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scipy.stats.norm.pdf examples
It looks like you made a few small but significant errors. You either are choosing your x vector wrong or you swapped your stddev and mean. 14 Samples. y = P.normpdf( bins, mu, sigma) l = P.plot(bins, y, 'k--', linewidth=1.5) # # create a histogram by providing the bin edges (unequally spa To shift and/or scale the distribution use the loc and scale parameters. Specifically, norm.pdf(x, loc, scale) is identically equivalent to norm.pdf(y) / scale with y Note that on 32-bit machines, 2**1023 is the largest integer power of 2 which can be converted to a Python float. matplotlib.mlab. bivariate_normal (X, Y, 34 Samples. y = normpdf( bins, mu, sigma) line, = ax.plot(bins, y, 'r--') line.set_linewidth(1) ax.set_xlabel('Smarts') ax.set_ylabel('Probability') ax There's one in scipy.stats: >>> import scipy.stats >>> scipy.stats.norm(0, import math def normpdf(x, mean, sd): var = float(sd)**2 pi Description. Y = normpdf(X,mu,sigma) computes the pdf at each of the values in X using the normal distribution with mean mu and standard deviation sigma . To shift and/or scale the distribution use the loc and scale parameters. Specifically, norm.pdf(x, loc, scale) is identically equivalent to norm.pdf(y) / scale with y import matplotlib.pyplot as plt import numpy as np import matplotlib.mlab as mlab So, you can write plt.plot(range, norm.pdf(range, 0, 2)) . You got tricked by pythons integer division arithmetics! Here is some working code
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