I have a data set that looks like that...(shortened for the question here).

```py
DataFrame Name = ObjectIntTest

```

[Most of the columns/labels are varios objects](https://i.stack.imgur.com/YapQc.png)

Label ISIN = Various ISINs like IE00B3XXRP09, IE00BF553838,LU0950668870

To run a logistic regression I need to convert for example this various ISINs to integers
(the integer/float then always needs to be the same for the ISIN IE00B3XXRP09 and IE00BF553838)

I tried various ways like these ones but I only get errors:

```py
ObjectIntTest['ISIN'] = ObjectIntTest.ISIN.astype(int) = ValueError: invalid literal for int() with base 10: 'IE00B3XXRP09'

ObjectIntTest['ISIN'] = ObjectIntTest['ISIN'].astype(float) = ValueError: could not convert string to float: 'IE00B3XXRP09'

ObjectIntTest['ISIN'] = ObjectIntTest['ISIN'].astype(str).astype(int) = ValueError: invalid literal for int() with base 10: 'IE00B3XXRP09'

ObjectIntTest = pd.to_numeric(ObjectIntTest['ISIN']) = ValueError: Unable to parse string "IE00B3XXRP09" at position 0

```

Sorry I'm new to python and programming but need to find as solution to make this data usable for logistic regression...
Thanks for your help.