Google不正确地解析CSV，而朱庇特笔记本则解析得很好
Google不正确地解析CSV，而朱庇特笔记本则解析得很好
提问于 2022-09-16 06:59:11
我想在Google Collab上读一本带有Pandas的csv。我使用了标准编码：
train = pd.read_csv("here-I-enter-my-Google-Drive-URL-to-the-file", sep=",", lineterminator='\n', header=0)但它会返回：
ParserError: Error tokenizing data. C error: Expected 90 fields in line 3, saw 228关键是，这是我第一次尝试Google，我在网上搜索了这个问题。大多数人认为这是因为数据中可能有一个逗号(除了分隔列的部分)，但是我在几个文本和电子表格阅读器上使用了“Find”函数进行搜索，但是没有发现--实际上，任何电子表格阅读器都能正确地读取它，就像朱庇特笔记本一样。
让我在这里复制文件中的前10行，这些行可以完全下载这里
Id,MSSubClass,MSZoning,LotFrontage,LotArea,Street,Alley,LotShape,LandContour,Utilities,LotConfig,LandSlope,Neighborhood,Condition1,Condition2,BldgType,HouseStyle,OverallQual,OverallCond,YearBuilt,YearRemodAdd,RoofStyle,RoofMatl,Exterior1st,Exterior2nd,MasVnrType,MasVnrArea,ExterQual,ExterCond,Foundation,BsmtQual,BsmtCond,BsmtExposure,BsmtFinType1,BsmtFinSF1,BsmtFinType2,BsmtFinSF2,BsmtUnfSF,TotalBsmtSF,Heating,HeatingQC,CentralAir,Electrical,1stFlrSF,2ndFlrSF,LowQualFinSF,GrLivArea,BsmtFullBath,BsmtHalfBath,FullBath,HalfBath,BedroomAbvGr,KitchenAbvGr,KitchenQual,TotRmsAbvGrd,Functional,Fireplaces,FireplaceQu,GarageType,GarageYrBlt,GarageFinish,GarageCars,GarageArea,GarageQual,GarageCond,PavedDrive,WoodDeckSF,OpenPorchSF,EnclosedPorch,3SsnPorch,ScreenPorch,PoolArea,PoolQC,Fence,MiscFeature,MiscVal,MoSold,YrSold,SaleType,SaleCondition,SalePrice
1,60,RL,65,8450,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Gd,TA,PConc,Gd,TA,No,GLQ,706,Unf,0,150,856,GasA,Ex,Y,SBrkr,856,854,0,1710,1,0,2,1,3,1,Gd,8,Typ,0,NA,Attchd,2003,RFn,2,548,TA,TA,Y,0,61,0,0,0,0,NA,NA,NA,0,2,2008,WD,Normal,208500
2,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,1Fam,1Story,6,8,1976,1976,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,Gd,TA,Gd,ALQ,978,Unf,0,284,1262,GasA,Ex,Y,SBrkr,1262,0,0,1262,0,1,2,0,3,1,TA,6,Typ,1,TA,Attchd,1976,RFn,2,460,TA,TA,Y,298,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,181500
3,60,RL,68,11250,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,162,Gd,TA,PConc,Gd,TA,Mn,GLQ,486,Unf,0,434,920,GasA,Ex,Y,SBrkr,920,866,0,1786,1,0,2,1,3,1,Gd,6,Typ,1,TA,Attchd,2001,RFn,2,608,TA,TA,Y,0,42,0,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,223500
4,70,RL,60,9550,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Crawfor,Norm,Norm,1Fam,2Story,7,5,1915,1970,Gable,CompShg,Wd Sdng,Wd Shng,None,0,TA,TA,BrkTil,TA,Gd,No,ALQ,216,Unf,0,540,756,GasA,Gd,Y,SBrkr,961,756,0,1717,1,0,1,0,3,1,Gd,7,Typ,1,Gd,Detchd,1998,Unf,3,642,TA,TA,Y,0,35,272,0,0,0,NA,NA,NA,0,2,2006,WD,Abnorml,140000
5,60,RL,84,14260,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,350,Gd,TA,PConc,Gd,TA,Av,GLQ,655,Unf,0,490,1145,GasA,Ex,Y,SBrkr,1145,1053,0,2198,1,0,2,1,4,1,Gd,9,Typ,1,TA,Attchd,2000,RFn,3,836,TA,TA,Y,192,84,0,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,250000
6,50,RL,85,14115,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1.5Fin,5,5,1993,1995,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,Wood,Gd,TA,No,GLQ,732,Unf,0,64,796,GasA,Ex,Y,SBrkr,796,566,0,1362,1,0,1,1,1,1,TA,5,Typ,0,NA,Attchd,1993,Unf,2,480,TA,TA,Y,40,30,0,320,0,0,NA,MnPrv,Shed,700,10,2009,WD,Normal,143000
