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  1. Notes/

NumPy

Table of Contents
  • https://pabloinsente.github.io/intro-numpy-fundamentals
  • h5py - It lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want.

Sort rows of 2D matrix #

a = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])
a
# array([[9, 8, 7],
#        [6, 5, 4],
#        [3, 2, 1]])
a[:, a[0, :].argsort()]
# array([[7, 8, 9],
#        [4, 5, 6],
#        [1, 2, 3]])

Double advanced indexing #

a = np.array([[1, 2], [3, 4]])

idxs_1 = np.array([1])
idxs_2 = np.array([0])

# does not work
a[idxs_1][idxs_2] = np.array([9, 9])

print(a)  # [[1 2] [3 4]]

# works
a[idxs_1[idxs_2]] = np.array([9, 9])

print(a)  # [[1 2] [9 9]]