Type casting with astype() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the NumPy array to float type using astype().

NumPy

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

Drag options to blanks, or click blank then click option'

Aastype

Bconvert

Ctofloat

Dcast

Attempts:

3 left

💡 Hint

Common Mistakes

Using a method name that does not exist like 'convert' or 'cast'.

Trying to change type by assignment instead of using a method.

The astype() method is used to cast a NumPy array to a different type.

2fill in blank

medium

Complete the code to convert the integer array to string type using astype().

NumPy

import numpy as np
arr = np.array([10, 20, 30])
arr_str = arr.[1](str)
print(arr_str)

Drag options to blanks, or click blank then click option'

Aastype

Bto_str

Cconvert

Dcast

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent method like 'to_str'.

Trying to convert by casting without a method.

Use astype(str) to convert the array elements to strings.

3fill in blank

hard

Fix the error in the code to convert the float array to integer type using astype().

NumPy

import numpy as np
arr = np.array([1.5, 2.7, 3.1])
arr_int = arr.[1](int)
print(arr_int)

Drag options to blanks, or click blank then click option'

Acast

Bastypee

Cconvert

Dastype

Attempts:

3 left

💡 Hint

Common Mistakes

Misspelling the method name as 'astypee'.

Using a method that does not exist.

The correct method name is astype. Misspelling it causes an error.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, only for words longer than 3 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = { [1] : len([2]) for [1] in words if len([1]) > 3 }
print(lengths)

Drag options to blanks, or click blank then click option'

Aword

Blen

Cwords

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Using the list name instead of the loop variable inside len().

Use the same variable word as the key and inside len() to get the length of each word.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their float lengths as values, only for words longer than 3 characters.

NumPy

import numpy as np
words = ['apple', 'bat', 'carrot', 'dog']
lengths = { [1] : np.array([len([2])]).[3](float)[0] for [2] in words if len([2]) > 3 }
print(lengths)

Drag options to blanks, or click blank then click option'

Aword.upper()

Bword

Castype

Dwords

Attempts:

3 left

💡 Hint

Common Mistakes

Using the list name instead of the loop variable.

Forgetting to convert length to float.

Using wrong method names.

The key is the uppercase word using word.upper(), the loop variable is word, and the length is converted to float using astype(float).

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