UnicodeDecodeError when reading CSV file in Pandas with Python

The Question :

489 people think this question is useful

I’m running a program which is processing 30,000 similar files. A random number of them are stopping and producing this error…

File "C:\Importer\src\dfman\importer.py", line 26, in import_chr
     data = pd.read_csv(filepath, names=fields)
File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 400, in parser_f
     return _read(filepath_or_buffer, kwds)
File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 205, in _read
     return parser.read()
   File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 608, in read
     ret = self._engine.read(nrows)
File "C:\Python33\lib\site-packages\pandas\io\parsers.py", line 1028, in read
     data = self._reader.read(nrows)
File "parser.pyx", line 706, in pandas.parser.TextReader.read (pandas\parser.c:6745)
File "parser.pyx", line 728, in pandas.parser.TextReader._read_low_memory (pandas\parser.c:6964)
File "parser.pyx", line 804, in pandas.parser.TextReader._read_rows (pandas\parser.c:7780)
File "parser.pyx", line 890, in pandas.parser.TextReader._convert_column_data (pandas\parser.c:8793)
File "parser.pyx", line 950, in pandas.parser.TextReader._convert_tokens (pandas\parser.c:9484)
File "parser.pyx", line 1026, in pandas.parser.TextReader._convert_with_dtype (pandas\parser.c:10642)
File "parser.pyx", line 1046, in pandas.parser.TextReader._string_convert (pandas\parser.c:10853)
File "parser.pyx", line 1278, in pandas.parser._string_box_utf8 (pandas\parser.c:15657)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xda in position 6: invalid    continuation byte

The source/creation of these files all come from the same place. What’s the best way to correct this to proceed with the import?

The Question Comments :

The Answer 1

937 people think this answer is useful

read_csv takes an encoding option to deal with files in different formats. I mostly use read_csv('file', encoding = "ISO-8859-1"), or alternatively encoding = "utf-8" for reading, and generally utf-8 for to_csv.

You can also use one of several alias options like 'latin' instead of 'ISO-8859-1' (see python docs, also for numerous other encodings you may encounter).

See relevant Pandas documentation, python docs examples on csv files, and plenty of related questions here on SO. A good background resource is What every developer should know about unicode and character sets.

To detect the encoding (assuming the file contains non-ascii characters), you can use enca (see man page) or file -i (linux) or file -I (osx) (see man page).

The Answer 2

124 people think this answer is useful

Simplest of all Solutions:

import pandas as pd
df = pd.read_csv('file_name.csv', engine='python')

Alternate Solution:

  • Open the csv file in Sublime text editor or VS Code.
  • Save the file in utf-8 format.

In sublime, Click File -> Save with encoding -> UTF-8

Then, you can read your file as usual:

import pandas as pd
data = pd.read_csv('file_name.csv', encoding='utf-8')

and the other different encoding types are:

encoding = "cp1252"
encoding = "ISO-8859-1"

The Answer 3

24 people think this answer is useful

Pandas allows to specify encoding, but does not allow to ignore errors not to automatically replace the offending bytes. So there is no one size fits all method but different ways depending on the actual use case.

  1. You know the encoding, and there is no encoding error in the file. Great: you have just to specify the encoding:

    file_encoding = 'cp1252'        # set file_encoding to the file encoding (utf8, latin1, etc.)
    pd.read_csv(input_file_and_path, ..., encoding=file_encoding)
  2. 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. 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, ..., encoding='latin1')
  3. You know that most of the file is written with a specific encoding, but it also contains encoding errors. 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. Pandas has no provision for a special error processing, but Python open function has (assuming Python3), and read_csv accepts a file like object. 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.)
    input_fd = open(input_file_and_path, encoding=file_encoding, errors = 'backslashreplace')
    pd.read_csv(input_fd, ...)

The Answer 4

18 people think this answer is useful
with open('filename.csv') as f:

after executing this code you will find encoding of ‘filename.csv’ then execute code as following

data=pd.read_csv('filename.csv', encoding="encoding as you found earlier"

there you go

The Answer 5

7 people think this answer is useful

In my case, a file has USC-2 LE BOM encoding, according to Notepad++. It is encoding="utf_16_le" for python.

Hope, it helps to find an answer a bit faster for someone.

The Answer 6

5 people think this answer is useful

Try specifying the engine=’python’. It worked for me but I’m still trying to figure out why.

df = pd.read_csv(input_file_path,...engine='python')

The Answer 7

4 people think this answer is useful

In my case this worked for python 2.7:

data = read_csv(filename, encoding = "ISO-8859-1", dtype={'name_of_colum': unicode}, low_memory=False) 

And for python 3, only:

data = read_csv(filename, encoding = "ISO-8859-1", low_memory=False) 

The Answer 8

4 people think this answer is useful

Please try to add


This will help. Worked for me. Also, make sure you’re using the correct delimiter and column names.

You can start with loading just 1000 rows to load the file quickly.

