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Servers - Platforms. Cloud file storage is also available via Chwala/iRony components of Kolab with the capability to integrate various storage backends.

Servers - Platforms

Files are accessible via storage layer access options, WebDAV and Kolab web interface integrating Roundcube. Tarsnap is not recommended on PRISM Break due to its strict copyright on the client that makes it difficult to replace the service in the event Tarsnap is shut down. BitTorrent Sync, MEGA, and SpiderOak are services that are built on either partially or fully proprietary software.

They will not be recommended on PRISM Break until they open source the entirety of their codebase. With closed source software, you need to have 100% trust in the vendor because there's nothing except for their morality in the way of them leaking your personal information. Another alternative to cloud storage is local backup with external hard drives and USB flash drives.

Introduction to Cloud Infrastructure Technologies. Buying Choices: Data Science at the Command Line: Facing the Future with Time-Tested Tools. Feed. Tony Robbins (tonyrobbins) AJ Monte (@theoptionoracle) Chmod Man Page. Change access permissions, change mode.

chmod Man Page

Syntax chmod [Options]... Mode [,Mode]... file... chmod [Options]... Numeric_Mode file... chmod [Options]... --reference=RFile file... Options -f, --silent, --quiet suppress most error messages -v, --verbose output a diagnostic for every file processed -c, --changes like verbose but report only when a change is made --reference=RFile use RFile's mode instead of MODE values -R, --recursive change files and directories recursively --help display help and exit --version output version information and exit chmod changes the permissions of each given file according to mode, where mode describes the permissions to modify. Numeric mode: From one to four octal digits Any omitted digits are assumed to be leading zeros. Packages. Summarizing Data in Python with Pandas.

Like many, I often divide my computational work between Python and R.

Summarizing Data in Python with Pandas

For a while, I've primarily done analysis in R. And with the power of data frames and packages that operate on them like reshape, my data manipulation and aggregation has moved more and more into the R world as well. Perhaps my favorite tool of all has been plyr, which allows you to easily split up a data set into subsets based on some criteria, apply a function or set of functions to those pieces, and combine those results back together (a.k.a. "split-apply-combine"). For example, I often use this to split up a data set by treatment, calculate some summary stats for each treatment, and put these statistics back together for comparison. Analyzing Python Job Market with Pandas.

In this post I’m doing some simple data analytics of job market for python programmes.

Analyzing Python Job Market with Pandas

I will be using Python Pandas My dataset comes from reed.co.uk - UK job board. I created simple Scrapy project that crawls reed.co.uk python job section, and parses all ads it finds. While crawling I set high download delay of 2 seconds, low number of max concurrent requests per domain and added descriptive user agent header linking back to my blog. Pandas: powerful Python data analysis toolkit — pandas 0.16.1 documentation. Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.

pandas: powerful Python data analysis toolkit — pandas 0.16.1 documentation

It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. Scraping and Analyzing Baseball Data with R. We get a lot of emails from people who are interested in analyzing sports data.

Scraping and Analyzing Baseball Data with R

The usual suspects are moneyball types--SABRmetrics enthusiasts with a love of baseball and a penchant for R. Luckily for us, baseball data is very accessible. 11 Python Libraries You Might Not Know. There are tons of Python packages out there.

11 Python Libraries You Might Not Know

So many that no one man or woman could possibly catch them all. PyPi alone has over 47,000 packages listed! Recently, with so many data scientists making the switch to Python, I couldn't help but think that while they're getting some of the great benefits of pandas, scikit-learn, and numpy, they're missing out on some older yet equally helpful Python libraries. In this post, I'm going to highlight some lesser-known libraries. Even you experienced Pythonistas should take a look, there might be one or two in there you've never seen! 1) delorean Delorean is a really cool date/time library.

Rodeo: A data science IDE for Python. Today we're excited to introduce a new project: Rodeo.

Rodeo: A data science IDE for Python

Rodeo is an IDE that's built expressly for doing data science in Python. Think of it as a light weight alternative to the IPython Notebook. We've been using it for projects internally, but today we're releasing it to the public! Bastille. Chemtrails.

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