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Update numpy.md #306

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10 changes: 9 additions & 1 deletion _tutorials/numpy.md
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,8 @@ The biggest advantage of NumPy is its ability to handle numerical arrays. For ex
a = [1, 2, 3, 4, 5]
b = []
for i in a:
b.append(a**2)
b.append(i**2)
print(b)
```

and you will get `[1, 4, 9, 16, 25]` for `b`. Now, if you want to do the same with a 2-dimensional array, the base Python to do this is:
Expand All @@ -40,6 +41,7 @@ b = [[],[]]
for i in range(len(a)):
for j in range(len(a[i])):
b[i].append(a[i][j]**2)
print(b)
```

This would give you `b` equal to `[[1, 4], [9, 16]]`. To do the same with a 3D array you would need 3 nested loops and to do it in 4D would require 4 nested loops. However, with NumPy you can take the square of an array of any dimensions using the same line of code and no loops:
Expand Down Expand Up @@ -158,6 +160,9 @@ for i in [a, b]:
i[:, :, 0] += 1.
elif dimensions == 4:
i[:, :, :, 0] += 1.

print(a)
print(b)
```
While there is nothing wrong with this code, and it will do what we want, there is a better method:
```python
Expand All @@ -169,6 +174,9 @@ b = np.zeros((3, 3, 3, 3))
# add 1 to the first columns
for i in [a, b]:
i[..., 0] += 1.

print(a)
print(b)
```
`...` is called the ellipsis, and it is used to select all unspecified dimensions.

Expand Down