Python - How to convert from object to float

Question

I had an XLSX file with 2 columns namely months and revenue and saved it as a CSV file. By using pandas to read my csv file, the revenue column has now turned into object. How can I change this column to float?

data = pd.DataFrame

dat['revenue']

7980.79
Nan
1000.25
17800.85
.....
Nan 
2457.85
6789.33

This is the column I want to change but it has been given me different errors

I tried, astype, to_numeric but no success.

Some of the errors I got is:

Cannot parse a string '798.79'

Answer 1

Now using nucsit026's answer to create a slightly different dataFrame with strings

dic = {'revenue':['7980.79',np.nan,'1000.25','17800.85','None','2457.85','6789.33']}
print(df)
print(df['revenue'].dtypes

Output:

    revenue
0   7980.79
1   NaN
2   1000.25
3   17800.85
4   None
5   2457.85
6   6789.33

dtype('O')

try this:

df['revenue']=pd.to_numeric(data['revenue'], errors='coerce').fillna(0, downcast='infer')

it will replace nan with 0s

Output:

0     7980.79
1        0.00
2     1000.25
3    17800.85
4        0.00
5     2457.85
6     6789.33
Name: revenue, dtype: float64

EDIT:

From your shared error if quotes are the problem you can use

df['revenue']=df['revenue'].str.strip("'")

and then try to convert to float using above mentioned code

EDIT2

OP had some spaces in the column values like this

Month  Revenue
Apr-13 16 004 258.24
May-13
Jun-13 16 469 157.71
Jul-13 19 054 861.01
Aug-13 20 021 803.71
Sep-13 21 285 537.45
Oct-13 22 193 453.80
Nov-13 21 862 298.20
Dec-13 10 053 557.64
Jan-14 17 358 063.34
Feb-14 19 469 161.04
Mar-14 22 567 078.21
Apr-14 20 401 188.64

In this case use following code:

df['revenue']=df['revenue'].replace(' ', '', regex=True)

and then perform the conversion

Answer 2

From above link:

dic = {'revenue':[7980.79,None,1000.25,17800.85,None,2457.85,6789.33]}
df = pd.DataFrame(dic)
df['revenue'] = df.revenue.astype(float)
df

output

    revenue
0   7980.79
1   NaN
2   1000.25
3   17800.85
4   NaN
5   2457.85
6   6789.33