我有一个嵌套的Python列表，如下所示：
my_list = [[3.74, 5162, 13683628846.64, 12783387559.86, 1.81],
[9.55, 116, 189688622.37, 260332262.0, 1.97],
[2.2, 768, 6004865.13, 5759960.98, 1.21],
[3.74, 4062, 3263822121.39, 3066869087.9, 1.93],
[1.91, 474, 44555062.72, 44555062.72, 0.41],
[5.8, 5006, 8254968918.1, 7446788272.74, 3.25],
[4.5, 7887, 30078971595.46, 27814989471.31, 2.18],
[7.03, 116, 66252511.46, 81109291.0, 1.56],
[6.52, 116, 47674230.76, 57686991.0, 1.43],
[1.85, 623, 3002631.96, 2899484.08, 0.64],
[13.76, 1227, 1737874137.5, 1446511574.32, 4.32],
[13.76, 1227, 1737874137.5, 1446511574.32, 4.32]]
然后我导入Numpy，并将打印选项设置为(suppress=True)。当我创建一个数组时：
my_array = numpy.array(my_list)
我不能为我的生活压制科学记谱法：
[[ 3.74000000e+00 5.16200000e+03 1.36836288e+10 1.27833876e+10
1.81000000e+00]
[ 9.55000000e+00 1.16000000e+02 1.89688622e+08 2.60332262e+08
1.97000000e+00]
[ 2.20000000e+00 7.68000000e+02 6.00486513e+06 5.75996098e+06
1.21000000e+00]
[ 3.74000000e+00 4.06200000e+03 3.26382212e+09 3.06686909e+09
1.93000000e+00]
[ 1.91000000e+00 4.74000000e+02 4.45550627e+07 4.45550627e+07
4.10000000e-01]
[ 5.80000000e+00 5.00600000e+03 8.25496892e+09 7.44678827e+09
3.25000000e+00]
[ 4.50000000e+00 7.88700000e+03 3.00789716e+10 2.78149895e+10
2.18000000e+00]
[ 7.03000000e+00 1.16000000e+02 6.62525115e+07 8.11092910e+07
1.56000000e+00]
[ 6.52000000e+00 1.16000000e+02 4.76742308e+07 5.76869910e+07
1.43000000e+00]
[ 1.85000000e+00 6.23000000e+02 3.00263196e+06 2.89948408e+06
6.40000000e-01]
[ 1.37600000e+01 1.22700000e+03 1.73787414e+09 1.44651157e+09
4.32000000e+00]
[ 1.37600000e+01 1.22700000e+03 1.73787414e+09 1.44651157e+09
4.32000000e+00]]
如果我直接创建一个简单的numpy数组：
new_array = numpy.array([1.5, 4.65, 7.845])
我没有问题，打印如下：
[ 1.5 4.65 7.845]
有谁知道我的问题是什么？
答案 0 :(得分：203)
我猜您需要的是np.set_printoptions(suppress=True)，有关详细信息，请参阅此处：
http://pythonquirks.blogspot.fr/2009/10/controlling-printing-in-numpy.html
对于SciPy.org numpy文档，其中包含所有函数参数（上面链接中未详细说明抑制），请参见此处：https://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
答案 1 :(得分：21)
以下是对正在进行的操作的解释，滚动到底部进行代码演示。
将参数suppress=True传递给函数set_printoptions仅适用于符合分配给它的默认8个字符空间的数字，如下所示：
import numpy as np
np.set_printoptions(suppress=True) #prevent numpy exponential
#notation on print, default False
# tiny med large
a = np.array([1.01e-5, 22, 1.2345678e7]) #notice how index 2 is 8
#digits wide
print(a) #prints [ 0.0000101 22. 12345678. ]
但是，如果传入的宽度超过8个字符，则会再次施加指数表示法，如下所示：
np.set_printoptions(suppress=True)
a = np.array([1.01e-5, 22, 1.2345678e10]) #notice how index 2 is 10
#digits wide, too wide!
