This is an implementation of DataFrames, Series and data wrangling methods for the Go programming language. The API is still in flux so use at your own risk.The term DataFrame typically refers to a tabular dataset that can be viewed as a two dimensional table. Often the columns of this dataset refers to a list of features, while the rows represent a number of measurements. As the data on the real world is not perfect, DataFrame supports non measurements or NaN elements.
https://github.com/kniren/gotaTags | data-structure collections go-collection |
Implementation | Go |
License | Apache |
Platform | Windows MacOS Linux |
Capsule aims to become a full-fledged (immutable) collections library for Java 8+ that is solely built around persistent tries. The library is designed for standalone use and for being embedded in domain-specific languages. Capsule still has to undergo some incubation before it can ship as a well-rounded collection library. Nevertheless, the code is stable and performance is solid.
hashmap trie immutable immutable-collections persistent-data-structure hashset performance data-structure collections map list champThis repository contains immutable collection types for Go. It includes List, Map, and SortedMap implementations. Immutable collections can provide efficient, lock free sharing of data by requiring that edits to the collections return new collections. The collection types in this library are meant to mimic Go built-in collections such asslice and map. The primary usage difference between Go collections and immutable collections is that immutable collections always return a new collection on mutation so you will need to save the new reference.
immutable collectionsThe missing set collection for the Go language. Until Go has sets built-in...use this.I have to give some credit for helping seed the idea with this post on stackoverflow.
set threadsafe datastructures data-structure collections go-collectionGo-datastructures is a collection of useful, performant, and threadsafe Go datastructures.Interval tree for collision in n-dimensional ranges. Implemented via a red-black augmented tree. Extra dimensions are handled in simultaneous inserts/queries to save space although this may result in suboptimal time complexity. Intersection determined using bit arrays. In a single dimension, inserts, deletes, and queries should be in O(log n) time.
data-structure collections go-collectionEclipse Collections is a collections framework for Java. It has JDK-compatible List, Set and Map implementations with a rich API, additional types not found in the JDK like Bags, Multimaps, and set of utility classes that work with any JDK compatible Collections, Arrays, Maps, or Strings. The iteration protocol was inspired by the Smalltalk collection framework. It maximizes the power of Java 8 Lambda expressions and method references with rich APIs directly available on your collections.
collections java-collection collections-library utility data-structureBoom Filters are probabilistic data structures for processing continuous, unbounded streams. This includes Stable Bloom Filters, Scalable Bloom Filters, Counting Bloom Filters, Inverse Bloom Filters, Cuckoo Filters, several variants of traditional Bloom filters, HyperLogLog, Count-Min Sketch, and MinHash.Classic Bloom filters generally require a priori knowledge of the data set in order to allocate an appropriately sized bit array. This works well for offline processing, but online processing typically involves unbounded data streams. With enough data, a traditional Bloom filter "fills up", after which it has a false-positive probability of 1.
bloom-filter stable-bloom-filters cuckoo-filter probabilistic-programming counting-bloom-filters scalable-bloom-filters count-min-sketch data-stream filter data-structure collections go-collectionImplementation of various data structures and algorithms in Go.Containers are either ordered or unordered. All ordered containers provide stateful iterators and some of them allow enumerable functions.
data-structure map tree set list stack iterator enumerable sort avl-tree red-black-tree b-tree binary-heap collections go-collectionThis library provides high-quality Java implementations of mutable and immutable data structures, each sharing a common API. Rather than using the existing collection interfaces in java.util such as List or Map, it provides its own interfaces (IList, IMap, ISet) that provide functional semantics - each update to a collection returns a reference to a new collection.
data-structure collections immutable map list java-collectionsBuckets is a complete, fully tested and documented data structure library written in pure JavaScript.
data-structures collections collection linked-list dictionary map multimap stack queue set bag binary-heap priority-queue binary-search binary-search-tree tree tree-structure buckets data structure linked list heap priority binary data-structure hashmapAn improved version of HyperLogLog for the count-distinct problem, approximating the number of distinct elements in a multiset using 20-50% less space than other usual HyperLogLog implementations.This work is based on "Better with fewer bits: Improving the performance of cardinality estimation of large data streams - Qingjun Xiao, You Zhou, Shigang Chen".
