### 02-numpy.md: Update numpy.diff example to use patient data (#782)

```* Update 02-numpy.md

Changing `numpy.diff` example to use data from the inflammation dataset.

As well as adding introduction to usage `axis` parameter of `numpy.diff` in the 2D multi-dimensinal array.

* Update 02-numpy.md

Change `row_start` variable name to `patient3_week1`.

Modify wording for the 2D array `numpy.diff` example question.

* Adding decimer indicator for number within numpy array

* Add printing statement for viewing of patient3_week1 data```
parent 264020e0
 ... ... @@ -665,38 +665,44 @@ which is the average inflammation per patient across all days. > with NumPy. > > The `numpy.diff()` function takes an array and returns the differences > between two successive values. First we consider a one-dimensional > array of length 5. This could be part of some row `i` of our inflammation data, > i.e. `row_start = data[i,:5]`. > > between two successive values. Let's use it to examine the changes > each day across the first week of patient 3 from our inflammation dataset. > > ~~~ > row_start = numpy.array([ 0, 2, 5, 9, 14]) > patient3_week1 = data[3, :7] > print(patient3_week1) > ~~~ > {: .language-python} > > Calling `numpy.diff(row_start)` would do the following calculations > ~~~ > [0. 0. 2. 0. 4. 2. 2.] > ~~~ > {: .output} > > Calling `numpy.diff(patient3_week1)` would do the following calculations > > ~~~ > [ 2 - 0, 5 - 2, 9 - 5, 14 - 9 ] > [ 0 - 0, 2 - 0, 0 - 2, 4 - 0, 2 - 4, 2 - 2 ] > ~~~ > {: .language-python} > > and return the 4 difference values in a new array. > and return the 6 difference values in a new array. > > ~~~ > numpy.diff(row_start) > numpy.diff(patient3_week1) > ~~~ > {: .language-python} > > ~~~ > array([2, 3, 4, 5]) > array([ 0., 2., -2., 4., -2., 0.]) > ~~~ > {: .output} > > Note that the array of differences is shorter by one element (length 4). > Note that the array of differences is shorter by one element (length 6). > > When applying `numpy.diff` to our 2D inflammation array `data`, which axis > would it make sense to use this function along? > When calling `numpy.diff` with a multi-dimensional array, an `axis` argument may > be passed to the function to specify which axis to process. When applying > `numpy.diff` to our 2D inflammation array `data`, which axis would we specify? > > > ## Solution > > Since the row axis (0) is patients, it does not make sense to get the ... ...
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