如果数据缺少DIAGNOS的值，则可能发生TypeError: 'float' object is not iterable.例如，当数据如下所示:

The TypeError: 'float' object is not iterable could happen if the data is missing a value for DIAGNOS. For example, when data looks like this:

LopNr   AR  var3    va4 var5    var6    var7    var8    var9    var10   DIAGNOS
6   2011    a   a   a   a   a   a   a   a   S834
6   2011    a   a   a   a   a   a   a   a   
6   2011    a   a   a   a   a   a   a   a   K21 S834

然后

    In [68]: treatments = pd.read_table('data', usecols=[0,1,10])

In [69]: treatments
Out[69]: 
       LopNr    AR   DIAGNOS
0          6  2011      S834
1          6  2011       NaN
2          6  2011  K21 S834

[3 rows x 3 columns]

DIAGNOS列中的NaN是问题的根源，因为str.split(' ')保留NaN:

The NaN in the DIAGNOS column is the source of the problem, since str.split(' ') preserves the NaN:

In [70]: diagnoses = treatments['DIAGNOS'].str.split(' ')

In [71]: diagnoses
Out[72]: 
0         [S834]
1            NaN
2    [K21, S834]
Name: DIAGNOS, dtype: object

当调用diganose.map(tobacco)时，NaN被传递给tobacco函数.由于NaN是浮点且不可迭代，因此for x in lst循环会引发TypeError.

The NaN gets passed to the tobacco function when diganose.map(tobacco) is called. Since NaN is a float and not iterable, the for x in lst loop raises the TypeError.

为避免此错误，请替换treatments['DIAGNOS']中的NaN:

To avoid this error, replace the NaNs in treatments['DIAGNOS']:

import pandas as pd

def tobacco(lst):
    return any((('C30' <= x < 'C40') or ('F17' <= x <'F18')) for x in lst)

treatments = pd.read_table('data', usecols=[0,1,10])
treatments['DIAGNOS'].fillna('', inplace=True)
diagnoses = treatments['DIAGNOS'].str.split(' ')
treatments['tobacco'] = diagnoses.map(tobacco)
print(treatments)

收益

       LopNr    AR   DIAGNOS tobacco
0          6  2011      S834   False
1          6  2011             False
2          6  2011  K21 S834   False

[3 rows x 4 columns]