Pandas - 标记化数据预期 1 个字段看到多个
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Pandas - Tokenizing Data Expected 1 field saw multiple
提问by Ahmed Haque
A bit confused why I am getting this error. I thought skiprows should have taken care of me.
有点困惑为什么我会收到这个错误。我认为skiprows应该照顾我。
Error:
错误：
CParserError: Error tokenizing data. C error: Expected 1 fields in line 6, saw 13
Line:
线：
df_data = pd.read_csv(infile.name, skiprows=[6], sep=',')
CSV:
CSV：
Header: 1asdf
Header: 2fac
Header: 3aaz
Header: 4ssw
Header: 5aaa
0.0,-64,192,152,27023,3,0,26275,31473,149,67,77,0.0
0.04050016403198242,-64,192,148,27021,3,0,26274,31471,149,67,77,0.038919925689697266
0.08100008964538574,-64,192,148,27017,3,0,26275,31467,149,67,77,0.07783985137939453
0.12150001525878906,-60,192,148,27019,3,0,26277,31467,149,67,77,0.1167600154876709
0.16199994087219238,-60,192,144,27015,3,0,26277,31463,149,67,77,0.15567994117736816
0.2025001049041748,-60,192,148,27075,3,0,26319,31463,149,67,77,0.19460034370422363
回答by Python Beginner WG
I got the same error message. In my case it was because commas were used as decimal marks and the cells were seperated by semicolon. In my case sep=";"solved the problem:
pd.read_csv(infile.name, sep=";")
我收到了同样的错误信息。就我而言，这是因为逗号用作小数点，并且单元格用分号分隔。在我的情况下 sep=";"解决了这个问题：
pd.read_csv(infile.name, sep=";")
回答by chrisb
If you pass a list to skiprows, it interprets it as 'skip the rows in this list (0 indexed)'. Pass an integer instead. You probably also want header=Noneso your first row of data doesn't become the column names.
如果您将列表传递给 skiprows，它会将其解释为“跳过此列表中的行（0 索引）”。而是传递一个整数。您可能还希望header=None第一行数据不会成为列名。
pd.read_csv(infile.name, skiprows=6, header=None)
回答by YouAreAwesome
This error comes when you have more columns entries than specified in schema.
当您的列条目多于架构中指定的条目时，就会出现此错误。
That means - In your particulate column you should have delimiter in it.
这意味着 - 在您的微粒列中，您应该有分隔符。
In this way interpreter assumes that new column is coming but in reality we dont have any so the exception is thrown as runtime.
通过这种方式，解释器假定新列即将到来，但实际上我们没有任何列，因此异常作为运行时抛出。
Solution for that:
解决方案：
- Best one is, ask your input source generator to solve this.
Second would be, if you have permission to skip the records then using this -
df = pd.read_csv(file_loc, sep=',', keep_default_na=False)
- 最好的方法是，让您的输入源生成器解决这个问题。
其次是，如果您有权跳过记录，则使用此 -
df = pd.read_csv(file_loc, sep=',', keep_default_na=False)
