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How to create an array with arrays in one function

I am trying to create an output that will be an array that contains 5 "sub-arrays". Every array should include 10 random numbers between 0 and 10.
I have this code:
def count_tweets():
big_array = []
for i in range(5):
array = []
for p in range(10):
array.append(random.randint(0,10))
big_array.append(array)
print(big_array)
I get a result like:
[[4, 2, 7, 1, 3, 2, 6, 9, 3, 10]]
[[4, 2, 7, 1, 3, 2, 6, 9, 3, 10], [5, 10, 7, 10, 7, 2, 1, 4, 8, 3]]
[[4, 2, 7, 1, 3, 2, 6, 9, 3, 10], [5, 10, 7, 10, 7, 2, 1, 4, 8, 3], [2, 7, 1, 3, 8, 5, 7, 6, 0, 0]]
[[4, 2, 7, 1, 3, 2, 6, 9, 3, 10], [5, 10, 7, 10, 7, 2, 1, 4, 8, 3], [2, 7, 1, 3, 8, 5, 7, 6, 0, 0], [0, 1, 9, 9, 4, 2, 10, 4, 3, 8]]
[[4, 2, 7, 1, 3, 2, 6, 9, 3, 10], [5, 10, 7, 10, 7, 2, 1, 4, 8, 3], [2, 7, 1, 3, 8, 5, 7, 6, 0, 0], [0, 1, 9, 9, 4, 2, 10, 4, 3, 8], [3, 7, 3, 5, 4, 0, 2, 8, 6, 2]]
But instead it should be like:
[[0,2,6,7,9,4,6,1,10,5],[1,3,5,9,8,7,6,9,0,10],[3,5,1,7,9,4,7,2,7,9],[10,2,8,5,6,9,2,3,5,9],[4,5,2,9,8,7,5,1,3,5]]
I cannot seem to get the indentation correct. How do I fix the code?

So what you did was put the print() statement inside a loop, which will print each time it runs.
import random
def count_tweets():
big_array = []
for i in range(5):
array = []
for p in range(10):
array.append(random.randint(0,10))
big_array.append(array)
print(big_array)
count_tweets()
Hope this helps :)

You got it right, just slide the print out of the for loop.(delete four spaces before print())

