Pandas dataframe read_csv on bad data

Question

I want to read in a very large csv (cannot be opened in excel and edited easily) but somewhere around the 100,000th row, there is a row with one extra column causing the program to crash. This row is errored so I need a way to ignore the fact that it was an extra column. There is around 50 columns so hardcoding the headers and using names or usecols isn't preferable. I'll also possibly encounter this issue in other csv's and want a generic solution. I couldn't find anything in read_csv unfortunately. The code is as simple as this:

def loadCSV(filePath):     dataframe = pd.read_csv(filePath, index_col=False, encoding='iso-8859-1', nrows=1000)     datakeys = dataframe.keys();     return dataframe, datakeys 

Answer 1

pass error_bad_lines=False to skip erroneous rows:

error_bad_lines : boolean, default True Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these “bad lines” will dropped from the DataFrame that is returned. (Only valid with C parser)