【numpy】【ndarray】指定每个元素保留小数点后多少位【around】【round】_array限制小数位数

作者：繁依Fanyi0 | 2024-06-16 11:15:57

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array限制小数位数

dump_svmlight_file() 函数内部无法指定输出结果保留小数点后多少位，因此需提前调用函数限制小数点位数，相当于 python 原生的 round() 函数，引出 numpy.around() 函数。

numpy.around¶

numpy.around(a, decimals=0, out=None)

Evenly round to the given number of decimals.

参数

参数	a : array_like Input data. decimals : int, optional Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. See `doc.ufuncs` (Section “Output arguments”) for details.
返回值	rounded_array : ndarray An array of the same type as a, containing the rounded values. Unless out was specified, a new array is created. A reference to the result is returned. The real and imaginary parts of complex numbers are rounded separately. The result of rounding a float is a float.

a : array_like

Input data.

decimals : int, optional

Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point.

out : ndarray, optional

Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. See doc.ufuncs (Section “Output arguments”) for details.

返回值

rounded_array : ndarray

An array of the same type as a, containing the rounded values. Unless out was specified, a new array is created. A reference to the result is returned.

The real and imaginary parts of complex numbers are rounded separately. The result of rounding a float is a float.

See also

ndarray.round

equivalent method

ceil, fix, floor, rint, trunc

Notes

For values exactly halfway between rounded decimal values, NumPy rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0, -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due to the inexact representation of decimal fractions in the IEEE floating point standard [R9] and errors introduced when scaling by powers of ten.

Examples


>>> np.around([0.37, 1.64])
array([ 0.,  2.])
>>> np.around([0.37, 1.64], decimals=1)
array([ 0.4,  1.6])
>>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
array([ 0.,  2.,  2.,  4.,  4.])
>>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
array([ 1,  2,  3, 11])
>>> np.around([1,2,3,11], decimals=-1)
array([ 0,  0,  0, 10])

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