it-ebooks - A Little Book of Python for Multivariate Analysis

Although there are a number of ways of getting Python to your system, for a hassle free install and quick start using, I highly recommend downloading and installing Anaconda [https://www.continuum.io/downloads] by Continuum [https://www.continuum.io] , which is a Python distribution that contains the core packages plus a large number of packages for scientific computing and tools to easily update them, install new ones, create virtual environments, and provide IDEs such as this one, the Jupyter notebook [https://jupyter.org] (formerly known as ipython notebook).

This notebook was created with python 2.7 version. For exact details, including versions of the other libraries, see the %watermark directive below.

The first thing that you will want to do to analyse your multivariate data will be to read it into Python, and to plot the data. For data analysis an I will be using the Python Data Analysis Library [http://pandas.pydata.org] (pandas, imported as pd ), which provides a number of useful functions for reading and analyzing the data, as well as a DataFrame storage structure, similar to that found in other popular data analytics languages, such as R.

For example, the file http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data contains data on concentrations of 13 different chemicals in wines grown in the same region in Italy that are derived from three different cultivars. The data set looks like this:

There is one row per wine sample. The first column contains the cultivar of a wine sample (labelled 1, 2 or 3), and the following thirteen columns contain the concentrations of the 13 different chemicals in that sample. The columns are separated by commas, i.e. it is a comma-separated (csv) file without a header row.

The data can be read in a pandas dataframe using the read_csv() function. The argument header=None tells the function that there is no header in the beginning of the file.