ValueError: could not convert string to float: '1.598.248'

  my_col_name
0   1.598.248
1   1.323.373
2   1.628.781
3   1.551.707
4   1.790.930
5   1.877.985
6   1.484.103

With the df above, you can try below code, with 3 steps: (1) change column type to string, (2) do string replace character, (3) change column type to float

col = 'my_col_name'
df[col] = df[col].astype('str')
df[col] = df[col].str.replace('.','')
df[col] = df[col].astype('float')
print(df)

Please note the above will result in a warning: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.

So you could use below code with regex=True, also, I've combined in 1 line:

df[col] = df[col].astype('str').str.replace('.','', regex=True).astype('float')
print(df)

Output

   my_col_name
0   15982480.0
1   13233730.0
2   16287810.0
3   15517070.0
4   17909300.0
5   18779850.0
6   14841030.0

You will need to remove the full stops. You can use pandas replace method then convert it into a float:

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

Example

>>> df = pd.DataFrame({'A': ['1.1.1', '2.1.2', '3.1.3', '4.1.4']})
>>> df

       A
0  1.1.1
1  2.1.2
2  3.1.3
3  4.1.4

>>> df['col'] = df['col'].replace('\.', '', regex=True).astype('float')
>>> df['A']

       A
0  111.0
1  212.0
2  313.0
3  414.0

>>> df['A'].dtype

float64

I'm assuming that because you have two full stops that the data is of type string. However, this should work even if you have some integers or floats in that column as well.