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@apio/timeframes
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Readme
Dataframe-like API for timeseries-like data
Install through NPM
npm i @apio/timeframes
There are a few examples that you can check out in the examples directory.
import { TimeSerie } from '@apio/timeframes'
// Pass an array of points
// a point is a tuple [DateLike, PointValue]
// DateLike is value that can be passed to new Date()
// PointValue is any value, but preferably a number (to compute sums averages ecc)
const ts = new TimeSerie([["2022-01-01", 12],["2022-01-02", 13]])
const sum = ts.sum()
const avg = ts.avg()
import { TimeFrame } from '@apio/timeframes'
// Each item is a row
const rows = [
{time: "2022-01-01", value1:10, value2:140},
{time: "2022-01-02", value1:41}, // here value2 is null
{time: "2022-01-03", value1:78, value2:301},
]
const ts = new TimeFrame(rows)
// column() extracts columns as timeseries
const total1 = ts.column("value1").sum()
FAQs
Library for dealing with timeseries data
The npm package @apio/timeframes receives a total of 2 weekly downloads. As such, @apio/timeframes popularity was classified as not popular.
We found that @apio/timeframes demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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Streamline your login process and enhance security by enabling Single Sign-On (SSO) on the Socket platform, now available for all customers on the Enterprise plan, supporting 20+ identity providers.
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