tele | NAME tele -- Provisioning at a distance | DevOps library

Give this a try I think it should be able to handle a few millions of rows.

chain.from_iterable can solve this problem.

The function can be written in SQL, that might be slightly faster:

Use this in your code to see that how many times your loop is going to be run print(len(content))

If you do so, you will find that the length of your loop (which is equal to length of content variable) is 2, and as you put the print(link,email) inside your loop, it will run twice and you see the printed result two times... in other words, you are printing the result 2 times. to fix that, remove the indentation for print(link,email) to put it outside the loop and it will be fixed.

It's was a mistake if u use price=100 or another u must use:

I would suggest having the database as follows:

EQ_LIST

The script is translating, but the text you've provided is a proper name and even if its translated it looks almost the same. I've checked it with the code below:

Functions and variables in Lua live in the same namespace, so you can't have a function and a variable both called Beam in the same place.

Welcome to the site.

The "such that" sub-setting can be done pretty easily in pyomo. You cannot "bury" an if statement in your model because those values are unknown at the time the model is built.... Not sure how AMPL handles such things. The trick is to use core python to construct arbitrarily complicated subsets and use those to construct your model. Fortunately, python's list/set comprehensions are immensely powerful and make this pretty straightforward. Below is a snippet that shows this. 2 methods... Well, 2nd is just an enhancement. Note that you don't necessarily need to bring the subset into your model, but sometimes it is good for consistency and T/S, and using the "within" keyword in Method 2 allows error checking (buffoonery prevention...). Note that the 2 constraints constructed in the model are equivalent/redundant... Just shown for consistency. [Aside: active is a keyword in pyomo, so I used act.]

Edit: There is probably a legit 3rd method here to by using a for-loop over the desired indices (with the inner loop for t_prime controlled by the loop over t with individual calls to make constraint for each combo, but that seems much clumsier than making your own subset... you would be just replicating the set comprehension. Seems inferior to what is shown below...

Regarding solvers, GLPK and CBC are both free and great. Installs can be a little challenging depending on type of machine. On a mac with homebrew, it is a snap.