Dataset loading

In this example, we demonstrate pyramid’s built-in toy datasets that can be used for benchmarking or experimentation. Pyramid has several built-in datasets that exhibit seasonality, non-stationarity, and other time series nuances.

Out:

Lynx array:
[ 269  321  585  871 1475 2821 3928 5943 4950 2577  523   98  184  279
  409 2285 2685 3409 1824  409  151   45   68  213  546 1033 2129 2536
  957  361  377  225  360  731 1638 2725 2871 2119  684  299  236  245
  552 1623 3311 6721 4254  687  255  473  358  784 1594 1676 2251 1426
  756  299  201  229  469  736 2042 2811 4431 2511  389   73   39   49
   59  188  377 1292 4031 3495  587  105  153  387  758 1307 3465 6991
 6313 3794 1836  345  382  808 1388 2713 3800 3091 2985 3790  674   81
   80  108  229  399 1132 2432 3574 2935 1537  529  485  662 1000 1590
 2657 3396]

Lynx series head:
1821     269
1822     321
1823     585
1824     871
1825    1475
dtype: int64

print(__doc__)

# Author: Taylor Smith <taylor.smith@alkaline-ml.com>

import pyramid as pm

# #############################################################################
# You can load the datasets via load_<name>
lynx = pm.datasets.load_lynx()
print("Lynx array:")
print(lynx)

# You can also get a series, if you rather
print("\nLynx series head:")
print(pm.datasets.load_lynx(as_series=True).head())

# Several other datasets:
heart_rate = pm.datasets.load_heartrate()
wineind = pm.datasets.load_wineind()
woolyrnq = pm.datasets.load_woolyrnq()

Total running time of the script: ( 0 minutes 0.002 seconds)