I have a dataframe created form a JSON output that looks like this:
Total Revenue Average Revenue Purchase count Rate Date Monday 1,304.40 CA$ 20.07 CA$ 2,345 1.54 % The value stored are received as string from the JSON. I am trying to:
1) Remove all characters in the entry (ex: CA$ or %) 2) convert rate and revenue columns to float 3) Convert count columns as int
I tried to do the following:
df[column] = (df[column].str.split()).apply(lambda x: float(x[0])) It works fine except when I have a value with a coma (ex: 1,465 won't work whereas 143 would).
I tried to use several function to replace the "," by "", etc. Nothing worked so far. I always receive the following error:
ValueError: could not convert string to float: '1,304.40'
2 Answers
These strings have commas as thousands separators so you will have to remove them before the call to float:
df[column] = (df[column].str.split()).apply(lambda x: float(x[0].replace(',', ''))) This can be simplified a bit by moving split inside the lambda:
df[column] = df[column].apply(lambda x: float(x.split()[0].replace(',', ''))) Another solution with list comprehension, if need apply string functions working only with Series (columns of DataFrame) like str.split and str.replace:
df = pd.concat([df[col].str.split() .str[0] .str.replace(',','').astype(float) for col in df], axis=1) #if need convert column Purchase count to int df['Purchase count'] = df['Purchase count'].astype(int) print (df) Total Revenue Average Revenue Purchase count Rate Date Monday 1304.4 20.07 2345 1.54 
