blocks|key|2792179|text|解析器被文件头搞糊涂了。它读取第一行，并从该行推断列数。但是前两行并不代表文件中的实际数据。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2792180|用data+=+pd.read_csv(path,+skiprows=2)试试吧|offset|length|style|CODE|2792181|entityMap^0|0|1|10|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@$D|L|E|M|F|G]]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

The parser is getting confused by the header of the file. It reads the first row and infers the number of columns from that row. But the first two rows aren't representative of the actual data in the file.

Try it with <code>data = pd.read_csv(path, skiprows=2)</code>

blocks|key|178850|text|你也可以尝试一下；|type|unstyled|depth|inlineStyleRanges|entityRanges|data|178851|data+=+pd.read_csv('file1.csv',+error_bad_lines=False)|code-block|syntax|javascript|178852|请注意，这将导致有问题的行被跳过。|178853|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

you could also try;

<pre><code>data = pd.read_csv('file1.csv', error_bad_lines=False)
</code></pre>

Do note that this will cause the offending lines to be skipped.

blocks|key|181210|text|我也遇到了同样的问题。在同一个源文件上使用pd.read_table()似乎是可行的。我无法追踪原因，但对于我的情况来说，这是一个有用的解决办法。也许更有见识的人可以更好地解释它为什么会起作用。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|181211|编辑:我发现当你的文件中有一些与实际数据格式不同的文本时，这个错误就会出现。这通常是页眉或页脚信息(超过一行，因此skip_header不起作用)，它们不会与实际数据(使用read_csv时)用相同数量的逗号分隔。使用read_table使用制表符作为分隔符，这可以绕过用户当前的错误，但会引入其他错误。|181212|我通常通过将额外的数据读取到文件中，然后使用read_csv()方法来解决这个问题。|181213|根据实际文件的不同，确切的解决方案可能会有所不同，但这种方法在某些情况下适用于我|181214|entityMap^0|L|F|0|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@$9|O|A|P|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|Q|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|R|8|@]|D|@]|E|$]]|$1|J|3|K|5|6|7|S|8|@]|D|@]|E|$]]|$1|L|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|M|$]]

I came across the same issue. Using <code>pd.read_table()</code> on the same source file seemed to work. I could not trace the reason for this but it was a useful workaround for my case. Perhaps someone more knowledgeable can shed more light on why it worked.

Edit:
I found that this error creeps up when you have some text in your file that does not have the same format as the actual data. This is usually header or footer information (greater than one line, so skip_header doesn't work) which will not be separated by the same number of commas as your actual data (when using read_csv). Using read_table uses a tab as the delimiter which could circumvent the users current error but introduce others.

I usually get around this by reading the extra data into a file then use the read_csv() method.

The exact solution might differ depending on your actual file, but this approach has worked for me in several cases

blocks|key|181216|text|这可能是一个问题|type|unstyled|depth|inlineStyleRanges|entityRanges|data|181217|正如@TomAugspurger注意到的一样，在第一行中显示数据中的分隔符|181218|要解决此问题，请尝试在调用read_csv时指定sep和/或header参数。例如,|offset|length|style|CODE|181219|df+=+pandas.read_csv(filepath,+sep='delimiter',+header=None)|code-block|syntax|javascript|181220|在上面的代码中，sep定义了分隔符，header=None告诉pandas源数据没有标题/列标题行。the+docs如是说：“如果文件不包含标题行，那么您应该显式地传递header=None”。在这种情况下，pandas会自动为每个字段{0,1,2，...}创建整数索引。|181221|根据文档，分隔符的事情应该不是问题。文档说“如果sep是None而没有指定，将尝试自动确定这一点。”然而，我在这方面并不走运，包括带有明显分隔符的实例。|181222|另一种解决方案可能是尝试自动检测分隔符|181223|#+use+the+first+2+lines+of+the+file+to+detect+separator
temp_lines+=+csv_file.readline()+%2B+'\n'+%2B+csv_file.readline()
dialect+=+csv.Sniffer().sniff(temp_lines,+delimiters=';,')

#+remember+to+go+back+to+the+start+of+the+file+for+the+next+time+it's+read
csv_file.seek(0)+

df+=+pd.read_csv(csv_file,+sep=dialect.delimiter)|181224|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html^0|0|0|D|8|O|3|U|6|0|0|8|3|I|B|1E|8|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|15|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|16|8|@$F|17|G|18|H|I]|$F|19|G|1A|H|I]|$F|1B|G|1C|H|I]]|9|@]|A|$]]|$1|J|3|K|5|L|7|1D|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|1E|8|@$F|1F|G|1G|H|I]|$F|1H|G|1I|H|I]]|9|@$F|1J|G|1K|1|1L]]|A|$]]|$1|Q|3|R|5|6|7|1M|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1N|8|@]|9|@]|A|$]]|$1|U|3|V|5|L|7|1O|8|@]|9|@]|A|$M|N]]|$1|W|3|-4|5|6|7|1P|8|@]|9|@]|A|$]]]|X|$Y|$5|Z|10|11|A|$12|13]]]]

It might be an issue with
<ul>
<li>the delimiters in your data</li>
<li>the first row, as @TomAugspurger noted</li>
</ul>
To solve it, try specifying the <code>sep</code> and/or <code>header</code> arguments when calling <code>read_csv</code>. For instance,
<pre><code>df = pandas.read_csv(filepath, sep='delimiter', header=None)
</code></pre>
In the code above, <code>sep</code> defines your delimiter and <code>header=None</code> tells pandas that your source data has no row for headers / column titles. Thus saith <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html" rel="noreferrer">the docs</a>: &quot;If file contains no header row, then you should explicitly pass header=None&quot;. In this instance, pandas automatically creates whole-number indices for each field {0,1,2,...}.
According to the docs, the delimiter thing should not be an issue. The docs say that &quot;if sep is None [not specified], will try to automatically determine this.&quot; I however have not had good luck with this, including instances with obvious delimiters.
Another solution may be to try auto detect the delimiter
<pre><code># use the first 2 lines of the file to detect separator
temp_lines = csv_file.readline() + '\n' + csv_file.readline()
dialect = csv.Sniffer().sniff(temp_lines, delimiters=';,')

# remember to go back to the start of the file for the next time it's read
csv_file.seek(0) 

df = pd.read_csv(csv_file, sep=dialect.delimiter)

</code></pre>

blocks|key|3004520|text|这绝对是分隔符的问题，因为大多数csv+CSV都是使用sep='/t'创建的，所以尝试使用制表符(\t)使用分隔符/t进行read_csv。因此，尝试使用以下代码行打开。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3004521|data=pd.read_csv("File_path",+sep='\t')|code-block|syntax|javascript|3004522|entityMap^0|R|8|1C|4|1L|2|1P|8|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|L|$]]

This is definitely an issue of delimiter, as most of the csv CSV are got create using <code>sep='/t'</code> so try to <code>read_csv</code> using the tab character <code>(\t)</code> using separator <code>/t</code>. so, try to open using following code line.

