A Python library for unevenly-spaced time series analysis
Project description
A Python library for unevenly-spaced time series analysis. Greatly inspired by traces.
Get Started Notebook.
Usage
from ticts import TimeSeries
ts = TimeSeries({
'2019-01-01': 1,
'2019-01-01 00:10:00': 2,
'2019-01-01 00:11:00': 3,
})
assert ts['2019-01-01 00:05:00'] == 1
ts['2019-01-01 00:04:00'] = 10
assert ts['2019-01-01 00:05:00'] == 10
assert ts + ts == 2 * ts
from datetime import timedelta
onemin = timedelta(minutes=1)
ts_evenly_spaced = ts.sample(freq=onemin)
# if pandas installed:
df = ts.to_dataframe()
Installation
pip install ticts
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
ticts-0.2.1.tar.gz
(131.3 kB
view hashes)
Built Distribution
ticts-0.2.1-py2.py3-none-any.whl
(10.4 kB
view hashes)
Close
Hashes for ticts-0.2.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 511a00845f1025f7016a60bb5eab1912b14697b047475ffb6709998b0149a580 |
|
MD5 | c78283a9cd5f8f139c2850b7d74ec0c8 |
|
BLAKE2b-256 | 91cde570e6bab68d3dd09dde0db559eee77034127de16961ea2daaa8acce7d6e |