我正在尝试使用循环遍历两个列表.不幸的是，第二个for循环不起作用:它仅检查列表中的第一项，而不检查其余部分. 你能告诉我为什么吗?
I am trying to use a loop for iterating through two lists. Unfortunately, the second for loop does not work: it only checks the first item within the list, but not with rest. Could you please tell me why?
谢谢
列表:
low_cars_engines=['Audi', 'Bentley', 'Bugatti', 'Porsche', 'Skoda'] low_planes_engines=['Pratt & Whitney','Rolls-Royce','GE Aviation']我想基于if语句在原始数据集中添加两列(汽车和飞机):
I would like to add two more columns (Cars and Planes) to my original dataset based on if statements:
- 如果列表'Engine to check'中的对象在low_cars_engines列表中，则它是汽车，否则，它不是；
- 如果列表检查的引擎"中的对象位于low_planes_engines列表中，则该对象为飞机，否则为飞机.
第一个for循环工作正常，但第二个则不能:即使在low_planes_engines列表中，我也无法为检查的引擎"列表中的项分配值(它始终使我否").
The first for loop works fine, but not the second: I am not able to assign a value to an item in the list 'Engine to check' even if it is within the list low_planes_engines (it gives me always No).
能否请您告诉我什么地方出了问题，以及是否可以仅使用一个for循环而不是两个?我希望保留相同的结构，或保留df.at[index,'_'].现在，第二个循环仅打印/检查list_lowsplanes_engines列表中的第一项(即Pratt& Whitney)，而其余部分则不进行检查.
Could you please tell me what is wrong and if it would be possible to use only one for loop rather than two? I would prefer to keep the same structure, or keep df.at[index,'_']. Right now the second loop print/check only the first item of the list low_planes_engines (i.e. Pratt & Whitney) and does not go through the rest.
由于数据集类似于:
Audi CFM International Rolls-Royce Bentley Volkswagen Toyota Suzuki Porsche并且不包含该元素，Planes下的所有行均设置为No.
and it does not include that element, all the rows under Planes are set to No.
推荐答案在使用Pandas时，不应使用循环. DataFrame不能按顺序访问.不过，您需要一些NumPy:
You should not use loops when you work with Pandas. DataFrames are not designed to be accessed sequentially. You need some NumPy, though:
import numpy as np df['Cars'] = np.where(df['Engine to check'].isin(low_cars_engines), 'Yes', 'No') df['Planes'] = np.where(df['Engine to check'].isin(low_planes_engines), 'Yes', 'No')结果:
# Engine to check Cars Planes # 0 Audi Yes No # 1 CFM International No No # 2 Rolls-Royce No Yes # 3 Bentley Yes No # 4 Volkswagen No No # 5 Toyota No No # 6 Suzuki No No # 7 Porsche Yes No您可能也不应使用是"和否".请改用布尔值True和False，因为它们将来会更易于使用:
You probably should not use "Yes" and "No," either. Use boolean values True and False instead, as they are easier to work with in the future:
df['Cars'] = df['Engine to check'].isin(low_cars_engines) df['Planes'] = df['Engine to check'].isin(low_planes_engines)最后，如果DataFrame中的所有内容严格都是汽车或飞机，则只需要一列.另一个将是补码.
Finally, if everything in the DataFrame is strictly a car or a plane, only one column is required. The other will be the complement.