为什么简单的列乘法在Python 3.5、0.23中不起作用？

我想创建一个计算列，从每小时值中提取每月值

我试过这个

---

df['Monthly']=df['SpotPrice'].乘以（744）

这个

---

df['Monthly']=df['SpotPrice'].乘以（744，级别=1）

这个

---

df['Monthly']=df['SpotPrice']*744

还有其他一些荒谬的变化，我复制/粘贴了其他的答案，这些答案肯定会被标记为重复的，但没有任何效果

我有：

Python 3.5.0 (v3.5.0:374f501f4567, Sep 13 2015, 02:27:37) [MSC v.1900 64 bit (AMD64)] on win32
..
>>> pandas.__version__
'0.23.0'

更新

我现有的数据框架如下所示

    AvailabilityZone InstanceType ProductDescription SpotPrice                 Timestamp
40        us-east-2b     t2.micro         Linux/UNIX  0.003500 2018-07-07 22:09:52+00:00
940       us-east-2c     t2.micro         Linux/UNIX  0.003500 2018-07-06 22:09:47+00:00
941       us-east-2b     t2.micro         Linux/UNIX  0.003500 2018-07-06 22:09:47+00:00
942       us-east-2a     t2.micro         Linux/UNIX  0.003500 2018-07-06 22:09:47+00:00
39        us-east-2c     t2.micro         Linux/UNIX  0.003500 2018-07-07 22:09:52+00:00
41        us-east-2a     t2.micro         Linux/UNIX  0.003500 2018-07-07 22:09:52+00:00
115       us-east-2a     t2.small         Linux/UNIX  0.006900 2018-07-07 21:25:10+00:00
114       us-east-2b     t2.small         Linux/UNIX  0.006900 2018-07-07 21:25:10+00:00
113       us-east-2c     t2.small         Linux/UNIX  0.006900 2018-07-07 21:25:10+00:00
945       us-east-2a     t2.micro            Windows  0.008100 2018-07-06 22:09:38+00:00
44        us-east-2a     t2.micro            Windows  0.008100 2018-07-07 22:09:39+00:00
43        us-east-2b     t2.micro            Windows  0.008100 2018-07-07 22:09:39+00:00
42        us-east-2c     t2.micro            Windows  0.008100 2018-07-07 22:09:39+00:00
944       us-east-2b     t2.micro            Windows  0.008100 2018-07-06 22:09:38+00:00
943       us-east-2c     t2.micro            Windows  0.008100 2018-07-06 22:09:38+00:00
877       us-east-2c    t2.medium         Linux/UNIX  0.013900 2018-07-07 00:33:17+00:00
879       us-east-2a    t2.medium         Linux/UNIX  0.013900 2018-07-07 00:33:17+00:00
878       us-east-2b    t2.medium         Linux/UNIX  0.013900 2018-07-07 00:33:17+00:00
103       us-east-2a     t2.small            Windows  0.015900 2018-07-07 21:26:17+00:00
101       us-east-2c     t2.small            Windows  0.015900 2018-07-07 21:26:17+00:00
102       us-east-2b     t2.small            Windows  0.015900 2018-07-07 21:26:17+00:00
998       us-east-2b     t2.small            Windows  0.015900 2018-07-06 21:25:19+00:00
999       us-east-2a     t2.small            Windows  0.015900 2018-07-06 21:25:19+00:00
997       us-east-2c     t2.small            Windows  0.015900 2018-07-06 21:25:19+00:00
511       us-east-2c     c4.large         Linux/UNIX  0.017200 2018-07-07 11:33:18+00:00

输出通常如下所示：

    AvailabilityZone                        ...                                                                    Monthly
57        us-east-2a                        ...                          0.0035000.0035000.0035000.0035000.0035000.0035...
56        us-east-2b                        ...                          0.0035000.0035000.0035000.0035000.0035000.0035...
55        us-east-2c                        ...                          0.0035000.0035000.0035000.0035000.0035000.0035...
958       us-east-2a                        ...                          0.0035000.0035000.0035000.0035000.0035000.0035...
957       us-east-2b                        ...                          0.0035000.0035000.0035000.0035000.0035000.0035...
956       us-east-2c                        ...                          0.0035000.0035000.0035000.0035000.0035000.0035...
131       us-east-2a                        ...                          0.0069000.0069000.0069000.0069000.0069000.0069...
129       us-east-2c                        ...                          0.0069000.0069000.0069000.0069000.0069000.0069...
130       us-east-2b                        ...                          0.0069000.0069000.0069000.0069000.0069000.0069...
961       us-east-2a                        ...                          0.0081000.0081000.0081000.0081000.0081000.0081...
959       us-east-2c                        ...                          0.0081000.0081000.0081000.0081000.0081000.0081...
60        us-east-2a                        ...                          0.0081000.0081000.0081000.0081000.0081000.0081...
59        us-east-2b                        ...                          0.0081000.0081000.0081000.0081000.0081000.0081...
58        us-east-2c                        ...                          0.0081000.0081000.0081000.0081000.0081000.0081...
960       us-east-2b                        ...                          0.0081000.0081000.0081000.0081000.0081000.0081...
894       us-east-2b                        ...                          0.0139000.0139000.0139000.0139000.0139000.0139...
895       us-east-2a                        ...                          0.0139000.0139000.0139000.0139000.0139000.0139...
893       us-east-2c                        ...                          0.0139000.0139000.0139000.0139000.0139000.0139...
118       us-east-2b                        ...                          0.0159000.0159000.0159000.0159000.0159000.0159...
117       us-east-2c                        ...                          0.0159000.0159000.0159000.0159000.0159000.0159...
119       us-east-2a                        ...                          0.0159000.0159000.0159000.0159000.0159000.0159...
890       us-east-2c                        ...                          0.0172000.0172000.0172000.0172000.0172000.0172...
527       us-east-2c                        ...                          0.0172000.0172000.0172000.0172000.0172000.0172...
104       us-east-2b                        ...                          0.0177000.0177000.0177000.0177000.0177000.0177...
251       us-east-2a                        ...                          0.0178000.0178000.0178000.0178000.0178000.0178...
74        us-east-2c                        ...                          0.0178000.0178000.0178000.0178000.0178000.0178...

预期输出将是一个框架，其中包含一个名为“Monthly”的新列，该列的SpotPrice值将乘以744。

更新2 这是我的完整剧本

import sys
import boto3
import json
import pandas as pd
region = sys.argv[1]
client = boto3.client('ec2', region)
response = client.describe_spot_price_history()
df = pd.DataFrame(response['SpotPriceHistory'])
df = df.sort_values(by = 'SpotPrice')
df = df.head(50)
df['Monthly'] = df['SpotPrice'] * 744
print(df)

---

@Primusa评论中提供的答案：

---

df['Monthly']=df['SpotPrice'].aType（float）*744

“不工作”并不能告诉我们多少。您的示例是做什么的？您希望它们做什么？@JohnGordon刚刚添加了。@ScottBoston添加了输入和实际与预期输出SpotPrice是str而不是数字类型。df.info（）返回什么？

df['Monthly']=df['SpotPrice'].astype（float）*744

正如Anton vBR在评论中提到的那样，虽然这会起作用，但在导入时清理数据被认为是最佳做法，而不是在使用时。如果您反复使用此专栏，则必须记住在使用时强制使用，这将是一件恼人的事情，而且可能是危险的。（危险是因为

0.250.25

可能会导致崩溃，

2525

可能不会。）@DSM“导入时清理”在代码中是什么样子？