Convert number strings with commas in pandas DataFrame to float

Question

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']]
df=pandas.DataFrame(a)

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

df[0].apply(locale.atof)

works as expected. I get a Series of floats.

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

df.apply(locale.atof)

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

and

df[0:1].apply(locale.atof)

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?

Asked By: pheon

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Source

Answer 1

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)
Out[12]:
      0        1
0  1200  4200.00
1  7000    -0.03
2     5     0.00

Answered By: Andy Hayden

Answer 2

You may use the pandas.Series.str.replace method:

df.iloc[:,:].str.replace(',', '').astype(float)

This method can remove or replace the comma in the string.

Answered By: shen ke

Answer 3

You can convert one column at a time like this :

df['colname'] = df['colname'].str.replace(',', '').astype(float)

Answered By: ghollah kioko

Answer 4

This will work for strings such as ‘-55,00’ or ‘5.500,00’ and convert them to floats -55.00 and 5500.00, respectively.

df['colname'] = df['colname'].str.replace('.','', regex=True).str.replace(',', '.', regex=True).astype(float)

Answered By: Robert Van Ysendyck

Convert number strings with commas in pandas DataFrame to float

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