英文:
Select rows in Pandas and iterate thru them to change value in column based in value in another row
问题
我有一个数据框，其中每一行代表一个对象。
我想选择具有特定列中特定值的所有行。然后，我想遍历所选行，并根据行中另一列中的值添加一个值到另一列中。
我会使用loc来选择行，但然后我不知道如何迭代它们以检查值并更改它们。
这可能是一个广泛的问题，但我是Pandas的新手。我搜索了类似的问题，但没有找到我要找的答案。
示例数据
| id | name | city | address | is customer |
|---|---|---|---|---|
| 1 | John | Berlin | bla | |
| 2 | Jake | Paris | bla | |
| 3 | Jim | Berlin | Club |
我想只选择城市为“Berlin”的行。
然后在客户列表中查找名字：
list_of_customers = ["John","Jake"]
如果找到名字，就在“is customer”列中添加“True”。
| id | name | city | address | is customer |
|---|---|---|---|---|
| 1 | John | Berlin | bla | True |
| 2 | Jake | Paris | bla | |
| 3 | Jim | Berlin | Club |
我更喜欢更改原始的数据框而不是创建一个新的。
英文:
I have a data frame where every row represents an object.
I want to select all rows that have a specific value in a column. Then I want to iterate thru the selected rows and and add a value in a column based on a value in another column on the row.
I would use loc to select the rows, but then I have no idea how to iterate over them to check value and change them.
This might be a broad question, but I am new to Pandas. I did search for similar question, but did not find what I was searching for.
Example Data
| id | name | city | address | is customer |
|---|---|---|---|---|
| 1 | John | Berlin | bla | |
| 2 | Jake | Paris | bla | |
| 3 | Jim | Berlin | Club |
I want to select only rows with "Berlin" as city.
Then lookup the name in the list of customers:
list_of_customers = ["John","Jake"]
And if the name is found ad a "True" in the column "is customer"
| id | name | city | address | is customer |
|---|---|---|---|---|
| 1 | John | Berlin | bla | True |
| 2 | Jake | Paris | bla | |
| 3 | Jim | Berlin | Club |
I prefer to change the original DF not create a new one.
答案1
得分: 1
现在你澄清了你的问题，我可以帮助：
import numpy as np
list_of_customers = ["john","jake"]
df['is customer'] = np.where(df['name'].str.lower().isin(list_of_customers), True, False)
英文:
Now that you clarified your question I can help:
import numpy as np
list_of_customers = ["john","jake"]
df ['is customer'] = np.where(df['name'].str.lower().isin(list_of_customers), True, False)
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