I have two simple one-dimensional arrays in NumPy. I should be able to concatenate them using numpy.concatenate. But I get this error for the code below:
我有两个简单的一维数组。我应该能够使用numpi .concatenate将它们连接起来。但是我得到下面代码的错误:
TypeError: only length-1 arrays can be converted to Python scalars
TypeError:只能将长度为1的数组转换为Python标量
Code
import numpy
a = numpy.array([1, 2, 3])
b = numpy.array([5, 6])
numpy.concatenate(a, b)
Why?
为什么?
4 个解决方案
#1
219
The line should be:
线应该是:
numpy.concatenate([a,b])
The arrays you want to concatenate need to passed in as a sequence, not as separate arguments.
需要连接的数组需要作为序列传入,而不是作为单独的参数传入。
From the NumPy documentation:
从NumPy文档:
numpy.concatenate((a1, a2, ...), axis=0)
numpy。连接((a1,a2,…),轴= 0)
Join a sequence of arrays together.
一起加入一系列阵列。
It was trying to interpret your b
as the axis parameter, which is why it complained it couldn't convert it into a scalar.
它试图把b解释为轴参数,这就是为什么它抱怨不能把它转换成标量的原因。
#2
20
The first parameter to concatenate
should itself be a sequence of arrays to concatenate:
要连接的第一个参数本身应该是要连接的数组序列:
numpy.concatenate((a,b)) # Note the extra parentheses.
#3
7
An alternative ist to use the short form of "concatenate" which is either "r_[...]" or "c_[...]" as shown in the example code beneath (see http://wiki.scipy.org/NumPy_for_Matlab_Users for additional information):
另一种方法是使用缩写形式“concatenate”,即“r_[…]”。]”或“c_(…]“如下面的示例代码所示(参见http://wiki.scipy.org/NumPy_for_Matlab_Users获取其他信息):
%pylab
vector_a = r_[0.:10.] #short form of "arange"
vector_b = array([1,1,1,1])
vector_c = r_[vector_a,vector_b]
print vector_a
print vector_b
print vector_c, '\n\n'
a = ones((3,4))*4
print a, '\n'
c = array([1,1,1])
b = c_[a,c]
print b, '\n\n'
a = ones((4,3))*4
print a, '\n'
c = array([[1,1,1]])
b = r_[a,c]
print b
print type(vector_b)
Which results in:
结果:
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
[1 1 1 1]
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 1. 1. 1. 1.]
[[ 4. 4. 4. 4.]
[ 4. 4. 4. 4.]
[ 4. 4. 4. 4.]]
[[ 4. 4. 4. 4. 1.]
[ 4. 4. 4. 4. 1.]
[ 4. 4. 4. 4. 1.]]
[[ 4. 4. 4.]
[ 4. 4. 4.]
[ 4. 4. 4.]
[ 4. 4. 4.]]
[[ 4. 4. 4.]
[ 4. 4. 4.]
[ 4. 4. 4.]
[ 4. 4. 4.]
[ 1. 1. 1.]]
#4
7
There are several possibilities for concatenating 1D arrays, e.g.,
连接1D数组有几种可能性,例如,
numpy.r_[a, a],
numpy.stack([a, a]).reshape(-1),
numpy.hstack([a, a]),
numpy.concatenate([a, a])
All those options are equally fast for large arrays; for small ones, concatenate
has a slight edge:
对于大型数组,所有这些选项都同样快速;对于小的,连接有一个小的边缘:
The plot was created with perfplot:
这个情节是用完美的情节创作的:
import numpy
import perfplot
perfplot.show(
setup=lambda n: numpy.random.rand(n),
kernels=[
lambda a: numpy.r_[a, a],
lambda a: numpy.stack([a, a]).reshape(-1),
lambda a: numpy.hstack([a, a]),
lambda a: numpy.concatenate([a, a])
],
labels=['r_', 'stack+reshape', 'hstack', 'concatenate'],
n_range=[2**k for k in range(19)],
xlabel='len(a)',
logx=True,
logy=True,
)
#1
219
The line should be:
线应该是:
numpy.concatenate([a,b])
The arrays you want to concatenate need to passed in as a sequence, not as separate arguments.
需要连接的数组需要作为序列传入,而不是作为单独的参数传入。
From the NumPy documentation:
从NumPy文档:
numpy.concatenate((a1, a2, ...), axis=0)
numpy。连接((a1,a2,…),轴= 0)
Join a sequence of arrays together.
一起加入一系列阵列。
It was trying to interpret your b
as the axis parameter, which is why it complained it couldn't convert it into a scalar.
它试图把b解释为轴参数,这就是为什么它抱怨不能把它转换成标量的原因。
#2
20
The first parameter to concatenate
should itself be a sequence of arrays to concatenate:
要连接的第一个参数本身应该是要连接的数组序列:
numpy.concatenate((a,b)) # Note the extra parentheses.
#3
7
An alternative ist to use the short form of "concatenate" which is either "r_[...]" or "c_[...]" as shown in the example code beneath (see http://wiki.scipy.org/NumPy_for_Matlab_Users for additional information):
另一种方法是使用缩写形式“concatenate”,即“r_[…]”。]”或“c_(…]“如下面的示例代码所示(参见http://wiki.scipy.org/NumPy_for_Matlab_Users获取其他信息):
%pylab
vector_a = r_[0.:10.] #short form of "arange"
vector_b = array([1,1,1,1])
vector_c = r_[vector_a,vector_b]
print vector_a
print vector_b
print vector_c, '\n\n'
a = ones((3,4))*4
print a, '\n'
c = array([1,1,1])
b = c_[a,c]
print b, '\n\n'
a = ones((4,3))*4
print a, '\n'
c = array([[1,1,1]])
b = r_[a,c]
print b
print type(vector_b)
Which results in:
结果:
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
[1 1 1 1]
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 1. 1. 1. 1.]
[[ 4. 4. 4. 4.]
[ 4. 4. 4. 4.]
[ 4. 4. 4. 4.]]
[[ 4. 4. 4. 4. 1.]
[ 4. 4. 4. 4. 1.]
[ 4. 4. 4. 4. 1.]]
[[ 4. 4. 4.]
[ 4. 4. 4.]
[ 4. 4. 4.]
[ 4. 4. 4.]]
[[ 4. 4. 4.]
[ 4. 4. 4.]
[ 4. 4. 4.]
[ 4. 4. 4.]
[ 1. 1. 1.]]
#4
7
There are several possibilities for concatenating 1D arrays, e.g.,
连接1D数组有几种可能性,例如,
numpy.r_[a, a],
numpy.stack([a, a]).reshape(-1),
numpy.hstack([a, a]),
numpy.concatenate([a, a])
All those options are equally fast for large arrays; for small ones, concatenate
has a slight edge:
对于大型数组,所有这些选项都同样快速;对于小的,连接有一个小的边缘:
The plot was created with perfplot:
这个情节是用完美的情节创作的:
import numpy
import perfplot
perfplot.show(
setup=lambda n: numpy.random.rand(n),
kernels=[
lambda a: numpy.r_[a, a],
lambda a: numpy.stack([a, a]).reshape(-1),
lambda a: numpy.hstack([a, a]),
lambda a: numpy.concatenate([a, a])
],
labels=['r_', 'stack+reshape', 'hstack', 'concatenate'],
n_range=[2**k for k in range(19)],
xlabel='len(a)',
logx=True,
logy=True,
)