Python Minizinc gives sometimes a different result than Minizinc IDE

Depending on the input data I sometimes obtain a different result (different objective and solution) when using the Minizinc Python library or the Minizinc IDE.
My Python code is the following:
from minizinc import Instance, Model, Solver
import datetime
gecode = Solver.lookup("gecode")
model = Model()
model.add_file("./model.mzn")
model.add_file("./data.dzn", parse_data=True)
instance = Instance(gecode, model)
TIMELIMIT = datetime.timedelta(seconds=60)
result = instance.solve(timeout=TIMELIMIT)
print(result.status)
result.solution
As far as I can tell, my solver parameters are the same in the IDE, I use the same model and same data.
I can see the following command in the IDE:
Command: minizinc --json-stream --param-file-no-push /private/var/folders/hf/_75w1jh170x75y2_2vwt60lc0000gn/T/mzn_KneEzb.mpc model.mzn data.dzn
Configuration:
{
"intermediate-solutions": true,
"solver": "org.gecode.gecode#6.3.0",
"time-limit": 60000
}
In one specific input data instance, IDE finds an optimal at obj = 80, and Python finds obj = 104.
In another instance of input data, both IDE and Python find the same obj.
What can explain this different result?
Update: I am adding the MiniZinc model and data:
model.mzn
int: nNodes = nC+2*nV;
% ============== Vehicle Routing Problem ============= %
include "globals.mzn";
int: nC; int: nV; int: timeBudget;
set of int: VEHICLE = 1..nV;
set of int: CUSTOMER = 1..nC;
set of int: NODES = 1..nNodes;
set of int: START_DEPOT_NODES = nC+1..nC+nV;
set of int: END_DEPOT_NODES = nC+nV+1..nC+2*nV;
set of int: TIME = 0..timeBudget;
array[NODES] of int: serviceTime;
array[NODES, NODES] of int: distance;
% Time windows
% departure time must be between TimeWindows[client_i, 1] and TimeWindows[client_i, 2]
array[1..nC, 1..2] of int: TimeWindows;
% -------- variables ------------- %
array[NODES] of var VEHICLE: vehicle;
array[NODES] of var NODES: successor;
array[NODES] of var TIME: arrivalTime;
% -------- Start Nodes, End Nodes constraints ---------- %
constraint forall (n in START_DEPOT_NODES) % associate each start
( vehicle[n] = n-nC ); % node with a vehicle
constraint forall (n in END_DEPOT_NODES) % associate each end
( vehicle[n] = n-nC-nV ); % node with a vehicle
% -------- Successor Nodes constraints ---------- %
constraint forall (n in nC+nV+1..nC+2*nV-1) % successors of end nodes
( successor[n] = n-nV+1 ); % are start nodes
constraint successor[nC+2*nV] = nC+1;
constraint forall (n in START_DEPOT_NODES) % vehicles leave the
( arrivalTime[n] = 0 ); % depot at time zero
constraint circuit(successor); % hamiltonian circuit
constraint forall (n in CUSTOMER) % use the same vehicle
( vehicle[successor[n]] = vehicle[n] ); % along a subtour
constraint forall (n in 1..nC+nV)
( arrivalTime[n] + serviceTime[n] + distance[n,successor[n]]
<= arrivalTime[successor[n]] ); % time constraints
% -------- Time windows constraints -------------- %
constraint
forall(i in 1..nC)(
TimeWindows[i, 1] <= arrivalTime[i]
);
constraint
forall(i in 1..nC)(
TimeWindows[i, 2] >= arrivalTime[i]
);
% -------- objective ------------ %
% minimize distance (could be travel time in hours)
var int: obj = (sum (n in NODES) (distance[n,successor[n]]));
solve minimize obj; % expected overall travel time of each vehicle
% -------- output ------------ %
output ["obj = \(obj) \n"];
data.dzn
nV =5; nC = 40; timeBudget = 5000;
serviceTime = [ 6, 7, 3, 10, 2, 5, 2, 9, 7, 6, 5, 7, 7, 9, 9, 3, 7, 9, 1, 2, 5, 4, 7, 6, 3, 4, 6, 6, 2, 1, 10, 1, 5, 4, 10, 2, 10, 9, 2, 10, 5, 6, 4, 9, 9, 10, 6, 6, 6, 5];
distance = [| 0, 3, 7, 2, 10, 1, 6, 1, 10, 7, 3, 9, 5, 10, 5, 6, 2, 4, 3, 6, 5, 4, 4, 8, 7, 8, 8, 7, 8, 8, 9, 5, 2, 2, 9, 1, 2, 3, 1, 6, 6, 1, 1, 5, 3, 1, 5, 1, 4, 9
|5, 0, 7, 7, 6, 9, 1, 2, 2, 7, 10, 1, 9, 7, 5, 9, 4, 9, 8, 8, 4, 2, 10, 6, 1, 2, 7, 7, 4, 6, 5, 7, 9, 1, 10, 8, 6, 9, 1, 8, 6, 10, 7, 5, 7, 4, 10, 6, 8, 2
|7, 7, 0, 8, 10, 10, 6, 5, 9, 7, 5, 3, 2, 8, 10, 6, 10, 9, 8, 2, 1, 9, 10, 7, 4, 6, 1, 5, 8, 8, 9, 1, 2, 2, 6, 3, 2, 7, 1, 3, 6, 9, 7, 3, 6, 6, 6, 3, 3, 8
|7, 3, 6, 0, 10, 5, 8, 2, 5, 7, 1, 10, 8, 2, 5, 6, 9, 7, 1, 2, 1, 3, 3, 10, 10, 3, 9, 7, 2, 3, 4, 10, 10, 7, 6, 4, 9, 9, 8, 4, 1, 5, 6, 3, 10, 4, 5, 9, 3, 7
|5, 4, 9, 4, 0, 9, 8, 1, 1, 4, 9, 2, 10, 4, 4, 9, 7, 6, 8, 10, 5, 5, 5, 7, 8, 10, 10, 7, 3, 1, 6, 2, 4, 6, 5, 3, 10, 1, 2, 7, 3, 1, 2, 8, 1, 5, 10, 1, 5, 5
|2, 4, 4, 4, 7, 0, 8, 9, 3, 2, 8, 7, 7, 7, 5, 9, 9, 8, 3, 2, 3, 8, 3, 2, 8, 4, 1, 6, 4, 3, 2, 3, 6, 4, 2, 10, 6, 5, 5, 5, 10, 6, 4, 10, 10, 10, 8, 3, 2, 4
|5, 3, 6, 3, 7, 1, 0, 9, 10, 5, 4, 10, 4, 8, 9, 8, 6, 4, 1, 9, 2, 1, 4, 4, 6, 6, 9, 1, 9, 1, 7, 2, 1, 5, 4, 3, 6, 4, 4, 10, 4, 10, 7, 10, 4, 7, 8, 1, 3, 4
