Why array creation matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array from a Python list.

NumPy

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

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

Aseries

Barray

Cmatrix

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'array' causes an AttributeError.

Using 'matrix' creates a different NumPy object, not a simple array.

Use np.array to create a NumPy array from a list.

2fill in blank

medium

Complete the code to create a 3x3 array of zeros using NumPy.

NumPy

import numpy as np
zeros_array = np.[1]((3, 3))
print(zeros_array)

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

Azeros

Bempty

Cones

Dfull

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ones' creates an array of ones, not zeros.

Using 'empty' creates an uninitialized array with random values.

np.zeros creates an array filled with zeros of the given shape.

3fill in blank

hard

Fix the error in the code to create an array of ones with shape 2x4.

NumPy

import numpy as np
arr = np.ones[1](2, 4)
print(arr)

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

A2, 4

B[2, 4]

C(2, 4)

D{2, 4}

Attempts:

3 left

💡 Hint

Common Mistakes

Using square brackets creates a list, which is not accepted as shape.

Passing dimensions without parentheses causes a TypeError.

The shape argument must be a tuple, so use parentheses around the dimensions.

4fill in blank

hard

Fill both blanks to create a NumPy array of integers from 0 to 9 and reshape it to 2 rows and 5 columns.

NumPy

import numpy as np
arr = np.[1](10).[2]((2, 5))
print(arr)

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

Aarange

Breshape

Carray

Dflatten

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'array' instead of 'arange' does not generate a sequence automatically.

Using 'flatten' changes shape to 1D, not reshapes to 2D.

np.arange(10) creates numbers from 0 to 9, and reshape((2, 5)) changes the shape to 2 rows and 5 columns.

5fill in blank

hard

Fill all three blanks to create a 4x4 identity matrix, convert it to float type, and print its shape.

NumPy

import numpy as np
identity = np.[1](4).[2](dtype=[3])
print(identity.shape)

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

Aidentity

Bastype

Cfloat64

Dzeros

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'zeros' creates a zero matrix, not identity.

Passing dtype directly to identity instead of using astype causes an error.

np.identity(4) creates a 4x4 identity matrix, astype(float64) converts its type to float, and shape prints its dimensions.

Practice

1. Why is it important to use np.array() to create arrays in NumPy instead of just using Python lists?

easy

A. Because NumPy arrays allow fast math operations on many numbers at once.

B. Because Python lists cannot store numbers.

C. Because np.array() creates strings, not numbers.

D. Because Python lists are faster than NumPy arrays.

Solution

1. Step 1: Understand Python lists vs NumPy arrays

   Python lists store items but are slow for math on many numbers.

2. Step 2: Recognize NumPy arrays' advantage

   NumPy arrays allow fast, efficient math on large sets of numbers.

3. Final Answer:

   Because NumPy arrays allow fast math operations on many numbers at once. -> Option A

4. Quick Check:

   NumPy arrays speed up math = A [OK]

Hint: NumPy arrays speed math on many numbers, lists do not [OK]

Common Mistakes:

• Thinking Python lists can't store numbers
• Confusing array creation with string creation
• Believing lists are faster for math

2. Which of the following is the correct way to create a NumPy array from a Python list [1, 2, 3]?

easy

A. np.array{1, 2, 3}

B. array([1, 2, 3])

C. np.list([1, 2, 3])

D. np.array([1, 2, 3])

Solution

1. Step 1: Recall correct NumPy array syntax

   The function to create arrays is np.array() with parentheses and a list inside.

2. Step 2: Check each option

   np.array([1, 2, 3]) uses correct syntax. array([1, 2, 3]) misses np.. np.list([1, 2, 3]) uses wrong function. np.array{1, 2, 3} uses braces instead of parentheses.

3. Final Answer:

   np.array([1, 2, 3]) -> Option D

4. Quick Check:

   Correct function and syntax = A [OK]

Hint: Use np.array() with parentheses and list inside [OK]

Common Mistakes:

• Omitting np. prefix
• Using curly braces instead of parentheses
• Using wrong function names

3. What will be the output of this code?

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

medium

A. (4,)

B. (1, 4)

C. (2, 2)

D. Error

Solution

1. Step 1: Understand the array structure

   The array is created from a list of two lists, each with 2 elements, so it is 2 rows and 2 columns.

2. Step 2: Check the shape attribute

   arr.shape returns the dimensions as a tuple (rows, columns), which is (2, 2).

3. Final Answer:

   (2, 2) -> Option C

4. Quick Check:

   2 rows and 2 columns shape = (2, 2) [OK]

Hint: Shape shows rows and columns count as tuple [OK]

Common Mistakes:

• Confusing shape with size
• Expecting a flat tuple for nested lists
• Thinking shape returns error

4. Find the error in this code that tries to create a NumPy array:

import numpy as np
arr = np.array(1, 2, 3)
print(arr)

medium

A. The print statement is missing parentheses.

B. np.array() needs a single list or array argument, not multiple separate arguments.

C. np.array() cannot create arrays from numbers.

D. The import statement is incorrect.

Solution

1. Step 1: Check np.array() argument format

   np.array() expects one argument, usually a list or array, not multiple separate values.

2. Step 2: Identify the error in the code

   The code passes three separate arguments instead of one list, causing a TypeError.

3. Final Answer:

   np.array() needs a single list or array argument, not multiple separate arguments. -> Option B

4. Quick Check:

   Single argument list required = C [OK]

Hint: np.array() takes one list or array argument [OK]

Common Mistakes:

• Passing multiple arguments instead of one list
• Thinking np.array() can't take numbers
• Misreading print syntax

5. You have a list of lists representing daily temperatures for a week:

temps = [[20, 21, 19], [22, 20, 18], [19, 20, 21], [21, 22, 20], [20, 19, 22], [18, 20, 21], [19, 21, 20]]

How can you create a NumPy array from this list and find the average temperature for each day?

hard

A. "import numpy as np\ntemps_array = np.array(temps)\ndaily_avg = temps_array.mean(axis=1)\nprint(daily_avg)"

B. "import numpy as np\ntemps_array = np.array(temps)\ndaily_avg = temps_array.mean(axis=0)\nprint(daily_avg)"

C. "import numpy as np\ntemps_array = np.array(temps)\ndaily_avg = temps_array.sum(axis=1)\nprint(daily_avg)"

D. "import numpy as np\ntemps_array = np.array(temps)\ndaily_avg = temps_array.sum(axis=0)\nprint(daily_avg)"

Solution

1. Step 1: Create NumPy array from list of lists

   Use np.array(temps) to convert the list of daily temperatures into a 2D array.

2. Step 2: Calculate average temperature per day

   Use mean(axis=1) to average across each row (each day), since rows represent days.

3. Final Answer:

   import numpy as np temps_array = np.array(temps) daily_avg = temps_array.mean(axis=1) print(daily_avg) -> Option A

4. Quick Check:

   Mean per row (day) = D [OK]

Hint: Use mean(axis=1) to average rows (days) in 2D array [OK]

Common Mistakes:

• Using mean(axis=0) which averages columns (times)
• Using sum instead of mean
• Not converting list to array first