#exponential notation where we've told it not to!
print(a) #prints [1.01000000e-005 2.20000000e+001 1.23456780e+10]
这里有set_printoptions(formatter=...)来指定打印和舍入的选项。告诉set_printoptions只打印裸露的浮动：
np.set_printoptions(suppress=True,
formatter={'float_kind':'{:f}'.format})
a = np.array([1.01e-5, 22, 1.2345678e30]) #notice how index 2 is 30
#digits wide.
#Ok good, no exponential notation in the large numbers:
print(a) #prints [0.000010 22.000000 1234567799999999979944197226496.000000]
我们强制抑制了指数表示法，但它没有舍入或对齐，因此请指定额外的格式选项：
np.set_printoptions(suppress=True,
formatter={'float_kind':'{:0.2f}'.format}) #float, 2 units
#precision right, 0 on left
a = np.array([1.01e-5, 22, 1.2345678e30]) #notice how index 2 is 30
#digits wide
print(a) #prints [0.00 22.00 1234567799999999979944197226496.00]
强制抑制ndarray中所有指数概念的一个缺点是，如果你的ndarray在无限远处得到一个巨大的浮动值，并且你打印出来，你就会在页面满了的时候被炸成碎片数字。
from pprint import pprint
import numpy as np
#chaotic python list of lists with very different numeric magnitudes
my_list = [[3.74, 5162, 13683628846.64, 12783387559.86, 1.81],
[9.55, 116, 189688622.37, 260332262.0, 1.97],
[2.2, 768, 6004865.13, 5759960.98, 1.21],
[3.74, 4062, 3263822121.39, 3066869087.9, 1.93],
[1.91, 474, 44555062.72, 44555062.72, 0.41],
[5.8, 5006, 8254968918.1, 7446788272.74, 3.25],
[4.5, 7887, 30078971595.46, 27814989471.31, 2.18],
[7.03, 116, 66252511.46, 81109291.0, 1.56],
[6.52, 116, 47674230.76, 57686991.0, 1.43],
[1.85, 623, 3002631.96, 2899484.08, 0.64],
[13.76, 1227, 1737874137.5, 1446511574.32, 4.32],
[13.76, 1227, 1737874137.5, 1446511574.32, 4.32]]
#convert python list of lists to numpy ndarray called my_array
my_array = np.array(my_list)
#This is a little recursive helper function converts all nested
#ndarrays to python list of lists so that pretty printer knows what to do.
def arrayToList(arr):
if type(arr) == type(np.array):
#If the passed type is an ndarray then convert it to a list and
#recursively convert all nested types
return arrayToList(arr.tolist())
else:
#if item isn't an ndarray leave it as is.
