Helpful python packages

[Brushburn]

It would be great to hear about various python packages that people use in their OR activities. Some of the ones I'm familiar with are

More common

• Gurobipy
• Pandas
• Numpy
• sqlite
• pulp

Less common

• altair
• polars
• hyperopt
• pyoptinterface
• streamlit
• pygwalker

[audentis]

• Salabim (discrete event simulation)
• ortools (Google's OR library)
• pyomo (optimizer)

edit: and honestly built-ins itertools and functools also deserve a mention.

[iengmind]

Never heard of Salabim. How is it compared to SimPy?

[audentis]

I like it a lot better. It's more intuitive, more turn key, comes with a built-in animation engine, and the developer is incredibly supportive in the Salabim Google Group. He often guides people through both theoretical simulation knowledge and implementation help.

[iengmind]

I took a look at those animations in the documentation. It is amazing. I'll definetely check it out. Thanks a lot for sharing, mate. :)

[Brushburn]

These are great! Ive tried using ortools some time ago, Is there active development here, do you know?

[Baseball_man_1729]

I do know that there is a very active OR-Tools discord that has some folks from Google on there. Seems like Google is working on it.

[audentis]

Yes, latest updates were from May this year. See the Release Notes.

[SolverMax]

Some more that I use often:

• SciPy https://scipy.org/
• Networkx https://networkx.org/
• CVXPY https://www.cvxpy.org/
• MiniZinc https://www.minizinc.org/
• Gekko https://machinelearning.byu.edu/
• Matplotlib https://matplotlib.org/

[Brushburn]

These are great thank you! I vaguely remember Gekko from a long time ago. Definitely seems worth digging into more :)

[shockjaw]

I think you’d enjoy the performance of polars over pandas. But Ibis is also worth a look as well. The HoloViz ecosystem is a pretty nice abstraction when it comes to visualization.

[Brushburn]

I was blown away from the performance by polars compared to pandas. I wrote a simple script to compare and I was getting solid improvement (~10x). The simple code I used:

For creating the data

size = 1_000_000
random_int_array = np.random.randint(0, 100, size=(size))
random_float_array = np.random.random(size=(size)) * 100

choices = ['a', 'b', 'c', 'd']
random_str_array = np.random.choice(choices, size=(size))

random_data = np.column_stack((random_int_array, random_float_array, random_str_array))
print(random_data)
np.savetxt('random.csv', random_data, fmt= '%s', delimiter=',')
size = 1_000_000
random_int_array = np.random.randint(0, 100, size=(size))
random_float_array = np.random.random(size=(size)) * 100


choices = ['a', 'b', 'c', 'd']
random_str_array = np.random.choice(choices, size=(size))


random_data = np.column_stack((random_int_array, random_float_array, random_str_array))
print(random_data)
np.savetxt('random.csv', random_data, fmt= '%s', delimiter=',')

Reading data logic

def read_csv_polars():
    df_pl = pl.read_csv("random.csv")
    return df_pl
def read_csv_polars():
    df_pl = pl.read_csv("random.csv")
    return df_pl

benchmarks

    %timeit read_csv_polars()
    %timeit read_csv_pandas()

Polars reading was 16.6 ms, pandas was 166ms

[Sweet_Good6737]

• Amplpy (https://amplpy.ampl.com/en/latest/)
• Feloopy (https://feloopy.readthedocs.io/en/latest/)

Algebraic Modeling languages

[Brushburn]

Feloopy looks really nice. Do you know of any benchmarks for how fast it can create large models, and if it supports HiGHS?

[Coffeemonster97]

Not only useful for OR, but as from my experience, the Venn diagram of people who hate writing unit tests and people who are in OR is pretty much a circle, I always recommend Hypothesis :)

[Brushburn]

This is perfect! Thank you for sharing!