diff --git a/episodes/02-numpy.md b/episodes/02-numpy.md
index 21db9cc36..f281834b7 100644
--- a/episodes/02-numpy.md
+++ b/episodes/02-numpy.md
@@ -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)
@@ -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