<pre><code>data=pd.read_csv("File_path", sep='\t')
</code></pre>

blocks|key|3004546|text|我也有这个问题，但可能是因为不同的原因。我的CSV中有一些尾随的逗号，这些逗号添加了熊猫试图读取的附加列。使用以下代码可以工作，但它会忽略错误的代码行：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3004547|data+=+pd.read_csv('file1.csv',+error_bad_lines=False)|code-block|syntax|javascript|3004548|如果你想保留这些代码行，一种丑陋的处理错误的技巧是这样做的：|3004549|line+++++=+[]
expected+=+[]
saw++++++=+[]+++++
cont+++++=+True+

while+cont+==+True:+++++
++++try:
++++++++data+=+pd.read_csv('file1.csv',skiprows=line)
++++++++cont+=+False
++++except+Exception+as+e:++++
++++++++errortype+=+e.message.split('.')[0].strip()++++++++++++++++++++++++++++++++
++++++++if+errortype+==+'Error+tokenizing+data':++++++++++++++++++++++++
+++++++++++cerror++++++=+e.message.split(':')[1].strip().replace(',','')
+++++++++++nums++++++++=+[n+for+n+in+cerror.split('+')+if+str.isdigit(n)]
+++++++++++expected.append(int(nums[0]))
+++++++++++saw.append(int(nums[2]))
+++++++++++line.append(int(nums[1])-1)
+++++++++else:
+++++++++++cerror++++++=+'Unknown'
+++++++++++print+'Unknown+Error+-+222'

if+line+!=+[]:
++++#+Handle+the+errors+however+you+want|3004550|我接着写了一个脚本，将这些行重新插入到行中，因为在上面的代码中，错误的行将由变量‘DataFrame’给出。这一切都可以通过简单地使用csv阅读器来避免。希望熊猫开发人员能够在未来更容易地处理这种情况。|3004551|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

I had this problem as well but perhaps for a different reason. I had some trailing commas in my CSV that were adding an additional column that pandas was attempting to read. Using the following works but it simply ignores the bad lines:

<pre><code>data = pd.read_csv('file1.csv', error_bad_lines=False)
</code></pre>

If you want to keep the lines an ugly kind of hack for handling the errors is to do something like the following:

<pre><code>line = []
expected = []
saw = [] 
cont = True 

while cont == True: 
 try:
 data = pd.read_csv('file1.csv',skiprows=line)
 cont = False
 except Exception as e: 
 errortype = e.message.split('.')[0].strip() 
 if errortype == 'Error tokenizing data': 
 cerror = e.message.split(':')[1].strip().replace(',','')
 nums = [n for n in cerror.split(' ') if str.isdigit(n)]
 expected.append(int(nums[0]))
 saw.append(int(nums[2]))
 line.append(int(nums[1])-1)
 else:
 cerror = 'Unknown'
 print 'Unknown Error - 222'

if line != []:
 # Handle the errors however you want
</code></pre>

I proceeded to write a script to reinsert the lines into the DataFrame since the bad lines will be given by the variable 'line' in the above code. This can all be avoided by simply using the csv reader. Hopefully the pandas developers can make it easier to deal with this situation in the future.

blocks|key|2792272|text|我自己也有过几次这样的问题。几乎每一次，原因都是我试图打开的文件一开始就不是正确保存的CSV。我所说的“适当”，是指每一行都有相同数量的分隔符或列。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2792273|通常情况下，这是因为我在Excel中打开了CSV，然后错误地保存了它。尽管文件扩展名仍然是.csv，但纯CSV格式已被更改。|2792274|用pandas+to_csv保存的任何文件都会被正确格式化，应该不会有这个问题。但如果你用另一个程序打开它，它可能会改变结构。|2792275|希望这能有所帮助。|2792276|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

I've had this problem a few times myself. Almost every time, the reason is that the file I was attempting to open was not a properly saved CSV to begin with. And by "properly", I mean each row had the same number of separators or columns. 

Typically it happened because I had opened the CSV in Excel then improperly saved it. Even though the file extension was still .csv, the pure CSV format had been altered. 

Any file saved with pandas to_csv will be properly formatted and shouldn't have that issue. But if you open it with another program, it may change the structure. 

Hope that helps.

blocks|key|180321|text|尽管此问题不是这种情况，但此错误也可能与压缩数据一起出现。显式设置kwarg+compression的值解决了我的问题。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|180322|result+=+pandas.read_csv(data_source,+compression='gzip')|code-block|syntax|javascript|180323|entityMap^0|X|5|13|B|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

Although not the case for this question, this error may also appear with compressed data. Explicitly setting the value for <code>kwarg</code> <code>compression</code> resolved my problem.

<pre><code>result = pandas.read_csv(data_source, compression='gzip')
</code></pre>

blocks|key|182917|text|CSV文件的列数可能是可变的，read_csv从前几行中推断出列数。在这种情况下有两种方法来解决这个问题：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|182918|1)将CSV文件更改为具有最大列数的虚拟第一行(并指定header=[0])|182919|2)或者使用names+=+list(range(0,N))，其中N是最大列数。|182920|entityMap^0|F|8|0|R|A|0|6|O|0^^$0|@$1|2|3|4|5|6|7|L|8|@$9|M|A|N|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|R|8|@$9|S|A|T|B|C]]|D|@]|E|$]]|$1|J|3|-4|5|6|7|U|8|@]|D|@]|E|$]]]|K|$]]

Your CSV file might have variable number of columns and <code>read_csv</code> inferred the number of columns from the first few rows. Two ways to solve it in this case:

1) Change the CSV file to have a dummy first line with max number of columns (and specify <code>header=[0]</code>)

2) Or use <code>names = list(range(0,N))</code> where N is the max number of columns.

blocks|key|178915|text|在尝试读取制表符分隔的包含空格、逗号和引号的表时，我遇到了类似的问题：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|178916|1115794+4218++++"k__Bacteria",+"p__Firmicutes",+"c__Bacilli",+"o__Bacillales",+"f__Bacillaceae",+""
1144102+3180++++"k__Bacteria",+"p__Firmicutes",+"c__Bacilli",+"o__Bacillales",+"f__Bacillaceae",+"g__Bacillus",+""
368444++2328++++"k__Bacteria",+"p__Bacteroidetes",+"c__Bacteroidia",+"o__Bacteroidales",+"f__Bacteroidaceae",+"g__Bacteroides",+""



import+pandas+as+pd
#+Same+error+for+read_table
counts+=+pd.read_csv(path_counts,+sep='\t',+index_col=2,+header=None,+engine+=+'c')

pandas.io.common.CParserError:+Error+tokenizing+data.+C+error:+out+of+memory|code-block|syntax|javascript|178917|这说明它与C解析引擎(这是默认的)有关。也许改用python+one会改变一切。|178918|counts+=+pd.read_table(path_counts,+sep='\t',+index_col=2,+header=None,+engine='python')

Segmentation+fault+(core+dumped)|178919|这是一个不同的错误。|178920|如果我们继续尝试从表中删除空格，python-engine中的错误将再次更改：|178921|1115794+4218++++"k__Bacteria","p__Firmicutes","c__Bacilli","o__Bacillales","f__Bacillaceae",""
1144102+3180++++"k__Bacteria","p__Firmicutes","c__Bacilli","o__Bacillales","f__Bacillaceae","g__Bacillus",""
368444++2328++++"k__Bacteria","p__Bacteroidetes","c__Bacteroidia","o__Bacteroidales","f__Bacteroidaceae","g__Bacteroides",""


