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}

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

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 }

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

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 >.

Practice

1. What is the main purpose of aggregation in data analysis using numpy?

easy

A. To remove missing values from data

B. To create new columns in a dataset

C. To summarize many data points into simple values like sums or averages

D. To sort data in ascending order

Solution

1. Step 1: Understand aggregation meaning

   Aggregation means combining many values into one summary value.

2. Step 2: Identify common aggregation uses

   Common aggregations are sum, mean, max, min, which summarize data quickly.

3. Final Answer:

   To summarize many data points into simple values like sums or averages -> Option C

4. Quick Check:

   Aggregation = summary values [OK]

Hint: Aggregation means summarizing data points simply [OK]

Common Mistakes:

• Confusing aggregation with sorting
• Thinking aggregation creates new columns
• Mixing aggregation with data cleaning

2. Which of the following is the correct syntax to calculate the mean of a numpy array arr?

easy

A. arr.mean()

B. mean(arr)

C. numpy.mean(arr)

D. arr.average()

Solution

1. Step 1: Recall numpy array methods

   Numpy arrays have a method called mean() to calculate average.

2. Step 2: Check syntax correctness

   Calling arr.mean() is correct; mean(arr) is invalid without import, numpy.mean(arr) requires prefix, arr.average() does not exist.

3. Final Answer:

   arr.mean() -> Option A

4. Quick Check:

   Array method for mean = arr.mean() [OK]

Hint: Use array.method() for aggregation in numpy [OK]

Common Mistakes:

• Using mean(arr) without numpy prefix or import
• Using arr.average() which is not a numpy method
• Confusing function and method syntax

3. What is the output of this code?

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

medium

A. 5

B. 15

C. [1 2 3 4 5]

D. Error

Solution

1. Step 1: Understand the array values

   The array contains numbers 1, 2, 3, 4, 5.

2. Step 2: Calculate the sum of array elements

   Sum = 1+2+3+4+5 = 15.

3. Final Answer:

   15 -> Option B

4. Quick Check:

   Sum of 1 to 5 = 15 [OK]

Hint: Sum adds all elements in the array [OK]

Common Mistakes:

• Confusing sum with max or count
• Expecting array output instead of single value
• Syntax errors from missing import

4. Identify the error in this code snippet:

import numpy as np
arr = np.array([1, 2, 3, 4])
result = arr.mean
print(result)

medium

A. mean() method does not exist in numpy

B. Using print instead of return

C. Array should be a list for mean to work

D. Missing parentheses when calling mean method

Solution

1. Step 1: Check how mean is called

   In the code, arr.mean is assigned without parentheses, so it is a method reference, not a value.

2. Step 2: Understand method call syntax

   To get the mean value, arr.mean() must be called with parentheses before assignment.

3. Final Answer:

   Missing parentheses when calling mean method -> Option D

4. Quick Check:

   Method call needs () [OK]

Hint: Call methods with () to get results [OK]

Common Mistakes:

• Assigning method without calling it
• Thinking print causes error
• Believing mean only works on lists

5. You have a 2D numpy array representing sales data for 3 products over 4 days:

import numpy as np
sales = np.array([[10, 20, 30, 40],
                  [5, 15, 25, 35],
                  [2, 4, 6, 8]])

Which code correctly calculates the total sales per product?

hard

A. sales.sum(axis=1)

B. sales.sum(axis=0)

C. sales.mean(axis=1)

D. sales.max(axis=0)

Solution

1. Step 1: Understand array shape and axis meaning

   The array shape is (3 products, 4 days). Axis 0 is down rows (products), axis 1 is across columns (days per product).

2. Step 2: Choose axis to sum per product

   Summing along axis=1 adds sales across days for each product, giving total sales per product.

3. Final Answer:

   sales.sum(axis=1) -> Option A

4. Quick Check:

   Sum per product = sum across columns (axis=1) [OK]

Hint: Sum axis=1 sums rows (per product) in 2D array [OK]

Common Mistakes:

• Using axis=0 sums per day, not per product
• Using mean instead of sum for totals
• Confusing axis numbering in numpy