Python Dataframe计算列不返回数值

我有一个数据帧（df），头部看起来像：

  Quarter   Body    Total requests  Requests on-hold    Total requests received (excluding on-hold)
1 2019_Q3      A                93                 5    
2 2019_Q3      B               228                 2    
3 2019_Q3      C               180                 7    
4 2019_Q3      D                31                 3    
5 2019_Q3      E               555                 0    

每个字段的类型为：

df.dtypes  
Quarter                                                                                         object
Body                                                                                            object
Total requests                                                                                  object
Requests Processed                                                                              object
Requests on-hold                                                                                object
Total requests received (excluding on-hold)                                                    float64

我正在尝试计算

总请求数-保留请求数

并将结果插入

列中收到的总请求数（不包括保留请求）

，因此我希望的输出如下所示：

  Quarter   Body    Total requests  Requests on-hold    Total requests received (excluding on-hold)
1 2019_Q3      A                93                 5                                            88
2 2019_Q3      B               228                 2                                           226
3 2019_Q3      C               180                 7                                           173
4 2019_Q3      D                31                 3                                            28
5 2019_Q3      E               555                 0                                           555

我正在尝试使用以下方法创建接收到的总请求（不包括保留）colum数据：

df['Total requests received (excluding on-hold)'] = df['Total requests'] - df['Requests on-hold']

但是我得到的是每个条目的

NaN

，而不是一个值

  Quarter   Body    Total requests  Requests on-hold    Total requests received (excluding on-hold)
1 2019_Q3      A                93                 5                                           NaN
2 2019_Q3      B               228                 2                                           NaN
3 2019_Q3      C               180                 7                                           NaN
4 2019_Q3      D                31                 3                                           NaN
5 2019_Q3      E               555                 0                                           NaN

我注意到Total requests和requests on hold的类型是object，所以我尝试使用

df["Total requests"] = pd.to_numeric(df["Total requests"])
df["Requests on-hold"] = pd.to_numeric(df["Requests on-hold"])

没有成功。我如何解决这个问题

注意：当我添加以下代码以转换为数值时（在计算之前）：

我收到错误消息：

    df["Total requests"] = pd.to_numeric(df["Total requests"])

  File "C:\Anaconda_Python 3.7\2019.03\lib\site-packages\pandas\core\tools\numeric.py", line 122, in to_numeric
    raise TypeError('arg must be a list, tuple, 1-d array, or Series')

TypeError: arg must be a list, tuple, 1-d array, or Series

---

它似乎有一些拖拉空白，所以请尝试通过

剥离
来删除它：

df["Total requests"] = pd.to_numeric(df["Total requests"].str.strip())
df["Requests on-hold"] = pd.to_numeric(df["Requests on-hold"].str.strip())

如果可能的话，先将一些带有尾随值的非数值
剥离
，然后添加参数
errors='concurve'
，以便将它们转换为
NaN
s：

df["Total requests"] = pd.to_numeric(df["Total requests"].str.strip(), errors='coerce')
df["Requests on-hold"] = pd.to_numeric(df["Requests on-hold"].str.strip(), errors='coerce')

如果不是尾随空格：

df["Total requests"] = pd.to_numeric(df["Total requests"], errors='coerce')
df["Requests on-hold"] = pd.to_numeric(df["Requests on-hold"], errors='coerce')

当使用
pd.to_numeric
时，您会得到什么？Hi yatu-我已经将收到的错误添加到问题主体中
df[“总请求”]
不是
pd.Series
？？在尝试输入数字之前，请检查df[“Total requests”]的dtypedtype是否为对象
df["Total requests"] = pd.to_numeric(df["Total requests"], errors='coerce')
df["Requests on-hold"] = pd.to_numeric(df["Requests on-hold"], errors='coerce')