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.

Practice

1. What does the astype() method do in NumPy?

easy

A. It changes the data type of a NumPy array and returns a new array.

B. It sorts the elements of a NumPy array in ascending order.

C. It removes duplicate elements from a NumPy array.

D. It reshapes the NumPy array into a new shape.

Solution

1. Step 1: Understand the purpose of astype()

   The astype() method is used to convert the data type of elements in a NumPy array.

2. Step 2: Recall the behavior of astype()

   It returns a new array with the changed data type and does not modify the original array.

3. Final Answer:

   It changes the data type of a NumPy array and returns a new array. -> Option A

4. Quick Check:

   astype() changes type and returns new array [OK]

Hint: Remember: astype() changes type and returns a new array [OK]

Common Mistakes:

• Thinking astype() modifies the original array in place
• Confusing astype() with sorting or reshaping functions
• Assuming astype() removes duplicates

2. Which of the following is the correct syntax to convert a NumPy array arr to float type using astype()?

easy

A. arr.convert(float)

B. astype(arr, float)

C. arr.to_float()

D. arr.astype(float)

Solution

1. Step 1: Recall the method call syntax for astype()

   The astype() method is called on the array object like arr.astype().

2. Step 2: Check the argument for data type

   The argument should be the desired data type, here float, passed inside parentheses.

3. Final Answer:

   arr.astype(float) -> Option D

4. Quick Check:

   Correct syntax is arr.astype(type) [OK]

Hint: Call astype() on array with desired type inside parentheses [OK]

Common Mistakes:

• Using astype as a standalone function instead of a method
• Using incorrect method names like convert or to_float
• Passing data type outside parentheses

3. What will be the output of the following code?

import numpy as np
arr = np.array([1.7, 2.3, 3.9])
new_arr = arr.astype(int)
print(new_arr)

medium

A. [2 3 4]

B. [1.7 2.3 3.9]

C. [1 2 3]

D. Error: Cannot convert float to int

Solution

1. Step 1: Understand astype(int) on float array

   Converting floats to int using astype(int) truncates the decimal part (does not round).

2. Step 2: Apply truncation to each element

   1.7 becomes 1, 2.3 becomes 2, 3.9 becomes 3.

3. Final Answer:

   [1 2 3] -> Option C

4. Quick Check:

   astype(int) truncates floats to integers [OK]

Hint: astype(int) truncates decimals, does not round [OK]

Common Mistakes:

• Assuming astype(int) rounds instead of truncates
• Expecting original array to change
• Thinking conversion causes error

4. Identify the error in the following code snippet:

import numpy as np
arr = np.array([1, 2, 3])
new_arr = arr.astype('int32')
print(new_arr.astype('float64'))

medium

A. Error because 'int32' is not a valid dtype string

B. No error; code runs and prints float64 array

C. Error because astype() cannot be chained

D. Error because original array is modified

Solution

1. Step 1: Check dtype string validity

   'int32' and 'float64' are valid NumPy dtype strings for 32-bit int and 64-bit float.

2. Step 2: Verify chaining of astype()

   Chaining astype() calls is allowed because each returns a new array.

3. Final Answer:

   No error; code runs and prints float64 array -> Option B

4. Quick Check:

   Valid dtype strings and chaining astype() work [OK]

Hint: astype() returns new array; chaining is allowed [OK]

Common Mistakes:

• Thinking dtype strings like 'int32' are invalid
• Believing astype() modifies original array
• Assuming astype() cannot be chained

5. You have a NumPy array arr = np.array([10, 20, 30]) of integers. You want to save memory by converting it to the smallest unsigned integer type that can hold these values. Which astype() call is best?

hard

A. arr.astype('uint8')

B. arr.astype('int8')

C. arr.astype('float16')

D. arr.astype('uint16')

Solution

1. Step 1: Check the range of values in arr

   Values are 10, 20, 30, all positive and less than 255.

2. Step 2: Choose smallest unsigned integer type

   uint8 holds values 0 to 255, so it fits all values and saves memory.

3. Step 3: Compare with other options

   int8 supports -128 to 127 but is signed; float16 uses more memory; uint16 is larger than needed.

4. Final Answer:

   arr.astype('uint8') -> Option A

5. Quick Check:

   Use smallest unsigned int type fitting data [OK]

Hint: Pick smallest unsigned int type covering max value [OK]

Common Mistakes:

• Choosing signed int type that wastes range or causes errors
• Using float type which uses more memory
• Ignoring value ranges when selecting dtype