Data types - use Technical Quotient

1. Understand

Why does np.power(100, 9, dtype=np.int32) result in an incorrect negative number?

NumPy automatically converts integer types to floating-point for large numbers.

Incorrect; NumPy does not automatically convert integers to floating-point numbers; the dtype must be explicitly specified to avoid overflow.

The calculation is incorrect; it should yield a positive number.
The 32-bit integer range is exceeded, causing overflow.
np.power() function is incompatible with 32-bit integers.

Incorrect; np.power() function works with 32-bit integers, but the issue is with overflow.

2. Apply

You need to calculate 100 ** 50 and ensure the result is accurate without overflow. Which code snippet should you use?

np.power(100, 50, dtype=np.int64)\nprint(np.iinfo(np.int64).max)

Incorrect; even with a 64-bit integer, the calculation will overflow, as 100 ** 50 exceeds the maximum value for np.int64.

result = np.power(100, 50)\nprint(result)

Incorrect; without specifying dtype, NumPy may default to np.int64 (depending on the platform), which will not accurately represent 100 ** 50 due to overflow.

np.power(100, 50, dtype=np.float64)\nprint(np.finfo(np.float64).max)

Correct; using dtype=np.float64 allows for a large range of values, including very large numbers, avoiding integer overflow and providing an inexact but large enough representation.

result = 100 ** 50\nprint(result.dtype)

1. Understand

2. Apply

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