Type promotion in operations in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is type promotion in numpy operations?

Type promotion is when numpy automatically converts data types during operations to a common type that can hold all values without loss.

beginner

What happens when you add an integer array and a float array in numpy?

Numpy promotes the integer array to float type before adding, so the result is a float array.

beginner

Why does numpy promote types during operations?

To avoid losing information by ensuring the result can represent all input values correctly.

intermediate

What is the result type when you multiply a float32 array with an int64 array in numpy?

The result is promoted to float64 because numpy chooses a common type that can hold both values safely.

intermediate

How does numpy handle type promotion with boolean and integer arrays?

Boolean values are promoted to integers (0 or 1) before the operation, so the result is an integer array.

What type does numpy promote when adding int32 and float64 arrays?

A float64

B int32

C float32

D int64

Numpy promotes to the more general type, float64, to avoid losing decimal information.

When multiplying a boolean array with an int array, what is the result type?

A boolean

B integer

C float

D string

Booleans are promoted to integers (0 or 1) before multiplication, so the result is integer.

If you add a float32 array and a float64 array, what will be the output type?

A float32

B object

C int64

D float64

Numpy promotes to the higher precision float64 to keep accuracy.

Why does numpy promote types during operations?

A To speed up calculations

B To save memory

C To avoid losing data

D To convert all to strings

Type promotion ensures no data is lost during operations.

What happens when you add an int8 array and a uint16 array?

A Result is int32

B Result is uint16

C Result is float64

D Result is int8

Numpy promotes to int32 to safely hold values from both types.

Explain how numpy decides the output data type when performing operations on arrays with different types.

Think about how numpy chooses a type that can hold all input values safely.

Describe what happens when you perform arithmetic operations between boolean and numeric numpy arrays.

Remember how True and False behave as numbers in numpy.