How to resolve 'KeyError' issue in Pandas dataframe when using a custom function in a loop?

Unable to find out the problem of a defined function that is used in a loop.

The program I am trying to develop, is about a supply chain management that involves planing and managing deliveries and warehouse capacities. I have written a function that is working outside the main code, but when I try to use it in the main procedure, it faces with an error.
Assume that there is a pandas dataframe called df like below:

df = 
{'Name': {0: 'a', 1: 'c', 2: 'j', 3: 'd', 4: 'e'},
'Type': {0: 1, 1: 1, 2: 1, 3: 2, 4: 2},
'Number of Beams': {0: 60, 1: 60, 2: 60, 3: 60, 4: 60},
'Number of Columns': {0: 25, 1: 25, 2: 25, 3: 25, 4: 25},
'Total Weight': {0: 120, 1: 125, 2: 130, 3: 145, 4: 145},
'Warehouse1 Distance to Site Location': {0: 968, 1: 447, 2: 580, 3: 245, 4: 100},
'Warehouse2 Distance to Site Location': {0: 220, 1: 513, 2: 123, 3: 35, 4: 940},
'Warehouse3 Distance to Site Location': {0: 215, 1: 617, 2: 319, 3: 175, 4: 228},
'Distance to Site Location': {0: 215, 1: 447, 2: 123, 3: 35, 4: 100},
'Date of Registeration': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0},
'Earliest Time to Deliver': {0: 8, 1: 9, 2: 7, 3: 8, 4: 8},
'Latest Time to Deliver': {0: 10, 1: 10, 2: 11, 3: 12, 4: 9},
'Frame Cost': {0: 3720, 1: 3875, 2: 4030, 3: 4495, 4: 4495},
'Transportation Cost': {0: 516, 1: 1117.5, 2: 319.8, 3: 101.5, 4: 290},
'Date of Delivery': {0: 8, 1: 9, 2: 7, 3: 8, 4: 8}}

And there are some variables that need to be defined as below:

current_day = 0
Charge = [250, 130, 140, 200]
Capacities = {1:{"Warehouse1":250, "Warehouse2":250, "Warehouse3":250}, 
2:{"Warehouse1":130, "Warehouse2":130, "Warehouse3":130}, 
3:{"Warehouse1":140, "Warehouse2":140, "Warehouse3":140},
4:{"Warehouse1":200, "Warehouse2":200, "Warehouse3":200}}
Daily_Capacities = []
Indicator = 0

The function that I mentioned before is:

def Select_Warhouse(df, li):
# Initialize variables
min_col = None
min_val = float('inf')
results = []
# Loop over dictionaries
for dict_item in li:
for key in dict_item.keys():
# Check if key is a partial match with any column name
matching_col = [col for col in df.columns if key in col]
if matching_col:
# Retrieve column name and value
col_name = matching_col[0]
col_val = df.loc[0, col_name]
results.append((col_name, col_val))
# Update minimum value if necessary
if col_val < min_val:
min_col = col_name
min_val = col_val
output = [el for el in li if list(el.keys())[0] in min_col.split()[0]]
return str(*output[0].keys())

Finally, the loop that tries to determine a feasible distribution plan for delivery is this:

while len(df) > 0:
if current_day % 15 == 0 and current_day != 0 and Indicator == 0:
for i, j in zip(Capacities, Charge):
for key in Capacities[i]:
Capacities[i][key] = Capacities[i][key] + j
Indicator = 0
select = df[df['Date of Delivery'] == current_day]
if len(select) == 0:
Daily_Capacities.append(copy.deepcopy(Capacities))        
current_day += 1
continue
if len(select) > 1:
Indicator = 1
select = select[select['Total Weight'] == select['Total Weight'].max()]
if len(select) > 1:
select = select[select['Latest Time to Deliver'] == select['Latest Time to Deliver'].min()]
if len(select) > 1:
select = select.sample()
Available_Warehouses = []
for key, value in Capacities[select['Type'].iloc[0]].items():
if select['Total Weight'].iloc[0] <= value:
Available_Warehouses.append({key:value})
if len(Available_Warehouses) == 0:
df.loc[select.index, 'Date of Delivery'] += 1
if Indicator == 1:
continue        
Daily_Capacities.append(copy.deepcopy(Capacities))
current_day += 1
continue
elif len(Available_Warehouses) == 1:
Warehouse = str(*Available_Warehouses[0].keys())
elif len(Available_Warehouses) > 1:
Warehouse = Select_Warhouse(select, Available_Warehouses)
Feasible_Distribution_Plan = Feasible_Distribution_Plan.append(select.iloc[0], ignore_index= True)
df.drop(select.index, inplace= True)
Capacities[select['Type'].iloc[0]][Warehouse] -= select.iloc[0]['Total Weight']
if Indicator == 1:
continue
for key in dailycapacities:
Daily_Capacities.append(copy.deepcopy(Capacities))        
current_day += 1             
Feasible_Distribution_Plan['Date of Delivery'] = Feasible_Distribution_Plan['Date of Delivery'].astype('int32')
Feasible_Distribution_Plan

When I run the program, it faces an error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3801             try:
-> 3802                 return self._engine.get_loc(casted_key)
3803             except KeyError as err:
~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 0
The above exception was the direct cause of the following exception:
KeyError                                  Traceback (most recent call last)
<ipython-input-8-61768d41f965> in <module>
41 
42     elif len(Available_Warehouses) > 1:
---> 43         Warehouse = Select_Warhouse(select, Available_Warehouses)
44 
45     Feasible_Distribution_Plan = Feasible_Distribution_Plan.append(select.iloc[0], ignore_index= True)
<ipython-input-7-54081f8dbf92> in Select_Warhouse(df, li)
13                 # Retrieve column name and value
14                 col_name = matching_col[0]
---> 15                 col_val = df.loc[0, col_name]
16                 results.append((col_name, col_val))
17                 # Update minimum value if necessary
~\anaconda3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1064             key = tuple(com.apply_if_callable(x, self.obj) for x in key)
1065             if self._is_scalar_access(key):
-> 1066                 return self.obj._get_value(*key, takeable=self._takeable)
1067             return self._getitem_tuple(key)
1068         else:
~\anaconda3\lib\site-packages\pandas\core\frame.py in _get_value(self, index, col, takeable)
3922             #  results if our categories are integers that dont match our codes
3923             # IntervalIndex: IntervalTree has no get_loc
-> 3924             row = self.index.get_loc(index)
3925             return series._values[row]
3926 
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3802                 return self._engine.get_loc(casted_key)
3803             except KeyError as err:
-> 3804                 raise KeyError(key) from err
3805             except TypeError:
3806                 # If we have a listlike key, _check_indexing_error will raise
KeyError: 0

I'm kinda new to python, and can not solve the problem. The function is working outside the loop but it is not working in it. Do you have any idea?
ps: If you have any question about the program logic, please feel free to ask.

When you use your func in loop,you're trying to access df.loc[0, col_name] which the index = 0 might not be in the passing dataframe in the loop.
To solve the problem you should change the line df.loc[0, col_name] in your defined function to df.iloc[0][col_name]
Also, in your code, there's no dailycapacities, but you've used it in the last for of the loop. That one needs to get fixed, too.