Module 4 · Lesson 3

Sorting

np.sort(arr) returns a sorted copy; arr.sort() sorts in place and returns None.
np.argsort(arr) doesn’t sort anything — it returns the indices that would sort the array. This is the tool you reach for when you need to sort one array “by” another (e.g. sort names by score).
Both accept axis on 2-D arrays, sorting each row/column independently (each row sorted on its own does not sort the matrix as a whole).

arr = np.array([3, 1, 4, 1, 5])
print("np.sort (copy):   ", np.sort(arr), "| original untouched:", arr)

order = np.argsort(arr)
print("argsort indices:  ", order)
print("names sorted by score, using argsort as a key:")
names = np.array(["c", "a", "d", "b", "e"])
scores = np.array([30, 90, 10, 90, 50])
print(names[np.argsort(scores)])

Output:

np.sort (copy):    [1 1 3 4 5] | original untouched: [3 1 4 1 5]
argsort indices:   [1 3 0 2 4]
names sorted by score, using argsort as a key:
['d' 'c' 'e' 'a' 'b']

Write top_n_names(names, times, n) that returns the n names (as a NumPy array of strings) belonging to the smallest n values in times — i.e. the fastest n runners — ordered from fastest to slowest. names and times are same-length 1-D arrays.

💡 Hint

np.argsort(times) gives ascending order (fastest first, since lower time is better); slice the first n of names[order].

✅ Solution

def top_n_names(names, times, n):
    order = np.argsort(times)
    return names[order][:n]

Write sort_rows_desc(grid) that returns a copy of grid with each row sorted in descending order (columns no longer correspond to anything meaningful across rows — that's expected).

💡 Hint

Sort ascending along axis=1 with np.sort, then reverse each row with [:, ::-1].

✅ Solution

def sort_rows_desc(grid):
    return np.sort(grid, axis=1)[:, ::-1]

Sorting

Rank the runners

Sort each row independently