79946 # Create a new column based on calculations that change between rows?

<h3>Question</h3>

I would like to calculate a sum of variables for a given day. Each day contains a different calculation, but all the days use the variables consistently.

There is a df which specifies my variables and a df which specifies how calculations will change depending on the day.

How can I create a new column containing answers from these different equations?

```import pandas as pd import numpy as np conversion = [["a",5],["b",1],["c",10]] conversion_table = pd.DataFrame(conversion,columns=['Variable','Cost']) data1 = [[1,"3a+b"],[2,"c"],[3,"2c"]] to_solve = pd.DataFrame(data1,columns=['Day','Q1']) desired = [[1,16],[2,10],[3,20]] desired_table=pd.DataFrame(desired,columns=['Day','Q1 solved']) ```

I have separated my variables and equations based on row. Can I loop though these equations to find non-numerics and re-assign them?

```#separate out equations and values for var in conversion_table["Variable"]: cost=(conversion_table.loc[conversion_table['Variable'] == var, 'Cost']).mean() for row in to_solve["Q1"]: equation=row ```

A simple suggestion, perhaps you need to rewrite a part of your code. Not sure if your want something like this:

```a = 5 b = 1 c = 10 # Rewrite the equation that is readable by Python # e.g. replace 3a+b by 3*a+b data1 = [[1,"3*a+b"], [2,"c"], [3,"2*c"]] desired_table = pd.DataFrame(data1, columns=['Day','Q1']) desired_table['Q1 solved'] = desired_table['Q1'].apply(lambda x: eval(x)) desired_table ```

Output:

``` Day Q1 Q1 solved 0 1 3*a+b 16 1 2 c 10 2 3 2*c 20 ```

If it's possible to have the equations changed to equations with `*` then you could do this.

Get the mapping from the

`mapping = dict(zip(conversion_table['Variable'], conversion_table['Cost'])`

the eval the function and replace variables with numeric from the mapping

```desired_table['Q1 solved'] = to_solve['Q1'].map(lambda x: eval(''.join([str(mapping[i]) if i.isalpha() else str(i) for i in x]))) ``` ```0 16 1 10 2 20 ```