|9, 1, 5, 4, 6, 5, 10, 0, 4, 8, 10, 7, 5, 10, 7, 5, 4, 4, 10, 7, 8, 6, 2, 7, 9, 6, 2, 3, 9, 8, 3, 9, 4, 6, 5, 1, 4, 6, 7, 6, 9, 1, 9, 5, 5, 5, 5, 6, 7, 1
|5, 6, 4, 2, 9, 1, 5, 4, 0, 3, 3, 3, 6, 1, 4, 10, 1, 10, 10, 4, 8, 4, 7, 2, 9, 4, 4, 4, 4, 10, 8, 9, 9, 7, 9, 3, 9, 9, 3, 7, 1, 9, 2, 1, 3, 9, 8, 4, 1, 8
|10, 5, 8, 5, 7, 2, 1, 1, 5, 0, 7, 2, 5, 10, 5, 7, 1, 7, 7, 8, 3, 1, 3, 3, 9, 10, 1, 7, 1, 3, 4, 1, 8, 8, 4, 4, 5, 3, 3, 2, 3, 9, 4, 2, 3, 8, 5, 7, 9, 9
|9, 5, 8, 1, 2, 3, 1, 5, 10, 4, 0, 8, 10, 9, 3, 5, 1, 2, 10, 4, 9, 2, 1, 1, 10, 1, 4, 7, 7, 8, 10, 2, 6, 7, 8, 7, 4, 3, 5, 7, 7, 8, 10, 2, 3, 4, 7, 6, 6, 7
|8, 10, 3, 2, 9, 4, 2, 3, 7, 4, 8, 0, 9, 5, 1, 6, 7, 7, 9, 5, 4, 2, 3, 8, 7, 1, 2, 10, 9, 5, 4, 4, 4, 9, 3, 4, 9, 9, 6, 9, 6, 10, 9, 3, 8, 3, 9, 3, 6, 2
|1, 1, 6, 3, 6, 1, 9, 3, 9, 2, 6, 1, 0, 5, 10, 5, 1, 7, 7, 5, 3, 1, 7, 3, 7, 3, 5, 10, 1, 8, 3, 6, 2, 6, 2, 1, 2, 5, 8, 8, 10, 4, 6, 1, 10, 2, 3, 3, 1, 1
|3, 5, 3, 4, 7, 7, 10, 9, 5, 3, 5, 3, 8, 0, 5, 6, 5, 9, 8, 8, 4, 1, 6, 8, 5, 9, 7, 5, 7, 5, 5, 1, 3, 6, 7, 9, 9, 10, 2, 3, 2, 6, 8, 9, 10, 3, 2, 1, 4, 5
|6, 5, 6, 8, 6, 5, 2, 9, 5, 3, 10, 8, 4, 6, 0, 10, 2, 2, 3, 3, 7, 5, 5, 9, 4, 2, 10, 10, 4, 4, 2, 3, 4, 8, 10, 6, 1, 1, 1, 9, 8, 1, 9, 5, 6, 9, 7, 5, 7, 8
|6, 9, 8, 1, 8, 10, 7, 1, 4, 2, 8, 1, 4, 3, 2, 0, 3, 7, 4, 9, 5, 2, 10, 2, 8, 9, 3, 3, 2, 8, 9, 4, 1, 10, 2, 10, 9, 6, 10, 2, 9, 9, 9, 2, 4, 8, 7, 9, 9, 5
|8, 3, 4, 5, 5, 5, 4, 6, 6, 2, 6, 6, 5, 3, 4, 6, 0, 7, 1, 7, 1, 8, 5, 10, 8, 8, 9, 6, 10, 3, 10, 6, 4, 6, 8, 9, 9, 9, 1, 7, 3, 5, 4, 7, 3, 7, 2, 10, 10, 7
|10, 7, 1, 4, 7, 6, 2, 5, 1, 9, 7, 9, 6, 9, 6, 2, 3, 0, 5, 4, 3, 4, 2, 5, 5, 1, 1, 1, 1, 10, 4, 5, 6, 2, 2, 7, 10, 9, 4, 3, 4, 3, 9, 2, 6, 10, 9, 6, 4, 2
|2, 10, 10, 8, 5, 4, 4, 2, 1, 7, 7, 9, 2, 2, 7, 1, 7, 4, 0, 9, 5, 3, 10, 4, 10, 9, 7, 3, 4, 1, 8, 6, 1, 8, 2, 6, 1, 1, 6, 7, 3, 3, 10, 1, 10, 5, 3, 1, 5, 9
|8, 4, 2, 6, 5, 5, 6, 2, 1, 3, 10, 10, 10, 4, 3, 3, 1, 4, 6, 0, 6, 4, 3, 10, 4, 5, 3, 10, 1, 6, 6, 8, 4, 8, 4, 7, 4, 5, 9, 1, 3, 6, 1, 5, 10, 3, 10, 7, 7, 5
|3, 9, 8, 5, 2, 7, 9, 9, 10, 4, 9, 10, 10, 2, 8, 2, 9, 2, 2, 8, 0, 2, 5, 3, 9, 10, 9, 8, 2, 2, 5, 4, 7, 6, 5, 7, 6, 1, 4, 10, 6, 4, 4, 8, 4, 5, 4, 3, 10, 5
|4, 2, 5, 7, 7, 5, 9, 8, 2, 9, 5, 6, 5, 4, 4, 1, 1, 5, 8, 8, 4, 0, 2, 10, 8, 6, 8, 8, 3, 8, 2, 4, 6, 7, 1, 4, 1, 1, 6, 3, 3, 10, 9, 7, 1, 8, 10, 4, 1, 7