_csv.Error:+'+++'+expected+after+'"'|178922|很明显，熊猫在解析我们的行时遇到了问题。要使用python引擎解析表，我需要事先从表中删除所有空格和引号。与此同时，C-engine不断崩溃，即使行中有逗号。|178923|为了避免创建一个带有替换的新文件，我这样做了，因为我的表很小：|178924|from+io+import+StringIO
with+open(path_counts)+as+f:
++++input+=+StringIO(f.read().replace('",+""',+'').replace('"',+'').replace(',+',+',').replace('\0',''))
++++counts+=+pd.read_table(input,+sep='\t',+index_col=2,+header=None,+engine='python')|178925|tl;dr|offset|length|style|BOLD|178926|更改解析引擎，尽量避免在数据中使用任何非分隔的引号/逗号/空格。|178927|entityMap^0|0|0|0|0|0|0|0|0|0|0|0|5|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|17|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|18|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|1A|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|1B|8|@]|9|@]|A|$]]|$1|O|3|P|5|D|7|1C|8|@]|9|@]|A|$E|F]]|$1|Q|3|R|5|6|7|1D|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1E|8|@]|9|@]|A|$]]|$1|U|3|V|5|D|7|1F|8|@]|9|@]|A|$E|F]]|$1|W|3|X|5|6|7|1G|8|@$Y|1H|Z|1I|10|11]]|9|@]|A|$]]|$1|12|3|13|5|6|7|1J|8|@]|9|@]|A|$]]|$1|14|3|-4|5|6|7|1K|8|@]|9|@]|A|$]]]|15|$]]

I've had a similar problem while trying to read a tab-delimited table with spaces, commas and quotes:

<pre><code>1115794 4218 "k__Bacteria", "p__Firmicutes", "c__Bacilli", "o__Bacillales", "f__Bacillaceae", ""
1144102 3180 "k__Bacteria", "p__Firmicutes", "c__Bacilli", "o__Bacillales", "f__Bacillaceae", "g__Bacillus", ""
368444 2328 "k__Bacteria", "p__Bacteroidetes", "c__Bacteroidia", "o__Bacteroidales", "f__Bacteroidaceae", "g__Bacteroides", ""



import pandas as pd
# Same error for read_table
counts = pd.read_csv(path_counts, sep='\t', index_col=2, header=None, engine = 'c')

pandas.io.common.CParserError: Error tokenizing data. C error: out of memory
</code></pre>

This says it has something to do with C parsing engine (which is the default one). Maybe changing to a python one will change anything 

<pre><code>counts = pd.read_table(path_counts, sep='\t', index_col=2, header=None, engine='python')

Segmentation fault (core dumped)
</code></pre>

Now that is a different error. 
If we go ahead and try to remove spaces from the table, the error from python-engine changes once again:

<pre><code>1115794 4218 "k__Bacteria","p__Firmicutes","c__Bacilli","o__Bacillales","f__Bacillaceae",""
1144102 3180 "k__Bacteria","p__Firmicutes","c__Bacilli","o__Bacillales","f__Bacillaceae","g__Bacillus",""
368444 2328 "k__Bacteria","p__Bacteroidetes","c__Bacteroidia","o__Bacteroidales","f__Bacteroidaceae","g__Bacteroides",""


_csv.Error: ' ' expected after '"'
</code></pre>

And it gets clear that pandas was having problems parsing our rows. To parse a table with python engine I needed to remove all spaces and quotes from the table beforehand. Meanwhile C-engine kept crashing even with commas in rows.
 
 
To avoid creating a new file with replacements I did this, as my tables are small:
 

<pre><code>from io import StringIO
with open(path_counts) as f:
 input = StringIO(f.read().replace('", ""', '').replace('"', '').replace(', ', ',').replace('\0',''))
 counts = pd.read_table(input, sep='\t', index_col=2, header=None, engine='python')
</code></pre>

tl;dr
 
Change parsing engine, try to avoid any non-delimiting quotes/commas/spaces in your data.

blocks|key|182938|text|我有一个带有前置行号的数据集，我使用了index_col：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|182939|pd.read_csv('train.csv',+index_col=0)|code-block|syntax|javascript|182940|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I had a dataset with prexisting row numbers, I used index_col: 

<pre><code>pd.read_csv('train.csv', index_col=0)
</code></pre>

blocks|key|180365|text|尝试：pandas.read_csv(path,+sep+=+','+,header=None)|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|180366|entityMap^0|3|19|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

try: <code>pandas.read_csv(path, sep = ',' ,header=None)</code>

blocks|key|2792359|text|有时问题不在于如何使用python，而在于如何使用原始数据。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2792360|我收到了这个错误消息|2792361|Error+tokenizing+data.+C+error:+Expected+18+fields+in+line+72,+saw+19.|code-block|syntax|javascript|2792362|事实证明，在列描述中有时会有逗号。这意味着需要清理CSV文件或使用其他分隔符。|2792363|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|O|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|P|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Sometimes the problem is not how to use python, but with the raw data. 
I got this error message 

<pre><code>Error tokenizing data. C error: Expected 18 fields in line 72, saw 19.
</code></pre>

It turned out that in the column description there were sometimes commas. This means that the CSV file needs to be cleaned up or another separator used.

blocks|key|182971|text|我遇到了一个类似的错误，问题是我的csv文件中有一些转义引号，需要适当地设置escapechar参数。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|182972|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I had a similar error and the issue was that I had some escaped quotes in my csv file and needed to set the escapechar parameter appropriately.

blocks|key|2792412|text|使用pandas.read_csv('CSVFILENAME',header=None,sep=',+')|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2792413|尝试从链路读取csv数据时|2792414|http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data|2792415|我将数据从站点复制到我的the文件中。它有额外的空格，所以使用了sep+='，‘，它起作用了:)|2792416|entityMap|0|LINK|mutability|MUTABLE|url^0|2|1F|0|0|0|21|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|V|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|W|8|@]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|J|3|K|5|6|7|10|8|@]|D|@]|E|$]]|$1|L|3|-4|5|6|7|11|8|@]|D|@]|E|$]]]|M|$N|$5|O|P|Q|E|$R|I]]]]

use 
<code>pandas.read_csv('CSVFILENAME',header=None,sep=', ')</code>

when trying to read csv data from the link 

<a href="http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data" rel="nofollow noreferrer">http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data</a> 

I copied the data from the site into my csvfile. It had extra spaces so used sep =', ' and it worked :)

blocks|key|2792495|text|我发现在处理类似的解析错误时，另一种方法很有用，它使用CSV模块将数据重新路由到pandas+df中。例如：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2792496|import+csv
import+pandas+as+pd
path+=+'C:/FileLocation/'
file+=+'filename.csv'
f+=+open(path%2Bfile,'rt')
reader+=+csv.reader(f)

#once+contents+are+available,+I+then+put+them+in+a+list
csv_list+=+[]
for+l+in+reader:
++++csv_list.append(l)
f.close()
#now+pandas+has+no+problem+getting+into+a+df
df+=+pd.DataFrame(csv_list)|code-block|syntax|javascript|2792497|我发现CSV模块对格式不佳的逗号分隔文件更健壮一些，因此已经成功地使用这种方法解决了类似的问题。|2792498|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

