Universal functions - use Technical Quotient

2. Understand

What is the purpose of the np.promote_types() function in NumPy?

It finds the smallest data type to which both inputs can be safely cast.

Correct; np.promote_types() identifies the smallest data type that can safely accommodate both input types.

It calculates the result of an operation between two arrays.
It checks if safe casting between two data types is possible.

Incorrect; checking for safe casting is the role of np.can_cast().

It identifies the minimum necessary scalar type for an operation.

Incorrect; identifying the minimum necessary scalar type is the role of np.min_scalar_type().

3. Apply

You have two arrays, arr1 = np.array([1, 2, 3], dtype=np.int8) and arr2 = np.array([1.1, 2.2, 3.3], dtype=np.float32). You need to perform an addition operation that respects the precision of both data types. Which of the following code snippets will ensure the correct data type for the result?

result_dtype = np.promote_types(arr1.dtype, arr2.dtype)\narr1 = arr1.astype(result_dtype)\nresult = arr1 + arr2

Correct; promotes arr1 to the dtype that can safely include arr2 values, ensuring the operation retains precision.

result_dtype = np.result_type(arr1.dtype, arr2.dtype)\nresult = np.add(arr1, arr2, dtype=result_dtype)

Incorrect; assigning dtype in np.add() does not ensure proper promotion; the result dtype is determined by the input array types.

result_dtype = np.min_scalar_type(arr1, arr2)\nresult = np.add(arr1.astype(result_dtype), arr2.astype(result_dtype))

Incorrect; np.min_scalar_type() does not accept arrays as arguments and is not intended for promoting types based on two arrays.

can_cast = np.can_cast(arr1.dtype, arr2.dtype)\nif can_cast:\n\tresult = arr1 + arr2\nelse:\n\tresult = arr1.astype(float) + arr2.astype(float)

Incorrect; the approach does not correctly handle type promotion based on precision; also, np.can_cast() usage is misconstrued.

4. Analyze

You're merging data from two sources: arrA = np.array([0, 1, 2], dtype=np.uint8) and arrB = np.array([0, -1, -2], dtype=np.int8). You plan to use a universal function (ufunc) to process these arrays together and are concerned about the output data type, especially avoiding data loss or unexpected type promotions.Considering NumPy's type promotion rules, what will be the output data type if you perform an addition operation between arrA and arrB without explicitly specifying the data type?

np.int16

Correct; due to mixed signedness and to avoid data loss, NumPy promotes to a larger signed type, here np.int16, accommodating both input ranges.

np.int8

Incorrect; np.int8 could not accommodate the unsigned values of arrA without data loss.

np.uint8

Incorrect; np.uint8 cannot represent the negative values from arrB.

np.float32

Incorrect; while np.float32 can represent the data without loss, NumPy's type promotion prioritizes integer preservation over floating-point promotion in this context.

1. Remember

2. Understand

3. Apply

4. Analyze

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