# AMPL Python API#

`amplpy`

is an interface that allows developers to access the features of AMPL from within Python.
For a quick introduction to AMPL see Quick Introduction to AMPL.

In the same way that AMPL’s syntax matches naturally the mathematical description of the model,
the input and output data matches naturally Python lists, sets, dictionaries, `pandas`

and `numpy`

objects.

All model generation and solver interaction is handled directly by AMPL, which leads to great stability and speed; the library just acts as an intermediary, and the added overhead (in terms of memory and CPU usage) depends mostly on how much data is sent and read back from AMPL, the size of the expanded model as such is irrelevant.

With `amplpy`

you can model and solve large scale optimization problems in Python with the performance of heavily optimized C code
without losing model readability. The same model can be deployed on applications
built on different languages by just switching the API used.

Note

Many Jupyter notebooks with examples are available on the AMPL Model Colaboratory and the new book Hands-On Mathematical Optimization with AMPL in Python 🐍.

You should also check out our collection of interactive Streamlit Apps and learn how easy you can build your own apps.

## Installation & minimal example#

```
# Install Python API for AMPL
$ python -m pip install amplpy --upgrade
# Install solver modules (e.g., HiGHS, CBC, Gurobi)
$ python -m amplpy.modules install highs cbc gurobi
# Activate your license (e.g., free https://ampl.com/ce license)
$ python -m amplpy.modules activate <license-uuid>
# Import in Python
$ python
>>> from amplpy import AMPL
>>> ampl = AMPL() # instantiate AMPL object
```

Note

You can use a free Community Edition license, which allows **free
and perpetual use of AMPL with Open-Source solvers**. There are also free AMPL for Courses licenses that give unlimited
access to all commercial solvers for teaching.

```
# Minimal example:
from amplpy import AMPL
import pandas as pd
ampl = AMPL()
ampl.eval(r"""
set A ordered;
param S{A, A};
param lb default 0;
param ub default 1;
var w{A} >= lb <= ub;
minimize portfolio_variance:
sum {i in A, j in A} w[i] * S[i, j] * w[j];
s.t. portfolio_weights:
sum {i in A} w[i] = 1;
""")
tickers, cov_matrix = # ... pre-process data in Python
ampl.set["A"] = tickers
ampl.param["S"] = pd.DataFrame(cov_matrix, index=tickers, columns=tickers)
ampl.solve(solver="gurobi", gurobi_options="outlev=1")
assert ampl.solve_result == "solved"
sigma = ampl.get_value("sqrt(sum {i in A, j in A} w[i] * S[i, j] * w[j])")
print(f"Volatility: {sigma*100:.1f}%")
# ... post-process solution in Python
```

## Contents#

- Introduction
- Initial Setup
- Quick start
- Complete listing
- Needed modules and AMPL environment creation
- Load model and data from files
- Load model using eval
- Load the data using Pandas objects
- Load the data using lists and dictionaries
- Solve a problem
- Get an AMPL entity in the programming environment (get objective value)
- Modify model data (assign values to parameters)
- Get numeric values from variables
- Get arbitrary values via ampl expressions

- Class structure
- API Reference
- Examples
- Example files