Search Results for "python data analysis" - Page 27
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Showing 1158 open source projects for "python data analysis"
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fooltrader
Quant framework for stock
Build a standard data schema, and then implement various connectors to import systems you are familiar with for analysis. fooltrader is a quantitative analysis trading system designed using big data technology, including data capture, cleaning, structuring, calculation, display, backtesting and trading. Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. ... -
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Ponder
C++ reflection library with Lua binding, and JSON and XML
...It provides an abstraction for most of the high-level concepts of C++ like classes, enumerations, properties, functions, and objects. By wrapping all these concepts into abstract structures, Ponder provides an extra layer of flexibility to programs and allows them to expose and manipulate their data structures at runtime. Many applications can take advantage of Ponder, in order to automate tasks that would otherwise require a huge amount of work. For example, Ponder can be used to expose and edit objects' attributes in a graphical user interface. It can also be used to do automatic binding of C++ classes to script languages such as Python or Lua. ... -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The concept of model interpretability in the field of machine learning is still new, largely subjective, and, at times, controversial. Model interpretation is the ability to explain and validate the decisions of a predictive model to enable fairness, accountability, and transparency in algorithmic decision-making. ... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more... -
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Build Time Tracker Plugin
Gradle plugin to continuously track and report your build times
The build‑time‑tracker‑plugin for Gradle continuously logs build performance data in a monoidal format, enabling statistical analysis across machines. This helps teams understand where build time is spent, compare build performance across environments, and identify bottlenecks. build-time-tracker writes a continuous log that is monoidal and can be collected from various machines to run statistical analyses. Importantly, the written files contain identifying information about the machine the build happened on so you can compare apples with apples. ... -
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BinCoMerge
Compare binary files in hexadecimal view
BinCoMerge is a binary files diff at byte resolution. It shows hexadecimal and raw ASCII data. BinCoMerge is seeking for common blocks from the bigger ones to the lower ones. It works by levels of minimum block sizes which are powers of 2. For example, with a file sizing between 16 KiB and 32 KiB, Bincomerge will firstly seek for all common blocks bigger than 16 KiB. Then, it will seek for all common bigger than 8 KiB, then 4KiB, 2KiB, 1024 Bytes... Because it works by levels, BinCoMerge... -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
The cnn-text-classification-tf repository by Denny Britz is a well-known educational implementation of convolutional neural networks for text classification using TensorFlow, aimed at helping developers and researchers understand how CNNs can be applied to natural language processing tasks. Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution... -
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cquery
C/C++ language server supporting multi-million line code base
C/C++ language server supporting multi-million line code base, powered by libclang. Emacs, Vim, VSCode, and others with language server protocol support. Cross-references, completion, diagnostics, semantic highlighting, and more. cquery is a highly-scalable, low-latency language server for C/C++/Objective-C. It is tested and designed for large codebases like Chromium. cquery provides accurate and fast semantic analysis without interrupting workflow. cquery implements almost the entire... -
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GDL - GNU Data Language, a free IDL (Interactive Data Language, see http://ittvis.com/idl/) compatible incremental compiler.
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Leo is an outlining-oriented project manager and browser. With Leo you can organize programs, web sites, URL's, pictures or any other kind of data. Leo is fully scriptable in Python. Leo is 100% pure Python. It requires the Qt gui toolkit.
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Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.
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pyhanlp
Chinese participle
pyhanlp is a Python interface for HanLP (Han Language Processing) that lets you use a mature Java-based NLP toolkit from Python workflows without rebuilding the underlying algorithms. It is commonly used for Chinese-language NLP tasks where you want production-grade tokenization and linguistic analysis, but still want the convenience of Python scripting. -
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Zhao
A compilation of "The Princely Party Relationship Network"
zhao is a repository that consolidates research, data, and insights related to Zhao, which is likely an individual’s research collection, notes, or curated resources on deep learning, AI, or computational topics (name and content context suggest specialized study). The project may include code examples, experiment results, references to academic papers, mathematical notes, and supporting scripts to explore specific ML methods, benchmarks, or theoretical findings. Because it aggregates... -
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A shell for using the methods of Contextual Logic to do qualitative data analysis, mathematical research on the theory underlying Conceptual Knowledge Processing, or learning Formal Concept Analysis. It uses the framework provided by the Tockit project..