|1, 10, 1, 5, 4, 8, 8, 5, 5, 9, 7, 9, 1, 8, 4, 7, 8, 3, 5, 6, 2, 4, 0, 1, 10, 3, 10, 7, 8, 4, 10, 4, 10, 1, 10, 5, 1, 9, 8, 5, 6, 7, 6, 7, 9, 10, 9, 5, 8, 2
|7, 1, 8, 8, 9, 4, 9, 10, 1, 5, 6, 8, 8, 10, 9, 7, 10, 5, 3, 4, 10, 6, 5, 0, 6, 2, 6, 8, 2, 5, 5, 3, 4, 7, 6, 6, 2, 1, 1, 7, 8, 7, 9, 3, 6, 5, 2, 10, 2, 4
|3, 6, 6, 8, 9, 8, 4, 9, 2, 4, 3, 5, 8, 4, 1, 4, 9, 9, 3, 8, 2, 5, 10, 2, 0, 4, 5, 8, 5, 7, 5, 4, 4, 5, 1, 5, 2, 3, 2, 8, 4, 6, 9, 3, 8, 4, 1, 7, 5, 8
|9, 2, 7, 6, 1, 4, 9, 2, 5, 7, 2, 5, 2, 5, 5, 3, 6, 4, 8, 4, 8, 6, 10, 1, 6, 0, 6, 4, 3, 3, 4, 3, 7, 3, 3, 7, 8, 10, 7, 1, 2, 5, 4, 5, 8, 4, 7, 7, 2, 9
|2, 10, 9, 3, 1, 9, 7, 10, 8, 5, 10, 8, 4, 6, 9, 2, 3, 9, 4, 10, 9, 8, 7, 4, 6, 8, 0, 8, 7, 2, 2, 9, 7, 6, 9, 4, 3, 8, 4, 2, 8, 1, 1, 9, 4, 10, 2, 8, 8, 2
|1, 4, 4, 8, 8, 8, 3, 6, 7, 8, 1, 2, 10, 2, 2, 5, 4, 4, 8, 8, 2, 8, 5, 5, 2, 9, 9, 0, 4, 6, 10, 8, 3, 5, 2, 8, 4, 7, 2, 3, 4, 6, 3, 9, 6, 3, 8, 10, 3, 2
|9, 10, 1, 9, 8, 9, 6, 3, 3, 8, 9, 8, 7, 6, 6, 7, 10, 8, 5, 6, 6, 4, 2, 6, 9, 2, 10, 10, 0, 5, 9, 4, 3, 10, 9, 8, 3, 1, 5, 8, 1, 9, 7, 5, 4, 6, 10, 10, 7, 2
|9, 4, 6, 2, 2, 2, 2, 6, 6, 5, 5, 1, 6, 5, 6, 4, 7, 6, 4, 9, 2, 10, 9, 10, 2, 4, 1, 9, 1, 0, 5, 9, 3, 8, 10, 7, 4, 3, 10, 1, 8, 1, 7, 10, 5, 6, 1, 10, 10, 3
|4, 10, 3, 7, 3, 7, 2, 9, 10, 2, 2, 4, 3, 9, 5, 5, 9, 8, 3, 5, 2, 9, 7, 1, 4, 6, 2, 5, 4, 6, 0, 6, 1, 4, 5, 10, 8, 3, 2, 8, 7, 6, 10, 2, 5, 1, 7, 4, 10, 3
|6, 8, 3, 6, 2, 3, 5, 1, 10, 6, 8, 4, 6, 5, 7, 1, 6, 7, 1, 3, 7, 6, 10, 5, 5, 5, 10, 7, 2, 10, 9, 0, 1, 9, 2, 2, 8, 3, 6, 7, 3, 10, 5, 5, 7, 4, 6, 7, 7, 10
|3, 1, 5, 9, 6, 8, 5, 8, 4, 9, 3, 3, 6, 6, 7, 6, 1, 8, 7, 10, 2, 2, 8, 7, 4, 6, 3, 2, 6, 3, 4, 9, 0, 2, 7, 2, 7, 3, 3, 4, 3, 9, 3, 2, 4, 10, 1, 7, 2, 7
|10, 10, 4, 3, 5, 1, 7, 10, 6, 3, 5, 10, 3, 9, 7, 2, 5, 10, 4, 3, 10, 1, 7, 9, 1, 1, 5, 4, 3, 3, 7, 2, 5, 0, 10, 10, 1, 9, 2, 3, 6, 6, 7, 1, 9, 6, 5, 1, 1, 10
|5, 8, 7, 3, 2, 1, 5, 1, 1, 4, 9, 4, 6, 3, 9, 10, 1, 3, 4, 1, 9, 2, 10, 6, 10, 7, 6, 6, 10, 7, 5, 3, 2, 8, 0, 3, 3, 5, 8, 5, 2, 6, 2, 10, 5, 4, 3, 7, 1, 7
|10, 7, 7, 3, 7, 3, 2, 3, 2, 5, 7, 8, 1, 8, 10, 1, 1, 2, 7, 2, 6, 10, 9, 7, 10, 1, 7, 8, 9, 5, 3, 1, 10, 3, 5, 0, 6, 3, 5, 9, 4, 7, 3, 7, 7, 7, 1, 6, 9, 5
|8, 7, 8, 7, 10, 1, 3, 5, 4, 1, 8, 10, 8, 1, 7, 5, 1, 8, 5, 9, 3, 10, 9, 4, 7, 4, 2, 9, 1, 5, 8, 6, 8, 10, 1, 7, 0, 8, 8, 8, 1, 1, 3, 1, 1, 4, 9, 3, 8, 6
