import plotly.plotly as py import plotly.graph_objs as go import numpy as np
np.random.randn() allows you to sample from the normal distribution. It can take an integer or a
shape for its input.
import plotly.plotly as py import plotly.graph_objs as go num_of_points = 200 normal_pts_x = np.random.randn(num_of_points) normal_pts_y = np.random.randn(num_of_points) trace1 = go.Scatter( x=normal_pts_x, y=normal_pts_y, mode='markers', marker = dict( size=11, color=[normal_pts_x[i] * normal_pts_y[i] for i in range(len(normal_pts_x))], colorscale='Portland' ), name='Normally Sampled Points' ) py.iplot([trace1], filename='numpy-randn')
Help on built-in function randn: randn(...) randn(d0, d1, ..., dn) Return a sample (or samples) from the "standard normal" distribution. If positive, int_like or int-convertible arguments are provided, `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled with random floats sampled from a univariate "normal" (Gaussian) distribution of mean 0 and variance 1 (if any of the :math:`d_i` are floats, they are first converted to integers by truncation). A single float randomly sampled from the distribution is returned if no argument is provided. This is a convenience function. If you want an interface that takes a tuple as the first argument, use `numpy.random.standard_normal` instead. Parameters ---------- d0, d1, ..., dn : int, optional The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned. Returns ------- Z : ndarray or float A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from the standard normal distribution, or a single such float if no parameters were supplied. See Also -------- random.standard_normal : Similar, but takes a tuple as its argument. Notes ----- For random samples from :math:`N(\mu, \sigma^2)`, use: ``sigma * np.random.randn(...) + mu`` Examples -------- >>> np.random.randn() 2.1923875335537315 #random Two-by-four array of samples from N(3, 6.25): >>> 2.5 * np.random.randn(2, 4) + 3 array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random