np.ones() for one-filled arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.ones() with shape (3, 2)

What is the output of this code snippet?

NumPy

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

A

[[1 1]
 [1 1]
 [1 1]]

B

[[1. 1.]
 [1. 1.]
 [1. 1.]]

C [1. 1. 1. 1. 1. 1.]

D

[[0. 0.]
 [0. 0.]
 [0. 0.]]

💡 Hint

np.ones() creates an array filled with 1.0 by default with the given shape.

np.ones((3, 2)) creates a 2D array with 3 rows and 2 columns filled with float 1.0 values.

❓ data_output

intermediate

Shape and dtype of np.ones array

What are the shape and data type of the array created by np.ones((4,))?

NumPy

import numpy as np
arr = np.ones((4,))
print(arr.shape, arr.dtype)

A (1, 4) float32

B (4, 1) int64

C (4,) float64

D (4,) int32

💡 Hint

Default dtype for np.ones is float64 unless specified.

np.ones((4,)) creates a 1D array with 4 elements of type float64 by default.

🔧 Debug

advanced

Error in np.ones() usage

What error does this code produce?

NumPy

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

A TypeError: data type not understood

B TypeError: 'int' object is not iterable

C TypeError: _ones() takes from 1 to 2 positional arguments but 3 were given

D TypeError: data type not specified

💡 Hint

np.ones(shape, dtype) interprets the second argument as dtype; check if it's valid.

np.ones(3, 2) passes shape=3 and dtype=2, but 2 is not a valid dtype, raising TypeError: data type not understood.

🚀 Application

advanced

Creating a 3D array of ones with dtype int

Which code creates a 3x3x3 array filled with integer ones?

A np.ones((3,3,3), dtype=int)

B np.ones((3,3,3), type=int)

C np.ones([3,3,3], type=int)

D np.ones(3,3,3, dtype=int)

💡 Hint

dtype is the correct keyword for data type in numpy arrays.

Option A correctly passes shape as a tuple and dtype as int. Others have syntax errors or wrong keywords.

🧠 Conceptual

expert

Memory usage difference between np.ones and np.zeros

Which statement about memory usage is true when comparing np.ones((1000,1000)) and np.zeros((1000,1000)) with default dtype?

A np.ones uses less memory because ones are stored more efficiently.

B np.ones uses more memory because it stores 1s instead of 0s.

C np.zeros uses less memory because zeros can be compressed internally.

D Both use the same amount of memory because they store the same number of elements with the same dtype.

💡 Hint

Consider how numpy stores array data in memory regardless of values.

Both arrays have the same shape and dtype, so they use the same memory size. Values do not affect memory size in numpy arrays.

Practice

1. What does the np.ones() function do in NumPy?

easy

A. Creates an array filled with ones

B. Creates an array filled with zeros

C. Creates an array filled with random numbers

D. Creates an empty array

Solution

1. Step 1: Understand the function purpose

   np.ones() is designed to create arrays filled with the number one.

2. Step 2: Compare with other array creation functions

   Functions like np.zeros() create arrays filled with zeros, but np.ones() specifically fills with ones.

3. Final Answer:

   Creates an array filled with ones -> Option A

4. Quick Check:

   np.ones() = array of ones [OK]

Hint: Remember: ones means all elements are 1 [OK]

Common Mistakes:

• Confusing np.ones() with np.zeros()
• Thinking it creates random values
• Assuming it creates an empty array

2. Which of the following is the correct syntax to create a 2x3 array of ones with integer type using np.ones()?

easy

A. np.ones((2, 3), dtype=int)

B. np.ones([2, 3], type=int)

C. np.ones(2, 3, dtype='int')

D. np.ones(2, 3, int)

Solution

1. Step 1: Check the shape argument format

   The shape must be given as a tuple, so (2, 3) is correct.

2. Step 2: Check the dtype argument syntax

   The correct keyword is dtype, and the value can be int or 'int'.

3. Final Answer:

   np.ones((2, 3), dtype=int) -> Option A

4. Quick Check:

   Shape as tuple and dtype keyword correct [OK]

Hint: Use tuple for shape and 'dtype' keyword [OK]

Common Mistakes:

• Using list instead of tuple for shape
• Using 'type' instead of 'dtype'
• Passing shape as separate arguments

3. What is the output of the following code?

import numpy as np
arr = np.ones((2, 2), dtype=float)
print(arr)

medium

A. [[1 1]
[1 1]]

B. [[1. 1.]
[1. 1.]]

C. [[1 1 1]
[1 1 1]]

D. [[0. 0.]
[0. 0.]]

Solution

1. Step 1: Understand the shape and dtype

   The shape is (2, 2), so the array has 2 rows and 2 columns. The dtype is float, so elements are floats.

2. Step 2: Predict the printed output

   Each element is 1.0, printed as 1. with a decimal point. So the output is a 2x2 array of 1.0 values.

3. Final Answer:

   [[1. 1.] [1. 1.]] -> Option B

4. Quick Check:

   Shape (2,2) and float type means 1.0 values [OK]

Hint: Float dtype shows decimal points in output [OK]

Common Mistakes:

• Confusing integer and float output formats
• Misreading shape as (2,3)
• Expecting zeros instead of ones

4. Identify the error in this code snippet:

import numpy as np
arr = np.ones(3, 4)
print(arr)

medium

A. print() cannot display numpy arrays

B. Missing dtype argument

C. np.ones() cannot create 2D arrays

D. Shape must be a tuple, so use np.ones((3, 4))

Solution

1. Step 1: Check the shape argument

   The code passes two separate arguments 3 and 4, but np.ones() expects a single shape argument as a tuple.

2. Step 2: Correct the shape argument

   Use np.ones((3, 4)) to create a 3 by 4 array of ones.

3. Final Answer:

   Shape must be a tuple, so use np.ones((3, 4)) -> Option D

4. Quick Check:

   Shape argument must be tuple [OK]

Hint: Shape is always a tuple in np.ones() [OK]

Common Mistakes:

• Passing shape as separate arguments
• Forgetting parentheses around shape tuple
• Thinking dtype is mandatory

5. You want to create a 3D mask array of shape (2, 3, 4) filled with ones of boolean type using np.ones(). Which code snippet correctly does this?

hard

A. np.ones((2, 3, 4), type=bool)

B. np.ones([2, 3, 4], type=bool)

C. np.ones((2, 3, 4), dtype=bool)

D. np.ones(2, 3, 4, dtype=bool)

Solution

1. Step 1: Confirm the shape argument format

   The shape must be a tuple (2, 3, 4) to create a 3D array.

2. Step 2: Use correct dtype keyword and value

   The dtype keyword must be dtype and the value bool to create a boolean array.

3. Final Answer:

   np.ones((2, 3, 4), dtype=bool) -> Option C

4. Quick Check:

   Tuple shape and dtype=bool correct [OK]

Hint: Use tuple shape and dtype=bool for boolean arrays [OK]

Common Mistakes:

• Using list instead of tuple for shape
• Using 'type' instead of 'dtype'
• Passing shape as separate arguments