Iterate data pandas between two tables

I’m trying to add a new column to my customers table (table). This new column is the sum of pending orders for a product (SKU). Pending orders are in another table (tableOV). I managed to do this, but it is taking a long time to calculate. I would like to know if there is a more efficient way.

table

[]

tableOV

[]

table with new pendent sum column

[]

my code

table = pd.DataFrame(productsCustomerCd)

tableOV['CC'] = tableOV['CC'].astype(str)
tableOV['SKU'] = tableOV['SKU'].astype(str)
table['cc'] = table['cc'].astype(str)

sumPendentList = []
for i in table.itertuples():
    sumPendent = tableOV.loc[(tableOV['CC']==i.cc)].loc[(tableOV['SKU']==i.sku), 'Pendent'].sum()
    sumPendentList.append(sumPendent)

    print(i.cc, i.sku, sumPendent)

table['Pendent'] = sumPendentList

print(table)

I’m trying to add a new column to my customers table (table). This new column is the sum of pending orders for a product (SKU). Pending orders are in another table (tableOV). I managed to do this, but it is taking a long time to calculate. I would like to know if there is a more efficient way.

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