Fast data store for Pandas timeseries data
Project description
PyStore - Fast data store for Pandas timeseries data
PyStore is a simple (yet powerful) datastore for Pandas dataframes, and while it can store any Pandas object, it was designed with storing timeseries data in mind.
It’s built on top of Pandas, Numpy, Dask, and Parquet (via Fastparquet), to provide an easy to use datastore for Python developers that can easily query millions of rows per second per client.
==> Check out this Blog post for the reasoning and philosophy behind PyStore, as well as a detailed tutorial with code examples.
==> Follow this PyStore tutorial in Jupyter notebook format.
Quickstart
Install PyStore
Install using pip:
$ pip install pystore --upgrade --no-cache-dirInstall using conda:
$ conda install -c ranaroussi pystoreINSTALLATION NOTE: If you don’t have Snappy installed (compression/decompression library), you’ll need to you’ll need to install it first.
Using PyStore
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pystore
import quandl
# Set storage path (optional, default is `~/pystore`)
pystore.set_path("~/pystore")
# List stores
pystore.list_stores()
# Connect to datastore (create it if not exist)
store = pystore.store('mydatastore')
# List existing collections
store.list_collections()
# Access a collection (create it if not exist)
collection = store.collection('NASDAQ')
# List items in collection
collection.list_items()
# Load some data from Quandl
aapl = quandl.get("WIKI/AAPL", authtoken="your token here")
# Store the first 100 rows of the data in the collection under "AAPL"
collection.write('AAPL', aapl[:100], metadata={'source': 'Quandl'})
# Reading the item's data
item = collection.item('AAPL')
data = item.data # <-- Dask dataframe (see dask.pydata.org)
metadata = item.metadata
df = item.to_pandas()
# Append the rest of the rows to the "AAPL" item
collection.append('AAPL', aapl[100:])
# Reading the item's data
item = collection.item('AAPL')
data = item.data
metadata = item.metadata
df = item.to_pandas()
# --- Query functionality ---
# Query avaialable symbols based on metadata
collection.list_items(some_key='some_value', other_key='other_value')
# --- Snapshot functionality ---
# Snapshot a collection
# (Point-in-time named reference for all current symbols in a collection)
collection.create_snapshot('snapshot_name')
# List available snapshots
collection.list_snapshots()
# Get a version of a symbol given a snapshot name
collection.item('AAPL', snapshot='snapshot_name')
# Delete a collection snapshot
collection.delete_snapshot('snapshot_name')
# ...
# Delete the item from the current version
collection.delete_item('AAPL')
# Delete the collection
store.delete_collection('NASDAQ')Concepts
PyStore provides namespaced collections of data. These collections allow bucketing data by source, user or some other metric (for example frequency: End-Of-Day; Minute Bars; etc.). Each collection (or namespace) maps to a directory containing partitioned parquet files for each item (e.g. symbol).
A good practice it to create collections that may look something like this:
collection.EOD
collection.ONEMINUTE
Requirements
Python >= 3.5
Pandas
Numpy
Dask
Fastparquet
Snappy (Google’s compression/decompression library)
multitasking
PyStore was tested to work on *nix-like systems, including macOS.
Dependencies:
PyStore uses Snappy, a fast and efficient compression/decompression library from Google. You’ll need to install Snappy on your system before installing PyStore.
* See the python-snappy Github repo for more information.
*nix Systems:
APT: sudo apt-get install libsnappy-dev
RPM: sudo yum install libsnappy-devel
macOS:
First, install Snappy’s C library using Homebrew:
$ brew install snappyThen, install Python’s snappy using conda:
$ conda install python-snappy -c conda-forge…or, using pip:
$ CPPFLAGS="-I/usr/local/include -L/usr/local/lib" pip install python-snappyWindows:
Windows users should checkout Snappy for Windows and this Stackoverflow post for help on installing Snappy and python-snappy.
Roadmap
PyStore currently offers support for local filesystem. I plan on adding support for Amazon S3 (via s3fs), Google Cloud Storage (via gcsfs) and Hadoop Distributed File System (via hdfs3) in the future.
Acknowledgements
PyStore is hugely inspired by Man AHL’s Arctic which uses MongoDB for storage and allow for versioning and other features. I highly reommend you check it out.
License
PyStore is licensed under the Apache License, Version 2.0. A copy of which is included in LICENSE.txt.
I’m very interested in your experience with PyStore. Please drop me an note with any feedback you have.
Contributions welcome!
- Ran Aroussi
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