We give a brief overview of the core of the SciPy tool stack: numpy, scipy, matplotlib and IPython. We then interactively explore different components of SciPy, such as interpolation, optimization, integration, statistics and image processing. To conclude, we briefly discuss some external tools that form part of the eco-system, such as nose and Sphinx.
Ability to edit and run Python scripts, familiarity with NumPy arrays
IPython, NumPy (>= 1.5.1), SciPy (>= 0.8.0), matplotlib 
Make sure that you can execute the following commands before the tutorial:
import numpy print numpy.__version__ import scipy print scipy.__version__ import matplotlib.pyplot as plt plt.plot([1, 2, 3]) plt.show()
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