python之pd.to_numeric 将整个系列转换为 NaN

我正在尝试使用 pd.to_numeric 转换列，但出于某种原因，它会将所有值(一个除外)转换为 NaN:

In[]: pd.to_numeric(portfolio["Principal Remaining"],errors="coerce") 
Out[]:  
1           NaN 
2           NaN 
3           NaN 
4           NaN 
5           NaN 
6           NaN 
7           NaN 
8           NaN 
9           NaN 
10          NaN 
11          NaN 
12          NaN 
13          NaN 
14          NaN 
15          NaN 
16          NaN 
17          NaN 
18       836.61 
19          NaN 
20          NaN 
      ...   
Name: Principal Remaining, Length: 32314, dtype: float64 

想知道为什么会这样吗？原始数据如下所示:

1         18,052.02 
2         27,759.85 
3         54,061.75 
4         89,363.61 
5         46,954.46 
6         64,295.64 
7        100,000.00 
8         27,905.98 
9         13,821.48 
10        16,937.89 
        ...     
Name: Principal Remaining, Length: 32314, dtype: object 

请您参考如下方法:

read_csv 与 thousands=','

df = pd.read_csv('file.csv', thousands=',') 

这解决了读取数据时的问题。

replace 和to_numeric

df['Principal Remaining'] = pd.to_numeric( 
    df['Principal Remaining'].str.replace(',', ''), errors='coerce') 

如果第一个选项不是一个选择，您需要先使用 str.replace 去掉逗号，然后调用 pd. to_numeric 如此处所示。