return arr
#suppress exponential notation, define an appropriate float formatter
#specify stdout line width and let pretty print do the work
np.set_printoptions(suppress=True,
formatter={'float_kind':'{:16.3f}'.format}, linewidth=130)
pprint(arrayToList(my_array))
打印：
array([[ 3.740, 5162.000, 13683628846.640, 12783387559.860, 1.810],
[ 9.550, 116.000, 189688622.370, 260332262.000, 1.970],
[ 2.200, 768.000, 6004865.130, 5759960.980, 1.210],
[ 3.740, 4062.000, 3263822121.390, 3066869087.900, 1.930],
[ 1.910, 474.000, 44555062.720, 44555062.720, 0.410],
[ 5.800, 5006.000, 8254968918.100, 7446788272.740, 3.250],
[ 4.500, 7887.000, 30078971595.460, 27814989471.310, 2.180],
[ 7.030, 116.000, 66252511.460, 81109291.000, 1.560],
[ 6.520, 116.000, 47674230.760, 57686991.000, 1.430],
[ 1.850, 623.000, 3002631.960, 2899484.080, 0.640],
[ 13.760, 1227.000, 1737874137.500, 1446511574.320, 4.320],
[ 13.760, 1227.000, 1737874137.500, 1446511574.320, 4.320]])
import numpy as np
#chaotic python list of lists with very different numeric magnitudes
my_list = [[3.74, 5162, 13683628846.64, 12783387559.86, 1.81],
[9.55, 116, 189688622.37, 260332262.0, 1.97],
[2.2, 768, 6004865.13, 5759960.98, 1.21],
[3.74, 4062, 3263822121.39, 3066869087.9, 1.93],
[1.91, 474, 44555062.72, 44555062.72, 0.41],
[5.8, 5006, 8254968918.1, 7446788272.74, 3.25],
[4.5, 7887, 30078971595.46, 27814989471.31, 2.18],
[7.03, 116, 66252511.46, 81109291.0, 1.56],
[6.52, 116, 47674230.76, 57686991.0, 1.43],
[1.85, 623, 3002631.96, 2899484.08, 0.64],
[13.76, 1227, 1737874137.5, 1446511574.32, 4.32],
[13.76, 1227, 1737874137.5, 1446511574.32, 4.32]]
import sys
#convert python list of lists to numpy ndarray called my_array
my_array = np.array(my_list)
#following two lines do the same thing, showing that np.savetxt can
#correctly handle python lists of lists and numpy 2D ndarrays.
np.savetxt(sys.stdout, my_list, '%16.2f')
np.savetxt(sys.stdout, my_array, '%16.2f')
打印：
3.74 5162.00 13683628846.64 12783387559.86 1.81
9.55 116.00 189688622.37 260332262.00 1.97
2.20 768.00 6004865.13 5759960.98 1.21
3.74 4062.00 3263822121.39 3066869087.90 1.93
1.91 474.00 44555062.72 44555062.72 0.41
5.80 5006.00 8254968918.10 7446788272.74 3.25
4.50 7887.00 30078971595.46 27814989471.31 2.18
7.03 116.00 66252511.46 81109291.00 1.56
6.52 116.00 47674230.76 57686991.00 1.43
1.85 623.00 3002631.96 2899484.08 0.64
13.76 1227.00 1737874137.50 1446511574.32 4.32
13.76 1227.00 1737874137.50 1446511574.32 4.32
答案 2 :(得分：20)
对于1D和2D数组，您可以使用np.savetxt使用特定格式字符串进行打印：
>>> import sys
>>> x = numpy.arange(20).reshape((4,5))
>>> numpy.savetxt(sys.stdout, x, '%5.2f')
0.00 1.00 2.00 3.00 4.00
5.00 6.00 7.00 8.00 9.00
10.00 11.00 12.00 13.00 14.00
15.00 16.00 17.00 18.00 19.00
你在v1.3中使用numpy.set_printoptions或numpy.array2string的选项非常笨重且有限（例如无法抑制大数字的科学记数法）。使用numpy.set_printoptions（formatter = ..）和numpy.array2string（style = ..），看起来这将随着未来版本而改变。
答案 3 :(得分：0)
您可以编写一个将科学记数法转换为常规的函数，例如
def sc2std(x):
s = str(x)
if 'e' in s:
num,ex = s.split('e')
if '-' in num:
negprefix = '-'
else:
negprefix = ''
num = num.replace('-','')
if '.' in num:
dotlocation = num.index('.')
else:
dotlocation = len(num)
newdotlocation = dotlocation + int(ex)
num = num.replace('.','')
if (newdotlocation < 1):
return negprefix+'0.'+'0'*(-newdotlocation)+num
if (newdotlocation > len(num)):
return negprefix+ num + '0'*(newdotlocation - len(num))+'.0'
return negprefix + num[:newdotlocation] + '.' + num[newdotlocation:]
else:
return s
答案 4 :(得分：-1)
添加一个“”“即可完成打印：
print(myarray ,"")
将抑制科学计数法。