Welcome to CVXPY — CVXPY 0.4.11 documentation
Welcome to CVXPY� Join the CVXPY mailing list and Gitter chat for the best CVXPY support! Try the new, improved CVXPY 1.0, available here. Please report any bugs you find! CVXPY is a Python-embedded modeling language for convex optimization problems. It allows you to express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers. For e
Welcome — Theano 0.9.0 documentation
Welcome¶ Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions. transparent use of a GPU – Perform data-intensive computations much faster than on a CPU. efficient symbolic differentiation – Theano does your d
ggplot | Home
ggplot from ŷhat ggplot is a plotting system for Python based on R's ggplot2 and the Grammar of Graphics. It is built for making profressional looking, plots quickly with minimal code. ggplot is easy to learn from ggplot import * ggplot(aes(x='date', y='beef'), data=meat) +\ geom_line() +\ stat_smooth(colour='blue', span=0.2) ggplot is fun ggplot(diamonds, aes(x='carat', y='price', color='cut')) +
Making 1 million requests with python-aiohttp
Apr 22, 2016 - by Paweł Miech - about: asyncio, aiohttp, python In this post I’d like to test limits of python aiohttp and check its performance in terms of requests per minute. Everyone knows that asynchronous code performs better when applied to network operations, but it’s still interesting to check this assumption and understand how exactly it is better and why it’s is better. I’m going to che
C++ Has Become More Pythonic
C++ has changed a lot in recent years. The last two revisions, C++11 and C++14, introduce so many new features that, in the words of Bjarne Stroustrup, “It feels like a new language.” It’s true. Modern C++ lends itself to a whole new style of programming – and I couldn’t help noticing it has more of a Python flavor. Ranged-based for loops, type deduction, vector and map initializers, lambda expres
6 Lesser Known Python Data Analysis Libraries
Posted on: 19-04-2016 Python offers a great environment and rich set of libraries to developers while working with data. There are tons of useful libraries out there for novice or experienced developers or analysts for helping out with processing or visualizing datasets. Some of the libraries are really popular and used by millions of developers, for example - Pandas, Numpy, Scikit-learn, NTLK etc
Comparing Python Clustering Algorithms
Comparing Python Clustering Algorithms¶(Why you should use HDBSCAN)¶There are a lot of clustering algorithms to choose from. The standard sklearn clustering suite has thirteen different clustering classes alone. So what clustering algorithms should you be using? As with every question in data science and machine learning it depends on your data. A number of those thirteen classes in sklearn are sp
Useful Pandas Snippets | Computers are for People
Even after almost two years of working with Pandas, the incredibly useful Python data analysis library, I still need to look up syntax for some common tasks. Finally got around to putting everything on a single “useful Pandas snippets” cheat sheet: these are essential tools for munging federal budget data.
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%load_ext watermark %watermark -a 'Sebastian Raschka' -v -d -p pandas
Essays Ken Reitz
Update: I developed Pipenv to solve these problems. Check it out. When developing Python applications today, it’s standard practice to have a requirements.txt file in the root of your repository. This file can be used in different ways, and typically takes one of these two forms: A list of top-level dependencies a project has, often without versions specified. A complete list of all dependencies a
Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting (GBM) in Python
Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting (GBM) in Python Overview Learn parameter tuning in gradient boosting algorithm using Python Understand how to adjust bias-variance trade-off in machine learning for gradient boosting Introduction If you have been using GBM as a ‘black box’ till now, maybe it’s time for you to open it and see, how it actually works! This artic
Welcome to Bokeh — Bokeh documentation
Bokeh documentation# Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming datasets. With Bokeh, you can create JavaScript-powered visualizations without writing any JavaScript yourself. Finding the right documentation resources# Bokeh’s documentation consists
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Growler/README.rst at dev · pyGrowler/Growler
GitHub - tienfuc/gdcmdtools: Google drive command-line tools
Chainer: A flexible framework for neural networks
Usage — delorean 1.0.0 documentation
GitHub - hugapi/hug: Embrace the APIs of the future. Hug aims to make developing APIs as simple as possible, but no simpler.
Infix Operators in Python
anaconda-notebook/Dockerfile at master · rothnic/anaconda-notebook
docker-images/Dockerfile at master · ContinuumIO/docker-images · GitHub