data-structure collections go-collectionPackage bitset implements bitsets, a mapping between non-negative integers and boolean values. It should be more efficient than map[uint] bool.It provides methods for setting, clearing, flipping, and testing individual integers.
bitset data-structure collections go-collectionA Bloom filter is a representation of a set of n items, where the main requirement is to make membership queries; i.e., whether an item is a member of a set.A Bloom filter has two parameters: m, a maximum size (typically a reasonably large multiple of the cardinality of the set to represent) and k, the number of hashing functions on elements of the set. (The actual hashing functions are important, too, but this is not a parameter for this implementation). A Bloom filter is backed by a BitSet; a key is represented in the filter by setting the bits at each value of the hashing functions (modulo m). Set membership is done by testing whether the bits at each value of the hashing functions (again, modulo m) are set. If so, the item is in the set. If the item is actually in the set, a Bloom filter will never fail (the true positive rate is 1.0); but it is susceptible to false positives. The art is to choose k and m correctly.
bloom bloom-filters bloom-filter data-structure collections go-collectionDynamic Data is a portable class library which brings the power of Reactive Extensions (Rx) to collections. Rx is extremely powerful but out of the box provides nothing to assist with managing collections. In most applications there is a need to update the collections dynamically. Typically a collection is loaded and after the initial load, asynchronous updates are received. The original collection will need to reflect these changes. In simple scenarios the code is simple. However, typical applications are much more complicated and may apply a filter, transform the original dto and apply a sort. Even with these simple every day operations the complexity of the code is quickly magnified. Dynamic data has been developed to remove the tedious code of dynamically maintaining collections. It has grown to become functionally very rich with at least 60 collection based operations which amongst other things enable filtering, sorting, grouping, joining different sources, transforms, binding, pagination, data virtualisation, expiration, disposal management plus more.
reactive-extensions reactive-collections rx dynamic-data rx-observable mvvm reactive-programmingDynamic Data is a portable class library which brings the power of Reactive Extensions (Rx) to collections.Rx is extremely powerful but out of the box provides nothing to assist with managing collections. In most applications there is a need to update the collections dynamically. Typically a collection is loaded and after the initial load, asynchronous updates are received. The original collection will need to reflect these changes. In simple scenarios the code is simple. However, typical applications are much more complicated and may apply a filter, transform the original dto and apply a sort. Even with these simple every day operations the complexity of the code is quickly magnified. Dynamic data has been developed to remove the tedious code of dynamically maintaining collections. It has grown to become functionally very rich with at least 60 collection based operations which amongst other things enable filtering, sorting, grouping, joining different sources, transforms, binding, pagination, data virtualisation, expiration, disposal management plus more.
reactive-extensions reactive-collections rx dynamic-data c-sharp rx-observableThe main goal of Boon is to reduce the productivity delta between Java and Python/Groovy/Ruby. The idea is to just create a lower ceremony APIs. Less Java like APIs, and more getting things done quickly APIs. If reading a file takes one line of code in Ruby/Python/Groovy, then it will also in Java. If slice notation is part of those language, then Boon will have it to.
java-collections data-structure utility json-parser collectionsAgrona provides a library of data structures and utility methods that are a common need when building high-performance applications in Java and C++. It supports Buffers, Map, Sets, Cache, Queues and lot more.
collections java-collection collections-library utility data-structureImmutable data cannot be changed once created, leading to much simpler application development, no defensive copying, and enabling advanced memoization and change detection techniques with simple logic. Persistent data presents a mutative API which does not update the data in-place, but instead always yields new updated data.
immutable persistent lazy data data-structure functional collection stateless sequence iterationGoque provides embedded, disk-based implementations of stack and queue data structures. Motivation for creating this project was the need for a persistent priority queue that remained performant while growing well beyond the available memory of a given machine. While there are many packages for Go offering queues, they all seem to be memory based and/or standalone solutions that are not embeddable within an application.
stack queue data-structure collections leveldbMemento push and pop for Backbone.js models and collections structures. A view may offer some editing capabilities that directly modify a structure (model or collection), directly. If you want to cancel the changes after they have already been applied to the structure, you will have to make a round trip to the back-end server or other origin of the structures's data to do so.
Java Concurrency Tools for the JVM. This project aims to offer some concurrent data structures currently missing from the JDK.
concurrency data-structures queues lock-free wait-free awesome collections java-collection data-structure
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