nlm function equivalent in Python (Non-Linear-Minimization)

I test that if you use the same data, you can get the similar minimum. In R:

data <- c(0L, 1L, 0L, 1L, 1L, 2L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 2L, 
1L, 3L, 1L, 1L, 0L, 3L, 2L, 0L, 1L, 2L, 1L, 2L, 1L, 3L, 3L, 0L, 
0L, 0L, 3L, 1L, 0L, 0L, 0L, 1L, 3L, 2L, 0L, 3L, 2L, 2L, 2L, 0L, 
0L, 0L, 0L, 3L, 3L, 3L, 5L, 1L, 3L, 0L, 1L, 2L, 3L, 1L, 3L, 5L, 
3L, 2L, 2L, 1L, 2L, 5L, 10L, 5L, 8L, 8L, 10L, 8L, 7L, 10L, 6L, 
5L, 9L, 6L, 3L, 5L, 9L, 7L, 10L, 5L, 4L, 6L, 8L, 8L, 6L, 8L, 
5L, 4L, 13L, 9L, 9L, 4L, 10L)
PAR <- c(0.693147180559945, 1.38629436111989, 1.94591014905531, 0.510825623765991, 
-0.405465108108164)
f(PAR) 
# 239.3489

Output:

res = nlm(f,PAR);
res 

$minimum
[1] 228.2598

$estimate
[1] -1.3531276  0.6409517  1.9662910 -0.6879096  0.5427875

$gradient
[1] -1.228761e-05 -4.965273e-05  1.113862e-04  1.637090e-05 -2.287948e-05

$code
[1] 1

$iterations
[1] 32

In Python

data = np.array([0, 1, 0, 1, 1, 2, 1, 1, 0, 0, 1, 1, 0, 0, 2, 
          1, 3, 1, 1, 0, 3, 2, 0, 1, 2, 1, 2, 1, 3, 3, 0, 
          0, 0, 3, 1, 0, 0, 0, 1, 3, 2, 0, 3, 2, 2, 2, 0, 
          0, 0, 0, 3, 3, 3, 5, 1, 3, 0, 1, 2, 3, 1, 3, 5, 
          3, 2, 2, 1, 2, 5, 10, 5, 8, 8, 10, 8, 7, 10, 6, 
          5, 9, 6, 3, 5, 9, 7, 10, 5, 4, 6, 8, 8, 6, 8, 
          5, 4, 13, 9, 9, 4, 10])
PAR = np.array([ 0.69314718,  1.38629436,  1.94591015,  0.51082562, -0.40546511])

f(PAR)
#239.34891885662626

Output

res = minimize(f, PAR, method='Nelder-Mead', tol=1e-6);
res

final_simplex: (array([[-1.35304276,  0.64096297,  1.96629305, -0.68786541,  0.54278448],
       [-1.35304245,  0.64096303,  1.96629305, -0.68786503,  0.54278438],
       [-1.35304179,  0.64096308,  1.96629302, -0.68786502,  0.54278439],
       [-1.35304231,  0.64096294,  1.96629301, -0.6878652 ,  0.54278434],
       [-1.35304278,  0.64096297,  1.966293  , -0.68786511,  0.5427845 ],
       [-1.3530437 ,  0.64096284,  1.96629297, -0.68786577,  0.54278438]]), array([228.25982274, 228.25982274, 228.25982274, 228.25982274,
       228.25982274, 228.25982274]))
           fun: 228.259822735201
       message: 'Optimization terminated successfully.'
          nfev: 768
           nit: 478
        status: 0
       success: True
             x: array([-1.35304276,  0.64096297,  1.96629305, -0.68786541,  0.54278448])