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jsondata
Modular JSON by trees and branches, pointers and patches
The 'jsondata' package provides for the modular in-memory processing of JSON data by trees, branches, pointers, and patches. The main interface classes are: - JSONData - Core for RFC7159 based data structures. Provides modular data components. - JSONDataSerializer - Core for RFC7159 based data persistence. Provides modular data serialization. - JSONPointer - RFC6901 for addressing by pointer paths. Provides pointer arithmetics. - JSON Relative Pointer -... -
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FlexibleParser_Java
Group of multi-purpose Java-converted-from-C# parsing libraries.
FlexibleParser (Java) is currently formed by the following independent libraries/parts: - UnitParser. It allows to easily deal with a wide variety of situations involving units of measurement. - NumberParser. It provides a common framework for all the .NET numeric types. AUTHORSHIP & COPYRIGHT I, Alvaro Carballo Garcia (varocarbas), am the sole author of each single bit of this code. All the contents of this repository can be considered public domain. For more information about my... -
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FlexibleParser
FlexibleParser is a group of multi-purpose .NET parsing libraries.
FlexibleParser (C#/.NET) is currently formed by the following independent libraries/parts: - UnitParser. It allows to easily deal with a wide variety of situations involving units of measurement. - NumberParser. It provides a common framework for all the .NET numeric types. - DateParser. It enhances the default .NET date/time support. AUTHORSHIP & COPYRIGHT I, Alvaro Carballo Garcia (varocarbas), am the sole author of each single bit of this code. All the contents of this... -
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AgileReview
AgileReview - A OneClick Code Review Eclipse Plugin
AgileReview - A OneClick Code Review Eclipse Plugin -
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CSBB-v3.0
CSBB - Computational Suite for Bioinformaticians and Biologists
CSBB is a command line-based bioinformatics suite to analyze biological data acquired through varied avenues of biological experiments. CSBB is implemented in Perl, while it also leverages the use of R, java, python and ruby in background for specific modules. Major focus of CSBB is to allow users from biology and bioinformatics community, to get benefited by performing down-stream analysis tasks while eliminating the need to write programming code. -
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pivottablejs
Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook
PivotTable.js is a Javascript Pivot Table and Pivot Chart library with drag-drop interactivity, and it can now be used with Jupyter/IPython Notebook via the pivottablejs module. I first built PivotTable.js with a plan to build an in-browser data analysis tool, and got as far as one where you could load up a CSV file in the browser for display. Since then, however, the Jupyter project has gathered steam and now provides a browser-based interface to some of the most powerful data processing libraries in the world, so it makes sense to interface with it. -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. It includes data preprocessing helpers, training scripts, and configuration options so developers can experiment with different filter sizes, dropout rates, and hyperparameters to optimize performance for their dataset. -
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transitfeed
Tools for reading, validating & writing transit schedule info in GTFS
transitfeed helps to make public transit information projects more successful for agencies and other interested parties. It provides a library to help you parse, validate, and generate General Transit Feed Spec (GTFS) feed files. It contains several parts, including TransitFeed (Python package for reading, writing, and validating feeds); Feedvalidator (command line tool that checks a GTFS feed for problems); ScheduleViewer (application for exploring a feed on a map) and more; each with their... -
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HackMyResume
Generate polished résumés and CVs
Create polished résumés and CVs in multiple formats from your command line or shell. Author in clean Markdown and JSON, export to Word, HTML, PDF, LaTeX, plain text, and other arbitrary formats. Fight the power, save trees. Compatible with FRESH and JRS resumes. HackMyResume is a dev-friendly, local-only Swiss Army knife for resumes and CVs. Use it to generate HTML, Markdown, LaTeX, MS Word, PDF, plain text, JSON, XML, YAML, print, smoke signal, carrier pigeon, and other arbitrary-format... -
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Edward
A probabilistic programming language in TensorFlow
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming.
