Writing a pandas DataFrame to CSV file

To delimit by a tab you can use the sep argument of to_csv:

df.to_csv(file_name, sep='\t')

To use a specific encoding (e.g. 'utf-8') use the encoding argument:

df.to_csv(file_name, sep='\t', encoding='utf-8')

In many cases you will want to remove the index and add a header:

df.to_csv(file_name, sep='\t', encoding='utf-8', index=False, header=True)

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.

Somewhat like:

df.to_csv(file_name, encoding='utf-8', index=False)

So if your DataFrame object is something like:

  Color  Number
0   red     22
1  blue     10

The csv file will store:

Color,Number
red,22
blue,10

instead of (the case when the default value True was passed)

,Color,Number
0,red,22
1,blue,10

To write a pandas DataFrame to a CSV file, you will need DataFrame.to_csv. This function offers many arguments with reasonable defaults that you will more often than not need to override to suit your specific use case. For example, you might want to use a different separator, change the datetime format, or drop the index when writing. to_csv has arguments you can pass to address these requirements.

Here's a table listing some common scenarios of writing to CSV files and the corresponding arguments you can use for them.

Footnotes

1. The default separator is assumed to be a comma (','). Don't change this unless you know you need to.
2. By default, the index of df is written as the first column. If your DataFrame does not have an index (IOW, the df.index is the default RangeIndex), then you will want to set index=False when writing. To explain this in a different way, if your data DOES have an index, you can (and should) use index=True or just leave it out completely (as the default is True).
3. It would be wise to set this parameter if you are writing string data so that other applications know how to read your data. This will also avoid any potential UnicodeEncodeErrors you might encounter while saving.
4. Compression is recommended if you are writing large DataFrames (>100K rows) to disk as it will result in much smaller output files. OTOH, it will mean the write time will increase (and consequently, the read time since the file will need to be decompressed).

Something else you can try if you are having issues encoding to 'utf-8' and want to go cell by cell you could try the following.

Python 2

(Where "df" is your DataFrame object.)

for column in df.columns:
    for idx in df[column].index:
        x = df.get_value(idx,column)
        try:
            x = unicode(x.encode('utf-8','ignore'),errors ='ignore') if type(x) == unicode else unicode(str(x),errors='ignore')
            df.set_value(idx,column,x)
        except Exception:
            print 'encoding error: {0} {1}'.format(idx,column)
            df.set_value(idx,column,'')
            continue

Then try:

df.to_csv(file_name)

You can check the encoding of the columns by:

for column in df.columns:
    print '{0} {1}'.format(str(type(df[column][0])),str(column))

Warning: errors='ignore' will just omit the character e.g.

IN: unicode('Regenexx\xae',errors='ignore')
OUT: u'Regenexx'

Python 3

for column in df.columns:
    for idx in df[column].index:
        x = df.get_value(idx,column)
        try:
            x = x if type(x) == str else str(x).encode('utf-8','ignore').decode('utf-8','ignore')
            df.set_value(idx,column,x)
        except Exception:
            print('encoding error: {0} {1}'.format(idx,column))
            df.set_value(idx,column,'')
            continue