ValueError: MySQL identifier cannot be entirely numeric with Pandas to_sql

I am using pandas.to_sql to write data to an existing MySQL table. The code has been running in a crontab job for weeks without fail.

I started to get the following error: ValueError: MySQL identifier cannot be entirely numeric

My Code:

thisweek.to_sql(name='bs_reporting_weeklymetrics', con = cnx, flavor = 'mysql', if_exists = 'append', index=False)

As you can see the table name is not numeric.

Answer

This is caused by an update in pandas 0.16.1, where I was using a previous version prior (I think 0.14.XX) EDIT: this will be fixed in pandas 0.16.2

With this update there is new code in the io.sql package for to_sql that is checking the table name and all column names for numeric characters:

def _get_valid_mysql_name(name):
# Filter for unquoted identifiers 
# See http://dev.mysql.com/doc/refman/5.0/en/identifiers.html
uname = _get_unicode_name(name)
if not len(uname):
    raise ValueError("Empty table or column name specified")

basere = r'[0-9,a-z,A-Z$_]'
for c in uname:
    if not re.match(basere, c):
        if not (0x80 < ord(c) < 0xFFFF):
            raise ValueError("Invalid MySQL identifier '%s'" % uname)
if not re.match(r'[^0-9]', uname):
    raise ValueError('MySQL identifier cannot be entirely numeric')

return '`' + uname + '`'

re.match(r'[0-9], uname) returns as None if the uname value is only numeric OR STARTS with a numeric character. I think this is a bug because MySQL supports column names which contain and start with numeric characters (which I had ’90DayTrailingAvgRevenue’)

You can update the pandas code to use:

if re.match(r'[0-9][0-9]*$', uname):

inplace of that line. This changes the regular expression to look for 1 or more numeric characters and the end of line, so that it qualifies the entire name as numeric and not just the first character. It also switches to a positive match instead of negative, so I removed ‘not’

If you don’t want to mess with the pandas package, then I suggest renaming your columns to not start with numerics.

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