将包含字符串的熊猫系列转换为布尔值

问题描述：

我有一个名为df的数据框，

I have a DataFrame named df as

  Order Number       Status
1         1668  Undelivered
2        19771  Undelivered
3    100032108  Undelivered
4         2229    Delivered
5        00056  Undelivered

我想将Status列转换为布尔值(状态为Delivered时为True，状态为Undelivered时为False) 但是如果状态既不是未交付"也不是已交付"，则应将其视为NotANumber或类似的内容.

I would like to convert the Status column to boolean (True when Status is Delivered and False when Status is Undelivered) but if Status is neither 'Undelivered' neither 'Delivered' it should be considered as NotANumber or something like that.

我想使用字典

d = {
  'Delivered': True,
  'Undelivered': False
}

所以我可以轻松添加其他字符串，可以将其视为True或False.

so I could easily add other string which could be either considered as True or False.

答

您可以只使用map:

In [7]: df = pd.DataFrame({'Status':['Delivered', 'Delivered', 'Undelivered',
                                     'SomethingElse']})

In [8]: df
Out[8]:
          Status
0      Delivered
1      Delivered
2    Undelivered
3  SomethingElse

In [9]: d = {'Delivered': True, 'Undelivered': False}

In [10]: df['Status'].map(d)
Out[10]:
0     True
1     True
2    False
3      NaN
Name: Status, dtype: object

将包含字符串的熊猫系列转换为布尔值

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