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.