Python Pandas DataFrame：不可排序的类型：str() > int()
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Python Pandas DataFrame: unorderable types: str() > int()
提问by user3770271
I have downloaded the file mentioned in book "Python for Data Analysis" and was going through the example FEC database mentioned in page 278. I get the following Type error when I ran the command. My versions: Python 3.4; Pandas: 0.14.0. OS: Windows 8
我已经下载了“Python for Data Analysis”一书中提到的文件，并且正在浏览第 278 页中提到的示例 FEC 数据库。运行命令时出现以下类型错误。我的版本：Python 3.4；Pandas：0.14.0。操作系统：Windows 8
>>> fec=pd.read_csv('c:\python\P00000001-ALL.csv')
>>> (fec.contb_receipt_amt > 0).value_counts()
>>> TypeError: unorderable types: str() > int()
But it is not just this dataset. Any dataset that I am working with have similar problem. Int(Number) data types are being imported as objects like anything else and when run any comparison with numbers(>0) on them I get the above error. What is the work around? I tried importing with dtype option, which throws an error that says int64 or Float64 is not available. I am sure there is a right way of doing. How to load the data frame with the right data types.
但这不仅仅是这个数据集。我正在使用的任何数据集都有类似的问题。Int(Number) 数据类型像其他任何东西一样作为对象导入，当对它们运行与 numbers(>0) 的任何比较时，我得到上述错误。有什么工作？我尝试使用 dtype 选项导入，这会引发一个错误，指出 int64 或 Float64 不可用。我相信有正确的做法。如何使用正确的数据类型加载数据框。
Any help is appreciated.
任何帮助表示赞赏。
回答by Denis Cottin
I got this error for some not identical dates. I solved it using a type change first.
对于某些不相同的日期，我收到了此错误。我首先使用类型更改解决了它。
Try :
尝试 ：
fec[[contb_receipt_amt]] = fec[[contb_receipt_amt]].astype(str)
Then try again the count.
然后再试一次计数。