An alternative that I have found to be useful in dealing with similar parsing errors uses the CSV module to re-route data into a pandas df. For example:

<pre><code>import csv
import pandas as pd
path = 'C:/FileLocation/'
file = 'filename.csv'
f = open(path+file,'rt')
reader = csv.reader(f)

#once contents are available, I then put them in a list
csv_list = []
for l in reader:
 csv_list.append(l)
f.close()
#now pandas has no problem getting into a df
df = pd.DataFrame(csv_list)
</code></pre>

I find the CSV module to be a bit more robust to poorly formatted comma separated files and so have had success with this route to address issues like these.

blocks|key|2792524|text|下面的命令序列起作用了(我丢失了data+-no+header=None的第一行--但至少它会加载)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2792525|df+=+pd.read_csv(filename,+++++++++++++++++++usecols=range(0,+42))+df.columns+=+['YR',+'MO',+'DAY',+'HR',+'MIN',+'SEC',+'HUND',+++++++++++++++++++++++++'ERROR',+'RECTYPE',+'LANE',+'SPEED',+'CLASS',+++++++++++++++++++++++++'LENGTH',+'GVW',+'ESAL',+'W1',+'S1',+'W2',+'S2',+++++++++++++++++++++++++'W3',+'S3',+'W4',+'S4',+'W5',+'S5',+'W6',+'S6',+++++++++++++++++++++++++'W7',+'S7',+'W8',+'S8',+'W9',+'S9',+'W10',+'S10',+++++++++++++++++++++++++'W11',+'S11',+'W12',+'S12',+'W13',+'S13',+'W14']|offset|length|style|CODE|2792526|以下内容不起作用：|2792527|df+=+pd.read_csv(filename,++++++++++++++++++names=['YR',+'MO',+'DAY',+'HR',+'MIN',+'SEC',+'HUND',+++++++++++++++++++++++++'ERROR',+'RECTYPE',+'LANE',+'SPEED',+'CLASS',+++++++++++++++++++++++++'LENGTH',+'GVW',+'ESAL',+'W1',+'S1',+'W2',+'S2',+++++++++++++++++++++++++'W3',+'S3',+'W4',+'S4',+'W5',+'S5',+'W6',+'S6',+++++++++++++++++++++++++'W7',+'S7',+'W8',+'S8',+'W9',+'S9',+'W10',+'S10',+++++++++++++++++++++++++'W11',+'S11',+'W12',+'S12',+'W13',+'S13',+'W14'],++++++++++++++++++usecols=range(0,+42))|2792528|CParserError:标记数据时出错。C错误:+1605634行中应该有53个字段，但下面的54个字段不起作用：|2792529|df+=+pd.read_csv(filename,++++++++++++++++++header=None)|2792530|CParserError:标记数据时出错。C错误:预期1605634行中有53个字段，看到54个字段|2792531|因此，在您的问题中，您必须传递usecols=range(0,+2)|2792532|entityMap^0|0|0|DL|0|0|0|DV|0|0|0|1K|0|0|F|J|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|W|8|@$D|X|E|Y|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|Z|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|10|8|@$D|11|E|12|F|G]]|9|@]|A|$]]|$1|L|3|M|5|6|7|13|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|14|8|@$D|15|E|16|F|G]]|9|@]|A|$]]|$1|P|3|Q|5|6|7|17|8|@]|9|@]|A|$]]|$1|R|3|S|5|6|7|18|8|@$D|19|E|1A|F|G]]|9|@]|A|$]]|$1|T|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|U|$]]

following sequence of commands works (I lose the first line of the data -no header=None present-, but at least it loads):

<code>df = pd.read_csv(filename, 
 usecols=range(0, 42))
df.columns = ['YR', 'MO', 'DAY', 'HR', 'MIN', 'SEC', 'HUND',
 'ERROR', 'RECTYPE', 'LANE', 'SPEED', 'CLASS',
 'LENGTH', 'GVW', 'ESAL', 'W1', 'S1', 'W2', 'S2',
 'W3', 'S3', 'W4', 'S4', 'W5', 'S5', 'W6', 'S6',
 'W7', 'S7', 'W8', 'S8', 'W9', 'S9', 'W10', 'S10',
 'W11', 'S11', 'W12', 'S12', 'W13', 'S13', 'W14']</code>

Following does NOT work:

<code>df = pd.read_csv(filename,
 names=['YR', 'MO', 'DAY', 'HR', 'MIN', 'SEC', 'HUND',
 'ERROR', 'RECTYPE', 'LANE', 'SPEED', 'CLASS',
 'LENGTH', 'GVW', 'ESAL', 'W1', 'S1', 'W2', 'S2',
 'W3', 'S3', 'W4', 'S4', 'W5', 'S5', 'W6', 'S6',
 'W7', 'S7', 'W8', 'S8', 'W9', 'S9', 'W10', 'S10',
 'W11', 'S11', 'W12', 'S12', 'W13', 'S13', 'W14'],
 usecols=range(0, 42))
</code>

CParserError: Error tokenizing data. C error: Expected 53 fields in line 1605634, saw 54
Following does NOT work:

<code>df = pd.read_csv(filename,
 header=None)
</code>

CParserError: Error tokenizing data. C error: Expected 53 fields in line 1605634, saw 54

Hence, in your problem you have to pass <code>usecols=range(0, 2)</code>

blocks|key|181473|text|我收到一位同事发来的.csv，当我尝试使用pd.read_csv()读取csv时，也收到了类似的错误。它显然试图使用第一行为数据帧生成列，但有许多行包含的列比第一行所暗示的要多。我最终解决了这个问题，只需打开文件并将其重新保存为.csv，然后再次使用pd.read_csv()。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|181474|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I had received a .csv from a coworker and when I tried to read the csv using pd.read_csv(), I received a similar error. It was apparently attempting to use the first row to generate the columns for the dataframe, but there were many rows which contained more columns than the first row would imply. I ended up fixing this problem by simply opening and re-saving the file as .csv and using pd.read_csv() again.

blocks|key|181480|text|您可以执行此步骤来避免该问题-|type|unstyled|depth|inlineStyleRanges|entityRanges|data|181481|train+=+pd.read_csv('/home/Project/output.csv'+,+header=None)|code-block|syntax|javascript|181482|只需添加-+header=None|offset|length|style|CODE|181483|希望这能有所帮助！！|181484|entityMap^0|0|0|6|B|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@$I|T|J|U|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|V|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

You can do this step to avoid the problem - 

<pre><code>train = pd.read_csv('/home/Project/output.csv' , header=None)
</code></pre>

just add - <code>header=None</code>

Hope this helps!!

blocks|key|183083|text|这就是我所做的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|183084|sep='::'解决了我的问题：|offset|length|style|CODE|183085|data=pd.read_csv('C:\\Users\\HP\\Downloads\\NPL+ASSINGMENT+2+imdb_labelled\\imdb_labelled.txt',engine='python',header=None,sep='::')|code-block|syntax|javascript|183086|entityMap^0|0|0|8|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@$D|Q|E|R|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|S|8|@]|9|@]|A|$K|L]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

This is what I did.

