When I try:
df['Value'] = df['Value'].str.replace(',', '.').replace(' ', '').astype(float)
It would throw below error:
ValueError: could not convert string to float: '-1 750.5'
But when I use:
df['Value'] = df['Value'].apply(
lambda x: float(x.replace(',', '.').replace(' ', '')))
It would work. What I am doing wrong here?
Here is used Series.str.replace for default substring replacement and Series.replace for not substring replacement, so is possible for match one or more whitespaces instead ' ' use \s+ with:
#added second str.replace
df['Value'] = df['Value'].str.replace(',', '.').str.replace('\s+', '').astype(float)
#added regex=True for substring replacement in Series.replace
df['Value'] = df['Value'].str.replace(',', '.').replace('\s+', '', regex=True).astype(float)
#added regex=True for substring replacement in Series.replace with dictionary
df['Value'] = df['Value'].replace({',': '.', '\s+': ''}, regex=True).astype(float)
