Time series for dealing with window/point data sources, which has interpolation midful of gaps
A time series built upon pandas for dealing with window/point data sources, which has interpolation mindful of gap’s.
Each window is represented by valid_from, valid_to, value.
During interpolation, the window time range is transformed into a center point datetime.
A constraint on the data model is a predefined length of a window, this length is used to query all suitable data and compute gaps.
Gaps are determined and a mask is applied to the original data frame.
When performing a query on a data frame, missing data at the tail and head are filled in.
Below are a visual representation of data within the tests.
This project is compatible with Python 3.5+, Pandas 0.19.
This library is in alpha state and is subject to revision.
|Filename, size & hash||File type||Python version||Upload date|
|pandas-timeseries-0.0.6.tar.gz (3.3 kB) View hashes||Source||None|