NumPy float types: a demonstration of precision (Python 3)

NumPy float types: a demonstration of precision

The different NumPy float types allow us to store floats in different precision, dependent on the number of bits we allow the float to use. The larger the number of allowed bits, the more precision our array’s elements will have. E.g., np.float16 will use 16 bits (two bytes), while np.float64 takes up 64 bits (8 bytes).

Increased precision comes at the expense of memory and performance. Still, the rule of thumb is to err on the safe side and use np.float64 by default unless you have a good reason to use something else. E.g., if you can spare some precision and performance and memory usage are of the essence, use something smaller.

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