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,
})
not_in_index = '2019-01-01 00:05:00'
assert ts[not_in_index] == 1 # step function, previous value
ts['2019-01-01 00:04:00'] = 10
assert ts[not_in_index] == 10
assert ts + ts == 2 * ts
ts_evenly_spaced = ts.sample(freq='1Min')
# From ticts to pandas, and the other way around
assert ts.equals(
ts.to_dataframe().to_ticts(),
)
Installation
pip install ticts
Contributing
pip install pre-commit
pre-commit install --hook-type pre-push
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-0.3.4.tar.gz
(75.1 kB
view hashes)
Built Distribution
ticts-0.3.4-py2.py3-none-any.whl
(12.2 kB
view hashes)
Close
Hashes for ticts-0.3.4-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 02a188dc2cf1f7ef7cd8a2750d99eb6e455f4f70d42c820f2ad4739ba85fe9dd |
|
MD5 | dcdf47e0b3decb8b92fc53b781ec8d65 |
|
BLAKE2b-256 | 944716a236b1e223114ebb8905616940fd0f65ea9f562ab15dcf5da427e277e2 |