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Top 15 Python Libraries for Data Science in 2017 – ActiveWizards: machine learning company – Medium. As Python has gained a lot of traction in the recent years in Data Science industry, I wanted to outline some of its most useful libraries for data scientists and engineers, based on recent experience.

Top 15 Python Libraries for Data Science in 2017 – ActiveWizards: machine learning company – Medium

And, since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity. Core Libraries. 1. NumPy (Commits: 15980, Contributors: 522) Presentations & Blog Posts — Conda documentation. Package, dependency and environment management for any language: Python, R, Ruby, Lua, Scala, Java, Javascript, C/ C++, FORTRAN Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them.

Presentations & Blog Posts — Conda documentation

It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software. Conda is included in Anaconda and Miniconda. Conda is also included in the Continuum subscriptions of Anaconda, which provide on-site enterprise package and environment management for Python, R, Node.js, Java, and other application stacks. Conda is also available on pypi, although that approach may not be as up-to-date. Miniconda is a small “bootstrap” version that includes only conda, Python, and the packages they depend on. Anaconda includes conda, conda-build, Python, and over 150 automatically installed scientific packages and their dependencies.

GitHub - amontalenti/elements-of-python-style: Goes beyond PEP8 to discuss what makes Python code feel great. A Strunk & White for Python. Sharing Your Side Projects Online and Making Your Github the Best Résumé It Ca. Moving from R to Python: The Libraries You Need to Know. Why the switch?

Moving from R to Python: The Libraries You Need to Know

One of my favorite parts of machine learning in Python is that it got the benefit of observing the R community and then emulating the best parts of it. I'm a big believer that a language is only as helpful as its libraries. So in this post I'm going to go over some critical packages that I use almost every time I work in R, and their counterpart(s) in Python. glm, knn, randomForest, e1071 (yes, this is actually a meaningful package's name) -> scikit-learn One thing that is a blessing and a curse in R is that the machine learning algorithms are generally segmented by package. Reshape/reshape2, plyr/dplyr -> pandas This was actually the subject of one of our first posts. pandas took the best parts of data munging in R and turned it into a Python package. Ggplot2 -> ggplot + seaborn + bokeh. 10 interesting Python modules to learn in 2016.

In this article I will give you an introduction into some Python modules I think of as useful.

10 interesting Python modules to learn in 2016

Naturally this can vary in your case but anyway it is a good idea to look at them, maybe you will use them in the future. I won't give a thorough introduction for each module because it would blow-up the boundaries of this article. However I will try to give you a fair summary on the module: what id does and how you can use it in the future if you are interested. For a detailed introduction you have to wait for either future articles which explain these libraries in more depth or you have to search the web to find resources. And there are plenty of good resources out there. ( In some languages static typing is the only way to go. Python 3.5 introduced the standard for type annotations where you can annotate your code to display what type the function parameters should have and what type the function returns for example. A gallery of interesting IPython Notebooks · ipython/ipython Wiki. Practical Programming for Total Beginners. Python Cheat Sheet by DaveChild. The One-Stop Shop for Big Data.

Runestone Interactive. Scrapekit: Python Library for Writing Web Scrapers. Google's Python Class - Educational Materials. Welcome to Google's Python Class -- this is a free class for people with a little bit of programming experience who want to learn Python.

Google's Python Class - Educational Materials

The class includes written materials, lecture videos, and lots of code exercises to practice Python coding. These materials are used within Google to introduce Python to people who have just a little programming experience. The first exercises work on basic Python concepts like strings and lists, building up to the later exercises which are full programs dealing with text files, processes, and http connections. The class is geared for people who have a little bit of programming experience in some language, enough to know what a "variable" or "if statement" is.

Beyond that, you do not need to be an expert programmer to use this material. This material was created by Nick Parlante working in the engEDU group at Google. Learn Python The Hard Way. Learn Python Programming. Python Weekly: A Free, Weekly Python E-mail Newsletter. Online Python Tutor - Learn programming by visualizing code execution. Welcome to Python.org. Python's IDLE editor: How to Use - by Dr A. Dawson.

Copyright Dr A Dawson 2005 - 2016 This file is: Python_Editor_IDLE.htm First created: Tuesday 8th March 2005, 7:28 PT, ADLast updated: Saturday 31st January 2015, 9:05 PT, AD.

Python's IDLE editor: How to Use - by Dr A. Dawson

Dive Into Python 3. You are here: • Dive Into Python 3 Dive Into Python 3 covers Python 3 and its differences from Python 2.

Dive Into Python 3

Compared to Dive Into Python, it’s about 20% revised and 80% new material. The book is now complete, but feedback is always welcome. Table of Contents (expand) Also available on dead trees! The book is freely licensed under the Creative Commons Attribution Share-Alike license. Python Special Edition 1. Think Python: How to Think Like a Computer Scientist.

How to Think Like a Computer Scientist by Allen B.

Think Python: How to Think Like a Computer Scientist

Downey. PythonBooks - Learn Python the easy way ! A Byte of Python. You have seen how you can reuse code in your program by defining functions once.

A Byte of Python

What if you wanted to reuse a number of functions in other programs that you write? As you might have guessed, the answer is modules. There are various methods of writing modules, but the simplest way is to create a file with a .py extension that contains functions and variables. Another method is to write the modules in the native language in which the Python interpreter itself was written. For example, you can write modules in the C programming language and when compiled, they can be used from your Python code when using the standard Python interpreter. A module can be imported by another program to make use of its functionality. Example (save as module_using_sys.py): import sys print('The command line arguments are:')for i in sys.argv: print i print '\n\nThe PYTHONPATH is', sys.path, '\n' How It Works First, we import the sys module using the import statement. 11.1.