Standard Cauchy distribution with mode = 0.
Also known as the Lorentz distribution.
Parameters :  size : int or tuple of ints


Returns :  samples : ndarray or scalar

Notes
The probability density function for the full Cauchy distribution is
and the Standard Cauchy distribution just sets and
The Cauchy distribution arises in the solution to the driven harmonic oscillator problem, and also describes spectral line broadening. It also describes the distribution of values at which a line tilted at a random angle will cut the x axis.
When studying hypothesis tests that assume normality, seeing how the tests perform on data from a Cauchy distribution is a good indicator of their sensitivity to a heavytailed distribution, since the Cauchy looks very much like a Gaussian distribution, but with heavier tails.
References
[R168]  NIST/SEMATECH eHandbook of Statistical Methods, “Cauchy Distribution”, http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm 
[R169]  Weisstein, Eric W. “Cauchy Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/CauchyDistribution.html 
[R170]  Wikipedia, “Cauchy distribution” http://en.wikipedia.org/wiki/Cauchy_distribution 
Examples
Draw samples and plot the distribution:
>>> s = np.random.standard_cauchy(1000000)
>>> s = s[(s>25) & (s<25)] # truncate distribution so it plots well
>>> plt.hist(s, bins=100)
>>> plt.show()