I have a nested Python list that looks like the following:
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]]
I then import Numpy, and set print options to (suppress=True). When I create an array:
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)
I can't for the life of me suppress scientific notation:
我不能为我的生活压抑科学记数法：
[[ 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]]
If I create a simple numpy array directly:
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])
I have no problem and it prints as follows:
我没有问题，它打印如下：
[ 1.5 4.65 7.845]
Does anyone know what my problem is?
有谁知道我的问题是什么？
4 个解决方案
#1
147
I guess what you need is np.set_printoptions(suppress=True), for details see here: http://pythonquirks.blogspot.fr/2009/10/controlling-printing-in-numpy.html
我想你需要的是np.set_printoptions（suppress = True），详见这里：http：//pythonquirks.blogspot.fr/2009/10/controlling-printing-in-numpy.html
For SciPy.org numpy documentation, which includes all function parameters (suppress isn't detailed in the above link), see here: https://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
对于SciPy.org numpy的文档，其中包括所有功能参数（抑制未在上述链接中详述），在这里看到：https://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
#2
15
for 1D and 2D arrays you can use np.savetxt to print using a specific format string:
对于一维和二维阵列可以使用np.savetxt使用特定格式字符串打印：
>>> 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
Your options with numpy.set_printoptions or numpy.array2string in v1.3 are pretty clunky and limited (for example no way to suppress scientific notation for large numbers). It looks like this will change with future versions, with numpy.set_printoptions(formatter=..) and numpy.array2string(style=..).
您在V1.3 numpy.set_printoptions或numpy.array2string选项是相当笨重，限制（例如没有办法抑制了大量的科学记数法）。它看起来像这样将与未来版本发生变化，numpy.set_printoptions（格式= ..）和numpy.array2string（样式= ..）。
#3
9
Python Force-suppress all exponential notation when printing numpy ndarrays, wrangle text justification, rounding and print options:
What follows is an explanation for what is going on, scroll to bottom for code demos.
以下是对正在发生的事情作出解释，滚动到底部的代码演示。
Passing parameter suppress=True to function set_printoptions works only for numbers that fit in the default 8 character space allotted to it, like this:
传递参数抑制= True以功能set_printoptions只适用于符合分配给它，这样默认的8个字符的空格数：
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. ]
However if you pass in a number greater than 8 characters wide, exponential notation is imposed again, like this:
但是，如果你在多个通面宽大于8个字符，指数符号被再次征收，就像这样：
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]
numpy has a choice between chopping your number in half thus misrepresenting it, or forcing exponential notation, it chooses the latter.
numpy的有一半砍你的电话号码从而歪曲了，或者迫使指数符号，它选择了后者之间做出选择。
Here comes set_printoptions(formatter=...) to the rescue to specify options for printing and rounding. Tell set_printoptions to just print bare a bare float:
这里谈到set_printoptions（格式= ...）救援指定打印和舍入选项。告诉set_printoptions只是打印裸裸浮动：
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]
We've force-suppressed the exponential notation, but it is not rounded or justified, so specify extra formatting options:
我们已经力抑制指数符号，但它不是圆的或合理的，所以指定额外的格式化选项：
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]
The drawback for force-suppressing all exponential notion in ndarrays is that if your ndarray gets a huge float value near infinity in it, and you print it, you're going to get blasted in the face with a page full of numbers.
对于力抑制ndarrays所有指数概念的缺点是，如果你ndarray获取它无限接近一个巨大的浮点值，并打印它，你会得到轰出在脸上一整页的数字。
Full example Demo 1:
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))
Prints:
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]])
Full example Demo 2:
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')
Prints:
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
#4
0
You could write a function that converts a scientific notation to regular, something like
您可以编写一个将科学记数法转换为常规符号的函数
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
#1
147
I guess what you need is np.set_printoptions(suppress=True), for details see here: http://pythonquirks.blogspot.fr/2009/10/controlling-printing-in-numpy.html
我想你需要的是np.set_printoptions（suppress = True），详见这里：http：//pythonquirks.blogspot.fr/2009/10/controlling-printing-in-numpy.html
For SciPy.org numpy documentation, which includes all function parameters (suppress isn't detailed in the above link), see here: https://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
对于SciPy.org numpy的文档，其中包括所有功能参数（抑制未在上述链接中详述），在这里看到：https://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
#2
15
for 1D and 2D arrays you can use np.savetxt to print using a specific format string:
对于一维和二维阵列可以使用np.savetxt使用特定格式字符串打印：
>>> 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
Your options with numpy.set_printoptions or numpy.array2string in v1.3 are pretty clunky and limited (for example no way to suppress scientific notation for large numbers). It looks like this will change with future versions, with numpy.set_printoptions(formatter=..) and numpy.array2string(style=..).
您在V1.3 numpy.set_printoptions或numpy.array2string选项是相当笨重，限制（例如没有办法抑制了大量的科学记数法）。它看起来像这样将与未来版本发生变化，numpy.set_printoptions（格式= ..）和numpy.array2string（样式= ..）。
#3
9
Python Force-suppress all exponential notation when printing numpy ndarrays, wrangle text justification, rounding and print options:
What follows is an explanation for what is going on, scroll to bottom for code demos.
以下是对正在发生的事情作出解释，滚动到底部的代码演示。
Passing parameter suppress=True to function set_printoptions works only for numbers that fit in the default 8 character space allotted to it, like this:
传递参数抑制= True以功能set_printoptions只适用于符合分配给它，这样默认的8个字符的空格数：
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. ]
However if you pass in a number greater than 8 characters wide, exponential notation is imposed again, like this:
但是，如果你在多个通面宽大于8个字符，指数符号被再次征收，就像这样：
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]
numpy has a choice between chopping your number in half thus misrepresenting it, or forcing exponential notation, it chooses the latter.
numpy的有一半砍你的电话号码从而歪曲了，或者迫使指数符号，它选择了后者之间做出选择。
Here comes set_printoptions(formatter=...) to the rescue to specify options for printing and rounding. Tell set_printoptions to just print bare a bare float:
这里谈到set_printoptions（格式= ...）救援指定打印和舍入选项。告诉set_printoptions只是打印裸裸浮动：
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]
We've force-suppressed the exponential notation, but it is not rounded or justified, so specify extra formatting options:
我们已经力抑制指数符号，但它不是圆的或合理的，所以指定额外的格式化选项：
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]
The drawback for force-suppressing all exponential notion in ndarrays is that if your ndarray gets a huge float value near infinity in it, and you print it, you're going to get blasted in the face with a page full of numbers.
对于力抑制ndarrays所有指数概念的缺点是，如果你ndarray获取它无限接近一个巨大的浮点值，并打印它，你会得到轰出在脸上一整页的数字。
Full example Demo 1:
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))
Prints:
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]])
Full example Demo 2:
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')
Prints:
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
#4
0
You could write a function that converts a scientific notation to regular, something like
您可以编写一个将科学记数法转换为常规符号的函数
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