I have some strings in a column which originally uses commas as separators from thousands and from decimals and I need to convert this string into a float, how can I do it?

I firstly tried to replace all the commas for dots:

df['min'] = df['min'].str.replace(',', '.')

and tried to convert into float:

df['min']= df['min'].astype(float) 

but it returned me the following error:

ValueError                                Traceback (most recent call last)
<ipython-input-29-5716d326493c> in <module>
----> 1 df['min']= df['min'].astype(float)
      2 #df['mcom']= df['mcom'].astype(float)
      3 #df['max']= df['max'].astype(float)

~\anaconda3\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, errors)
   5544         else:
   5545             # else, only a single dtype is given
-> 5546             new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors,)
   5547             return self._constructor(new_data).__finalize__(self, method="astype")
   5548 

~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in astype(self, dtype, copy, errors)
    593         self, dtype, copy: bool = False, errors: str = "raise"
    594     ) -> "BlockManager":
--> 595         return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
    596 
    597     def convert(

~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, align_keys, **kwargs)
    404                 applied = b.apply(f, **kwargs)
    405             else:
--> 406                 applied = getattr(b, f)(**kwargs)
    407             result_blocks = _extend_blocks(applied, result_blocks)
    408 

~\anaconda3\lib\site-packages\pandas\core\internals\blocks.py in astype(self, dtype, copy, errors)
    593             vals1d = values.ravel()
    594             try:
--> 595                 values = astype_nansafe(vals1d, dtype, copy=True)
    596             except (ValueError, TypeError):
    597                 # e.g. astype_nansafe can fail on object-dtype of strings

~\anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
    993     if copy or is_object_dtype(arr) or is_object_dtype(dtype):
    994         # Explicit copy, or required since NumPy can't view from / to object.
--> 995         return arr.astype(dtype, copy=True)
    996 
    997     return arr.view(dtype)

ValueError: could not convert string to float: '1.199.75'

If it is possible, I would like to remove all dots and commas and then add the dots before the last two characters from the variables before converting into float.

Input:

df['min'].head()

9.50
10.00
3.45
1.095.50
13.25

Expected output:

9.50
10.00
3.45
1095.50
13.25

>Solution :

If you always have 2 decimal digits:

df['min'] = pd.to_numeric(df['min'].str.replace('.', '', regex=False)).div(100)

output (as new column min2 for clarity):

        min     min2
0      9.50     9.50
1     10.00    10.00
2      3.45     3.45
3  1.095.50  1095.50
4     13.25    13.25