# Python Installation¶

## Setup¶

The language itself consists of the Python interpreter itself, and a rather complete set of modules (one says, “Python comes with batteries included”).

In the training we might look into external modules, such as NumPy and/or Pandas, but installing these is not the focus of the current topic 2.

Note

While the training material covers Python versions 2 and 3 to a large extent, time has come to consider version 2 obsolete.

Please choose Python 3 when installing!

For the matter of the training, for diadactical purposes, I suggest we use the standard Python installation,

• Download Windows installer from here, and go through the installation process. Take care to check the “add python to path” box.

(For Linuxers, Python usually comes as part of your favorite distribution and is already installed.)

• If there is the need to install packages that are not contained in Python’s own set of packages, we will install them using pip.

Data scientists often use a distribution named Anaconda which brings the standard Python installation and a large set of set of pre-packaged external extensions 1 . If you are already familiar with Anaconda, then I don’t object.

## Programming Environment¶

As we are all programmers to a certain extent, we know what tools to use. For example, the training does not dictate which IDE (or editor) a participant uses. The exercises are not voluminous enough to justify that, after all; a simple text editor like Nodepad++ is sufficient.

That said, here’s a list of IDEs/editors that are frequently used for Python programming. It is in no particular order, and far from being complete.

• Visual Studio Code. See here for more.

• PyCharm. I frequently see people use it, so it cannot be all that bad.

• Eclipse and PyDev. Definitely a heavy weight (regarding memory footprint at least) among IDEs, Eclipse knows how to handle Python.

• Spyder. It is used by data scientists a lot. Running code in it feels like a Jupyter Notebook execution in that there are seemingly strange “cell” like dependencies. (Take this into account when you decide to go with it.)

• Emacs. (I had to say that.) Your trainer will use it to do occasional live hacking demos. Watching someone use it is ok, but learning how to use it requires a nontrivial amount of patience.

Footnotes

1

Anaconda also packages the R language which is also heavily used by data scientists.

2

See Python Package Index and Virtual Environments for how to install external packages.