python之使用 pandas .map 更改值_编程设计

我正在尝试使用 map 函数将数据中的字符串更改为数值。

这是数据:

    label   sms_message 
0   ham     Go until jurong point, crazy.. Available only ... 
1   ham     Ok lar... Joking wif u oni... 
2   spam    Free entry in 2 a wkly comp to win FA Cup fina... 
3   ham     U dun say so early hor... U c already then say... 
4   ham     Nah I don't think he goes to usf, he lives aro...

我正在尝试使用以下方法将“垃圾邮件”更改为 1 并将“火腿”更改为 0:

df['label'] = df.label.map({'ham':0, 'spam':1})

但结果是:

    label   sms_message 
0   NaN     Go until jurong point, crazy.. Available only ... 
1   NaN     Ok lar... Joking wif u oni... 
2   NaN     Free entry in 2 a wkly comp to win FA Cup fina... 
3   NaN     U dun say so early hor... U c already then say... 
4   NaN     Nah I don't think he goes to usf, he lives aro...

谁能找出问题所在？

请您参考如下方法:

你是对的，我想你执行了同一条语句两次(1 接 1 次)。在 Python 交互式终端上执行的以下语句阐明了这一点。

Note: If you pass dictionary, map() replaces all values from Series with NaN if it does not match with dictionary's keys (I think, you have also done the same i.e. executing the statement twice). Check pandas map(), apply().

Pandas documentation note: when arg is a dictionary, values in Series that are not in the dictionary (as keys) are converted to NaN.

>>> import pandas as pd 
>>> 
>>> d = { 
...     "label": ["ham", "ham", "spam", "ham", "ham"], 
...     "sms_messsage": [ 
...     "Go until jurong point, crazy.. Available only ...", 
...     "Ok lar... Joking wif u oni...", 
...     "Free entry in 2 a wkly comp to win FA Cup fina...", 
...     "U dun say so early hor... U c already then say...", 
...     "Nah I don't think he goes to usf, he lives aro..." 
...    ] 
... } 
>>> 
>>> df = pd.DataFrame(d) 
>>> df 
  label                                       sms_messsage 
0   ham  Go until jurong point, crazy.. Available only ... 
1   ham                      Ok lar... Joking wif u oni... 
2  spam  Free entry in 2 a wkly comp to win FA Cup fina... 
3   ham  U dun say so early hor... U c already then say... 
4   ham  Nah I don't think he goes to usf, he lives aro... 
>>> 
>>> df['label'] = df.label.map({'ham':0, 'spam':1}) 
>>> df 
   label                                       sms_messsage 
0      0  Go until jurong point, crazy.. Available only ... 
1      0                      Ok lar... Joking wif u oni... 
2      1  Free entry in 2 a wkly comp to win FA Cup fina... 
3      0  U dun say so early hor... U c already then say... 
4      0  Nah I don't think he goes to usf, he lives aro... 
>>> 
>>> df['label'] = df.label.map({'ham':0, 'spam':1}) 
>>> df 
   label                                       sms_messsage 
0    NaN  Go until jurong point, crazy.. Available only ... 
1    NaN                      Ok lar... Joking wif u oni... 
2    NaN  Free entry in 2 a wkly comp to win FA Cup fina... 
3    NaN  U dun say so early hor... U c already then say... 
4    NaN  Nah I don't think he goes to usf, he lives aro... 
>>>

获得相同结果的其他方法

>>> import pandas as pd 
>>> 
>>> d = { 
...     "label": ['spam', 'ham', 'ham', 'ham', 'spam'], 
...     "sms_message": ["M1", "M2", "M3", "M4", "M5"] 
... } 
>>> 
>>> df = pd.DataFrame(d) 
>>> df 
  label sms_message 
0  spam          M1 
1   ham          M2 
2   ham          M3 
3   ham          M4 
4  spam          M5 
>>>

1st way - using map() with dictionary parameter

>>> new_values = {'spam': 1, 'ham': 0} 
>>> 
>>> df 
  label sms_message 
0  spam          M1 
1   ham          M2 
2   ham          M3 
3   ham          M4 
4  spam          M5 
>>> 
>>> df.label = df.label.map(new_values) 
>>> df 
   label sms_message 
0      1          M1 
1      0          M2 
2      0          M3 
3      0          M4 
4      1          M5 
>>>

2nd way - using map() with function parameter

>>> df.label = df.label.map(lambda v: 0 if v == 'ham' else 1) 
>>> df 
   label sms_message 
0      1          M1 
1      0          M2 
2      0          M3 
3      0          M4 
4      1          M5 
>>>

3rd way - using apply() with function parameter

>>> df.label = df.label.apply(lambda v: 0 if v == "ham" else 1) 
>>> 
>>> df 
   label sms_message 
0      1          M1 
1      0          M2 
2      0          M3 
3      0          M4 
4      1          M5 
>>>

谢谢。