Notebook 01: pd.get_dummies() resulting in True/False values instead of 1/0 - Causing issues with creating model

[ralversity]

Not sure if I may have just done something wrong here, or if something has changed. But I noticed that when going through this I was having trouble creating the model. I discovered that the reason is that when I did this part:

It resulted in this:

I wound up changing the function to this and it fixed it for me, although not sure if this was the right thing to do or not:

[cwestergren]

What's the error that you get in creating the model? I believe that Python implements bool as a subclass to integer and should you, for example, use a Normalization layer and use your insurance_one_hot it will be [0,1] as output.

This example shows the integer subclass

And then applying normalization will just use the bool and give you a [0,1] float32 back.

[mayankbungla]

Facing same issue

[mrdbourke]

Hi @ralversity , @cwestergren and @uKnowKlaus ,

There has been an update to pd.get_dummies() to return bool dtypes by default (rather than float or int).

You can get the behaviour of the first screenshot by setting pd.get_dummies(dtype=int).

For example:

import pandas as pd

df = pd.DataFrame({'A': ['a', 'b', 'a'], 
                   'B': ['b', 'a', 'c'],
                   'C': [1, 2, 3]})
df_one_hot = pd.get_dummies(df, dtype=bool) # bool is default
df_one_hot

Output:

C	A_a	A_b	B_a	B_b	B_c
0	1	True	False	False	True
1	2	False	True	False	False
2	3	True	False	False	True

Change to dtype=int:

import pandas as pd

df = pd.DataFrame({'A': ['a', 'b', 'a'], 
                   'B': ['b', 'a', 'c'],
                   'C': [1, 2, 3]})
df_one_hot = pd.get_dummies(df, dtype=int)
df_one_hot

Output:

C	A_a	A_b	B_a	B_b	B_c
0	1	1	0	0	1
1	2	0	1	0	0
2	3	1	0	0	1

See the docs here: https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html

[mayankbungla]

Hey @mrdbourke,
Thanks for your reply. I already tried changing dtype to int and float it was still returning bool values. Tried restarting the kernel no effect whatsoever.

[cwestergren]

Do you get an error when applying normalisation though?

It's still a subclass of Integers, as seen at https://docs.python.org/3/c-api/bool.html

See my previous reply.

[mayankbungla]

@cwestergren I did use normalization as well but didn't work. IDK what's the issue with get_dummies.
Then I went with LabelEncoding.

[cwestergren]

Understood. If you want to share your code here please do, but label encoding would work too.

[mayankbungla]

[cwestergren]

Thanks. I'm after the point of error. It will still be a bool type, but internally it's
integers.

Can you share the error you get?

[mayankbungla]

Sorry, I didn't save the errors. I moved on with LabelEncoding so..

[cwestergren]

All good, happy coding :)

[samuelperezh]

Hey @uKnowKlaus I had the same issue but then I tried with 'int64' instead of 'int' and it worked!

[joaocastro95]

Thx everyone, I had this issue too

[ehvs]

@samuelperezh Hi, would you mind sharing the code you used with 'int64' ?

[PatilHarshita09]

Hey @uKnowKlaus I had the same issue but then I tried with 'int64' instead of 'int' and it worked!

np.int64 and 'run all cell' it worked for me

[shereenwalid]

I had the same issue and even after adding dtype=int however after adding df = df.astype(int) it worked perfectly well,
df = pd.get_dummies(df,sparse=False,dtype=int) df = df.astype(int)

[Aseem2004]

Just use the inbuilt dtype method along with pd.get_dummies() like:
df = pd.get_dummies(df,columns=['X','Y','Z'], dtype='int')

It works perfectly fine.