Numpy/Scipy Distributions and Statistical Operations: Examples & Reference

Last updated:
Table of Contents

For ways to sample from lists and distributions: Numpy sampling: Reference and Examples

Generate random int from 0 up to N

All integers from 0 (inclusive) to N-1 have equal probability

Template: np.random.randint(0, N)

import numpy as np

# generate a single int from 0 to 100 (exclusive)
np.random.randint(0,100)
# >>> 56

# generate 5 random ints from 0 to 100 (exclusive)
np.random.randint(0,100, size=5)
# >>> [29,52,89,9,22,3]

Generate random int in interval

Template: np.random.randint(A, B)

Again, all integers from A (inclusive) up to B-1 have equal probability

import numpy as np

# generate single int from 10 to 15 (exclusive)
np.random.randint(10,15)
# 12

# generate 5 random ints from 10 to 15 (exclusive)
np.random.randint(10,15, size=5)
# >>> [10,13,12,13,14]

Generate random float between 0 and 1

Generate a random number (float) from 0 (including) up to 0.99999... (depending on precision).

All real numbers from 0 to 0.99999... are generated with equal probability.

import numpy as np

# this is equivalent to np.random.uniform(low=0,high=1)
np.random.sample()
# 0.30220482

Evaluate x on a gaussian PDF

In other words, P(x) | P ~ Gaussian(0,1)

import scipy.stats as st

dist = st.norm(loc=0.0,scale=1.0)
dist.pdf(1.645)
# 0.10311081109198142

Evaluate x on a Gaussian CDF

In other words, what percentage of the density is to the left of x?

import scipy.stats as st

dist = st.norm(loc=0.0,scale=1.0)
dist.cdf(1.645)
# 0.95001509446087862

linspace VS arange

Use linspace if you care about the number of elements, use arange if you care about the step size

Generate N samples, evenly spaced

In this case generate 11 samples evenly spaced from 0.0 to 1.0

import numpy as np

# template is: linspace(start, stop, num=50)
x = np.linspace(0.0,1.0,11)
# array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])

Generate samples, evenly spaced with step size

template: arange(start, stop, step)

import numpy as np

# template is: arange(start, stop, step)
x = np.arange(0.0,10.0,3)
# array([ 0.,  3.,  6.,  9.])

Generate numbers in logarithmic scale

template: logspace(start, stop, num=50)

Generate numbers in logarithmic scale: 0.001, 0.01, 0.1, 1.0, 10.0, etc.

import numpy as np

start=-3
stop=2

# num_elements is calculated using
# the range (start,stop) you chose above
num_elements = np.abs([start,stop]).sum() + 1

x = np.logspace(start,stop,num)
# array([  1.00000000e-03,   1.00000000e-02,   1.00000000e-01,
#        1.00000000e+00,   1.00000000e+01,   1.00000000e+02])

References

Dialogue & Discussion