How it works...

Installing the SciPy stack has been challenging historically due to compilation dependencies, including the need for Fortran. Thus, we don't recommend that you compile and install from source code, unless you feel comfortable doing such things.

Now, the better question is, what did you just install? We installed the latest versions of NumPy, SciPy, matplotlib, IPython, IPython Notebook, pandas, SymPy, and nose. The following are their descriptions:

• SciPy: This is a Python-based ecosystem of open source software for mathematics, science, and engineering and includes a number of useful libraries for machine learning, scientific computing, and modeling.
• NumPy: This is the foundational Python package providing numerical computation in Python, which is C-like and incredibly fast, particularly when using multidimensional arrays and linear algebra operations. NumPy is the reason that Python can do efficient, large-scale numerical computation that other interpreted or scripting languages cannot do.
• matplotlib: This is a well-established and extensive 2D plotting library for Python that will be familiar to MATLAB users.
• IPython: This offers a rich and powerful interactive shell for Python. It is a replacement for the standard Python Read-Eval-Print Loop (REPL), among many other tools.
• Jupyter Notebook: This offers a browser-based tool to perform and record work done in Python with support for code, formatted text, markdown, graphs, images, sounds, movies, and mathematical expressions.
• pandas: This provides a robust data frame object and many additional tools to make traditional data and statistical analysis fast and easy.
• nose: This is a test harness that extends the unit testing framework in the Python standard library.