Getting error "invalid literal for int() with base 10: 'video'" when trying to convert categorical values to nominal values

Question

I have a Dataframe and I want to change 'video' and 'photo' as objects to 1 and 2 as int's.
I use the following code but get error:

invalid literal for int() with base 10: 'video'

Can you help me?

df = pd.read_csv('dataset.csv')    
df[['status_type']] = df[['status_type']].astype('int')

Does this answer your question? [Pandas: convert categories to numbers](https://stackoverflow.com/questions/38088652/pandas-convert-categories-to-numbers) — Recursing, Jul 12 '20 at 09:58
You can use map, ``df.status_type.map({"video":1, "photo": 2}).astype(int)`` — sushanth, Jul 12 '20 at 09:59
you can use `pd.factorize()` to change categories in to numbers [link](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.factorize.html) — Adhun Thalekkara, Jul 12 '20 at 10:03

score 0 · Accepted Answer · answered Dec 16 '21 at 10:38

0

You can use :

pd.factorize()

to change categories in to numbers.

answered Dec 16 '21 at 10:38

Archer

1 Answers1