The Answer 9

4 people think this answer is useful

Try changing the encoding. In my case, encoding = "utf-16" worked.

df = pd.read_csv("file.csv",encoding='utf-16')

The Answer 10

3 people think this answer is useful

Struggled with this a while and thought I’d post on this question as it’s the first search result. Adding the encoding="iso-8859-1" tag to pandas read_csv didn’t work, nor did any other encoding, kept giving a UnicodeDecodeError.

If you’re passing a file handle to pd.read_csv(), you need to put the encoding attribute on the file open, not in read_csv. Obvious in hindsight, but a subtle error to track down.

The Answer 11

3 people think this answer is useful

I am posting an answer to provide an updated solution and explanation as to why this problem can occur. Say you are getting this data from a database or Excel workbook. If you have special characters like La Cañada Flintridge city, well unless you are exporting the data using UTF-8 encoding, you’re going to introduce errors. La Cañada Flintridge city will become La Ca\xf1ada Flintridge city. If you are using pandas.read_csv without any adjustments to the default parameters, you’ll hit the following error

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf1 in position 5: invalid continuation byte

Fortunately, there are a few solutions.

Option 1, fix the exporting. Be sure to use UTF-8 encoding.

Option 2, if fixing the exporting problem is not available to you, and you need to use pandas.read_csv, be sure to include the following paramters, engine='python'. By default, pandas uses engine='C' which is great for reading large clean files, but will crash if anything unexpected comes up. In my experience, setting encoding='utf-8' has never fixed this UnicodeDecodeError. Also, you do not need to use errors_bad_lines, however, that is still an option if you REALLY need it.

pd.read_csv(<your file>, engine='python')

Option 3: solution is my preferred solution personally. Read the file using vanilla Python.

import pandas as pd

data = []

with open(<your file>, "rb") as myfile:
    # read the header seperately
    # decode it as 'utf-8', remove any special characters, and split it on the comma (or deliminator)
    header = myfile.readline().decode('utf-8').replace('\r\n', '').split(',')
    # read the rest of the data
    for line in myfile:
        row = line.decode('utf-8', errors='ignore').replace('\r\n', '').split(',')

# save the data as a dataframe
df = pd.DataFrame(data=data, columns = header)

Hope this helps people encountering this issue for the first time.

The Answer 12

1 people think this answer is useful

This answer seems to be the catch-all for CSV encoding issues. If you are getting a strange encoding problem with your header like this:

>>> f = open(filename,"r")
>>> reader = DictReader(f)
>>> next(reader)
OrderedDict([('\ufeffid', '1'), ... ])

Then you have a byte order mark (BOM) character at the beginning of your CSV file. This answer addresses the issue:

Python read csv – BOM embedded into the first key

The solution is to load the CSV with encoding="utf-8-sig":

>>> f = open(filename,"r", encoding="utf-8-sig")
>>> reader = DictReader(f)
>>> next(reader)
OrderedDict([('id', '1'), ... ])

Hopefully this helps someone.

The Answer 13

1 people think this answer is useful

I am posting an update to this old thread. I found one solution that worked, but requires opening each file. I opened my csv file in LibreOffice, chose Save As > edit filter settings. In the drop-down menu I chose UTF8 encoding. Then I added encoding="utf-8-sig" to the data = pd.read_csv(r'C:\fullpathtofile\filename.csv', sep = ',', encoding="utf-8-sig").

Hope this helps someone.

The Answer 14

1 people think this answer is useful

I have trouble opening a CSV file in simplified Chinese downloaded from an online bank, I have tried latin1, I have tried iso-8859-1, I have tried cp1252, all to no avail.

But pd.read_csv("",encoding ='gbk') simply does the work.

The Answer 15

1 people think this answer is useful

Another important issue that I faced which resulted in the same error was:

_values = pd.read_csv("C:\Users\Mujeeb\Desktop\file.xlxs")

^This line resulted in the same error because I am reading an excel file using read_csv() method. Use read_excel() for reading .xlxs

The Answer 16

1 people think this answer is useful

You can try this.

import csv
import pandas as pd
df = pd.read_csv(filepath,encoding='unicode_escape')

The Answer 17

0 people think this answer is useful

I am using Jupyter-notebook. And in my case, it was showing the file in the wrong format. The ‘encoding’ option was not working. So I save the csv in utf-8 format, and it works.

The Answer 18

0 people think this answer is useful

Try this:

import pandas as pd
with open('filename.csv') as f:
    data = pd.read_csv(f)

Looks like it will take care of the encoding without explicitly expressing it through argument

The Answer 19

0 people think this answer is useful

Check the encoding before you pass to pandas. It will slow you down, but…

with open(path, 'r') as f:
    encoding = f.encoding 

df = pd.read_csv(path,sep=sep, encoding=encoding)

In python 3.7

The Answer 20

0 people think this answer is useful

You can try with:

df = pd.read_csv('./file_name.csv', encoding='gbk')

The Answer 21

0 people think this answer is useful

Sometimes the problem is with the .csv file only. The file may be corrupted. When faced with this issue. ‘Save As’ the file as csv again.

0. Open the xls/csv file
1. Go to -> files 
2. Click -> Save As 
3. Write the file name 
4. Choose 'file type' as -> CSV [very important]
5. Click -> Ok 

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