Search and replace dots and commas in pandas dataframe

Question

This is my DataFrame:

d = {\'col1\': [\'sku 1.1\', \'sku 1.2\', \'sku 1.3\'], \'col2\': [\'9.876.543,21\', 654, \'321,01\']}
df = pd.DataFrame(data=d)
df

           


        

Answer 1

You can try

df = df.apply(lambda x: x.replace(',', '&'))
df = df.apply(lambda x: x.replace('.', ','))
df = df.apply(lambda x: x.replace('&', '.'))

Answer 2

You are always better off using standard system facilities where they exist. Knowing that some locales use commas and decimal points differently I could not believe that Pandas would not use the formats of the locale.

Sure enough a quick search revealed this gist, which explains how to make use of locales to convert strings to numbers. In essence you need to import locale and after you've built the dataframe call locale.setlocale to establish a locale that uses commas as decimal points and periods for separators, then apply the dataframe's applymapp method.

Answer 3

The best is use if possible parameters in read_csv:

df = pd.read_csv(file, thousands='.', decimal=',')

If not possible, then replace should help:

df['col2'] = (df['col2'].replace('\.','', regex=True)
                        .replace(',','.', regex=True)
                        .astype(float))