<code>sep='::'</code> solved my issue: 

<pre><code>data=pd.read_csv('C:\\Users\\HP\\Downloads\\NPL ASSINGMENT 2 imdb_labelled\\imdb_labelled.txt',engine='python',header=None,sep='::')
</code></pre>

blocks|key|183115|text|问题可能是文件问题，在我的例子中，问题在重命名文件后得到了解决。还没弄清楚原因..|type|unstyled|depth|inlineStyleRanges|entityRanges|data|183116|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Issue could be with file Issues, In my case, Issue was solved after renaming the file. yet to figure out the reason..

blocks|key|3005066|text|我有一个与此类似的案例和设置|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005067|train+=+pd.read_csv('input.csv'+,+encoding='latin1',engine='python')+|code-block|syntax|javascript|3005068|成功了|3005069|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

I had a similar case as this and setting

<pre><code>train = pd.read_csv('input.csv' , encoding='latin1',engine='python') 
</code></pre>

worked

blocks|key|181540|text|在参数中使用分隔符|type|unstyled|depth|inlineStyleRanges|entityRanges|data|181541|pd.read_csv(filename,+delimiter=",",+encoding='utf-8')|code-block|syntax|javascript|181542|它将显示为。|181543|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Use delimiter in parameter 

<pre><code>pd.read_csv(filename, delimiter=",", encoding='utf-8')
</code></pre>

It will read.

blocks|key|183151|text|当read_csv:+ParserError:错误标记数据时，我也遇到了同样的问题。我刚刚将旧的csv文件保存为新的csv文件。问题解决了！|type|unstyled|depth|inlineStyleRanges|entityRanges|data|183152|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I have the same problem when read_csv: ParserError: Error tokenizing data.
I just saved the old csv file to a new csv file. The problem is solved!

blocks|key|2792698|text|我遇到了这个问题，我试图在不传入列名的情况下读取CSV。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2792699|df+=+pd.read_csv(filename,+header=None)|code-block|syntax|javascript|2792700|我预先在列表中指定了列名，然后将它们传递给names，它立即解决了这个问题。如果没有设置列名，可以创建与数据中最大列数一样多的占位符名称。|offset|length|style|CODE|2792701|col_names+=+["col1",+"col2",+"col3",+...]
df+=+pd.read_csv(filename,+names=col_names)|2792702|entityMap^0|0|0|L|5|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@$I|T|J|U|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

I had this problem, where I was trying to read in a CSV without passing in column names.

<pre><code>df = pd.read_csv(filename, header=None)
</code></pre>

I specified the column names in a list beforehand and then pass them into <code>names</code>, and it solved it immediately. If you don't have set column names, you could just create as many placeholder names as the maximum number of columns that might be in your data.

<pre><code>col_names = ["col1", "col2", "col3", ...]
df = pd.read_csv(filename, names=col_names)
</code></pre>

blocks|key|181588|text|对我来说，问题是在我的CSV+intraday中添加了一个新的专栏。可接受的答案解决方案将不起作用，因为如果我使用，那么将来的每一行都将被丢弃。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|181589|这种情况下的解决方案是在pd.read_csv()中使用usecols参数。这样，我就可以只指定需要读入CSV的列，并且只要标题列存在(并且列名不变)，我的Python代码就会对将来的CSV更改保持弹性。|CODE|181590|callable+usecols：|181591|+-like或callable，optional返回列的子集。如果是类似列表，则所有元素必须是位置的(即文档列的整数索引)或与用户在名称中提供的列名或从文档标题行推断出的列名相对应的字符串。例如，一个有效的类似列表的usecols参数可以是0，1，2或'foo'，'bar'，'baz‘。元素顺序被忽略，因此usecols=0，1与1，0相同。若要从保留了元素顺序的数据实例化数据，请使用pd.read_csv(+data，usecols=+'foo‘，'bar')['foo'，'bar']表示'foo'，'bar’顺序中的列，或使用pd.read_csv(data，usecols='foo'，'bar')['bar'，'foo']表示'bar'，‘foo’顺序。|blockquote|181592|181593|181594|示例|181595|my_columns+=+['foo',+'bar',+'bob']
df+=+pd.read_csv(file_path,+usecols=my_columns)|code-block|syntax|javascript|181596|这样做的另一个好处是，如果我只使用具有18-20列的CSV的3-4列，我可以将更少的数据加载到内存中。|181597|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/pandas-dev/pandas/blob/v0.24.1/pandas/io/parsers.py#L110^0|1L|9|0|C|D|S|7|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@$9|17|A|18|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|19|8|@$9|1A|A|1B|B|H]]|D|@$9|1C|A|1D|1|1E]]|E|$]]|$1|I|3|J|5|6|7|1F|8|@]|D|@]|E|$]]|$1|K|3|L|5|M|7|1G|8|@]|D|@]|E|$]]|$1|N|3|-4|5|6|7|1H|8|@]|D|@]|E|$]]|$1|O|3|-4|5|6|7|1I|8|@]|D|@]|E|$]]|$1|P|3|Q|5|6|7|1J|8|@]|D|@]|E|$]]|$1|R|3|S|5|T|7|1K|8|@]|D|@]|E|$U|V]]|$1|W|3|X|5|6|7|1L|8|@]|D|@]|E|$]]|$1|Y|3|-4|5|6|7|1M|8|@]|D|@]|E|$]]]|Z|$10|$5|11|12|13|E|$14|15]]]]

The issue for me was that a new column was appended to my CSV intraday. The accepted answer solution would not work as every future row would be discarded if I used <code>error_bad_lines=False</code>. 

The solution in this case was to use the <a href="https://github.com/pandas-dev/pandas/blob/v0.24.1/pandas/io/parsers.py#L110" rel="nofollow noreferrer">usecols</a> parameter in <code>pd.read_csv()</code>. This way I can specify only the columns that I need to read into the CSV and my Python code will remain resilient to future CSV changes so long as a header column exists (and the column names do not change). 

<blockquote>
<pre><code>usecols : list-like or callable, optional 

Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or
strings that correspond to column names provided either by the user in
names or inferred from the document header row(s). For example, a
valid list-like usecols parameter would be [0, 1, 2] or ['foo', 'bar',
'baz']. Element order is ignored, so usecols=[0, 1] is the same as [1,
0]. To instantiate a DataFrame from data with element order preserved
use pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']] for
columns in ['foo', 'bar'] order or pd.read_csv(data, usecols=['foo',
'bar'])[['bar', 'foo']] for ['bar', 'foo'] order.
</code></pre>
</blockquote>

<h3>Example</h3>

<pre><code>my_columns = ['foo', 'bar', 'bob']
df = pd.read_csv(file_path, usecols=my_columns)
</code></pre>

Another benefit of this is that I can load way less data into memory if I am only using 3-4 columns of a CSV that has 18-20 columns.

