Python pandas.TimeSeries类代码示例

    def test_resample_5minute(self):
        rng = period_range('1/1/2000', '1/5/2000', freq='T')
        ts = TimeSeries(np.random.randn(len(rng)), index=rng)

        result = ts.resample('5min')
        expected = ts.to_timestamp().resample('5min')
        assert_series_equal(result, expected)

def save2csv(r, csvpath=None, fileNamePrefix=''):
    """
    Parse and save to csv
    """
    
    
    # Create Dataframe   
    try:
        d = {}
        for tup in r.json():
            d[dt.fromtimestamp(tup[0])] = tup[1]
        #pdb.set_trace()
        Ts = TimeSeries(data=d)
        # this line gives an error.  Should be checked, but for now I keep the nan's        
        # Ts = Ts[Ts != 'nan']
        
    except:
        print "-------> Problem with Flukso data parsing <-------"
        raise

    # save to file
    if csvpath is None:
        csvpath = os.getcwd()
    s = strftime("%Y-%m-%d_%H-%M-%S",Ts.index[0].timetuple())    
    e = strftime("%Y-%m-%d_%H-%M-%S",Ts.index[-1].timetuple())
    Ts.to_csv(os.path.join(csvpath, fileNamePrefix + '_FROM_' + s + 
                                    '_TO_' + e + '.csv'))    

 def test_pad_nan(self):
     x = TimeSeries([np.nan, 1., np.nan, 3., np.nan],
                    ['z', 'a', 'b', 'c', 'd'], dtype=float)
     x = x.fillna(method='pad')
     expected = TimeSeries([np.nan, 1.0, 1.0, 3.0, 3.0],
                             ['z', 'a', 'b', 'c', 'd'], dtype=float)
     assert_series_equal(x[1:], expected[1:])
     self.assert_(np.isnan(x[0]), np.isnan(expected[0]))

    def test_median(self):
        self.assertAlmostEqual(np.median(self.ts), self.ts.median())

        ts = self.ts.copy()
        ts[::2] = np.NaN

        self.assertAlmostEqual(np.median(ts.valid()), ts.median())

        # test with integers, test failure
        int_ts = TimeSeries(np.ones(10, dtype=int), index=range(10))
        self.assertAlmostEqual(np.median(int_ts), int_ts.median())

    def __init__(self, data, dtime,**kwargs):
	"""
	Time series w/ specific IO methods
	"""
        self.__dict__.update(kwargs)

        TimeSeries.__init__(self, data, index=dtime)
	#super(ObsTimeSeries,self).__init__(data,index=dtime)

	# Time coordinates
	self.nt = self.index.shape
	self.tsec = othertime.SecondsSince(self.index,\
		basetime = pd.datetime(self.baseyear,1,1))

    def test_resample_fill_missing(self):
        rng = PeriodIndex([2000, 2005, 2007, 2009], freq='A')

        s = TimeSeries(np.random.randn(4), index=rng)

        stamps = s.to_timestamp()

        filled = s.resample('A')
        expected = stamps.resample('A').to_period('A')
        assert_series_equal(filled, expected)

        filled = s.resample('A', fill_method='ffill')
        expected = stamps.resample('A', fill_method='ffill').to_period('A')
        assert_series_equal(filled, expected)

    def _split(self, frame):
        if self.share_afterward == 1:
            return

        splits = [self.share_afterward, 1.0]
        adj_day = self.ex_date - datetime.timedelta(days=1)
        indexes = []
        indexes.append(adj_day)
        indexes.append(datetime.date.today())
        
        splits = TimeSeries(splits, index=indexes)
        ri_splits = splits.reindex(frame.index, method='backfill')

        frame['adjclose'] = frame['adjclose'] / ri_splits

    def _divide(self, frame):
        if self.cash_afterward == 0:
            return

        cashes = [self.cash_afterward, 0.0]
        adj_day = self.ex_date - datetime.timedelta(days=1)
        indexes = []
        indexes.append(adj_day)
        indexes.append(datetime.date.today())
        
        cashes = TimeSeries(cashes, index=indexes)
        ri_cashes = cashes.reindex(frame.index, method='backfill')

        frame['adjclose'] = frame['adjclose'] - ri_cashes

    def _divide(self, frame):
        """divided close price to adjclose column

        WARNING
        =======
        frame should be chronological ordered otherwise wrong backfill.
        """
        if self.cash_afterward == 0:
            return

        cashes = [self.cash_afterward, 0.0]
        adj_day = self.ex_date - datetime.timedelta(days=1)
        indexes = []
        indexes.append(self.d2t(adj_day))
        indexes.append(self.d2t(datetime.date.today()))
        
        cashes = TimeSeries(cashes, index=indexes)
        ri_cashes = cashes.reindex(frame.index, method='backfill')

        frame['adjclose'] = frame['adjclose'] - ri_cashes

    def test_median(self):
        self._check_stat_op('median', np.median)

        # test with integers, test failure
        int_ts = TimeSeries(np.ones(10, dtype=int), index=range(10))
        self.assertAlmostEqual(np.median(int_ts), int_ts.median())