np.empty() for uninitialized arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.empty() with shape (2,2)

What is the output of the following code?

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

NumPy

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

A A 2x2 array with uninitialized random values

B A 2x2 array filled with zeros

C A 2x2 array filled with ones

D Raises a TypeError

💡 Hint

np.empty() creates an array without setting values, so it contains whatever was in memory.

np.empty() allocates memory for the array but does not initialize values, so the contents are random and depend on memory state.

❓ data_output

intermediate

Shape and dtype of np.empty array

What will be the shape and data type of the array created by this code?

import numpy as np
arr = np.empty((3,4), dtype=int)
print(arr.shape, arr.dtype)

NumPy

import numpy as np
arr = np.empty((3,4), dtype=int)
print(arr.shape, arr.dtype)

A (4, 3) float64

B (3, 4) int64

C (3, 4) float64

D (4, 3) int32

💡 Hint

The shape is given by the first argument, and dtype is set explicitly.

The array shape is (3,4) as specified, and dtype=int defaults to int64 on most 64-bit systems.

🔧 Debug

advanced

Why does np.empty() output contain unexpected values?

Consider this code:

import numpy as np
arr = np.empty(5)
print(arr)

Why does the output contain seemingly random numbers instead of zeros?

NumPy

import numpy as np
arr = np.empty(5)
print(arr)

A np.empty() does not initialize the array, so it contains leftover memory values

B np.empty() initializes the array with zeros but print shows garbage

C np.empty() creates an array of None values

D np.empty() raises an error if not initialized

💡 Hint

Think about what 'empty' means in this context.

np.empty() allocates memory but does not set values, so the array contains whatever was previously in that memory location.

🚀 Application

advanced

Using np.empty() for performance

You want to create a large array to fill with calculated values later. Which is the best reason to use np.empty() instead of np.zeros()?

A np.empty() validates input data types

B np.empty() automatically fills with zeros

C np.empty() uses less memory than np.zeros()

D np.empty() is faster because it skips initializing values

💡 Hint

Think about initialization cost.

np.empty() is faster because it just allocates memory without setting values, useful when you will overwrite all values anyway.

🧠 Conceptual

expert

Behavior of np.empty() with structured dtypes

What will be the output of this code?

import numpy as np
dtype = [('x', 'i4'), ('y', 'f4')]
arr = np.empty(2, dtype=dtype)
print(arr)

NumPy

import numpy as np
dtype = [('x', 'i4'), ('y', 'f4')]
arr = np.empty(2, dtype=dtype)
print(arr)

A Raises a TypeError due to structured dtype

B An array of zeros with integer and float fields

C An array of 2 elements with uninitialized integer and float fields

D An array filled with None values

💡 Hint

np.empty() behavior is the same regardless of dtype.

np.empty() allocates memory for structured arrays but does not initialize the fields, so values are random.