Module 4 · Lesson 4

Unique Values & Counting

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np.unique(arr) returns the sorted distinct values in an array. Pass return_counts=True to also get how many times each value occurred, or return_index=True to get the index of each value’s first occurrence. Both are commonly used together with the unique values to build a simple frequency table.

votes = np.array(["cat", "dog", "cat", "cat", "fox", "dog"])

values, counts = np.unique(votes, return_counts=True)
print("values:", values)
print("counts:", counts)

for value, count in zip(values, counts):
    print(f"{value}: {count}")

Output:

values: ['cat' 'dog' 'fox']
counts: [3 2 1]
cat: 3
dog: 2
fox: 1
4.1

Most common value

Write most_common(arr) that returns the most frequently occurring value in a 1-D array. Assume there's a unique winner (no ties to break).

💡 Hint

np.unique(arr, return_counts=True) gives you (values, counts) — find the index of the largest count with .argmax() and index into values.

✅ Solution
def most_common(arr):
    values, counts = np.unique(arr, return_counts=True)
    return values[counts.argmax()]
4.2

Values seen exactly once

Write singletons(arr) that returns, sorted, the values in a 1-D array that occur exactly once.

💡 Hint

Get (values, counts) from np.unique(..., return_counts=True), then boolean-index values with counts == 1.

✅ Solution
def singletons(arr):
    values, counts = np.unique(arr, return_counts=True)
    return values[counts == 1]