def _get_products(merchant_name):
    full_df = unnest(full_df,'Options','product code')
    return _cleanup_products_df_columns(full_df)

def unnest(df, col, col2, reset_index = False):
   for item in df[col]:
        if len(item) == 0:
            item.append('[]')

   col_flat = pd.DataFrame([[i, x]
                      for i, y in df[col].apply(list).iteritems()
                          for x in y ], columns=['I', col]
                           )
   col_flat = col_flat.set_index('I')
   print("col_flat::",col_flat)
   df = df.drop(col, 1)
   df = df.merge(col_flat, left_index=True, right_index=True)
   df['item_group_id'] = df['Product code']
   df['Product code'] += df[col].apply(lambda val: '' if val == "[]" else '-' + val)

   return df

 def _cleanup_products_df_columns(products_df):
    columns_map = merge_dictionary_and_list({
        'product code': 'merchant_product_id',
        'product name': 'name',
        'sale price': 'sale_price',
        'product url': 'web_url',
        'item_group_id':'item_group_id'
    }, ['category', 'description', 'image_urls', 'price', 'quantity', 'options'])

    products_df.columns = map(str.lower, products_df.columns)
    products_df = products_df.loc[:, products_df.columns.isin(list(columns_map.keys()))].reset_index(drop=True)
    products_df = products_df.rename(columns=columns_map)

    products_df['description'] = products_df['description'].apply(clean_description)
    products_df.drop_duplicates(subset=['merchant_product_id'], keep='first', inplace=True)

    return products_df.dropna(subset=['merchant_product_id', 'name'])

def main():
    MERCHANT_IDS = [1, 2]
    for idx in MERCHANT_IDS:
        merchant = get_merchant_by_id(idx)
        products_df = _get_products(merchant.name)
        products_df.to_csv(merchant.name + '.csv', encoding='utf-8', index=False)