Master the NumPy Basics

What will we cover in this Tutorial

If you are starting from scratch with NumPy and do not know what ndarray is, then you should read this tutorial first.

  • How to make arithmetics with ndarray.
  • Sliding and indexing of ndarray with 1-dimension.
  • Sliding and indexing of ndarray with 2-dimensions.

Arithmetics with NumPy

An amazing feature with ndarrays is that you do not need to make forloops for simple operations.

import numpy as np

a1 = np.array([[1., 2., 3.], [3., 2., 1.]])
a2 = np.array([[4., 5., 6.], [6., 5., 4.]])

print(a1)
print(a2)

print(a2 - a1)
print(a1*a2)
print(1/a1)
print(a2**0.5)

This looks too good to be true. Right?

The output is as you would expect.

[[1. 2. 3.]
 [3. 2. 1.]]
[[4. 5. 6.]
 [6. 5. 4.]]
[[3. 3. 3.]
 [3. 3. 3.]]
[[ 4. 10. 18.]
 [18. 10.  4.]]
[[1.         0.5        0.33333333]
 [0.33333333 0.5        1.        ]]
[[2.         2.23606798 2.44948974]
 [2.44948974 2.23606798 2.        ]]

Then you understand why all are so madly in love with NumPy.

This type of “batch” operation is called vectorization.

You can also make comparisons.

import numpy as np

a1 = np.array([[1., 2., 3.], [6., 5., 4.]])
a2 = np.array([[4., 5., 6.], [3., 4., 5.]])

print(a1 < a2)

Which gives what you expect.

[[ True  True  True]
 [False False  True]]

At least I hope you would expect the above.

Slicing and basic indexing

If you are familiar with Python lists, then this should not surprise you.

import numpy as np

a = np.arange(10)
print(a)
print(a[5])
print(a[2:5])

You guessed it.

[0 1 2 3 4 5 6 7 8 9]
5
[2 3 4]

But this might surprise you a bit.

import numpy as np

a = np.arange(10)
print(a)
a[4:7] = 10
print(a)

Resulting in.

[0 1 2 3 4 5 6 7 8 9]
[ 0  1  2  3 10 10 10  7  8  9]

That is quite a surprise.

You can take a “view” from it (also called a slice) like the following example shows.

import numpy as np

a = np.arange(10)
print(a)
a_slice = a[4:7]
print(a_slice)
a_slice[0:1] = 30
print(a_slice)
print(a)

Resulting in.

[0 1 2 3 4 5 6 7 8 9]
[4 5 6]
[30  5  6]
[ 0  1  2  3 30  5  6  7  8  9]

Slicing and indexing of 2-dimensions

First of, this seems similar to Python lists.

import numpy as np

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

print(a[1])
print(a[2][2])
print(a[2, 2])

Maybe the last statement is surprising, but it does the same as the above. That is, the effect of a[2][2] is the same as of a[2, 2].

[4 5 6]
9
9

Slicing the above ndarray will be done by rows.

import numpy as np

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

print(a[:2])

Which results in the following.

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

A bit more advanced to slice it as.

import numpy as np

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

print(a[:2, 1:])

Resulting in.

[[2 3]
 [5 6]]

It might not be clear the the second slice does fully vertical slices, which is illustrated by the following example.

import numpy as np

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

print(a[:, :1])

This will most likely surprise you. Right?

[[1]
 [4]
 [7]]

Makes sense, right?

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