Type casting with astype() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the astype() method do in NumPy?

The astype() method converts the data type of a NumPy array to another specified type, creating a new array with the new type.

beginner

How do you convert a NumPy array of floats to integers using astype()?

Use array.astype(int) to convert all elements from float to integer type.

beginner

Does astype() modify the original array or create a new one?

astype() creates a new array with the new type and does not change the original array.

intermediate

What happens if you convert a float array with decimal values to integers using astype()?

The decimal part is truncated (cut off), so values are converted to their integer part only.

intermediate

Can you use astype() to convert a NumPy array to a string type?

Yes, you can convert a NumPy array to string type by using astype(str).

What does array.astype(float) do?

A Changes the original array in place

B Converts array elements to float type

C Deletes the array

D Converts array elements to string type

astype(float) creates a new array with elements converted to float type without changing the original array.

If you convert np.array([1.9, 2.5, 3.7]) to int using astype(int), what is the result?

A [1, 2, 3]

B [1.9, 2.5, 3.7]

C [1, 2, 3] but as floats

D [2, 3, 4]

Converting floats to int truncates decimals, so values become [1, 2, 3].

Does astype() modify the original NumPy array?

A Yes, it changes the original array

B It deletes the original array

C No, it returns a new array with the new type

D It only works on lists

astype() returns a new array and leaves the original unchanged.

Which of these is a valid way to convert a NumPy array to string type?

A array.convert(str)

B array.astype(int)

C array.to_string()

D array.astype(str)

astype(str) converts array elements to string type.

What happens if you try to convert a string array with non-numeric text to int using astype(int)?

A It raises a ValueError

B Conversion succeeds with no errors

C It converts all to zero

D It converts to float instead

Trying to convert non-numeric strings to int raises a ValueError because conversion is not possible.

Explain how to use astype() to change the data type of a NumPy array and what happens to the original array.

Think about whether the original data changes or not.

Describe what happens when converting a float array with decimal values to integers using astype().

Consider how decimals are handled when changing to int.

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