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<a href="http://support.microsoft.com/kb/826772" rel="noreferrer">NORMSINV</a> (mentioned in a comment) is the inverse of the CDF of the standard normal distribution. Using <code>scipy</code>, you can compute this with the <code>ppf</code> method of the <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html" rel="noreferrer"><code>scipy.stats.norm</code></a> object. The acronym <code>ppf</code> stands for <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda362.htm#PPF" rel="noreferrer">percent point function</a>, which is another name for the <a href="https://en.wikipedia.org/wiki/Quantile_function" rel="noreferrer">quantile function</a>. <pre><code>In [20]: from scipy.stats import norm In [21]: norm.ppf(0.95) Out[21]: 1.6448536269514722 </code></pre> Check that it is the inverse of the CDF: <pre><code>In [34]: norm.cdf(norm.ppf(0.95)) Out[34]: 0.94999999999999996 </code></pre> By default, <code>norm.ppf</code> uses mean=0 and stddev=1, which is the "standard" normal distribution. You can use a different mean and standard deviation by specifying the <code>loc</code> and <code>scale</code> arguments, respectively. <pre><code>In [35]: norm.ppf(0.95, loc=10, scale=2) Out[35]: 13.289707253902945 </code></pre> If you look at the source code for <code>scipy.stats.norm</code>, you'll find that the <code>ppf</code> method ultimately calls <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.special.ndtri.html" rel="noreferrer"><code>scipy.special.ndtri</code></a>. So to compute the inverse of the CDF of the standard normal distribution, you could use that function directly: <pre><code>In [43]: from scipy.special import ndtri In [44]: ndtri(0.95) Out[44]: 1.6448536269514722 </code></pre>
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