|10, 4, 1, 4, 3, 8, 1, 10, 3, 9, 4, 3, 5, 10, 5, 10, 10, 2, 6, 10, 10, 1, 1, 4, 7, 7, 2, 1, 4, 8, 2, 9, 4, 7, 1, 7, 6, 0, 7, 10, 2, 1, 8, 5, 2, 7, 6, 7, 1, 9
|10, 6, 5, 8, 7, 6, 9, 5, 6, 8, 9, 9, 9, 1, 3, 4, 7, 9, 5, 7, 6, 1, 1, 5, 1, 1, 5, 10, 3, 6, 9, 3, 4, 4, 9, 10, 9, 1, 0, 10, 7, 5, 4, 10, 8, 6, 2, 10, 4, 5
|5, 9, 1, 7, 9, 9, 5, 9, 5, 4, 10, 5, 8, 2, 7, 1, 5, 8, 4, 7, 1, 7, 7, 5, 2, 4, 9, 5, 7, 5, 8, 4, 1, 7, 7, 8, 4, 7, 4, 0, 4, 1, 2, 1, 5, 7, 9, 1, 10, 2
|9, 5, 7, 7, 6, 7, 10, 7, 1, 3, 2, 8, 4, 1, 8, 2, 6, 2, 10, 3, 5, 10, 6, 4, 10, 7, 7, 5, 10, 2, 10, 6, 1, 7, 10, 9, 2, 8, 3, 5, 0, 3, 10, 4, 4, 9, 9, 5, 10, 6
|8, 9, 3, 5, 10, 8, 3, 9, 1, 10, 1, 9, 7, 7, 3, 1, 10, 9, 1, 3, 5, 10, 2, 8, 10, 5, 4, 10, 9, 4, 7, 5, 2, 7, 10, 1, 8, 5, 1, 4, 4, 0, 2, 4, 6, 4, 8, 10, 9, 3
|5, 2, 1, 2, 9, 8, 8, 5, 4, 8, 1, 2, 3, 9, 2, 5, 9, 5, 6, 9, 1, 3, 2, 4, 1, 9, 4, 6, 1, 5, 9, 6, 1, 6, 8, 8, 8, 6, 7, 9, 8, 7, 0, 10, 8, 3, 3, 4, 2, 10
|1, 4, 8, 7, 8, 10, 4, 7, 7, 4, 2, 1, 3, 4, 1, 3, 5, 10, 3, 5, 10, 6, 5, 7, 10, 3, 6, 2, 5, 2, 8, 7, 2, 10, 4, 7, 7, 7, 5, 4, 9, 2, 5, 0, 3, 1, 3, 7, 3, 6
|6, 10, 4, 6, 7, 8, 1, 4, 9, 10, 5, 1, 3, 7, 5, 5, 1, 9, 2, 1, 8, 8, 3, 1, 6, 2, 9, 6, 9, 9, 8, 3, 8, 2, 5, 8, 3, 9, 3, 4, 10, 10, 7, 3, 0, 9, 6, 5, 3, 4
|4, 1, 1, 4, 8, 2, 3, 5, 4, 4, 5, 2, 6, 6, 2, 4, 2, 10, 9, 7, 3, 6, 9, 6, 10, 10, 8, 1, 10, 8, 5, 3, 8, 10, 3, 9, 8, 3, 8, 10, 4, 9, 2, 5, 3, 0, 5, 9, 5, 4
|2, 9, 7, 10, 9, 1, 2, 9, 3, 8, 1, 4, 4, 10, 9, 1, 4, 4, 2, 8, 1, 9, 9, 7, 8, 7, 5, 2, 1, 6, 10, 5, 1, 8, 10, 1, 4, 4, 7, 1, 7, 3, 3, 7, 4, 10, 0, 1, 2, 5
|5, 9, 4, 6, 5, 5, 7, 5, 9, 5, 5, 9, 8, 2, 5, 5, 2, 9, 7, 4, 5, 5, 8, 5, 2, 1, 2, 7, 7, 2, 9, 10, 4, 2, 9, 6, 9, 4, 8, 4, 10, 8, 2, 9, 10, 10, 9, 0, 3, 5
|3, 10, 8, 7, 5, 5, 9, 2, 5, 8, 3, 8, 1, 1, 7, 4, 8, 3, 1, 4, 7, 4, 3, 9, 1, 8, 5, 9, 9, 7, 2, 6, 6, 10, 1, 6, 7, 3, 8, 1, 6, 10, 10, 1, 1, 5, 8, 2, 0, 2
|6, 6, 8, 8, 8, 10, 8, 10, 8, 3, 4, 5, 8, 4, 10, 6, 8, 10, 2, 8, 10, 7, 10, 3, 9, 9, 5, 9, 5, 8, 8, 10, 1, 1, 8, 4, 2, 2, 9, 10, 4, 5, 4, 1, 4, 5, 3, 3, 2, 0
|];
TimeWindows = [| 7, 504
|10, 582
|6, 552
|3, 531
|8, 538
|7, 520
|7, 514
|7, 585
|10, 599
|5, 585
|1, 549
|7, 531
|3, 526
|1, 553
|8, 562
|3, 575
|6, 534
|1, 574
|5, 554
|6, 577
|7, 532
|6, 605
|9, 531
|8, 509
|9, 529
|2, 532
|2, 559
|4, 584
|6, 535
|8, 574
|9, 580
|7, 571
|7, 511
|7, 596
|5, 607
|4, 536
|5, 593
|1, 575
|2, 565
|9, 525
|];

