Hi Everyone,
a beginner's question on how to perform some data substitution efficiently. I have a panel dataset, or in other words x individuals observed over a certain time span. For each column or individual, I need to substitute a certain value anytime a certain condition is satisfied. Both the condition and the value to be substituted into the panel dataset are individual specific. I can tackle the fact that the condition is individual specific but I cannot find a way to tackle the fact that the value to be substituted is individual specific without using a for – lop. Frankly, considering the size of the dataset the use of a for loop is perfectly acceptable in terms of the time needed to complete task but still it would be nice to learn a way to do this (a task I implement often) in a more efficient way.

Thanks in advance
Cristiano

 

import numpy as np
from copy import deepcopy

Data = np.array([[0,4,0],
                [2,5,7],
                [2,5,6]])
EditedData = deepcopy(Data)               

Condition = np.array([0, 5, 6])     # individual-specific condition
SubstituteData = np.array([1, 10,100])   
# The logic here
# if the value of any obssrvation for the 1st individual is 0, substitute 1,
#                                     the 2nd individual is 5, substitute 10
#                                     the 3rd individual is 6, substitute 100
      

# This wouldn't a problem if SubstituteData was not individual specific Data
# eg EditedData[Data==Condition] = 555

# As SubstituteData is individual specifc, I need to use a for loop
for i in range(np.shape(EditedData)[1]):
    TempData = EditedData[:, i]  # I introduce TempData to increase readability
    TempData[TempData == Condition[i]] = SubstituteData[i]
    EditedData[:, i] = TempData

   
print   EditedData