7,20,RL,75,10084,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,Stone,186,Gd,TA,PConc,Ex,TA,Av,GLQ,1369,Unf,0,317,1686,GasA,Ex,Y,SBrkr,1694,0,0,1694,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2004,RFn,2,636,TA,TA,Y,255,57,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,307000
8,60,RL,NA,10382,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,PosN,Norm,1Fam,2Story,7,6,1973,1973,Gable,CompShg,HdBoard,HdBoard,Stone,240,TA,TA,CBlock,Gd,TA,Mn,ALQ,859,BLQ,32,216,1107,GasA,Ex,Y,SBrkr,1107,983,0,2090,1,0,2,1,3,1,TA,7,Typ,2,TA,Attchd,1973,RFn,2,484,TA,TA,Y,235,204,228,0,0,0,NA,NA,Shed,350,11,2009,WD,Normal,200000
9,50,RM,51,6120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,7,5,1931,1950,Gable,CompShg,BrkFace,Wd Shng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,952,952,GasA,Gd,Y,FuseF,1022,752,0,1774,0,0,2,0,2,2,TA,8,Min1,2,TA,Detchd,1931,Unf,2,468,Fa,TA,Y,90,0,205,0,0,0,NA,NA,NA,0,4,2008,WD,Abnorml,129900
10,190,RL,50,7420,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,BrkSide,Artery,Artery,2fmCon,1.5Unf,5,6,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,GLQ,851,Unf,0,140,991,GasA,Ex,Y,SBrkr,1077,0,0,1077,1,0,1,0,2,2,TA,5,Typ,2,TA,Attchd,1939,RFn,1,205,Gd,TA,Y,0,4,0,0,0,0,NA,NA,NA,0,1,2008,WD,Normal,118000对如何解决这个问题有什么想法吗？
谢谢大家！
回答 3
Stack Overflow用户
回答已采纳
发布于 2022-09-20 08:31:57
在进行了更多的研究之后，我确实给出了一个答案，这可能会对你和每个人在处理不同在线来源的数据集时有所帮助：
这里我的例子是“失败”：
import pandas as pd
df=pd.read_csv('https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data?select=train.csv')这将产生类似的错误：“错误标记数据。c错误:第9行中预期的1个字段，saw 2”。
问题将是，为什么会发生错误，以及如何解决它，这里我所做的，使用on_bad_lines = "warn"：
df=pd.read_csv('https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data?select=train.csv',on_bad_lines="warn")这将产生一条信息：
‘跳线9:预期1字段，saw 2跳线10:预期1字段，saw 3\n跳线12:预期1字段，saw 4\n跳线27:预期1字段，saw 8\n跳线31……“
即使是艰难的df也会产生，这样我们就能够更好地解释数据并发现错误：
df
<!DOCTYPE html>
0 <html lang="en">
1 <head>
2 <title>House Prices - Advanced Regression Te...
3 <meta charset="utf-8" />
4 <meta name="turbolinks-cache-control" conten...
... ...
86 <div id="site-body" class="hide">
87 </div>
88 </main>
89 </body>
90 </html>在这个例子中，我们可以看到，该文件是作为html链接加载的，而不是作为csv加载的，这会生成错误，尽管在使用本地保存的文件时没有发生错误。
使用API凭据后(Kaggle、Google、Github等)可以正确加载文件，结果与预期的相同：
df.head()
Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
0 1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 12 2008 WD Normal 25000希望这可能有助于未来独立于在线资源或环境。
Stack Overflow用户
发布于 2022-09-16 14:53:34
我已经下载了dataset并在我的机器上复制了这个场景，在这里我介绍了我的实现，它运行得很好。希望它能帮到你。
Python版本: 3.7.14
熊猫版本: 1.3.5
环境: Google
执行情况：
import pandas as pd
from google.colab import files
uploaded = files.upload()
df = pd.read_csv('train.csv')
df.head(5)Stack Overflow用户
发布于 2022-09-16 07:17:39
我刚刚下载了这些数据，并尝试用您的代码在colab中导入它，它在那里工作得很好。也许试着把它重新放到你的google驱动器上，然后把它放到一个DataFrame上。
我遇到的一件事是，如果您在csv尚未完全导入时运行代码(导入完成之前，文件显示在数据部分)，则会中断
希望这至少能帮上忙
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/73741043
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