Performing Calculations From A Pandas Data Frame with Multiple Conditions - python-3.x

Question

Forgive the question as I'm a science major, not computer science and I'm teaching myself Python to help with a class project.
I have a Pandas data frame that I've imported from a .csv that looks like:
Item_ID Event_ID Value
27 83531 2533501.8
28 83531 1616262
31 83531 269829
32 83531 55.8
33 83531 269829
34 83531 4882
35 83531 269829
36 83531 4882
37 83531 55.8
38 83531 55.8
27 83532 7137904.8
28 83532 5873877.6
31 83532 497381
32 83532 55.7
33 83532 497381
34 83532 7568
35 83532 497381
36 83532 7568
37 83532 55.7
38 83532 55.7
This data is from a manual entry that is done multiple times daily where the Item_ID is type of measurement, Event_ID is the unique identifier for each "data entry event" by the user, and value is the value of the measurement.
I need to perform a number of calculations on each unique Event_Id.
Calc1 = ([28]/[27])*(([31]*[32])/[28])*(([33]-[34])/[33])
Calc2 = [36]/[35]
Calc3 = ([35]-[113])/[35]
Calc4 = [37]
Calc5 = [38]
Each number in the above formula represents an Item_ID. I want the replace the Item_ID in the formula with the value from the same row for each Event_ID.
This project was started a month ago and will run for 6 more weeks. By then, there will be to many data points to perform the calculations by hand.
As these calculations cannot be performed across Event_IDs, the formula for Event_ID 85831 would look like:
Calc1_Data = ([1616262]/[2533501.8])*(([269829]*[55.8])/[1616262])*(([269829]-[4882])/[269829])
Calc2_Data = [4882]/[269829]
Calc3_Data = ([497381]-[0])/[497381]) ***0 would be placed hear as Item_ID 113 does not exist for this
Event_ID
Calc4_Data = [55.7]
Calc5_Data = [55.7]
The results would then be put into a new data frame that I could then perform my analysis on.
Event_ID Clac1_Result Calc2_Result Calc3_Result Calc4_Result Calc5_Result
85829
85830
85331 RESULTS HERE
85332 RESULTS HERE
85833
85834
This is my first go at asking a question here since I've been able to find all of my other answers in the library docs or previously asked questions. If I didn't provide enough information let me know and I'll clarify if possible.
Thanks

Answer 1

You can use groupby followed by agg methods to do that.
First, define your calculations as functions:
# Define calculations
def Calc1(x):
return (x[28]/x[27])*((x[31]*x[32])/x[28])*((x[33]-x[34])/x[33])
def Calc2(x):
return x[36]/x[35]
# Calc3 = lambda x: (x[35]-x[113])/x[35] # commenting out because there's no 113 in the provided example
def Calc4(x):
return x[37]
def Calc5(x):
return x[38]
Then, perform the calculations using the groupby and agg:
df = df.set_index('Item_ID') # set 'Item_ID' to index so that we can use fewer code inside the functions
df = df.groupby('Event_ID').agg([Calc1, Calc2, Calc4, Calc5]) # group by Event_ID, and perform the set of specified calculations
df.columns = df.columns.droplevel(0) # reset column names
Output:
Calc1 Calc2 Calc4 Calc5
Event_ID
83531 5.835418 0.018093 55.8 55.8
83532 3.822212 0.015216 55.7 55.7