我有一个 Dataframe ，其中一列作为列表，另一列作为字典。但是，这是不一致的。它可能是单个元素或NULL

df = pd.DataFrame({'item_id':[1,1,1,2,3,4,4],
'shop_id':['S1','S2','S3','S2','S3','S1','S2'], 
'price_list':["{'10':['S1','S2'], '20':['S3'], '30':['S4']}","{'10':['S1','S2'], '20':['S3'], '30':['S4']}","{'10':['S1','S2'], '20':['S3'], '30':['S4']}",'50','NaN',"{'10':['S1','S2','S3'],'25':['S4']}","{'10':['S1','S2','S3'],'25':['S4']}"]})

+---------+---------+--------------------------------------------------+
| item_id | shop_id |                      price_list                  |
+---------+---------+--------------------------------------------------+
|       1 | S1      | {'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']} |
|       1 | S2      | {'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']} |
|       1 | S3      | {'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']} |
|       2 | S2      | 50                                               |
|       3 | S3      | NaN                                              |
|       4 | S1      | {'10': ['S1', 'S2', 'S3'], '25': ['S4']}         |
|       4 | S2      | {'10': ['S1', 'S2', 'S3'], '25': ['S4']}         |
+---------+---------+--------------------------------------------------+

我想把它扩大为：

+---------+---------+-------+
| item_id | shop_id | price |
+---------+---------+-------+
|       1 | S1      | 10    |
|       1 | S2      | 10    |
|       1 | S3      | 20    |
|       2 | S2      | 50    |
|       3 | S3      | NaN   |
|       4 | S1      | 10    |
|       4 | S2      | 10    |
+---------+---------+-------+

我试着用敷：

def get_price(row):
    if row['price_list'][0]=='{':
        prices = eval(row['price_list'])
        for key,value in prices.items():
            if str(row['shop_id']) in value:
                price = key
                break
            price =  np.nan
    else:
        price =  row["price_list"]
    return price

df['price'] = df.apply(lambda row: get_price(row),axis=1)

(The price_list列中的dictionary元素实际上是字符串，因此我需要首先将它们作为dict进行计算。）
但是，由于我的 Dataframe 非常大，上述方法需要花费大量时间。
因此，我尝试使用多重处理。我使用多重处理的方法如下：

def get_price(row):
    if row['price_list'][0]=='{':
        prices = eval(row['price_list'])
        for key,value in prices.items():
            if str(row['shop_id']) in value:
                price = key
                break
            price =  np.nan
    else:
        price =  row["price_list"]
    return price

def parallelize(data, get_price, num_of_processes):
    data_split = np.array_split(data, num_of_processes)
    pool = mp.Pool(num_of_processes)
    data = pd.concat(pool.map(get_price, data_split))
    pool.close()
    pool.join()
    return data

def run_on_subset(get_price, data_subset):
    data_subset['price'] = data_subset.apply(get_price, axis=1)
    return data_subset

def parallelize_on_rows(data, get_price, num_of_processes):
    return parallelize(data, partial(run_on_subset, get_price), num_of_processes)

df = parallelize_on_rows(df,get_price,num_processes)

现在，虽然我曾经在单核上运行apply，但它会一直运行下去。但在使用所有4个内核的多处理后，我会出现内存错误，内核死亡。
我有16GB的内存和4个内核。当脚本启动时，我已经使用了8GB。我运行的是64位Python 3.6。
我正在运行linux，只是使用多处理池。
如何在不使用更少内核的情况下，始终使用多处理来运行脚本？