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Time series for dealing with window/point data sources, which has interpolation midful of gaps

Project description

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Time Series

A time series built upon pandas for dealing with window/point data sources, which has interpolation mindful of gap’s.

Design

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.

Sample data

Below are a visual representation of data within the tests.

Example A0 - Single data day Example A1 - Non-numeric content Example A2 - Multiple with non-numeric content Example B0 - Missing window at the start Example B1 - Missing window in the middle Example B2 - Missing window at the end Example C - Gaps between windows Example D - No data Example E - Multiple columns Example F - Multiple with non-numeric content

Compatibility

This project is compatible with Python 3.5+, Pandas 0.19.

Development state

This library is in alpha state and is subject to revision.

Project details


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Files for pandas-timeseries, version 0.0.6
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