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py3nes

Python 3 NES Emulator

py4e

Web site for www.py4e.com and source to the Python 3.0 textbook

py4fi2nd

Jupyter Notebooks and codes for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

py65

Simulate 6502-based microcomputer systems in Python.

pybot-a-chatbot-for-answering-python-queries-using-nlp

Pybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.

pycadl

Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"

pychromeless

Python Lambda Chrome Automation (naming pending)

pycon.tw

PyCon TW official website

pycparser

:snake: Complete C99 parser in pure Python

pydata-book-club

pyentropy

Entropy for Python

pyfcemu

A NES Emulator Written in Python

pyfinmod

Financial modeling with Python and Pandas

pyfolio

Portfolio and risk analytics in Python

pyfolio-1

Performance attribution analysis, value investment

pyfts

A open source library for Fuzzy Time Series in Python

pyfunceble

The tool to check the availability or syntax of domains, IPv4 or URL.

pygameoflife

Conway's Game of Life using python's matplotlib and numpy

pyklout

Klout Interface API using Python

pylda

A Latent Dirichlet Allocation implementation in Python.

pylivetrader

Python live trade execution library with zipline interface.

pylw

Python Lightweight Web framework

pymarketstore

Python driver for MarketStore

pymc3_datasciencela

PyMC3 tutorial for DataScience LA (January 2017)

pymc3_quickstart_guide

pyport

Portfolio strategy explorer in development

pyportfolioopt

Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

pyql

Cython QuantLib wrappers

pyshorttextcategorization

Various Algorithms for Short Text Mining

pyspark_kafka_stock_data_analysis

My experiments with PySpark Structured Streaming and Kafka to process near real-time data of stocks