您可以将apply与list comprehension一起使用:

df1['A'] = df1['A'].apply(lambda x: [y if y <= 9 else 11 for y in x])
print (df1)
                                A
2017-01-01 02:00:00  [11, 11, 11]
2017-01-01 03:00:00    [3, 11, 9]

首先将更快的解决方案转换为numpy array，然后使用 numpy.where :

Faster solution is first convert to numpy array and then use numpy.where:

a = np.array(df1['A'].values.tolist())
print (a)
[[33 34 39]
 [ 3 43  9]]

df1['A'] = np.where(a > 9, 11, a).tolist()
print (df1)
                                A
2017-01-01 02:00:00  [11, 11, 11]
2017-01-01 03:00:00    [3, 11, 9]