Type casting with astype() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of type casting a NumPy array to float

What is the output of this code snippet?

import numpy as np
arr = np.array([1, 2, 3])
new_arr = arr.astype(float)
print(new_arr)

NumPy

import numpy as np
arr = np.array([1, 2, 3])
new_arr = arr.astype(float)
print(new_arr)

A [1, 2, 3]

B [1 2 3]

C [1.0 2.0 3.0]

D [1. 2. 3.]

💡 Hint

astype(float) converts integers to floating point numbers with decimal points.

The astype(float) method converts the integer array elements to floats, which print with a decimal point but no trailing zeros after the decimal.

❓ data_output

intermediate

Resulting dtype after type casting

After running this code, what is the dtype of the new array?

import numpy as np
arr = np.array([1.5, 2.5, 3.5])
new_arr = arr.astype(int)
print(new_arr.dtype)

NumPy

import numpy as np
arr = np.array([1.5, 2.5, 3.5])
new_arr = arr.astype(int)
print(new_arr.dtype)

A float64

B int64

C int32

D object

💡 Hint

astype(int) converts floats to integers by truncating decimals.

The default integer type on most 64-bit systems is int64, so astype(int) converts to int64 dtype.

🔧 Debug

advanced

Identify the error in type casting code

What error does this code raise?

import numpy as np
arr = np.array(['1', '2', 'three'])
new_arr = arr.astype(int)

NumPy

import numpy as np
arr = np.array(['1', '2', 'three'])
new_arr = arr.astype(int)

A TypeError: unsupported operand type(s) for astype

B SyntaxError: invalid syntax

C ValueError: invalid literal for int() with base 10: 'three'

D No error, outputs array([1, 2, 0])

💡 Hint

The string 'three' cannot be converted to an integer.

astype(int) tries to convert each string to int. 'three' is not a valid integer string, so it raises ValueError.

🚀 Application

advanced

Using astype() to optimize memory usage

You have a large NumPy array of floats with values between 0 and 255. Which astype() conversion will reduce memory usage without losing information?

A arr.astype(np.uint8)

B arr.astype(np.float64)

C arr.astype(np.int64)

D arr.astype(str)

💡 Hint

Values between 0 and 255 fit in an 8-bit unsigned integer.

Converting to np.uint8 uses 1 byte per element, reducing memory compared to float64 (8 bytes).

🧠 Conceptual

expert

Effect of astype() on array views and copies

Which statement about astype() is true?

A astype() always returns a new array copy with the requested type.

B astype() modifies the original array in place without copying.

C astype() returns a view of the original array with changed dtype.

D astype() changes the dtype of the original array without copying.

💡 Hint

astype() creates a new array even if the dtype is the same.

astype() always returns a new array copy with the requested type; it does not modify the original array or return a view.