The solution of a system of equations in Python depends on the type of equations you are dealing with.

If you have a system of linear equations, you can use the `numpy`

library to solve it as shown in the previous answer.

If you have a system of nonlinear equations, you can use the `scipy.optimize`

module. Here’s an example of how to solve a system of nonlinear equations in Python:

```
import numpy as np
from scipy.optimize import root
# Define the system of equations
def system(x):
y = np.zeros(2)
y[0] = x[0]**2 + x[1]**2 - 1
y[1] = x[0] - x[1]**3 - 1
return y
# Define the initial guess
x0 = np.array([0.5, 0.5])
# Solve the system of equations
sol = root(system, x0)
# Print the solution
print(sol.x)
```

In this example, we first import the `numpy`

and `scipy.optimize`

modules using `import numpy as np`

and `from scipy.optimize import root`

.

We define the system of equations using a function called `system`

. This function takes in a vector `x`

containing the variables `x`

and `y`

, and returns a vector `y`

containing the values of the equations.

We define an initial guess for the variables using `x0 = np.array([0.5, 0.5])`

.

We solve the system of equations using `sol = root(system, x0)`

. This function takes in the system of equations and the initial guess, and returns a solution as an object. We access the solution vector using `sol.x`

.

Finally, we print the solution using `print(sol.x)`

.

Note: You’ll need to modify the code to define your own system of equations and initial guess.

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