Pyomo: Looping Over A Variable Method

Question

I am trying to extract my variable values into unique lists so that I can pass them to a panadas dataframe and eventually export the dataframe to Excel after I solve my model. The idea is to have the variables of interest in a much more readable and familiar format.

My thought process right now looks like this:

1. Create an empty list and add new lists.
2. For each variable in the model, get the dictionary-key values and insert those in the list.
3. The resultant list, value_list, will have each variables values stored in its own list.

value_list = []

for v in model.component_objects(Var):
    value_list.append( list(model.v.get_values().values()) )

I would think that the loop variable would replace the v component in the body of for loop and then run the operation as if it was the explicit variable name, but this is not the case.

Ultimately I am trying to produce a dataframe of my solved variables with the first series being the index of tuples (i,j,t) up to (I,J,T), but it's a bit cumbersome to manually create a series object for each of my variables. Note that all variables as shown below:

list_of_series = [  pd.Series(pyomo_index_list),
                    pd.Series(list(model.susceptible.get_values().values())),
                    pd.Series(list(model.inf_treated.get_values().values())),
                    pd.Series(list(model.inf_b4treat.get_values().values())),
                    pd.Series(list(model.level1.get_values().values())),
                    pd.Series(list(model.juvenilleTotal.get_values().values()))   ]

If there was a way to create a loop so that model.<var>.get_values().values() was not hard coded, then the code would be more reproducible for models that have 15, 30, or 50 variables.

The final output would look like this, but as a pandas dataframe:

          Susceptible inf_treated inf_b4treat level1 JuvTotal   
Key                                                             
(1, 1, 0)          50           7         NaN    NaN       21   
(1, 1, 1)        50.2     8.22148      16.443      1     13.8   
(1, 1, 2)     52.5085     10.1232     20.2464      1     3.27   
(1, 1, 3)     44.0863     26.5758     26.5758      0    1.569   
(1, 2, 0)          30           7         NaN    NaN        8   
(1, 2, 1)        25.7     4.93289     9.86577      1      5.3   
(1, 2, 2)     24.8171     2.51488     5.02976      1     1.25   
(1, 2, 3)     22.9502     1.47623     1.47623      0    0.602   
(2, 1, 0)          20           9         NaN    NaN       18   
(2, 1, 1)        17.3     4.47514     8.95028      1     11.7   
(2, 1, 2)     21.7349     1.44328     2.88656      1     2.79   
(2, 1, 3)     21.7496    0.443511    0.443511      0    1.332   
(2, 2, 0)          35           8         NaN    NaN       16   
(2, 2, 1)        35.1     6.82927     13.6585      1      7.9   
(2, 2, 2)     33.9407     5.58183     11.1637      1     2.23   
(2, 2, 3)     29.6549     8.06275     8.06275      0    0.934 

Answer 1

v represent each of your variables. I assume that your model is called 'model':

from pyomo.environ import *
import pandas as pd 
# add the following to your python script
DF = pd.DataFrame()
    for v in model.component_objects(Var,active=True):
        for index in v:
            DF.at[index, v.name] = value(v[index])

This part of the code directly generates the pandas' data frame for you with columns named according to the name of your variables. I hope it works for you, I tried it in my own model and got the following data frame: