In Numpy, we want to generate an n-dimensional random vector under the constraint that the sum of the number of elements is N.

>>import numpy as np
>>>n=7
>>>N=3
>> random_number=np.random.rand(n)
>>print(random_number)
[ 0.61444461  0.44538832  0.80188737  0.57265561  0.05225624  0.07613083
  0.55669533]
>>print(np.sum(random_number))
3.11945832298
>>answer=N* random_number/np.sum (random_number)
>> print (answer)
[ 0.59091472  0.42833237  0.77117943  0.55072601  0.05025511  0.07321543
  0.53537692]
>>print(np.sum(answer))
3.0

import numpy as np

n = 7
N = 3

answer=np.random.dirichlet(np.ones(n))*N

print("n=", answer.size)
print("N=", answer.sum())
print(answer)

n=7
N = 3.0
[ 0.23253049  1.45542211  0.36459806  0.39199048  0.31374561  0.20020738
  0.04150587]

import numpy as np, numpy.random
// Let N=3 sum be 3, i.e., the range of random numbers below be 3. 
 The lower limit is 0.0 and the upper limit is 3.0, and np.random.uniform should not output the upper limit.
// n=6 The number of elements is set to 6.
values = numpy.array([0.0,N]);
x = np.random.uniform (0.0, N, size = n-2);
values = numpy.append(x, values);
values.sort();
print(values) 
// [0.0.83087151 1.27426042 1.47701249 2.835177233.] 0, 3. should be 0.0, 3.0.By repl.it.
results = numpy.array([]);
for i in range (1, 6):
     results = numpy.append(results, values[i]-values[i-1]);
print(np.sum(results));
// 3.0