我使用从excel导入了一个数据帧
data = pd.read_csv('transaction.csv')
并且有一个看起来像的数据帧
Date Time Transaction Item
0 2016-10-30 09:58:11 1 water
1 2016-10-30 10:05:34 2 french fries
2 2016-10-30 10:05:34 2 Icecream
3 2016-10-30 10:07:57 3 chocolate
4 2016-10-30 10:07:57 3 Cookies
我创建了一个字典,将每个项目分配给食物或饮料类别,如下所示:
Food = ('french fries', 'Icecream', 'chocolate', 'Cookies')
Drink = ('water')
Category = {Food : "Food", Drink : "Drink"}
我想将类别分配给另一列,但结果是NaN。我用了这个代码:
data['Classification'] = data['Item'].map(Category)
Date Time Transaction Item Food or Drink
0 2016-10-30 09:58:11 1 water NaN
1 2016-10-30 10:05:34 2 french fries NaN
2 2016-10-30 10:05:34 2 icecream NaN
3 2016-10-30 10:07:57 3 chocolate NaN
4 2016-10-30 10:07:57 3 cookies NaN
解决这个问题的最佳方法是什么?
通过dict.fromkeys
为每个类别创建字典,并将它们合并在一起:
Food = ('french fries', 'Icecream', 'chocolate', 'Cookies')
Drink = ('water',)
Category = {**dict.fromkeys(Food, "Food"), **dict.fromkeys(Drink, "Drink")}
print (Category)
{'french fries': 'Food', 'Icecream': 'Food',
'chocolate': 'Food', 'Cookies': 'Food', 'water': 'Drink'}
data['Classification'] = data['Item'].map(Category)
print (data)
Date Time Transaction Item Classification
0 2016-10-30 09:58:11 1 water Drink
1 2016-10-30 10:05:34 2 french fries Food
2 2016-10-30 10:05:34 2 Icecream Food
3 2016-10-30 10:07:57 3 chocolate Food
4 2016-10-30 10:07:57 3 Cookies Food