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 for–loops 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?