How to efficiently handle European decimal separators using the pandas read_csv function?

Question

I'm using read_csv to read CSV files into Pandas data frames. My CSV files contain large numbers of decimals/floats. The numbers are encoded using the European decimal notation:

1.234.456,78

This means that the '.' is used as the thousand separator and the ',' is the decimal mark.

Pandas 0.8. provides a read_csv argument called 'thousands' to set the thousand separator. Is there an additional argument to provide the decimal mark as well? If no, what is the most efficient way to parse a European style decimal number?

Currently I'm using string replace which I consider to be a significant performance penalty. The coding I'm using is this:

# Convert to float data type and change decimal point from ',' to '.'
f = lambda x: string.replace(x, u',', u'.')
df['MyColumn'] = df['MyColumn'].map(f)

Any help is appreciated.

Answer 1

You can use the converters kw in read_csv. Given /tmp/data.csv like this:

"x","y"                                                                         
"one","1.234,56"                                                                
"two","2.000,00"   

you can do:

In [20]: pandas.read_csv('/tmp/data.csv', converters={'y': lambda x: float(x.replace('.','').replace(',','.'))})
Out[20]: 
     x        y
0  one  1234.56
1  two  2000.00

Answer 2

For European style numbers, use the thousands and decimal parameters in pandas.read_csv.

For example:

pandas.read_csv('data.csv', thousands='.', decimal=',')

From the docs:

thousands :

str, optional Thousands separator.

decimal :

str, default ‘.’ Character to recognize as decimal point (e.g. use ‘,’ for European data).