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Update 02-numpy.md #1104

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69 changes: 42 additions & 27 deletions episodes/02-numpy.md
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Expand Up @@ -422,28 +422,50 @@ operation across an axis:

![](fig/python-operations-across-axes.png){alt="Per-patient maximum inflammation is computed row-wise across all columns usingnumpy.amax(data, axis=1). Per-day average inflammation is computed column-wise across all rows usingnumpy.mean(data, axis=0)."}

To support this functionality,
most array functions allow us to specify the axis we want to work on.
If we ask for the average across axis 0 (rows in our 2D example),
we get:
To find the **maximum inflammation reported for each patient**, you would apply the `max` function moving across the columns (axis 1). To find the **daily average inflammation reported across patients**, you would apply the `mean` function moving down the rows (axis 0).

To support this functionality, most array functions allow us to specify the axis we want to work on. If we ask for the max across axis 1 (columns in our 2D example), we get:

```python
print(numpy.max(data, axis=1))
```

```output
[18. 18. 19. 17. 17. 18. 17. 20. 17. 18. 18. 18. 17. 16. 17. 18. 19. 19.
17. 19. 19. 16. 17. 15. 17. 17. 18. 17. 20. 17. 16. 19. 15. 15. 19. 17.
16. 17. 19. 16. 18. 19. 16. 19. 18. 16. 19. 15. 16. 18. 14. 20. 17. 15.
17. 16. 17. 19. 18. 18.]
```

As a quick check, we can ask this array what its shape is. We expect 60 patient maximums:

```python
print(numpy.max(data, axis=1).shape)
```

```output
(60,)
```

The expression `(60,)` tells us we have an N×1 vector, so this is the maximum inflammation per day for each patients.

If we ask for the average across/down axis 0 (rows in our 2D example), we get:

```python
print(numpy.mean(data, axis=0))
```

```output
[ 0. 0.45 1.11666667 1.75 2.43333333 3.15
3.8 3.88333333 5.23333333 5.51666667 5.95 5.9
8.35 7.73333333 8.36666667 9.5 9.58333333
10.63333333 11.56666667 12.35 13.25 11.96666667
11.03333333 10.16666667 10. 8.66666667 9.15 7.25
7.33333333 6.58333333 6.06666667 5.95 5.11666667 3.6
3.3 3.56666667 2.48333333 1.5 1.13333333
0.56666667]
[ 0. 0.45 1.11666667 1.75 2.43333333 3.15
3.8 3.88333333 5.23333333 5.51666667 5.95 5.9
8.35 7.73333333 8.36666667 9.5 9.58333333 10.63333333
11.56666667 12.35 13.25 11.96666667 11.03333333 10.16666667
10. 8.66666667 9.15 7.25 7.33333333 6.58333333
6.06666667 5.95 5.11666667 3.6 3.3 3.56666667
2.48333333 1.5 1.13333333 0.56666667]
```

As a quick check,
we can ask this array what its shape is:
Check the array shape. We expect 40 averages, one for each day of the study:

```python
print(numpy.mean(data, axis=0).shape)
Expand All @@ -452,26 +474,19 @@ print(numpy.mean(data, axis=0).shape)
```output
(40,)
```

The expression `(40,)` tells us we have an N×1 vector,
so this is the average inflammation per day for all patients.
If we average across axis 1 (columns in our 2D example), we get:
Similarly, we can apply the `mean` function to axis 1 to get the patient's average inflammation over the duration of the study (60 values).

```python
print(numpy.mean(data, axis=1))
```

```output
[ 5.45 5.425 6.1 5.9 5.55 6.225 5.975 6.65 6.625 6.525
6.775 5.8 6.225 5.75 5.225 6.3 6.55 5.7 5.85 6.55
5.775 5.825 6.175 6.1 5.8 6.425 6.05 6.025 6.175 6.55
6.175 6.35 6.725 6.125 7.075 5.725 5.925 6.15 6.075 5.75
5.975 5.725 6.3 5.9 6.75 5.925 7.225 6.15 5.95 6.275 5.7
6.1 6.825 5.975 6.725 5.7 6.25 6.4 7.05 5.9 ]
[5.45 5.425 6.1 5.9 5.55 6.225 5.975 6.65 6.625 6.525 6.775 5.8
6.225 5.75 5.225 6.3 6.55 5.7 5.85 6.55 5.775 5.825 6.175 6.1
5.8 6.425 6.05 6.025 6.175 6.55 6.175 6.35 6.725 6.125 7.075 5.725
5.925 6.15 6.075 5.75 5.975 5.725 6.3 5.9 6.75 5.925 7.225 6.15
5.95 6.275 5.7 6.1 6.825 5.975 6.725 5.7 6.25 6.4 7.05 5.9 ]
```

which is the average inflammation per patient across all days.

::::::::::::::::::::::::::::::::::::::: challenge

## Slicing Strings
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