Search Results for "python data analysis" - Page 3

A Julia implementation of basic tools for time series analysis compatible with incomplete data. Advanced estimation and validation algorithms are included in MessyTimeSeriesOptim.

Indicators is a Julia package offering efficient implementations of many technical analysis indicators and algorithms. This work is inspired by the TTR package in R and the Python implementation of TA-Lib, and the ultimate goal is to implement all of the functionality of these offerings (and more) in Julia. This package has been written to be able to interface with both native Julia Array types, as well as the TS time series type from the Temporal package.

A Julia interface to Apache Spark. Spark.jl provides an interface to Apache Spark™ platform, including SQL / DataFrame and Structured Streaming. It closely follows the PySpark API, making it easy to translate existing Python code to Julia. Spark.jl supports multiple cluster types (in client mode), and can be considered as an analog to PySpark or RSpark within the Julia ecosystem. It supports running within on-premise installations, as well as hosted instances such as Amazon EMR and Azure...

Repository for predicting time series using methods from nonlinear dynamics and time series analysis. It uses DelayEmbeddings.

This package provides a flexible & efficient time series class, TS, for the Julia programming language. While still early in development, the overarching goal is for the class to be able to slice & dice data with the rapid prototyping speed of R's xts and Python's pandas packages, while retaining the performance one expects from Julia. See the documentation for a more in-depth look at the package and some of the pain points it may solve when doing technical research with time series data.

RigidBodyDynamics.jl is a rigid body dynamics library in pure Julia. It aims to be user friendly and performant, but also generic in the sense that the algorithms can be called with inputs of any (suitable) scalar types. This means that if fast numeric dynamics evaluations are required, a user can supply Float64 or Float32 inputs. However, if symbolic quantities are desired for analysis purposes, they can be obtained by calling the algorithms with e.g. SymPy.Sym inputs. If gradients are...

LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type. The project goal is to mirror the functionality of robust network and graph analysis libraries such as NetworkX while being simpler to use and more efficient than existing Julian graph libraries such as Graphs.jl. It is an explicit design decision that any data not required for graph manipulation (attributes and other information, for example) is expected to be stored outside of the graph structure itself. Such data lends itself to storage in more traditional and better-optimized mechanisms.