Python 2.7 Python dataframe sort_值不适用于第二项

我有一个包含销售数据的数据框

Order ID Order Date Order Priority  Order Quantity     Sales  
  928.0   1/1/2009           High            32.0    180.36 
10369.0   1/2/2009            Low            43.0  4,083.19
10144.0   1/2/2009       Critical            16.0    137.63             
32323.0   1/1/2009  Not Specified             9.0    872.48          
48353.0   1/2/2009       Critical             3.0    124.81         
51008.0   1/3/2009       Critical            15.0     85.56       
26756.0   1/2/2009       Critical            43.0     614.8         
18144.0   1/2/2009            Low             4.0  1,239.06         
22912.0   1/2/2009            Low            32.0  4,902.38
... 

我想按日期（从最早到最新）和销售额（从最大到最小）对值进行排序。我在PyCharm Edu 3.5.1（python 2.7）中编写了这段代码：

输出：

    Order ID Order Date Order Priority  Order Quantity      Sales  
    32323.0 2009-01-01  Not Specified             9.0     872.48          
    928.0   2009-01-01           High            32.0     180.36          
    26756.0 2009-01-02       Critical            43.0      614.8         
    22912.0 2009-01-02            Low            32.0   4,902.38         
    10369.0 2009-01-02            Low            43.0   4,083.19          
    10144.0 2009-01-02       Critical            16.0     137.63          
    48353.0 2009-01-02       Critical             3.0     124.81          
    18144.0 2009-01-02            Low             4.0   1,239.06         
    29376.0 2009-01-03  Not Specified             4.0     896.49
...

“订单日期”列已正确排序，“销售”列未按预期排序。对于1000分隔符，PyCharm似乎忽略了带的值。我是不是遗漏了什么

与参数

数千

一起使用，用于删除

，

在浮动中，以及

解析日期

用于将列转换为日期时间，因为列

销售

的值被读取为

字符串

s：

df = pd.read_csv('sales.csv', thousands=',', parse_dates=['Order Date'])
print (df)
   Order ID Order Date Order Priority  Order Quantity    Sales
0     928.0 2009-01-01           High            32.0   180.36
1   10369.0 2009-01-02            Low            43.0  4083.19
2   10144.0 2009-01-02       Critical            16.0   137.63
3   32323.0 2009-01-01  Not Specified             9.0   872.48
4   48353.0 2009-01-02       Critical             3.0   124.81
5   51008.0 2009-01-03       Critical            15.0    85.56
6   26756.0 2009-01-02       Critical            43.0   614.80
7   18144.0 2009-01-02            Low             4.0  1239.06
8   22912.0 2009-01-02            Low            32.0  4902.38

df = df.sort_values(by=['Order Date', 'Sales'], ascending=[True, False])
print (df)
   Order ID Order Date Order Priority  Order Quantity    Sales
3   32323.0 2009-01-01  Not Specified             9.0   872.48
0     928.0 2009-01-01           High            32.0   180.36
8   22912.0 2009-01-02            Low            32.0  4902.38
1   10369.0 2009-01-02            Low            43.0  4083.19
7   18144.0 2009-01-02            Low             4.0  1239.06
6   26756.0 2009-01-02       Critical            43.0   614.80
2   10144.0 2009-01-02       Critical            16.0   137.63
4   48353.0 2009-01-02       Critical             3.0   124.81
5   51008.0 2009-01-03       Critical            15.0    85.56

另一种解决方案是使用+或：

---

似乎需要

df=pd.read_csv（'sales.csv'，header=0，数千='，'）

将列

sales

转换为

float

s。

df = pd.read_csv('sales.csv', thousands=',', parse_dates=['Order Date'])
print (df)
   Order ID Order Date Order Priority  Order Quantity    Sales
0     928.0 2009-01-01           High            32.0   180.36
1   10369.0 2009-01-02            Low            43.0  4083.19
2   10144.0 2009-01-02       Critical            16.0   137.63
3   32323.0 2009-01-01  Not Specified             9.0   872.48
4   48353.0 2009-01-02       Critical             3.0   124.81
5   51008.0 2009-01-03       Critical            15.0    85.56
6   26756.0 2009-01-02       Critical            43.0   614.80
7   18144.0 2009-01-02            Low             4.0  1239.06
8   22912.0 2009-01-02            Low            32.0  4902.38

df = df.sort_values(by=['Order Date', 'Sales'], ascending=[True, False])
print (df)
   Order ID Order Date Order Priority  Order Quantity    Sales
3   32323.0 2009-01-01  Not Specified             9.0   872.48
0     928.0 2009-01-01           High            32.0   180.36
8   22912.0 2009-01-02            Low            32.0  4902.38
1   10369.0 2009-01-02            Low            43.0  4083.19
7   18144.0 2009-01-02            Low             4.0  1239.06
6   26756.0 2009-01-02       Critical            43.0   614.80
2   10144.0 2009-01-02       Critical            16.0   137.63
4   48353.0 2009-01-02       Critical             3.0   124.81
5   51008.0 2009-01-03       Critical            15.0    85.56

df['Order Date'] = pd.to_datetime(df['Order Date'])
df['Sales'] = df['Sales'].replace(',', '', regex=True).astype(float)
#if astype does not work because bad data
#df['Sales'] = pd.to_numeric(df['Sales'].replace(',', '', regex=True), errors='coerce')
df = df.sort_values(by=['Order Date', 'Sales'], ascending=[True, False])
print (df)
   Order ID Order Date Order Priority  Order Quantity    Sales
3   32323.0 2009-01-01  Not Specified             9.0   872.48
0     928.0 2009-01-01           High            32.0   180.36
8   22912.0 2009-01-02            Low            32.0  4902.38
1   10369.0 2009-01-02            Low            43.0  4083.19
7   18144.0 2009-01-02            Low             4.0  1239.06
6   26756.0 2009-01-02       Critical            43.0   614.80
2   10144.0 2009-01-02       Critical            16.0   137.63
4   48353.0 2009-01-02       Critical             3.0   124.81
5   51008.0 2009-01-03       Critical            15.0    85.56