Displaying 1 to 6 from 6 results

xarray - N-D labeled arrays and datasets in Python

  •    Python

xarray (formerly xray) is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the Common Data Model for self- describing scientific data in widespread use in the Earth sciences: xarray.Dataset is an in-memory representation of a netCDF file.

stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis

  •    Python

Whether you are an academic, data scientist, software developer, or time series enthusiast, STUMPY is straightforward to install and our goal is to allow you to get to your time series insights faster. See documentation for more information. Please see our API documentation for a complete list of available functions and see our informative tutorials for more comprehensive example use cases. Below, you will find code snippets that quickly demonstrate how to use STUMPY.

pandas-datareader - Extract data from a wide range of Internet sources into a pandas DataFrame.

  •    HTML

Up to date remote data access for pandas, works for multiple versions of pandas. As of v0.6.0 Yahoo!, Google Options, Google Quotes and EDGAR have been immediately deprecated due to large changes in their API and no stable replacement.

distributed - Distributed computation in Python

  •    Python

A library for distributed computation. See documentation for more details.




pydata-chicago2016-ml-tutorial - Machine learning with scikit-learn tutorial at PyData Chicago 2016

  •    Jupyter

This tutorial provides you with an introduction to machine learning in Python using the popular scikit-learn library. This tutorial will teach you the basics of scikit-learn. I will give you a brief overview of the basic concepts of classification and regression analysis, how to build powerful predictive models from labeled data. Although it's not a requirement for attending this tutorial, I highly recommend you to check out the accompanying GitHub repository at https://github.com/rasbt/pydata-chicago2016-ml-tutorial 1-2 days before the tutorial. During the session, we will not only talk about scikit-learn, but we will also go over some live code examples to get the knack of scikit-learn's API.

pymapd - Python client for OmniSci GPU-accelerated SQL engine and analytics platform

  •    Python

A wrapper for the pyomnisci libarary http://github.com/omnisci/pyomnisci, maintained for backwards compatibility. Existing scripts should be migrated to pyomnisci from pymapd, this library will not be updated moving forward.






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