Numpy Arrays in Python

Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!

Tips and Traps¶

The Pythonic way of checking whether a collection (string, list, set, dict, etc.) coll is non-empty is to use if coll. However, do NOT use if arr to check whether a numpy array is non-empty or not. Instead, you shoule use arr.size >0 to check whether a numpy array is non-empty or not.
bottleneck is a collection of fast, NaN-aware NumPy array functions written in C.

from operator import itemgetter
import numpy as np

Construct a numpy array from a list.

arr = np.array([0, 1, 2, 3, 4, 5, 6])
arr

array([0, 1, 2, 3, 4, 5, 6])

Join a sequence of arrays along a new axis using numpy.stack.

np.stack(
    [
        [1, 2, 3],
        [4, 5, 6],
    ],
    axis=0,
)

np.stack(
    [
        [1, 2, 3],
        [4, 5, 6],
    ],
    axis=1,
)

arr[0]

0

arr[1:4]

array([1, 2, 3])

itemgetter(2, 3, 5)(arr)

(2, 3, 5)

Reverse the order of elements in an array along the given axis. The shape of the array is preserved, but the elements are reordered.

Note: numpy.where works on both numpy.ndarray and list, however, it doesn’t work on a generator/iterator.

arr = np.array([9, 80, 1, 2, 7, 1000])
arr >= 30

array([False, True, False, False, False, True])

np.where(arr >= 30)

(array([1, 5]),)

np.where(arr >= 30)[0].min()

1

np.min(np.where(arr >= 30))

1

arr.sum()

21

arr.prod()

0

np.NaN, np.NAN and np.nan are the same.

bottleneck is a collection of fast, NaN-aware NumPy array functions written in C.

np.NaN is np.NAN

True

np.NAN is np.nan

True

mat = np.matrix(
    [
        [1, 2, 3],
        [4, 5, 6],
    ]
)

mat.diagonal()

matrix([[1, 5]])

np.diag(mat)

array([1, 5])