NumPy: manipulating numerical data

NumPy is the key Python package for creating and manipulating (multi-dimensional) numerical arrays. NumPy arrays are also the most important data objects in \(\omega radlib\). It has become a convention to import NumPy as follows:

[1]:
import numpy as np

Creating and inspecting NumPy arrays

The ndarray, a numerical array, is the most important data type in NumPy.

[2]:
a = np.array([0, 1, 2, 3])
print(a)
print(type(a))
[0 1 2 3]
<class 'numpy.ndarray'>

Inspect the shape (i.e. the number and size of the dimensions of an array).

[3]:
print(a.shape)
# This creates a 2-dimensional array
a2 = np.array([[0, 1], [2, 3]])
print(a2.shape)
(4,)
(2, 2)

There are various ways to create arrays: from lists (as above), using convenience functions, or from file.

[4]:
# From lists
a = np.array([0, 1, 2, 3])
print("a looks like:\n%r\n" % a)

# Convenience functions
b = np.ones(shape=(2, 3))
print("b looks like:\n%r\nand has shape %r\n" % (b, b.shape))

c = np.zeros(shape=(2, 1))
print("c looks like:\n%r\nand has shape %r\n" % (c, c.shape))

d = np.arange(2, 10)
print("d looks like:\n%r\nand has shape %r\n" % (d, d.shape))

e = np.linspace(0, 10, 5)
print("e looks like:\n%r\nand has shape %r\n" % (e, e.shape))
a looks like:
array([0, 1, 2, 3])

b looks like:
array([[1., 1., 1.],
       [1., 1., 1.]])
and has shape (2, 3)

c looks like:
array([[0.],
       [0.]])
and has shape (2, 1)

d looks like:
array([2, 3, 4, 5, 6, 7, 8, 9])
and has shape (8,)

e looks like:
array([ 0. ,  2.5,  5. ,  7.5, 10. ])
and has shape (5,)

You can change the shape of an array without changing its size.

[5]:
a = np.arange(10)
b = np.reshape(a, (2, 5))
print("Array a has shape %r.\nArray b has shape %r" % (a.shape, b.shape))
Array a has shape (10,).
Array b has shape (2, 5)

Indexing and slicing

You can index an ndarray in the same way as a list:

[6]:
a = np.arange(10)
print(a)
print(a[0], a[2], a[-1])
[0 1 2 3 4 5 6 7 8 9]
0 2 9

Just follow your intuition for indexing multi-dimensional arrays:

[7]:
a = np.diag(np.arange(3))
print(a, end="\n\n")

print("Second row, second column: %r\n" % a[1, 1])

# Setting an array item
a[2, 1] = 10  # third line, second column
print(a, end="\n\n")

# Acessing a full row
print("Second row:\n%r" % a[1])
[[0 0 0]
 [0 1 0]
 [0 0 2]]

Second row, second column: 1

[[ 0  0  0]
 [ 0  1  0]
 [ 0 10  2]]

Second row:
array([0, 1, 0])

Slicing is just a way to access multiple array items at once:

[8]:
a = np.arange(10)
print(a, end="\n\n")

print("1st:", a[2:9])
print("2nd:", a[2:])
print("3rd:", a[:5])
print("4th:", a[2:9:3])  # [start:end:step]
print("5th:", a[a > 5])  # using a mask
[0 1 2 3 4 5 6 7 8 9]

1st: [2 3 4 5 6 7 8]
2nd: [2 3 4 5 6 7 8 9]
3rd: [0 1 2 3 4]
4th: [2 5 8]
5th: [6 7 8 9]

Get further info on NumPy arrays here!