blocks|key|179162|text|我遇到过这个带有错误引号的错误。我使用映射软件，它会在导出逗号分隔的文件时用引号将文本项括起来。使用引号(例如，‘=英尺和“=英寸)的文本在引发分隔符冲突时可能会出现问题。请考虑以下示例，该示例指出5英寸的井日志打印质量很差：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|179163|UWI_key,Latitude,Longitude,Remark+US42051316890000,30.4386484,-96.4330734,"poor+5""|offset|length|style|CODE|179164|使用5"作为5+inch的缩写，最终只能是徒劳无功。Excel将简单地去掉额外的引号，但Pandas在没有上面提到的error_bad_lines=False参数的情况下进行了分解。|179165|entityMap^0|0|0|2B|0|2|2|6|6|1M|L|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|M|8|@$D|N|E|O|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|P|8|@$D|Q|E|R|F|G]|$D|S|E|T|F|G]|$D|U|E|V|F|G]]|9|@]|A|$]]|$1|J|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|K|$]]

I have encountered this error with a stray quotation mark. I use mapping software which will put quotation marks around text items when exporting comma-delimited files. Text which uses quote marks (e.g. ' = feet and " = inches) can be problematic when then induce delimiter collisions. Consider this example which notes that a 5-inch well log print is poor:

<code>UWI_key,Latitude,Longitude,Remark
US42051316890000,30.4386484,-96.4330734,"poor 5""</code>

Using <code>5"</code> as shorthand for <code>5 inch</code> ends up throwing a wrench in the works. Excel will simply strip off the extra quote mark, but Pandas breaks down without the <code>error_bad_lines=False</code> argument mentioned above.

blocks|key|3005283|text|我遇到过这个带有错误引号的错误。我使用映射软件，它会在导出逗号分隔的文件时用引号将文本项括起来。使用引号(例如‘=英尺和“=英寸)的文本可能会有问题。考虑这个例子，它注意到5英寸的油井日志打印质量很差：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005284|UWI_key,Latitude,Longitude,Remark+US42051316890000,30.4386484,-96.4330734,"poor+5""|offset|length|style|CODE|3005285|使用5"作为5+inch的缩写，最终只能是徒劳无功。Excel将简单地去掉额外的引号，但Pandas在没有上面提到的error_bad_lines=False参数的情况下进行了分解。|3005286|一旦您知道了错误的性质，在导入之前从文本编辑器(例如Sublime+text+3或Notepad%2B%2B)执行查找-替换可能是最容易的。|3005287|entityMap^0|0|0|2B|0|2|2|6|6|1M|L|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@$D|P|E|Q|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|R|8|@$D|S|E|T|F|G]|$D|U|E|V|F|G]|$D|W|E|X|F|G]]|9|@]|A|$]]|$1|J|3|K|5|6|7|Y|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|M|$]]

I have encountered this error with a stray quotation mark. I use mapping software which will put quotation marks around text items when exporting comma-delimited files. Text which uses quote marks (e.g. ' = feet and " = inches) can be problematic. Consider this example which notes that a 5-inch well log print is poor:

<code>UWI_key,Latitude,Longitude,Remark
US42051316890000,30.4386484,-96.4330734,"poor 5""</code>

Using <code>5"</code> as shorthand for <code>5 inch</code> ends up throwing a wrench in the works. Excel will simply strip off the extra quote mark, but Pandas breaks down without the <code>error_bad_lines=False</code> argument mentioned above.

Once you know the nature of your error, it may be easiest to do a Find-Replace from a text editor (e.g., Sublime Text 3 or Notepad++) prior to import.

blocks|key|180658|text|大多数有用的答案都已经提到了，但是我建议将pandas数据帧保存为parquet文件。拼图文件没有这个问题，同时它们的内存效率也很高。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|180659|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Most of the useful answers are already mentioned, however I suggest saving the pandas dataframes as parquet file. Parquet files don't have this problem and they are memory efficient at the same time.

blocks|key|180661|text|我使用的数据集使用了大量与格式无关的引号(")。我能够通过为read_csv()包含此参数来修复错误|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|180662|quoting=3+#+3+correlates+to+csv.QUOTE_NONE+for+pandas|code-block|syntax|javascript|180663|entityMap^0|U|A|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

The dataset that I used had a lot of quote marks (") used extraneous of the formatting. I was able to fix the error by including this parameter for <code>read_csv()</code>:

<pre class="lang-py prettyprint-override"><code>quoting=3 # 3 correlates to csv.QUOTE_NONE for pandas
</code></pre>

blocks|key|180683|text|下面的方法适用于我(我发布了这个答案，因为我在Google+Colaboratory+Notebook中特别遇到了这个问题)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|180684|df+=+pd.read_csv("/path/foo.csv",+delimiter=';',+skiprows=0,+low_memory=False)|code-block|syntax|javascript|180685|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

The following worked for me (I posted this answer, because I specifically had this problem in a Google Colaboratory Notebook):

<pre><code>df = pd.read_csv("/path/foo.csv", delimiter=';', skiprows=0, low_memory=False)
</code></pre>

blocks|key|2792941|text|简单解析：在excel中打开csv文件，并以不同名称的csv格式文件保存。再次尝试导入spyder，您的问题将得到解决！|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|2792942|entityMap^0|0|4|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

Simple resolution: Open the csv file in excel &amp; save it with different name file of csv format. Again try importing it spyder, Your problem will be resolved!

blocks|key|3005425|text|适用于那些在linux操作系统上使用Python+3时遇到类似问题的用户。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005426|pandas.errors.ParserError:+Error+tokenizing+data.+C+error:+Calling
read(nbytes)+on+source+failed.+Try+engine='python'.|code-block|syntax|javascript|3005427|尝试：|3005428|df.read_csv('file.csv',+encoding='utf8',+engine='python')|3005429|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

For those who are having similar issue with Python 3 on linux OS.

<pre><code>pandas.errors.ParserError: Error tokenizing data. C error: Calling
read(nbytes) on source failed. Try engine='python'.
</code></pre>

Try: 

<pre><code>df.read_csv('file.csv', encoding='utf8', engine='python')
</code></pre>

blocks|key|180751|text|据我所知，在查看您的文件后，问题是您试图加载的csv文件有多个表。存在空行或包含表格标题的行。试着看看这个Stackoverflow+answer。它展示了如何通过编程来实现这一点。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|180752|另一种动态方法是使用csv+module，一次读取每一行，并进行健全性检查/正则表达式，以推断该行是否为(标题/标题/值/空白)。使用这种方法还有一个优点，即可以根据需要拆分/追加/收集python对象中的数据。|180753|最简单的方法是在手动选择表格并将其复制到剪贴板后使用pandas函数pd.read_clipboard()，以防您可以在excel或其他文件中打开csv。|style|CODE|180754|无关的|BOLD|180755|此外，与您的问题无关，但因为没有人提到这一点:当从UCI加载一些数据集时，我也遇到了同样的问题，例如seeds_dataset.txt。在我的例子中，出现这个错误是因为一些分隔符的空格比真正的制表符\t多。例如，请参阅下面的第3行|180756|14.38+++14.21+++0.8951++5.386+++3.312+++2.462+++4.956+++1
14.69+++14.49+++0.8799++5.563+++3.259+++3.586+++5.219+++1
14.11+++14.1++++0.8911++5.42++++3.302+++2.7+++++5+++++++1|code-block|syntax|javascript|180757|因此，在分隔符模式中使用\t%2B而不是\t。|180758|data+=+pd.read_csv(path,+sep='\t%2B`,+header=None)|180759|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/34184841/python-pandas-read-csv-file-containing-multiple-tables#answer-34188535|1|https://docs.python.org/3/library/csv.html|2|http://archive.ics.uci.edu/ml/datasets/seeds^0|1H|K|0|0|A|A|1|0|Y|J|0|0|3|0|1E|H|2R|2|1E|H|2|0|0|C|3|I|2|0|0^^$0|@$1|2|3|4|5|6|7|19|8|@]|9|@$A|1A|B|1B|1|1C]]|C|$]]|$1|D|3|E|5|6|7|1D|8|@]|9|@$A|1E|B|1F|1|1G]]|C|$]]|$1|F|3|G|5|6|7|1H|8|@$A|1I|B|1J|H|I]]|9|@]|C|$]]|$1|J|3|K|5|6|7|1K|8|@$A|1L|B|1M|H|L]]|9|@]|C|$]]|$1|M|3|N|5|6|7|1N|8|@$A|1O|B|1P|H|I]|$A|1Q|B|1R|H|I]]|9|@$A|1S|B|1T|1|1U]]|C|$]]|$1|O|3|P|5|Q|7|1V|8|@]|9|@]|C|$R|S]]|$1|T|3|U|5|6|7|1W|8|@$A|1X|B|1Y|H|I]|$A|1Z|B|20|H|I]]|9|@]|C|$]]|$1|V|3|W|5|Q|7|21|8|@]|9|@]|C|$R|S]]|$1|X|3|-4|5|6|7|22|8|@]|9|@]|C|$]]]|Y|$Z|$5|10|11|12|C|$13|14]]|15|$5|10|11|12|C|$13|16]]|17|$5|10|11|12|C|$13|18]]]]

