More NumPy functions

NumPy has an enormous range of functionality, and we can only scratch the surface here.

However, here are a few examples of NumPy code for various tasks that you might commonly want to do with your code.

Creating Arrays

NumPy provides various functions for generating arrays:

np.arange()

np.arange() can be used to generate arrays that contain sequences of integers.

np.arange(5) # generate an array containing a sequence of integers

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

np.arange(1, 6) # specify the start and stop points

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

np.arange(2, 20, 2) # specify start, stop, and step size

array([ 2,  4,  6,  8, 10, 12, 14, 16, 18])

np.ones()

np.ones(10) # create an array of 10 ones

array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

np.zeros()

np.zeros(10) # create an array of 10 zeros

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

np.linspace()

np.linspace() will create an array of evenly spaced values (as floats)

# Create an array of wavelengths for spectral analysis
wavelengths = np.linspace(400, 700, 31)  # 31 points from 400 nm to 700 nm
print("Wavelengths (nm):", wavelengths)

Wavelengths (nm): [400. 410. 420. 430. 440. 450. 460. 470. 480. 490. 500. 510. 520. 530.
 540. 550. 560. 570. 580. 590. 600. 610. 620. 630. 640. 650. 660. 670.
 680. 690. 700.]

Note that np.linspace() takes a third optional argument that specifies the number of values to generate. This is different to np.arange() where the third optional argument specifies the step-size between values.