I am posting an answer to provide an updated solution and explanation as to why this problem can occur. In case of the question, I would try this approach specific for problematic CSV file and then maybe try to use the found working encoding for all others. This output also addresses the problem that an encoding like 'latin1' that runs through with ought any error, does not necessarily produce the wanted outcome. Trying each encoding on a small chunk of the data One starts with all the standard encodings available for the python version (in this case 3.7 python 3.7 standard encodings).Ī usable python list of the standard encodings for the different python version is provided here: Helpful Stack overflow answer This is a more general script approach for the stated question. Input_fd = open(input_file_and_path, encoding=file_encoding, errors = 'backslashreplace') Typical errors parameter to use here are 'ignore' which just suppresses the offending bytes or (IMHO better) 'backslashreplace' which replaces the offending bytes by their Python’s backslashed escape sequence: file_encoding = 'utf8' # set file_encoding to the file encoding (utf8, latin1, etc.) Pandas has no provision for a special error processing, but Python open function has (assuming Python3), and read_csv accepts a file like object. A real world example is an UTF8 file that has been edited with a non utf8 editor and which contains some lines with a different encoding. You know that most of the file is written with a specific encoding, but it also contains encoding errors. Ok, you only have to use Latin1 encoding because it accept any possible byte as input (and convert it to the unicode character of same code): pd.read_csv(input_file_and_path. You do not want to be bothered with encoding questions, and only want that damn file to load, no matter if some text fields contain garbage. Great: you have just to specify the encoding: file_encoding = 'cp1252' # set file_encoding to the file encoding (utf8, latin1, etc.) You know the encoding, and there is no encoding error in the file. So there is no one size fits all method but different ways depending on the actual use case. Pandas allows to specify encoding, but does not allow to ignore errors not to automatically replace the offending bytes. What's the best way to correct this to proceed with the import? The source/creation of these files all come from the same place. UnicodeDecodeError: 'utf-8' codec can't decode byte 0xda in position 6: invalid continuation byte File "C:\Importer\src\dfman\importer.py", line 26, in import_chrĭata = pd.read_csv(filepath, names=fields)įile "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 400, in parser_fįile "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 205, in _readįile "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 608, in readįile "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 1028, in readįile "parser.pyx", line 706, in (pandas\parser.c:6745)įile "parser.pyx", line 728, in ._read_low_memory (pandas\parser.c:6964)įile "parser.pyx", line 804, in ._read_rows (pandas\parser.c:7780)įile "parser.pyx", line 890, in ._convert_column_data (pandas\parser.c:8793)įile "parser.pyx", line 950, in ._convert_tokens (pandas\parser.c:9484)įile "parser.pyx", line 1026, in ._convert_with_dtype (pandas\parser.c:10642)įile "parser.pyx", line 1046, in ._string_convert (pandas\parser.c:10853)įile "parser.pyx", line 1278, in pandas.parser._string_box_utf8 (pandas\parser.c:15657)
A random number of them are stopping and producing this error. I'm running a program which is processing 30,000 similar files.
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