Tag Archives: NumPy

Routes Python for Data Analysis Wrangling with Pandas NumPy

Python, a high level language developed by Guido van Rossum, is known for its easy readability. The core philosophies of the language are simple – simplicity over complexity; beauty over ugliness, explicit over implicit and other similar aphorisms. The most important philosophy of the language is “Readability Counts”, which means that the syntaxes and codes […]


Binglide: Visual reverse engineering tool showing some of the current techniques.

via wapiflapi/binglide · GitHub.

binglide is a visual reverse engineering tool. It is designed to offer a quick overview of the different data types that are present in a file. The screenshot bellow shows a small portion of the php5 binary that has a weird signature pattern:

Kayak: Library for Deep Neural Networks

via HIPS/Kayak · GitHub.

This is a library that implements some useful modules and provides automatic differentiation utilities for learning deep neural networks. It is similar in spirit to tools like Theano and Torch. The objective of Kayak is to be simple to use and extend, for rapid prototyping in Python. It is unlikely to be faster than these other tools, although it is competitive and sometimes faster in performance when the architectures are highly complex. It will certainly not be faster on convolutional architectures for visual object detection and recognition tasks than, e.g., Alex Krizhevsky’s CUDA Convnet or Caffe. The point of Kayak is to be able to experiment in Python with patterns that look a lot like what you’re already used to with Numpy. It makes it easy to manage batches of data and compute gradients with backpropagation.

Enthought Canopy Python Package Index

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An Enthought Canopy subscription provides Python 2.7.6, easy installation and updates of over 250 pre-built and tested scientific and analytic Python packages such as NumPy, Pandas, SciPy, Matplotlib, and IPython, PLUS an integrated analysis environment, graphical debugger, and online Python Essentials and Python Development Tools training courses. Academic users may request a free Canopy Academic license.

Canopy Express provides over 100 of these core packages free to all users. See the table below for a list of packages available in Canopy and Canopy Express (via the Canopy Package Manager). Both versions are available for Windows, Mac, and Linux users.

Scientific Computing with Hy: Linear Regressions

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A tutorial adventure with Python 3, cytoolz, NumPy, and matplotlib

This tutorial was adapted from the Linear Regression tutorial for the Clojure scientific computing platform Incanter. It has been significantly modified to take advantage of the following, however:

  • Python 3
  • Hy – Clojure-Flavoured Python
  • cytoolz – a Cython implementation of the famous pytoolz suite
  • NumPy – a full-featured numeric library for Python
  • matplotlib – a Python mathematical plotting library, originally inspired by MATLAB

Okay, to be honest, we don’t really use cytoolz in the linear regression exercise; but we really wanted to 🙂 The opportunity just didn’t arise. Seriously, though, cytoolz some some pretty sweet stuff, and provides fast utility functions you may be missing if you’re coming (or returning!) to Python after living in the land of functional programming. In particular, it borrows heavily from the Clojure API (which is, quite bluntly, awesome).

Artificial Dataset Generation for Machine Learning with Python and Numpy / Theano

Click to Read: In this quick post I just wanted to share some Python code which can be used to benchmark, test, and develop Machine Learning algorithms with any size of data.

Really Fast Analysis with NumPy and MongoDB.

Click to Read: NumPy arrays combine the speed of C with the convenience of Python. It is the fundamental package for scientific and statistical computing in Python. MongoDB’s scale, speed, and flexibility make it ideal for storing large amounts of data. However, the official MongoDB driver is not optimized for loading MongoDB documents into NumPy arrays. Enter “Monary”, which allows you to easily examine and manipulate data using NumPy arrays. We will explore how Monary can accelerate your scientific analysis while providing you with the scale and flexibility of MongoDB and the ease of Python.