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matplotlib

matplotlib: plotting with Python

mwparserfromhell

A Python parser for MediaWiki wikicode

nltk_data

NLTK Data

nomicon

The Dark Arts of Advanced and Unsafe Rust Programming

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandas-datareader

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

polars

Dataframes powered by a multithreaded, vectorized query engine, written in Rust

pydrive

Google Drive API Python wrapper library

pysher

Pusher Client implemented in Python3

python-packaging-user-guide

Python Packaging User Guide

quickpipeline

Quickpipeline is a python module for quick preprocessing of features for further use in machine learning tasks

ringbuf

Lock-free SPSC FIFO ring buffer with direct access to inner data

robust-gbdt

Code for paper: Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance

ru_punkt

Russian language support for NLTK's PunktSentenceTokenizer

rust-snappy

Snappy compression implemented in Rust (including the Snappy frame format).

scikit-learn

scikit-learn: machine learning in Python

serde

Serialization framework for Rust

smartstring

Compact inlined strings for Rust.

sqlalchemy-clickhouse

statsmodels

Statsmodels: statistical modeling and econometrics in Python

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

websocket-client

websocket client for python

wide

A crate to help you go wide. By which I mean use SIMD stuff.

wurlitzer

Capture C-level stdout/stderr in Python

xam

:dart: Personal data science and machine learning toolbox

xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

xgboost.jl

XGBoost Julia Package