Ways to create simulated numerical data based on small sample (using Python)? For an internship, I'm being asked to simulate the electrical consumption for virtual appliances (e.g. fridges, freezers).

The company currently has a bunch of recorded second by second data from several different appliances. The data displays strong patterns. It has only two variables - time and (W) consumed. I will need to simulate a large amount of data to train the algorithm. What are the best ways to do this in Python? I have seen some modules that have functionality in that vein but they don't seem suitable for my data format.
You can find what you need in

numpy.random


For example, generating a 100x100 matrix with normal data:

data = numpy.random.normal(mean, sigma, shape=(100, 100))


see here

[EDIT]: If you want more advanced way of generating data similar to an existing dataset, you can look into GANs.