Why aggregation matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the sum of all elements in the array.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
total = arr.[1]()

Drag options to blanks, or click blank then click option'

Asum

Bmean

Cmax

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of sum() which calculates average, not total.

Using max() or min() which find largest or smallest values, not sum.

The sum() function adds all elements in the array to give the total.

2fill in blank

medium

Complete the code to find the average value of the array elements.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
avg = arr.[1]()

Drag options to blanks, or click blank then click option'

Amean

Bsum

Cmedian

Dstd

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() which adds values but does not average.

Using median() which finds middle value, not average.

The mean() function calculates the average of all elements.

3fill in blank

hard

Fix the error in the code to find the maximum value in the array.

NumPy

import numpy as np
arr = np.array([3, 7, 2, 9, 5])
max_value = arr.[1]()

Drag options to blanks, or click blank then click option'

Amean

Bmin

Csum

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using min() which finds smallest value.

Using mean() or sum() which do not find max.

The max() function returns the largest value in the array.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

NumPy

words = ['data', 'is', 'fun', 'science', 'ai']
lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > which filters shorter words.

Using word instead of len(word) for length.

We use len(word) to get the length, and filter words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

NumPy

data = {'a': 1, 'b': -2, 'c': 3}
result = { [1]: [2] for k, v in data.items() if v [3] 0 }

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys.

Using < instead of > for filtering values.

Using k instead of v for values.

Keys are converted to uppercase with k.upper(), values are v, and we filter values greater than 0 using >.