Function 1 to build Transport Routes

transport_plan.py

# Create Transport Plan
def transport_plan(data, dict_trucks, capacity_dict):
	# List of Stores per Truck for each DAY
	df_plan = pd.DataFrame(data.groupby(['Date', 'TruckID'])['Code'].apply(list))
	df_plan.columns = ['List_Code']
	# List of Box Quantity
	df_plan['List_BOX'] = data.groupby(['Date', 'TruckID'])['BOX'].apply(list)
	# Mean of FTL
	df_plan['FTL']  = data.groupby(['Date', 'TruckID'])['FTL'].mean()
	df_plan['Capacity(T)'] = df_plan['FTL'].map(capacity_dict)
	df_plan['List_Loading'] = data.groupby(['Date', 'TruckID'])['Loading(T)'].apply(list)
	df_plan['Count'] = df_plan['List_Loading'].apply(lambda t: len(t))
	df_plan['Total_tons(T)'] = data.groupby(['Date', 'TruckID'])['Loading(T)'].sum()

	# Distribute: one shipment per col
	# Stores
	d = df_plan['List_Code'].apply(pd.Series)
	for col in d:
	    df_plan["Store%d" % (col+1)] = d[col]
	# Boxes number    
	d = df_plan['List_BOX'].apply(pd.Series)
	for col in d:
	    df_plan["Box%d" % (col+1)] = d[col]
	# Shipments Tonnage
	d = df_plan['List_Loading'].apply(pd.Series)
	for col in d:
	    df_plan["Tons%d" % (col+1)] = d[col]

	# Fill NaN + Drop useless columns
	df_plan.fillna(0, inplace = True)
	if 1 == 0:
		df_plan.drop(['List_Code'], axis = 1, inplace = True)
		df_plan.drop(['List_BOX'], axis = 1, inplace = True)
		df_plan.drop(['List_Loading'], axis = 1, inplace = True)

	return df_plan