[英]Add new keys and values to an existing dictionary where the keys are matched
I would like to calculate the cost of product c = 20 $
and d = 30$
.我想计算产品
c = 20 $
和d = 30$
的成本。 Since I have meta data
and df as separated data frame I need to join them somehow where I can then get the price and the number of items bought for each product (eg c:5
) in a dictionary for each individual id, and then calculated the price of each product (eg for product c
1 * 20
)由于我有
meta data
和 df 作为单独的数据框,我需要以某种方式加入它们,然后我可以在字典中为每个单独的 id 获取价格和为每个产品购买的商品数量(例如c:5
),然后计算每个产品的价格(例如产品c
1 * 20
)
My First data frame我的第一个数据框
Metadata = {'product_name': ["c", "d"], 'product_price': [20, 30]} Metadata = pd.DataFrame(data=Metadata )
My second data frame我的第二个数据框
df = pd.DataFrame({'id':[1,2,3], 'product':[{'c':1}, {'d':3}, {'c':5, 'd':6}]})
Edited
Metadata
table into a dictionary:Metadata
表转换为字典: def get_product_price_dictionary(Metadata): product_info = Metadata product_price_dict = dict() for d in product_info.to_dict('records'): p_name = d["product_name"] p_price = d["product_price"] product_price_dict[p_name] = p_price return product_price_dict test = get_product_price_dictionary(Metadata) test
Output: Output:
{'c': 20, 'd': 30}
Then I get the keys inside my data frame.然后我在我的数据框中获取密钥。
list_keys = []
df_dic = df['product']
for i in range(len(df_dic)):
if df_dic.iloc[i] is not None:
each_dic = df_dic.iloc[i]
for key, value in each_dic.items():
list_keys.append(key)
list_keys_uique = list(set(list_keys))
list_keys_uique[0:5]
Output
['c', 'd']
I have recently get started working with python and now, I am really stuck in working with dictionary!我最近开始使用 python 现在,我真的被困在使用字典中了! to get the column called
product_cost
in the df data frame.在 df 数据框中获取名为
product_cost
的列。
And now I do not know how to precede with it!!!现在我不知道如何处理它!
I would not turn everything to dict as Pandas is already very fast.我不会将所有内容都转为 dict,因为 Pandas 已经非常快了。 You can search for certain values within a database using double equators together with the row name:
您可以使用双等号和行名在数据库中搜索某些值:
df[df['row']==key].value
I added a little piece of code which walks your database and calculates the total money of each transaction:我添加了一小段代码,它遍历您的数据库并计算每笔交易的总金额:
Metadata = {'product_name': ["c", "d"], 'product_price': [20, 30]}
Metadata = pd.DataFrame(data=Metadata )
df = pd.DataFrame({'id':[1,2,3], 'product':[{'c':1}, {'d':3}, {'c':5, 'd':6}]})
print (Metadata)
print (df)
for action in df['product']:
print ('action:', action)
total = 0
for product in action:
price = float(Metadata[Metadata['product_name']==product].product_price)
print (' product: %s, price: %.2f' % (product, price))
print (' count: %i, sum: %.2f' % (action[product], price * action[product]))
total += price * action[product]
print (' total: %.2f' % total)
Console output of the above code:上述代码的控制台output:
product_name product_price
0 c 20
1 d 30
id product
0 1 {'c': 1}
1 2 {'d': 3}
2 3 {'c': 5, 'd': 6}
action: {'c': 1}
product: c, price: 20.00
count: 1, sum: 20.00
total: 20.00
action: {'d': 3}
product: d, price: 30.00
count: 3, sum: 90.00
total: 90.00
action: {'c': 5, 'd': 6}
product: c, price: 20.00
count: 5, sum: 100.00
product: d, price: 30.00
count: 6, sum: 180.00
total: 280.00
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