Fix Python – Convert number strings with commas in pandas DataFrame to float


Asked By – pheon

I have a DataFrame that contains numbers as strings with commas for the thousands marker. I need to convert them to floats.

a = [['1,200', '4,200'], ['7,000', '-0.03'], [ '5', '0']]

I am guessing I need to use locale.atof. Indeed


works as expected. I get a Series of floats.

But when I apply it to the DataFrame, I get an error.


TypeError: (“cannot convert the series to “, u’occurred at index 0′)



gives another error:

ValueError: (‘invalid literal for float(): 1,200′, u’occurred at index 0’)

So, how do I convert this DataFrame of strings to a DataFrame of floats?

Now we will see solution for issue: Convert number strings with commas in pandas DataFrame to float


If you’re reading in from csv then you can use the thousands arg:

df.read_csv('foo.tsv', sep='\t', thousands=',')

This method is likely to be more efficient than performing the operation as a separate step.

You need to set the locale first:

In [ 9]: import locale

In [10]: from locale import atof

In [11]: locale.setlocale(locale.LC_NUMERIC, '')
Out[11]: 'en_GB.UTF-8'

In [12]: df.applymap(atof)
      0        1
0  1200  4200.00
1  7000    -0.03
2     5     0.00

This question is answered By – Andy Hayden

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