Nagare is a free and open-source web framework for developing web applications in Stackless Python. This allows web applications to be developed in much the same way as desktop applications, for rapid application development.
Nagare is a components based framework: a Nagare application is a composition of interacting components each one with its own state and workflow kept on the server.
Each component can have one or several views that are composed to generate the final web page. This enables the developers to reuse or write highly reusable components easily and quickly.
Nagare is also a continuation-based web framework which enables to code a web application like a desktop application, with no need to split its control flow in a multitude of controllers and with the automatic handling of the back, fork and refresh actions from the browser.
Its component model and use of the continuation come from the famous Seaside Smalltalk framework.
Furthermore, Nagare integrates the best tools and standard from the Python world. For example:
WSGI: binds the application to several possible publishers,
lxml: generates the DOM trees and brings to Nagare the full set of XML features (XSL, XPath, Schemas …),
setuptools: installs, deploys and extends the Nagare framework and the Nagare applications too,
Anaconda is a freemium open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment
The condacommand is the primary interface for managing installations of various packages. It can:
Query and search the Anaconda package index and current Anaconda installation.
Create new conda environments.
Install and update packages into existing conda environments.
Anaconda Cloud is where data scientists share their work. You can search and download popular Python and R packages and notebooks to jumpstart your data science work.
Anaconda is the world’s most popular Python data science platform. Anaconda, Inc. continues to lead open source projects like Anaconda, NumPy and SciPy that form the foundation of modern data science. Anaconda’s flagship product, Anaconda Enterprise, allows organizations to secure, govern, scale and extend Anaconda to deliver actionable insights that drive businesses and industries forward.
What is Seaborn? Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
A dataset-oriented API for examining relationships between multiple variables
Specialized support for using categorical variables to show observations or aggregate statistics
Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
Automatic estimation and plotting of linear regression models for different kinds dependent variables
Convenient views onto the overall structure of complex datasets
High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations
Concise control over matplotlib figure styling with several built-in themes
Tools for choosing color palettes that faithfully reveal patterns in your data
Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.
PyShark is a wrapper for the Wireshark CLI interface, tshark, so all of the Wireshark decoders are available to PyShark!
Python wrapper for tshark, allowing python packet parsing using wireshark dissectors.
There are quite a few python packet parsing modules, this one is different because it doesn't actually parse any packets, it simply uses tshark's (wireshark command-line utility) ability to export XMLs to use its parsing.
This package allows parsing from a capture file or a live capture, using all wireshark dissectors you have installed. Tested on windows/linux.
Example Code for Reading a File
import pyshark cap = pyshark.FileCapture('/tmp/mycapture.cap') cap >>> <FileCapture /tmp/mycapture.cap> print cap Packet (Length: 698) Layer ETH: Destination: aa:bb:cc:dd:ee:ff Source: 00:de:ad:be:ef:00 Type: IP (0x0800) Layer IP: Version: 4 Header Length: 20 bytes Differentiated Services Field: 0x00 (DSCP 0x00: Default; ECN: 0x00: Not-ECT (Not ECN-Capable Transport)) Total Length: 684 Identification: 0x254f (9551) Flags: 0x00 Fragment offset: 0 Time to live: 1 Protocol: UDP (17) Header checksum: 0xe148 [correct] Source: 192.168.0.1 Destination: 192.168.0.2
FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. FastText builds on modern Mac OS and Linux distributions. Since it uses C++11 features, it requires a compiler with good C++11 support.
Steps for Installing
- git clone https://github.com/facebookresearch/fastText.git - cd fastText - make
Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool.
What is text classification? The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed.
Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).
Django CMS is a modern web publishing platform built with Django, the web application framework “for perfectionists with deadlines”.
django CMS offers out-of-the-box support for the common features you’d expect from a CMS, but can also be easily customized and extended by developers to create a site that is tailored to their precise needs.
Integrate Django applications painlessly; build sophisticated sites with easy-to-use tools.
Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python.
Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build an interactive web-based application. Dash is simple enough that you can bind a user interface around your Python code in an afternoon.
Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile-ready.
1) Lightweight - Dash apps require very little boilerplate to get started: An app like this weighs in at just 40 lines of pure Python. Dash provides direct control 2) Direct Control - Dash provides a simple interface for tying UI controls, like sliders, dropdowns, and graphs, with your Python data analysis code. Dash is Composable and Modular 3) Completely Customizable - Every aesthetic element of a Dash app is customizable. Dash apps are built and published in the Web, so the full power of CSS is available.