Monthly Archives: July 2015

PyCharm with Tortoise SVN

If you’re like me, you’ve been coding in Python for a few years but non of your work has involved team efforts where source/version control would have been key. So much of the code is cobbled together, re-produced, recycled, sometimes documented through comments, often not… the list goes on.

Well, I’m breaking with all these bad habits. I’ve heard and read so much about PyCharm, I’m giving it a try, and I’m also starting with SVC. Getting Jetbrains’ Pycharm up and running is easy. You get it here (https://www.jetbrains.com/pycharm/). It claims “Best Python IDE”.

Then I installed Tortoise SVN, a client for Apache Subversion (SVN), available here (http://tortoisesvn.net/). I went with version 1.8.11.

Finally, once both are installed, in PyCharm, under File >> Settings => Version Control => You select the directory you’d like to put under version control and then pick your version control software (SVN). Done!

pycharm_tortoise

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Discovering Pandas (Python)

Py_data_AnalSo I’ve added up another book to my Python library. “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython”.

So far, it promises to be a good introduction to data science using Python tools. – The first thing you need to do to be able to follow with the exercises in the book is get pandas installed. Which is easy. The link is right here.

Pandas leverages Numpy, which I was familiar with in that I had heard of it. But I didn’t realize the significance of Numpy in terms of user community or the size of the package. In fact, I assumed it might ship as part of the standard library. Wrong!

But you don’t have to install Pandas or Numpy separately. I’ve just discovered pip. Again, likely due to the fact that I spend most of my Python time in the Standard Library. Once you have pip running with your Python installation of preference, getting new sotware is as easy as running “sudo apt-get install” in Linux.

You just type “pip install pandas” and let it download the necessary packages from the web and install them locally. If you’ve already downloaded a Python wheel (WHL), you can also point pip at that and install from the local file. For more about Wheels, which are replacing Eggs, go here.

Trying to install pandas though, I was at first getting an error related to “Windows C++ 10.0” (link). That turned out to be due to my trying to install 32-bit Numpy. So I did end up downloading a 64-bit version for my 64-bit Python 3.3 and then used pip to install from that WHL. The whole installation took no more than 10 minutes. Now, I’m ready to play with pandas.

numpy_via_pip

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