我有
python pandas和numpy的奇怪问题.
>>> np.float64(1) * np.float64(85000) * np.float64(7.543709)
641215.26500000001
>>> round( np.float64(1) * np.float64(85000) * np.float64(7.543709), 2 )
641215.26000000001
>>> np.round( np.float64(1) * np.float64(85000) * np.float64(7.543709), 2 )
641215.26000000001
如何舍入才能得到正确的结果641215.27?
最佳答案 Numpy的圆形方法有利于偶数,看看精简的numpy源代码:
def round_(a, decimals=0, out=None):
return around(a, decimals=decimals, out=out)
def around(a, decimals=0, out=None):
"""
Evenly round to the given number of decimals.
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 [1]_ 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])
"""
例:
如果你需要打印字符串,你可以格式化它,给你正确的答案:
import numpy as np
num = np.float64(1) * np.float64(85000) * np.float64(7.543709)
print(num)
print(float("{0:.2f}".format(num)))
print(np.round(num, 2))
print()
num += 0.02
print(num)
print(float("{0:.2f}".format(num)))
print(np.round(num, 2))
给你
641215.265
641215.27
641215.26
641215.285
641215.29
641215.28