As far as I can tell, and after taking a look at your file, the problem is that the csv file you're trying to load has multiple tables. There are empty lines, or lines that contain table titles. Try to have a look at this <a href="https://stackoverflow.com/questions/34184841/python-pandas-read-csv-file-containing-multiple-tables#answer-34188535">Stackoverflow answer</a>. It shows how to achieve that programmatically.

Another dynamic approach to do that would be to use the <a href="https://docs.python.org/3/library/csv.html" rel="nofollow noreferrer">csv module</a>, read every single row at a time and make sanity checks/regular expressions, to infer if the row is (title/header/values/blank). You have one more advantage with this approach, that you can split/append/collect your data in python objects as desired.

The easiest of all would be to use pandas function <code>pd.read_clipboard()</code> after manually selecting and copying the table to the clipboard, in case you can open the csv in excel or something.

Irrelevant:

Additionally, irrelevant to your problem, but because no one made mention of this: I had this same issue when loading some datasets such as <a href="http://archive.ics.uci.edu/ml/datasets/seeds" rel="nofollow noreferrer"><code>seeds_dataset.txt</code></a> from UCI. In my case, the error was occurring because some separators had more whitespaces than a true tab <code>\t</code>. See line 3 in the following for instance

<pre><code>14.38 14.21 0.8951 5.386 3.312 2.462 4.956 1
14.69 14.49 0.8799 5.563 3.259 3.586 5.219 1
14.11 14.1 0.8911 5.42 3.302 2.7 5 1
</code></pre>

Therefore, use <code>\t+</code> in the separator pattern instead of <code>\t</code>.

<pre class="lang-py prettyprint-override"><code>data = pd.read_csv(path, sep='\t+`, header=None)
</code></pre>

blocks|key|3005471|text|在我的例子中，这是因为csv文件的前两行和最后两行的格式与文件的中间内容不同。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005472|所以我要做的就是以字符串的形式打开csv文件，解析字符串的内容，然后使用read_csv获取数据帧。|offset|length|style|CODE|3005473|import+io
import+pandas+as+pd

file+=+open(f'{file_path}/{file_name}',+'r')
content+=+file.read()

#+change+new+line+character+from+'\r\n'+to+'\n'
lines+=+content.replace('\r',+'').split('\n')

#+Remove+the+first+and+last+2+lines+of+the+file
#+StringIO+can+be+considered+as+a+file+stored+in+memory
df+=+pd.read_csv(StringIO("\n".join(lines[2:-2])),+header=None)|code-block|syntax|javascript|3005474|entityMap^0|0|10|8|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@$D|Q|E|R|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|S|8|@]|9|@]|A|$K|L]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

In my case, it is because the format of the first and last two lines of the csv file is different from the middle content of the file.

So what I do is open the csv file as a string, parse the content of the string, then use <code>read_csv</code> to get a dataframe.

<pre><code>import io
import pandas as pd

file = open(f'{file_path}/{file_name}', 'r')
content = file.read()

# change new line character from '\r\n' to '\n'
lines = content.replace('\r', '').split('\n')

# Remove the first and last 2 lines of the file
# StringIO can be considered as a file stored in memory
df = pd.read_csv(StringIO("\n".join(lines[2:-2])), header=None)
</code></pre>

blocks|key|3005478|text|标记数据时出错。C错误:应为第3行中的%252个字段，请参见%25+12|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005479|该错误给出了解决问题的线索“第3行预期2个字段，saw+12"，saw+12表示第二行的长度为12，第一行的长度为2。|3005480|当您拥有如下所示的数据时，如果您跳过行，则大多数数据将被跳过|3005481|data+=+"""1,2,3
1,2,3,4
1,2,3,4,5
1,2
1,2,3,4"""|code-block|syntax|javascript|3005482|如果您不想跳过任何行，请执行以下操作|3005483|#First+lets+find+the+maximum+column+for+all+the+rows
with+open("file_name.csv",+'r')+as+temp_f:
++++#+get+No+of+columns+in+each+line
++++col_count+=+[+len(l.split(","))+for+l+in+temp_f.readlines()+]

###+Generate+column+names++(names+will+be+0,+1,+2,+...,+maximum+columns+-+1)
column_names+=+[i+for+i+in+range(max(col_count))]+

import+pandas+as+pd
#+inside+range+set+the+maximum+value+you+can+see+in+"Expected+4+fields+in+line+2,+saw+8"
#+here+will+be+8+
data+=+pd.read_csv("file_name.csv",header+=+None,names=column_names+)|3005484|使用range而不是手动设置名称，因为当您有许多列时，这将很麻烦。|3005485|此外，如果需要使用偶数数据长度，则可以使用0填充NaN值。例如：用于聚类(k-means)|3005486|new_data+=+data.fillna(0)|3005487|entityMap^0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|Y|8|@]|9|@]|A|$]]|$1|F|3|G|5|H|7|Z|8|@]|9|@]|A|$I|J]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|A|$]]|$1|M|3|N|5|H|7|11|8|@]|9|@]|A|$I|J]]|$1|O|3|P|5|6|7|12|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|13|8|@]|9|@]|A|$]]|$1|S|3|T|5|H|7|14|8|@]|9|@]|A|$I|J]]|$1|U|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|V|$]]

Error tokenizing data. C error: Expected 2 fields in line 3, saw 12

The error gives a clue to solve the problem " Expected 2 fields in line 3, saw 12", saw 12 means length of the second row is 12 and first row is 2.

