python - 为什么即使 pandas 中存在列，我也会收到 Key 错误？

Question

我的文件包含 Company、RecordID、Sale 等列。当我在将文件加载到 pandas dataframe 后第一次尝试查找其数据类型时，它列出了几个列的 float/int。所以我将它们更改为字符串，如下所示；

data = pd.read_csv(filepath)
    print(data.dtypes)

Company Code          object
SiteCode               int64
Product Name          object
RECORD ID             int64
Tank ID                int64
Date                   int64
Sale Volume          float64
Deliveries Volume      int64
Dip Volume           float64

然后我在将输出写入文件之前将它们的类型更改为字符串；

>   data['RECORD ID'] = data['RECORD ID'].astype(str)
>      data['Tank ID'] = data['Tank ID'].astype(str)

我明白了；

    return self._engine.get_loc(key)
  File "pandas/_libs/index.pyx", line 107, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 131, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 1607, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 1614, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'RECORD ID'

如果我评论 recordid 并让 Tankid 转换，那么我会得到与 TankId 相同的 key 错误，这是为什么？

这是示例 csv； SIRA RECORD ID 和 TANK ID 列给出问题

 Company Code,SiteCode,Product Name,SIRA RECORD ID,Tank ID,Date,Sale Volume,Deliveries Volume,Dip Volume
    XXX,20995,27PMAXDSL,3535352,4,20191004,4383.49,12902,16000
    XXX,20995,02ULP,3535351,3,20191004,8221.573,15996,9987.32
    XXX,20995,02ULP,3535350,2,20191004,7303.1,8201,11200

Answer 1

如果需要将所有列都转换为字符串，最好在 read_csv 中使用参数 dtype:

data = pd.read_csv(filepath, dtype=str)