The key of this seems to be the timeout. With a higher timeout both approaches should always give the same result. But that doesn't really explain why MiniZincIDE and MiniZinc-Python give different results, but it could be just some fluke.
I tested the model with the Chuffed solver (+ free search flag) and it took 3.4s:
...
----------
obj = 73
----------
==========
Google OR-tools took about the same time (with 12 threads it took 0.9s).
As mentioned in
Placing array of rectangles inside a given area using Minizinc , one might have to experiment with different solvers/search strategies and variants of the model to get faster times.
For this model it seems to suffice to simply change the solver (if that is a acceptable action).

Append arrays of different dimensions to get a single array

l have three vectors (numpy arrays), vector_1, vector_2, vector_3
as follow :
Dimension(vector1)=(200,2048)
Dimension(vector2)=(200,8192)
Dimension(vector3)=(200,32768)
l would like to append these vectors to get vector_4 :
Dimension(vector4)= (200,2048+8192+32768)= (200, 43008)
Add respectively vector1 then vector2 then vector3
l tries the following :
vector4=numpy.concatenate((vector1,vector2,vector3),axis=0)
ValueError: all the input array dimensions except for the concatenation axis must match exactly
and
vector4=numpy.append(vector4,[vector1,vector2,vectors3],axis=0)
TypeError: append() missing 1 required positional argument: 'values'

I believe you are looking for numpy.hstack.
>>> import numpy as np
>>> a = np.arange(4).reshape(2,2)
>>> b = np.arange(6).reshape(2,3)
>>> c = np.arange(8).reshape(2,4)
>>> a
array([[0, 1],
[2, 3]])
>>> b
array([[0, 1, 2],
[3, 4, 5]])
>>> c
array([[0, 1, 2, 3],
[4, 5, 6, 7]])
>>> np.hstack((a,b,c))
array([[0, 1, 0, 1, 2, 0, 1, 2, 3],
[2, 3, 3, 4, 5, 4, 5, 6, 7]])

