Python for statistics and data analysis

First, try it out

You can run Python in your browser without installing anything onto your computer:

If you want to install Python and a collection of libraries for data analysis onto your computer, one relatively simple approach is to use anaconda. See below for other options.

Learning Python

There are many excellent Python tutorials on the web. Some of our favorites are:

About Python

Python is a general-purpose programming language. It was not developed specifically as a language for data management and analysis, but it works very well for that purpose.

In contrast, Matlab, R, and Julia are "domain specific languages" (DSL's). They were specifically designed for scientific computing and data analysis.

Python implementations exist for many platforms and hardware configurations. The core language and libraries behave in a highly consistent way across a variety of platforms (Windows, Linux, MacOS, many others).

Python is not an exotic or revolutionary language. It should seem obvious and natural to anyone familiar with generic "pseudo-code". It has a few distinguishing features, most notably, the use of indentation rather than braces to define code blocks.

Python libraries

Python itself is not very useful for scientific work. However by using Python together with some powerful libraries, many things become possible.

Here are the libraries that we discuss in our workshops:

Numerical performance

Python is an interpreted language like R and Matlab. It is possible to write code that runs reasonably quickly, but it is also possible to write code that performs poorly. There are many ways to write Python code that both performs well and is easy to read and maintain. One general principle for achieving good performance is to take advantage of libraries like Numpy that are implemented in C.

It is possible to write C extensions to Python. This is made particularly easy by using a tool called Cython. However, most users will rarely if ever need to use Cython.

Python 2 versus Python 3

The Python community is currently progressing through a transition from the "2 series" Python implementations to the "3 series" Python implementations. Python 3 scripts may not run in Python 2, and Python 2 scripts may not run in Python 3. Nearly all libraries have been substantially modified to work in both Python 2 and Python 3, and this process is now largely complete.

There are many small changes and a few large changes from Python 2 to Python 3. Many of these changes are of little consequence to most users of Python for scientific purposes.

Tools for working with Python

The base Python interpreter has a simple command-line interface. Many powerful tools for working with Python scripts and interacting with the interpreter have been developed. These tools are surveyed here.

Python installation

Installing from a distribution

Installing Python and its scientific libraries from scratch is possible, but is challenging and time consuming. The easiest way to get up and running quickly with Python is to either use a cloud service, or to install a bundled distribution of the entire scientific Python stack. Here are some Python distributions that are suitable for data analytic work:

Installing Python from source

If you use Linux or MacOS and have a working compiler like gcc in your system, you can install core Python by first downloading the source tarball (pythonxxx.tar.gz) from, then following these steps (change pythonxxx to the specific file name):

tar --xz -xvf pythonxxx.tar.xz cd pythonxxx ./configure make make install

Note that you need to have root access on the machine to do this. If you do not have root access, it is possible to install everything in an arbitrary location by replacing the configure step above with

./configure --prefix=/path/to/location

If you install Python in a non-standard location you will need to use the full path to the executable to launch it (or make an alias in your .bashrc or other shell configuration file).

Next you need to install each of the core libraries. Briefly, download an archive file for each library, e.g. numpyxxx.tar.gz, then

gunzip numpyxxx.tar.gz tar -xvf numpyxxx.tar cd numpyxxx python install

Note that the gunzip and tar steps may differ depending on the archive format (.tar.gz, .tar.bz2, .tar.xz, .zip). The python command in step 4 should invoke whichever Python installation on your system you want to link to the libraries (i.e. if you have installed several Pythons, you need to use the right one here).

An alternative way to install the libraries (but not Python itself) is to use pip. To install numpy, for example, using pip, type pip install numpy into the command line of your computer. Note that you may have several Python distributions installed on your machine. Each distribution will have its own directory tree and libraries, and its own copy of pip. You need to use the pip that belongs to the python installation you intend to work with when installing libraries to be accessed by that installation.

If you are using Anaconda Python, you can use the command conda to install packages, e.g. conda install numpy.

vitualenv is a utility that allows you to easily maintain multiple independent Python environments. It is also useful if you want to maintain only a single environment in a non-standard location.

Installing on Windows using binaries

If you use Windows and have administrator access to your system, you can scour the web for the core language and various libraries in self-extracting executable (*.exe) format, and install them by running them (double click on the *.exe).

A good resource for up-to-date windows packages for Python is here

Using Python for data analysis in the cloud

Several services allow you to use Python without installing any software on your own machine. These services provide servers on which the scientific Python stack is installed. Users connect to these services through a web interface, so all you need to use them is a computer with a web browser. Many people see this approach as the future of scientific computing, but the current generation of cloud computing services for scientific Python has some limitations. Nevertheless, it is usable, especially for training and learning.