# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from ..compat import DTYPE
__all__ = [
'load_austres'
]
[docs]def load_austres(as_series=False, dtype=DTYPE):
"""Quarterly residential data.
Numbers (in thousands) of Australian residents measured quarterly from
March 1971 to March 1994.
Parameters
----------
as_series : bool, optional (default=False)
Whether to return a Pandas series. If False, will return a 1d
numpy array.
dtype : type, optional (default=np.float64)
The type to return for the array. Default is np.float64, which is used
throughout the package as the default type.
Returns
-------
rslt : array-like, shape=(n_samples,)
The austres vector.
Examples
--------
>>> from pmdarima.datasets import load_austres
>>> load_austres()
np.array([13067.3, 13130.5, 13198.4, 13254.2, 13303.7, 13353.9,
13409.3, 13459.2, 13504.5, 13552.6, 13614.3, 13669.5,
13722.6, 13772.1, 13832.0, 13862.6, 13893.0, 13926.8,
13968.9, 14004.7, 14033.1, 14066.0, 14110.1, 14155.6,
14192.2, 14231.7, 14281.5, 14330.3, 14359.3, 14396.6,
14430.8, 14478.4, 14515.7, 14554.9, 14602.5, 14646.4,
14695.4, 14746.6, 14807.4, 14874.4, 14923.3, 14988.7,
15054.1, 15121.7, 15184.2, 15239.3, 15288.9, 15346.2,
15393.5, 15439.0, 15483.5, 15531.5, 15579.4, 15628.5,
15677.3, 15736.7, 15788.3, 15839.7, 15900.6, 15961.5,
16018.3, 16076.9, 16139.0, 16203.0, 16263.3, 16327.9,
16398.9, 16478.3, 16538.2, 16621.6, 16697.0, 16777.2,
16833.1, 16891.6, 16956.8, 17026.3, 17085.4, 17106.9,
17169.4, 17239.4, 17292.0, 17354.2, 17414.2, 17447.3,
17482.6, 17526.0, 17568.7, 17627.1, 17661.5])
>>> load_austres(True).head()
0 13067.3
1 13130.5
2 13198.4
3 13254.2
4 13303.7
dtype: float64
Notes
-----
This is quarterly data, so *m* should be set to 4 when using in a seasonal
context.
References
----------
.. [1] P. J. Brockwell and R. A. Davis (1996)
"Introduction to Time Series and Forecasting." Springer
"""
rslt = np.array([
13067.3, 13130.5, 13198.4, 13254.2, 13303.7, 13353.9,
13409.3, 13459.2, 13504.5, 13552.6, 13614.3, 13669.5,
13722.6, 13772.1, 13832.0, 13862.6, 13893.0, 13926.8,
13968.9, 14004.7, 14033.1, 14066.0, 14110.1, 14155.6,
14192.2, 14231.7, 14281.5, 14330.3, 14359.3, 14396.6,
14430.8, 14478.4, 14515.7, 14554.9, 14602.5, 14646.4,
14695.4, 14746.6, 14807.4, 14874.4, 14923.3, 14988.7,
15054.1, 15121.7, 15184.2, 15239.3, 15288.9, 15346.2,
15393.5, 15439.0, 15483.5, 15531.5, 15579.4, 15628.5,
15677.3, 15736.7, 15788.3, 15839.7, 15900.6, 15961.5,
16018.3, 16076.9, 16139.0, 16203.0, 16263.3, 16327.9,
16398.9, 16478.3, 16538.2, 16621.6, 16697.0, 16777.2,
16833.1, 16891.6, 16956.8, 17026.3, 17085.4, 17106.9,
17169.4, 17239.4, 17292.0, 17354.2, 17414.2, 17447.3,
17482.6, 17526.0, 17568.7, 17627.1, 17661.5]).astype(dtype)
if as_series:
return pd.Series(rslt)
return rslt