Python-Workshop-3-NumPy.md

@@ -447,49 +447,67 @@ def _backsolve(matrix_XTX, matrix_XTY):
 
 # Statistics
 
+Suppose we need to compute some grade statistics for the following data.
 
-```python
-    #---------------------------------------------------------------------------
-    # Data
-    #---------------------------------------------------------------------------
+| Name     | Homework 1 | Homework 2 | Exam 1 | Exam 2 |
+| :------- | ----:      | ----:      | ----:  | ----:  |
+| John     | 100        | 98         | 100    | 90     |
+| Tom      | 100        | 0          | 70     | 90     |
+| Bob      | 100        | 70         | 90     | 80     |
+
+The first step is to convert the table into...
+
+  - a list of exercises
+
+    ```python
     exercises = ["Homework 1", "Homework 2", "Exam 1", "Exam 2"]
+    ```
+
+  - a list of names
+
+    ```python
     students = ["John", "Tom", "Bob"]
+    ```
+
+  - a Numpy `ndarray`
 
+    ```python
     grades = np.array([[100., 98, 100., 90.],
                        [100.,  0., 70., 90.],
                        [100., 70., 90., 80.]])
+    ```
+
+Numpy provides a number of statistics operations that can be applied by axis.
+For a two-dimensional `ndarray` there are two choices:
+
+  - by row (student)
 
-    #---------------------------------------------------------------------------
-    # Statistics by Student
-    #---------------------------------------------------------------------------
+    ```python
         avg_by_student = grades.mean(axis=1)
         min_by_student = grades.min(axis=1)
         max_by_student = grades.max(axis=1)
+    ```
 
-    print("| {:^8} | {:^4} | {:^4} | {:^5} |".format("Name", "Avg", "Min", "Max"))
-    print("|:---------|-----:|-----:|------:|")
-
-    grade_data = zip(students, avg_by_student, min_by_student, max_by_student)
-
-    for name, avg, g_min, g_max in grade_data:
-        print(f"| {name:<8} | {avg:>4.1f} | {g_min:>4.1f} | {g_max:>5.1f} |")
+    |   Name   | Avg  | Min  |  Max  |
+    |:---------|-----:|-----:|------:|
+    | John     | 97.0 | 90.0 | 100.0 |
+    | Tom      | 65.0 |  0.0 | 100.0 |
+    | Bob      | 85.0 | 70.0 | 100.0 |
 
-    print()
+  - by column (exercise)
 
-    #---------------------------------------------------------------------------
-    # Statistics by Exercise
-    #---------------------------------------------------------------------------
+    ```python
         avg_by_exercise = grades.mean(axis=0)
         max_by_exercise = grades.max(axis=0)
         std_by_exercise = grades.std(axis=0)
+    ```
 
-    print("| {:^12} | {:^5} | {:^5} | {:^8} |".format("Exercise", "Avg", "Max", "Std Dev"))
-    print("|:-------------|------:|------:|---------:|")
-
-    grade_data = zip(exercises, avg_by_exercise, max_by_exercise, std_by_exercise)
+    |   Exercise   |  Avg  |  Max  | Std Dev  |
+    |:-------------|------:|------:|---------:|
+    | Homework 1   | 100.0 | 100.0 |      0.0 |
+    | Homework 2   |  56.0 |  98.0 |     41.2 |
+    | Exam 1       |  86.7 | 100.0 |     12.5 |
+    | Exam 2       |  86.7 |  90.0 |      4.7 |
 
-    for exercise, avg, g_min, g_max in grade_data:
-        print(f"| {exercise:<12} | {avg:>5.1f} | {g_min:>5.1f} | {g_max:>8.1f} |")
-```