Commit 7608a9e9 authored by Aurélien Campéas's avatar Aurélien Campéas
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doc: initial README

closes #2
parent 5e02c04bb9bc
This is a library to store/retrieve pandas timeseries to/from a
postgres database, tracking their successive versions.
# Principles
There are many ways to represent timeseries in a relational database,
and `tshistory` provides two things:
* a base python API which abstracts away the underlying storage
* a postgres model, which uses JSONB fields to store the bulk of the
series data.
The core idea of tshistory is to handle successive versions of
timeseries as they grow in time, allowing to get older states of any
Series state can be indexed by either a timestamp (which typically
matches the moment a new insertion took place) or a `changeset id`
which denotes the exact change leading to a given version.
# Basic usage
Complete usage is shown in the test suite. However here's a simple
from datetime import datetime
from sqlalchemy import create_engine
import pandas as pd
from tshistory.tsio import TimeSerie
engine = create_engine('postgres://me:password@localhost/mydb')
tsh = TimeSerie()
serie = pd.Series([1, 2, 3],
pd.date_range(start=datetime(2017, 1, 1),
freq='D', periods=3))
# db insertion
tsh.insert(engine, serie, 'my_serie', '')
# db retrieval
assert tsh.get(engine, 'my_serie') == serie
This is good. Now, let's add more:
serie = pd.Series([7, 8, 9],
pd.date_range(start=datetime(2017, 1, 3),
freq='D', periods=3))
# db insertion
tsh.insert(engine, serie, 'my_serie', '')
# db retrieval
stored = tsh.get(engine, 'my_serie')
assert """
2017-01-01 1
2017-01-02 2
2017-01-03 7
2017-01-04 8
2017-01-04 9
Freq: D
""".strip() == stored.to_string().strip()
It is important to note that the third value was replaced, and the two
last values were just appended.
description-file =
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