Another problem with: PerformanceWarning: DataFrame is highly fragmented - python-3.x

Question

Since I am still learning Python, I am getting some optimisation errors here.
I keep getting the error
PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
and it does take for a while to load for what I am doing now.
Here is my code:
def Monte_Carlo_for_Tracking_Error(N,S,K,Ru,Rd,r,I,a):
ldv=[]
lhp=[]
lsp=[]
lod=[]
Tracking_Error_df=pd.DataFrame()
# Go through different time steps of rebalancing
for y in range(1,I+1):
i=0
# do the same step a amount of times
while i<a:
Sample_Stock_Prices=[]
Sample_Hedging_Portfolio=[]
Hedging_Portfolio_Value=np.zeros(N) # Initzialize Hedging PF
New_Path=Portfolio_specification(N,S,K,Ru,Rd,r) # Get a New Sample Path
Sample_Stock_Prices.append(New_Path[0])
Sample_Hedging_Portfolio.append(Changing_Rebalancing_Rythm(New_Path,y))
Call_Option_Value=[]
Call_Option_Value.append(New_Path[1])
Differences=np.zeros(N)
for x in range(N):
Hedging_Portfolio_Value[x]=Sample_Stock_Prices[0][x]*Sample_Hedging_Portfolio[0][x]
for z in range(N):
Differences[z]=Call_Option_Value[0][z]-Hedging_Portfolio_Value[z]
lhp.append(Hedging_Portfolio_Value)
lsp.append(np.asarray(Sample_Stock_Prices))
ldv.append(np.asarray(Sample_Hedging_Portfolio))
lod.append(np.asarray(Differences))
Tracking_Error_df[f'Index{i+(y-1)*200}']=Differences
i=i+1
return(Tracking_Error_df,lod,lsp,lhp,ldv)
Code starts to give me warnings when I try to run:
Simulation=MCTE(100,100,104,1.05,0.95,0,10,200)
Small part of the warning:
C:\Users\xxx\AppData\Local\Temp\ipykernel_1560\440260239.py:30: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
Tracking_Error_df[f'Index{i+(y-1)*200}']=Differences
C:\Users\xxx\AppData\Local\Temp\ipykernel_1560\440260239.py:30: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
Tracking_Error_df[f'Index{i+(y-1)*200}']=Differences
C:\Users\xxx\AppData\Local\Temp\ipykernel_1560\440260239.py:30: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
Tracking_Error_df[f'Index{i+(y-1)*200}']=Differences
I am using jupyter notebook for this. If somebody could help me optimise it, would appreciate it.
I tried to test the code and I am expecting to have a more performance-oriented version of it.