Generate White Noise in Python
You need to randomly generate a white noise time series in Python.
There are a couple ways to do this. We'll cover both.
Approach 1: Use the random module
You can use the standard
random module to generate the white noise series:
>>> import random >>> random.seed(14) >>> white_noise = [random.gauss(0.0, 1.0) for i in range(1000)]
Series has a
describe() method that allows you to verify (informally)
that this series has the statistical characteristics you'd expect of white noise.
>>> from pandas import Series >>> white_noise_series = Series(white_noise) >>> white_noise_series.describe() count 1000.000000 mean 0.004942 std 0.984969 min -2.848431 25% -0.635941 50% 0.006516 75% 0.651805 max 3.558710 dtype: float64
(See for example the empirical rule.)
Here's a plot:
>>> import matplotlib.pyplot as plt >>> plt.clf() >>> plt.plot(white_noise) [<matplotlib.lines.Line2D object at 0x127b20610>] >>> plt.show()
Approach 2: Use NumPy
You can do the same thing a little more simply using NumPy:
>>> import numpy as np >>> white_noise = np.random.normal(0.0, 1.0, 1000)
You can verify the statistics using a Pandas
Series as we did in approach 1 above. Plotting is
also the same as it was with approach 1.