Python testing.N属性代码示例

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import N [as 别名]
def test_frame_inferred(self):
        # inferred freq
        idx = date_range('1/1/1987', freq='MS', periods=100)
        idx = DatetimeIndex(idx.values, freq=None)

        df = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df.plot)

        # axes freq
        idx = idx[0:40].union(idx[45:99])
        df2 = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df2.plot)

        # N > 1
        idx = date_range('2008-1-1 00:15:00', freq='15T', periods=10)
        idx = DatetimeIndex(idx.values, freq=None)
        df = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df.plot) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import N [as 别名]
def test_frame_inferred(self):
        # inferred freq
        import matplotlib.pyplot as plt
        idx = date_range('1/1/1987', freq='MS', periods=100)
        idx = DatetimeIndex(idx.values, freq=None)

        df = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df.plot)

        # axes freq
        idx = idx[0:40] + idx[45:99]
        df2 = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df2.plot)

        # N > 1
        idx = date_range('2008-1-1 00:15:00', freq='15T', periods=10)
        idx = DatetimeIndex(idx.values, freq=None)
        df = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df.plot) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import N [as 别名]
def create_random_sample_set(n_samples, time_shift='120m', randomize_times=False, freq='60T'):
    # Create artificial data
    tm.K = 3
    tm.N = n_samples
    # Random data frame with an hourly index
    test_df = tm.makeTimeDataFrame(freq=freq)
    # Turn the index into a column labeled 'index'
    test_df = test_df.reset_index()
    if randomize_times:
        tm.K = 1
        # Subtract and adds random time deltas to the index column, to create the prediction and evaluation times
        rand_fact = tm.makeDataFrame().reset_index(drop=True).squeeze().iloc[:len(test_df)].abs()
        test_df['index'] = test_df['index'].subtract(rand_fact.apply(lambda x: x * pd.Timedelta(time_shift)))
        rand_fact = tm.makeDataFrame().reset_index(drop=True).squeeze().iloc[:len(test_df)].abs()
        test_df['index2'] = test_df['index'].add(rand_fact.apply(lambda x: x * pd.Timedelta(time_shift)))
    else:
        test_df['index2'] = test_df['index'].apply(lambda x: x + pd.Timedelta(time_shift))
    # Sort the data frame by prediction time
    test_df = test_df.sort_values('index')
    X = test_df[['A', 'B', 'C']]
    pred_times = test_df['index']
    exit_times = test_df['index2']
    return X, pred_times, exit_times 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import N [as 别名]
def setup_method(self, method):

        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
                                                                  'three']],
                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])
        self.frame = DataFrame(np.random.randn(10, 3), index=index,
                               columns=Index(['A', 'B', 'C'], name='exp'))

        self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                                       labels=[[0, 1, 2, 3]], names=['first'])

        # create test series object
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = lzip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

        tm.N = 100
        self.tdf = tm.makeTimeDataFrame()
        self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month,
                                     lambda x: x.day]).sum()

        # use Int64Index, to make sure things work
        self.ymd.index.set_levels([lev.astype('i8')
                                   for lev in self.ymd.index.levels],
                                  inplace=True)
        self.ymd.index.set_names(['year', 'month', 'day'], inplace=True) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import N [as 别名]
def test_pyint_engine(self):
        # GH 18519 : when combinations of codes cannot be represented in 64
        # bits, the index underlying the MultiIndex engine works with Python
        # integers, rather than uint64.
        N = 5
        keys = [tuple(l) for l in [[0] * 10 * N,
                                   [1] * 10 * N,
                                   [2] * 10 * N,
                                   [np.nan] * N + [2] * 9 * N,
                                   [0] * N + [2] * 9 * N,
                                   [np.nan] * N + [2] * 8 * N + [0] * N]]
        # Each level contains 4 elements (including NaN), so it is represented
        # in 2 bits, for a total of 2*N*10 = 100 > 64 bits. If we were using a
        # 64 bit engine and truncating the first levels, the fourth and fifth
        # keys would collide; if truncating the last levels, the fifth and
        # sixth; if rotating bits rather than shifting, the third and fifth.

        for idx in range(len(keys)):
            index = MultiIndex.from_tuples(keys)
            assert index.get_loc(keys[idx]) == idx

            expected = np.arange(idx + 1, dtype=np.intp)
            result = index.get_indexer([keys[i] for i in expected])
            tm.assert_numpy_array_equal(result, expected)

        # With missing key:
        idces = range(len(keys))
        expected = np.array([-1] + list(idces), dtype=np.intp)
        missing = tuple([0, 1] * 5 * N)
        result = index.get_indexer([missing] + [keys[i] for i in idces])
        tm.assert_numpy_array_equal(result, expected) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import N [as 别名]
def test_business_freq_convert(self):
        n = tm.N
        tm.N = 300
        bts = tm.makeTimeSeries().asfreq('BM')
        tm.N = n
        ts = bts.to_period('M')
        _, ax = self.plt.subplots()
        bts.plot(ax=ax)
        assert ax.get_lines()[0].get_xydata()[0, 0] == ts.index[0].ordinal
        idx = ax.get_lines()[0].get_xdata()
        assert PeriodIndex(data=idx).freqstr == 'M' 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import N [as 别名]
def test_business_freq_convert(self):
        n = tm.N
        tm.N = 300
        bts = tm.makeTimeSeries().asfreq('BM')
        tm.N = n
        ts = bts.to_period('M')
        ax = bts.plot()
        self.assertEqual(ax.get_lines()[0].get_xydata()[0, 0],
                         ts.index[0].ordinal)
        idx = ax.get_lines()[0].get_xdata()
        self.assertEqual(PeriodIndex(data=idx).freqstr, 'M')