Pandas - how to convert objects to float values?

Question

I am working with a pandas dataframe of football players. There is a column with the value of each player. The problem is the type of this column is an object and I want to convert it to float64. How can I do it? The variable is Release clause.

df_fifa['Release Clause']
0        €226.5M
1        €127.1M
2        €228.1M
3        €138.6M
4        €196.4M
          ...   
18202      €143K
18203      €113K
18204      €165K
18205      €143K
18206      €165K
Name: Release Clause, Length: 18207, dtype: object

I want to convert it to the complete number. E.g. €200M to 200.000.000 and €200k to 200.000.

I know the funcion should be

df_fifa['Release Clause'].astype(str).astype(int)

But first I should remove €, M & k. I tried this for removing but it didn't work

df_fifa['Release Clause'] = df_fifa['Release Clause'].replace("€","")

Anyone knows how to do it?

Thank you!

I tried this function df_fifa['Release Clause'].astype(str).astype(int) but obviously it didn't work. First I need to remove "€", "M" & "k" and I don't know how to do it. And I also need to differentiate M (those numbers should be x1.000.000) and k (those numbers should be x100.000). — Mario Poveda
– Mario Poveda, Commented Jan 3, 2022 at 14:47
I suggest editing the question if you want to add more information. You cannot format code in comments. SO users also like to see some effort - otherwise, they get the impression to be treated as a free coding service. — Mr. T
– Mr. T, Commented Jan 3, 2022 at 14:50

Corralien · Accepted Answer · 2022-01-03 15:10:33Z

2

Convert your symbol €, K and M to '', * 1e3 and * 1e6 and evaluate your expression with pd.eval:

mapping = {'€': '', 'K': ' * 1e3', 'M': ' * 1e6'}

df_fifa['Release Clause'] = \
    pd.eval(df_fifa['Release Clause'].replace(mapping, regex=True))
print(df_fifa)

# Output
       Release Clause
0         226500000.0
1         127100000.0
2         228100000.0
3         138600000.0
4         196400000.0
18202        143000.0
18203        113000.0
18204        165000.0
18205        143000.0
18206        165000.0

answered Jan 3, 2022 at 15:10

Corralien

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