NumPy Basics

Main idea

NumPy arrays support fast vectorized math.

Tip

Prefer array operations over Python loops.

Watch out

Shape mismatch errors.

Read this next

NumPy Basics is used in many real programs. Right now you are in Real-World Tools.

Core idea:
NumPy arrays support fast vectorized math.

Read the example code on this page. Then write your own short version.

Tip:
Prefer array operations over Python loops.

Watch out for:
Shape mismatch errors.

Your challenge:
Compute mean, min, max from numeric array.

Use the editor in the challenge lab. When your output looks correct and there is no error, press the green button to unlock the next topic.

Example code

Copy this example if it helps. Change it so it matches NumPy Basics.

# Topic: NumPy Basics
def main():
    sample = "edit me"
    # TODO: apply NumPy Basics concept here
    result = sample
    print("Result:", result)

if __name__ == "__main__":
    main()

Challenge

Compute mean, min, max from numeric array.

Before you can finish: your output should include at least 16 characters; at least 1 non-empty line(s); no crash traceback—fix errors until the program runs cleanly.

Use Run. Read the output. Change your code until the task is done.

Starter reference

# Challenge starter for NumPy Basics
def solve():
    # Write your solution here
    pass

print("Update solve() and run")

Your code

The first Run may load Python in your browser (one-time). Later runs are faster.

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