Somewhat like: df.to_csv(file_name, encoding='utf-8', index=False) So if your DataFrame object is something like: Only the first is required. When you are storing a DataFrame object into a csv file using the to_csv method, you probably wont be needing to store the preceding indices of each row of the DataFrame object.. You can avoid that by passing a False boolean value to index parameter.. If you have no way of finding out the correct encoding of the file, then try the following encodings, in this order: utf-8; iso-8859-1 (also known as latin-1) (This is the encoding of all census data and … In Pandas, we often deal with DataFrame, and to_csv() function comes to handy when we need to export Pandas DataFrame to CSV. 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" Solution 3: Pandas allows to specify encoding, but does not allow to ignore errors not to automatically replace the offending bytes. I am having troubles with Python 3 writing to_csv file ignoring encoding argument too.. To be more specific, the problem comes from the following code (modified to focus on the problem and be copy pastable): Pandas DataFrame to csv. Source from Kaggle character encoding. Using the alias ‘latin1’ instead of ‘ISO-8859-1’.. References: Relevant Pandas documentation, python docs examples on csv files, To export CSV file from Pandas DataFrame, the df.to_csv() function. If you are interested in learning Pandas and want to become an expert in Python Programming, then check out this Python Course to upskill yourself. It mostly use read_csv(‘file’, encoding = “ISO-8859-1”), alternatively encoding = “utf-8” for reading, and generally utf-8 for to_csv.. The answer is: They read_csv takes an encoding option with deal with files in the different formats. Importing a CSV file can be frustrating. The Pandas read_csv() function has an argument call encoding that allows you to specify an encoding to use when reading a file. For my case, I wanted to us the "backslashreplace" style, which converts non-UTF-8 characters into their backslash escaped byte sequences. appropriate (default None) * ``chunksize``: Number of rows to write at a time * ``date_format``: Format string for datetime objects * ``encoding_errors``: Behavior when the input string can’t be converted according to the encoding’s rules (strict, ignore, replace, etc.) See the syntax of to_csv() function. df.to_csv('path', header=True, index=False, encoding='utf-8') If you don't specify an encoding, then the encoding used by df.to_csv defaults to ascii in Python2, or utf-8 in Python3. @@ -1710,6 +1710,8 @@ function takes a number of arguments. new_df = original_df.applymap(lambda x: str(x).encode("utf-8", errors="ignore").decode("utf-8", errors="ignore")) I entirely expect this approach is imperfect and non-optimal, but it works. 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