Creating Arrays

Typing every value into np.array does not scale. NumPy has functions that build an array from a rule: a range, a fill value, an even spacing, or a diagonal. Shapes are passed as a tuple, such as (2, 3).

arange and reshape

np.arange works like range but returns an array. Give it a stop, or a start and a stop, or a start, a stop, and a step. The stop is not included. The step can be a float. Chain reshape to lay the values out in rows.

import numpy as np

print(np.arange(5))
print(np.arange(2, 8))
print(np.arange(10, 0, -3))
print(np.arange(0, 1, 0.25))
print(np.arange(12).reshape(3, 4))

Output:

[0 1 2 3 4]
[2 3 4 5 6 7]
[10  7  4  1]
[0.   0.25 0.5  0.75]
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

zeros, ones, and full

These fill a shape with one value. zeros and ones give floats unless you pass a dtype. full takes the shape and the fill value.

import numpy as np

print(np.zeros(3))
print(np.ones((2, 3), dtype=int))
print(np.full((2, 2), 7))

Output:

[0. 0. 0.]
[[1 1 1]
 [1 1 1]]
[[7 7]
 [7 7]]

linspace

np.linspace(start, stop, num) gives num evenly spaced values and includes the stop. num is 50 if you leave it out. Pass endpoint=False to leave the stop out, or retstep=True to also get the gap between values.

import numpy as np

print(np.linspace(0, 1, 5))
print(np.linspace(0, 1, 5, endpoint=False))
values, gap = np.linspace(10, 20, 3, retstep=True)
print(values, gap)

Output:

[0.   0.25 0.5  0.75 1.  ]
[0.  0.2 0.4 0.6 0.8]
[10. 15. 20.] 5.0

identity and eye

np.identity(n) is a square grid with ones on the main diagonal. np.eye(rows, cols, k) can be rectangular, and k moves the diagonal right when positive or left when negative.

import numpy as np

print(np.identity(3, dtype=int))
print(np.eye(3, 4, k=1, dtype=int))
print(np.eye(3, k=-1, dtype=int))

Output:

[[1 0 0]
 [0 1 0]
 [0 0 1]]
[[0 1 0 0]
 [0 0 1 0]
 [0 0 0 1]]
[[0 0 0]
 [1 0 0]
 [0 1 0]]

hstack and vstack

hstack joins arrays side by side. vstack stacks them as rows. Both take one tuple of arrays, so note the extra parentheses.

import numpy as np

left = np.array([1, 2, 3])
right = np.array([4, 5, 6])
print(np.hstack((left, right)))
print(np.vstack((left, right)))

Output:

[1 2 3 4 5 6]
[[1 2 3]
 [4 5 6]]

Key takeaways

• arange leaves the stop out, and linspace keeps it in

• Shapes are tuples: np.zeros((2, 3))

• eye with k shifts the diagonal

• hstack and vstack take a tuple of arrays

• Next: Random Arrays

• Previous: Array Attributes