Question

我有一个带有ID和Sell列的df。我想使用新的卖单列表来更新“卖出”列（并非所有原始商品都需要更新-只是其中一些）。在我看到的所有示例中，该值始终相同或来自一列。就我而言，我具有动态价值。

这就是我想要的：

file = ('something.csv') # Has 300 rows
IDList= [['453164259','453106168','453163869','453164463'] # [ID] 
SellList=[120,270,350,410] # Sells values
csv = path_pattern = os.path.join(os.getcwd(), file)
df = pd.read_csv(file)
df.loc[df['Id'].isin(IDList[x]), 'Sell'] = SellList[x] # Update the rows with the corresponding Sell value of the ID.
df.to_csv(file)

有什么想法吗？预先感谢

Answer 1

假设“ id”是一个字符串（如IDList中所述）且不是df的索引

IDList= [['453164259','453106168','453163869','453164463'] # [ID] 
SellList=[120,270,350,410]
id_dict={x:y for x,y in zip(IDList,SellList)}
for index,row in df.iterrows():
    if row['id'] in IDList:
         df.loc[str(index),'Sell']=id_dict[row['id']]

如果id为索引：

IDList= [['453164259','453106168','453163869','453164463'] # [ID] 
SellList=[120,270,350,410]
id_dict={x:y for x,y in zip(IDList,SellList)}
for index,row in df.iterrows():
    if index in IDList:
         df.loc[str(index),'Sell']=id_dict[index]

我所做的是使用IDlist和SellList创建了一个字典，然后使用iterrows（）在df上循环了

Answer 2

df = pd.read_csv('something.csv')
IDList= ['453164259','453106168','453163869','453164463']
SellList=[120,270,350,410]

这将有效地工作，特别是对于大文件：

df.set_index('id', inplace=True)
df.loc[IDList, 'Sell'] = SellList
df.reset_index() ## not mandatory, just in case you need 'id' back as a column

df.to_csv(file)

使用熊猫，根据ID和新值列表更新列值

2 个答案: