert_raises_regex(KeyError, 'Level E '):
                s.reset_index(level=['A', 'E'])

            # With single-level Index
            s = df.set_index('A')['B']

            result = s.reset_index(level=levels[0])
            tm.assert_frame_equal(result, df[['A', 'B']])

            result = s.reset_index(level=levels[:1])
            tm.assert_frame_equal(result, df[['A', 'B']])

            result = s.reset_index(level=levels[0], drop=True)
            tm.assert_series_equal(result, df['B'])

            with tm.assert_raises_regex(IndexError, 'Too many levels'):
                s.reset_index(level=[0, 1, 2])

    def test_reset_index_range(self):
        # GH 12071
        s = pd.Series(range(2), name='A', dtype='int64')
        series_result = s.reset_index()
        assert isinstance(series_result.index, RangeIndex)
        series_expected = pd.DataFrame([[0, 0], [1, 1]],
                                       columns=['index', 'A'],
                                       index=RangeIndex(stop=2))
        assert_frame_equal(series_result, series_expected)

    def test_reorder_levels(self):
        index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
                           labels=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
                                   [0, 1, 0, 1, 0, 1]],
                           names=['L0', 'L1', 'L2'])
        s = Series(np.arange(6), index=index)

        # no change, position
        result = s.reorder_levels([0, 1, 2])
        assert_series_equal(s, result)

        # no change, labels
        result = s.reorder_levels(['L0', 'L1', 'L2'])
        assert_series_equal(s, result)

        # rotate, position
        result = s.reorder_levels([1, 2, 0])
        e_idx = MultiIndex(levels=[['one', 'two', 'three'], [0, 1], ['bar']],
                           labels=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1],
                                   [0, 0, 0, 0, 0, 0]],
                           names=['L1', 'L2', 'L0'])
        expected = Series(np.arange(6), index=e_idx)
        assert_series_equal(result, expected)

        result = s.reorder_levels([0, 0, 0])
        e_idx = MultiIndex(levels=[['bar'], ['bar'], ['bar']],
                           labels=[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
                                   [0, 0, 0, 0, 0, 0]],
                           names=['L0', 'L0', 'L0'])
        expected = Series(np.arange(6), index=e_idx)
        assert_series_equal(result, expected)

        result = s.reorder_levels(['L0', 'L0', 'L0'])
        assert_series_equal(result, expected)

    def test_rename_axis_inplace(self):
        # GH 15704
        series = self.ts.copy()
        expected = series.rename_axis('foo')
        result = series.copy()
        no_return = result.rename_axis('foo', inplace=True)

        assert no_return is None
        assert_series_equal(result, expected)

    def test_set_axis_inplace(self):
        # GH14636

        s = Series(np.arange(4), index=[1, 3, 5, 7], dtype='int64')

        expected = s.copy()
        expected.index = list('abcd')

        for axis in 0, 'index':
            # inplace=True
            # The FutureWarning comes from the fact that we would like to have
            # inplace default to False some day
            for inplace, warn in (None, FutureWarning), (True, None):
                result = s.copy()
                kwargs = {'inplace': inplace}
                with tm.assert_produces_warning(warn):
                    result.set_axis(list('abcd'), axis=axis, **kwargs)
                tm.assert_series_equal(result, expected)

        # inplace=False
        result = s.set_axis(list('abcd'), axis=0, inplace=False)
        tm.assert_series_equal(expected, result)

        # omitting the "axis" parameter
        with tm.assert_produces_warning(None):
            result = s.set_axis(list('abcd'), inplace=False)
        tm.assert_series_equal(result, expected)

        # wrong values for the "axis" parameter
        for axis in 2, 'foo':
            with tm.assert_raises_regex(ValueError, 'No axis named'):
                s.set_axis(list('abcd'), axis=axis, inplace=False)

    def test_set_axis_prior_to_deprecation_signature(self):
        s = Series(np.arange(4), index=[1, 3, 5, 7], dtype='int64')

        expected = s.copy()
        expected.index = list('abcd')

        for axis in 0, 'index':
            with tm.assert_produces_warning(FutureWarning):
                result = s.set_axis(0, list('abcd'), inplace=False)
            tm.assert_series_equal(result, expected)
