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Python Visualization Libraries List. Ggplot 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. Seaborn Seaborn is a library for making attractive and informative statistical graphics in Python. Seaborn offers: matplotlib matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Bokeh Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Pygal pygal is a dynamic SVG charting library.

Python-igraph igraph is a collection of network analysis tools with the emphasis on efficiency, portability and ease of use. python-igraph is a python interface to the igraph. graph plotting functionality is provided by the Cairo library This is a part of community edited list at Pansop DSC Resources Additional Reading. Quora. How to Write a Spelling Corrector. The Python Challenge. Boltons — boltons 0.5.0 documentation.

A guide to analyzing Python performance « Huy Nguyen. While it’s not always the case that every Python program you write will require a rigorous performance analysis, it is reassuring to know that there are a wide variety of tools in Python’s ecosystem that one can turn to when the time arises. Analyzing a program’s performance boils down to answering 4 basic questions: How fast is it running?

Where are the speed bottlenecks? How much memory is it using? Where is memory leaking? Below, we’ll dive into the details of answering these questions using some awesome tools. Coarse grain timing with time Let’s begin by using a quick and dirty method of timing our code: the good old unix utility time. $ time python yourprogram.py real 0m1.028s user 0m0.001s sys 0m0.003s The meaning between the three output measurements are detailed in this stackoverflow article, but in short real - refers to the actual elasped time user - refers to the amount of cpu time spent outside of kernel sys - refers to the amount of cpu time spent inside kernel specific functions.

Machine Learning With Python. In this post, I'd like to share a few awesome machine-learning toolkits developed in python. Gensim is a free Python framework designed to automatically extract semantic topics from documents, as efficiently (computer-wise) and painlessly (human-wise) as possible. PyML – machine learning in Python PyML is an interactive object oriented framework for machine learning written in Python. PyML focuses on SVMs and other kernel methods. It is supported on Linux and Mac OS X. mlpy mlpy provides a wide range of state-of-the-art machine learning methods for supervisedand unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is Open Source, distributed under the GNU General Public License version 3. scikit-learn: machine learning in Python The Natural Language Toolkit (NLTK) is an open source Python library for Natural Language Processing.

Theano. 7 Python Libraries you should know about. , Reposted here. In my years of programming in Python and roaming around GitHub's Explore section, I've come across a few libraries that stood out to me as being particularly enjoyable to use. This blog post is an effort to further spread that knowledge. I specifically excluded awesome libs like requests, SQLAlchemy, Flask, fabricetc. because I think they're already pretty "main-stream".

If you know what you're trying to do, it's almost guaranteed that you'll stumble over the aforementioned. This is a list of libraries that in my opinion should be better known, but aren't. 1. pyquery (with lxml) pip install pyquery For parsing HTML in Python, Beautiful Soup is oft recommended and it does a great job. What immediately stands out is how fast lxml is. So either slow and easy to use or fast and hard to use, right? Wrong! Enter PyQuery Oh PyQuery you beautiful seductress: from pyquery import PyQuerypage = PyQuery(some_html)last_red_anchor = page('#container > a.red:last') 2. dateutil 3. fuzzywuzzy. Transforming Code into Beautiful, Idiomatic Python.