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November 28, 2017 19:40
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Numpy types ordering regarding their maximum values
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{ | |
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"I'm doing some type optimization and it raised me a question: how int and float types can be ordered with respect to their maximum values?" | |
] | |
}, | |
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"source": [ | |
"import numpy as np" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"From [this numpy doc](https://docs.scipy.org/doc/numpy-1.10.1/reference/arrays.scalars.html), I can reach the following types:" | |
] | |
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"int_types = [np.int8, np.int16, np.int32, np.int64]\n", | |
"float_types = [np.float16, np.float32, np.float64]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Using numpy's [iinfo](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.iinfo.html) and [iinfo](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.finfo.html), I can get their maximum values." | |
] | |
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"source": [ | |
"int_maxes = map(lambda x: np.iinfo(x).max, int_types)\n", | |
"float_maxes = map(lambda x: np.finfo(x).max, float_types)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
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"Putting all together:" | |
] | |
}, | |
{ | |
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"execution_count": 4, | |
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{ | |
"data": { | |
"text/plain": [ | |
"[(numpy.int8, 127),\n", | |
" (numpy.int16, 32767),\n", | |
" (numpy.int32, 2147483647),\n", | |
" (numpy.int64, 9223372036854775807),\n", | |
" (numpy.float16, 65504.0),\n", | |
" (numpy.float32, 3.4028235e+38),\n", | |
" (numpy.float64, 1.7976931348623157e+308)]" | |
] | |
}, | |
"execution_count": 4, | |
"metadata": {}, | |
"output_type": "execute_result" | |
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], | |
"source": [ | |
"maxes = list(zip(int_types, int_maxes)) + list(zip(float_types, float_maxes))\n", | |
"maxes" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"And sorting (biggest to smallest):" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-11-28T19:37:44.647072Z", | |
"start_time": "2017-11-28T19:37:44.531876Z" | |
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}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[(numpy.float64, 1.7976931348623157e+308),\n", | |
" (numpy.float32, 3.4028235e+38),\n", | |
" (numpy.int64, 9223372036854775807),\n", | |
" (numpy.int32, 2147483647),\n", | |
" (numpy.float16, 65504.0),\n", | |
" (numpy.int16, 32767),\n", | |
" (numpy.int8, 127)]" | |
] | |
}, | |
"execution_count": 5, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"sorted(maxes, key=lambda x: x[1], reverse=True)" | |
] | |
} | |
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