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Jun 16th, 2019
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  1. In[1]
  2. df = {'Loan Nego': [2019-03-01, 2019-03-01], 'New Maturity': [2019-03-11, 2019-03-29],'Loan Amount in OCUR': [1000, 2000]}
  3.  
  4. Out[1]
  5. Loan Nego New Maturity Loan Amount in OCUR
  6. 2019-03-01 2019-03-11 1000
  7. 2019-03-01 2019-03-29 2000
  8.  
  9. In[2]
  10. df.dtypes
  11.  
  12. Out[2]
  13.  
  14. New Maturity datetime64[ns]
  15. Loan Nego datetime64[ns]
  16. Loan Amount in OCUR float64
  17.  
  18. # Equation CLOF
  19. def clof(loan,maturity, amount):
  20. days = (maturity-loan).days
  21. return ((amount * days)/ 360) * (2.36/100)
  22.  
  23. df["New Interest"] = clof(df["Loan Nego"],df["New Maturity"],df["Loan Amount in OCUR"])
  24.  
  25. Loan Nego New Maturity Loan Amount in OCUR New Interest
  26. 2019-03-01 2019-03-11 1000 0.65
  27. 2019-03-01 2019-03-29 2000 3.671
  28.  
  29. days = (maturity-loan).dt.days
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