The error message is pretty much telling you exactly what is the problem:
ValueError: all the input array dimensions except for the concatenation axis must match exactly
But you are doing the opposite, the concatenation axis dimensions match exactly but the others don't. Consider:
In [3]: arr1 = np.random.randint(0,10,(20, 5))
In [4]: arr2 = np.random.randint(0,10,(20, 3))
In [5]: arr3 = np.random.randint(0,10,(20, 11))
Note the dimensions. Just give it the correct axis. So use the second rather than the first:
In [8]: arr1.shape, arr2.shape, arr3.shape
Out[8]: ((20, 5), (20, 3), (20, 11))
In [9]: np.concatenate((arr1, arr2, arr3), axis=1)
Out[9]:
array([[3, 1, 4, 7, 3, 6, 1, 1, 6, 7, 4, 6, 8, 6, 2, 8, 2, 5, 0],
[4, 2, 2, 1, 7, 8, 0, 7, 2, 2, 3, 9, 8, 0, 7, 3, 5, 9, 6],
[2, 8, 9, 8, 5, 3, 5, 8, 5, 2, 4, 1, 2, 0, 3, 2, 9, 1, 0],
[6, 7, 3, 5, 6, 8, 3, 8, 4, 8, 1, 5, 4, 4, 6, 4, 0, 3, 4],
[3, 5, 8, 8, 7, 7, 4, 8, 7, 3, 8, 7, 0, 2, 8, 9, 1, 9, 0],
[5, 4, 8, 3, 7, 8, 3, 2, 7, 8, 2, 4, 8, 0, 6, 9, 2, 0, 3],
[0, 0, 1, 8, 6, 4, 4, 4, 2, 8, 4, 1, 4, 1, 3, 1, 5, 5, 1],
[1, 6, 3, 3, 9, 2, 3, 4, 9, 2, 6, 1, 4, 1, 5, 6, 0, 1, 9],
[4, 5, 4, 7, 1, 4, 0, 8, 8, 1, 6, 0, 4, 6, 3, 1, 2, 5, 2],
[6, 4, 3, 2, 9, 4, 1, 7, 7, 0, 0, 5, 9, 3, 7, 4, 5, 6, 1],
[7, 7, 0, 4, 1, 9, 9, 1, 0, 1, 8, 3, 6, 0, 5, 1, 4, 0, 7],
[7, 9, 0, 4, 0, 5, 5, 9, 8, 9, 9, 7, 8, 8, 2, 6, 2, 3, 1],
[4, 1, 6, 5, 4, 5, 6, 7, 9, 2, 5, 8, 6, 6, 6, 8, 2, 3, 1],
[7, 7, 8, 5, 0, 8, 5, 6, 4, 4, 3, 5, 9, 8, 7, 9, 8, 8, 1],
[3, 9, 3, 6, 3, 2, 2, 4, 0, 1, 0, 4, 3, 0, 1, 3, 4, 1, 3],
[5, 1, 9, 7, 1, 8, 3, 9, 4, 7, 6, 7, 4, 7, 0, 1, 2, 8, 7],
[6, 3, 8, 0, 6, 2, 1, 8, 1, 0, 0, 3, 7, 2, 1, 5, 7, 0, 7],
[5, 4, 7, 5, 5, 8, 3, 2, 6, 1, 0, 4, 6, 9, 7, 3, 9, 2, 5],
[1, 4, 8, 5, 7, 2, 0, 2, 6, 2, 6, 5, 5, 4, 6, 1, 8, 8, 1],
[4, 4, 5, 6, 2, 6, 0, 5, 1, 8, 4, 5, 8, 9, 2, 1, 0, 4, 2]])
In [10]: np.concatenate((arr1, arr2, arr3), axis=1).shape
Out[10]: (20, 19)

Numpy create index/slicing programmatically from array

I can use numpy.mgrid as follows:
a = numpy.mgrid[x0:x1, y0:y1] # 2 dimensional
b = numpy.mgrid[x0:x1, y0:y1, z0:z1] # 3 dimensional
Now, I'd like to create the expression in brackets programmatically, because I do not know whether I have 1, 2, 3 or more dimensions. I'm looking for something like:
shape = np.array([[x0, x1], [y0, y1], ... maybe more dimensions ...])
idx = (s[0]:s[1] for s in shape)
a = numpy.mgrid[idx]
That gives at least a syntax error in the second line. How can I properly generate those indices/slices programmatically? (The mgrid here is rather an example/use case, the question is really about indexing in general.)

Use the slice object. For example:
shape = np.array([[0, 10], [0, 10]])
idx = tuple(slice(s[0],s[1], 1) for s in shape)
#yields the following
#(slice(0, 10, 1), slice(0, 10, 1))
np.mgrid[idx]
yields
array([[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
[5, 5, 5, 5, 5, 5, 5, 5, 5, 5],
[6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
[7, 7, 7, 7, 7, 7, 7, 7, 7, 7],
[8, 8, 8, 8, 8, 8, 8, 8, 8, 8],
[9, 9, 9, 9, 9, 9, 9, 9, 9, 9]],
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]])
Alternatively, you could use the Numpy shorthand np.s_, e.g. np.s_[0:10:1], instead of slice(1, 10, 1), but they are equivalent objects.

Combining multiple numpy arrays into one

I have this O/P as shown in the below pic
My O/P consists of around 100+ numpy arrays like the one shown above. I'm trying to combine all these 100+ numpy arrays into an single numpy array for further data processing. Any ideas on how to get this done???? I'm using python V3.4

You can use hstack and vstack:
In [29]: hstack((arange(10) for _ in range(10)))
Out[29]:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2,
3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5,
6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8,
9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1,
2, 3, 4, 5, 6, 7, 8, 9])
In [30]: vstack((arange(10) for _ in range(10)))
Out[30]:
array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])