When you have data like the one shown below, if you skip rows then most of the data will be skipped 

<pre><code>data = """1,2,3
1,2,3,4
1,2,3,4,5
1,2
1,2,3,4"""
</code></pre>

If you dont want to skip any rows do the following 

<pre><code>#First lets find the maximum column for all the rows
with open("file_name.csv", 'r') as temp_f:
 # get No of columns in each line
 col_count = [ len(l.split(",")) for l in temp_f.readlines() ]

### Generate column names (names will be 0, 1, 2, ..., maximum columns - 1)
column_names = [i for i in range(max(col_count))] 

import pandas as pd
# inside range set the maximum value you can see in "Expected 4 fields in line 2, saw 8"
# here will be 8 
data = pd.read_csv("file_name.csv",header = None,names=column_names )
</code></pre>

Use range instead of manually setting names as it will be cumbersome when you have many columns. 

Additionally you can fill up the NaN values with 0, if you need to use even data length. Eg. for clustering (k-means)

<pre><code>new_data = data.fillna(0)
</code></pre>

blocks|key|3005495|text|在我的例子中，分隔符不是默认的"，“，而是Tab。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005496|pd.read_csv(file_name.csv,+sep='\\t',lineterminator='\\r',+engine='python',+header='infer')|code-block|syntax|javascript|3005497|注意："\t“没有像某些来源所建议的那样工作。"\t“是必需的。|3005498|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

In my case the separator was not the default "," but Tab.

<pre><code>pd.read_csv(file_name.csv, sep='\\t',lineterminator='\\r', engine='python', header='infer')
</code></pre>

Note: "\t" did not work as suggested by some sources. "\\t" was required.

blocks|key|179323|text|我相信解决方案，|type|unstyled|depth|inlineStyleRanges|entityRanges|data|179324|,engine='python'
,+error_bad_lines+=+False|code-block|syntax|javascript|179325|如果它是虚拟列，并且您想要删除它，这将是很好的。在我的例子中，第二行实际上有更多的列，我希望集成这些列，并使列数=MAX(列)。|179326|请参考下面的解决方案，我在任何地方都看不到：|179327|try:
++++df_data+=+pd.read_csv(PATH,+header+=+bl_header,+sep+=+str_sep)
except+pd.errors.ParserError+as+err:
++++str_find+=+'saw+'
++++int_position+=+int(str(err).find(str_find))+%2B+len(str_find)
++++str_nbCol+=+str(err)[int_position:]
++++l_col+=+range(int(str_nbCol))
++++df_data+=+pd.read_csv(PATH,+header+=+bl_header,+sep+=+str_sep,+names+=+l_col)|179328|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|S|8|@]|9|@]|A|$E|F]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

I believe the solutions, 

<pre><code>,engine='python'
, error_bad_lines = False
</code></pre>

will be good if it is dummy columns and you want to delete it.
In my case, the second row really had more columns and I wanted those columns to be integrated and to have the number of columns = MAX(columns). 

Please refer to the solution below that I could not read anywhere:

<pre><code>try:
 df_data = pd.read_csv(PATH, header = bl_header, sep = str_sep)
except pd.errors.ParserError as err:
 str_find = 'saw '
 int_position = int(str(err).find(str_find)) + len(str_find)
 str_nbCol = str(err)[int_position:]
 l_col = range(int(str_nbCol))
 df_data = pd.read_csv(PATH, header = bl_header, sep = str_sep, names = l_col)
</code></pre>

blocks|key|3005556|text|你可以试一试；|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005557|data+=+pd.read_csv('file1.csv',+sep='\t')|code-block|syntax|javascript|3005558|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can try;
<pre><code>data = pd.read_csv('file1.csv', sep='\t')
</code></pre>

blocks|key|180818|text|有时在单元格中有逗号"，“。正因为如此，熊猫看不懂它.尝试使用分隔符";“|type|unstyled|depth|inlineStyleRanges|entityRanges|data|180819|df+=+pd.read_csv(r'yourpath',+delimiter=";")|code-block|syntax|javascript|180820|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Sometimes in a cell there is a comma &quot;,&quot;. Due to that pandas can' t read it.
Try delimiter with &quot;;&quot;
<pre><code>df = pd.read_csv(r'yourpath', delimiter=&quot;;&quot;)
</code></pre>

blocks|key|3005602|text|这看起来很丑陋，但您将拥有您的数据帧|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005603|import+re
path+=+'GOOG+Key+Ratios.csv'

try:
++++data+=+pd.read_csv(path)
except+Exception+as+e:
++++val+=+re.findall('tokenizing.{1,100}\s*Expected\s*(\d{1,2})\s*',str(e),re.I)
++++data+=+pd.read_csv(path,+skiprows=int(val[0])-1)|code-block|syntax|javascript|3005604|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This looks ugly but you will have your dataframe
<pre><code>import re
path = 'GOOG Key Ratios.csv'

try:
 data = pd.read_csv(path)
except Exception as e:
 val = re.findall('tokenizing.{1,100}\s*Expected\s*(\d{1,2})\s*',str(e),re.I)
 data = pd.read_csv(path, skiprows=int(val[0])-1)
</code></pre>

blocks|key|2793175|text|问题出在分隔符上。找到数据中使用的分隔符类型，并指定如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2793176|data+=+pd.read_csv('some_data.csv',+sep='\t')|code-block|syntax|javascript|2793177|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

The issue is with the delimiter. Find what kind of delimiter is used in your data and specify it like below:
<pre><code>data = pd.read_csv('some_data.csv', sep='\t')
</code></pre>

blocks|key|3005674|text|对于这个问题，我遇到了多种解决方案。很多人也给出了最好的解释。但对于初学者，我认为以下两种方法就足够了：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3005675|import+pandas+as+pd

#Method+1

data+=+pd.read_csv('file1.csv',+error_bad_lines=False)
#Note+that+this+will+cause+the+offending+lines+to+be+skipped.

#Method+2+using+sep

data+=+pd.read_csv('file1.csv',+sep='\t')|code-block|syntax|javascript|3005676|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I came across multiple solutions for this issue. Lot's of folks have given the best explanation for the answers also. But for the beginners I think below two methods will be enough :
<pre><code>import pandas as pd

#Method 1

data = pd.read_csv('file1.csv', error_bad_lines=False)
#Note that this will cause the offending lines to be skipped.

#Method 2 using sep

data = pd.read_csv('file1.csv', sep='\t')
</code></pre>

I'm trying to use pandas to manipulate a .csv file but I get this error:

<blockquote>
 pandas.parser.CParserError: Error tokenizing data. C error: Expected 2 fields in line 3, saw 12
</blockquote>

I have tried to read the pandas docs, but found nothing.

My code is simple:

<pre><code>path = 'GOOG Key Ratios.csv'
#print(open(path).read())
data = pd.read_csv(path)
</code></pre>

How can I resolve this? Should I use the <code>csv</code> module or another language ?

File is from <a href="http://financials.morningstar.com/ratios/r.html?t=GOOG&amp;region=usa&amp;culture=en-US" rel="noreferrer">Morningstar</a>

Python Pandas Error tokenizing data

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我试图使用pandas来操作一个.csv文件，但是我得到了这个错误： pandas.parser.CParserError:标记数据时出错。C错误:应为第3行中的%2个字段，请参见% 12我试着读了熊猫的文档，但什么也没找到。我的代码很简单：path = 'GOOG Key Ratios.csv'#print(open...

问Python Pandas标记化数据时出错
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