python - pd.to_numeric 将整个系列转换为 NaN

Question

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

Answer 1

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。