Pandas: Get Dummies

Question

I have the following dataframe:

   amount  catcode    cid      cycle      date     di  feccandid    type
0   1000    E1600   N00029285   2014    2014-05-15  D   H8TX22107   24K
1   5000    G4600   N00026722   2014    2013-10-22  D   H4TX28046   24K
2      4    C2100   N00030676   2014    2014-03-26  D   H0MO07113   24Z

I want to make dummy variables for the values in column type. There about 15. I have tried this:

pd.get_dummies(df['type'])

And it returns this:

           24A  24C  24E  24F  24K  24N  24P  24R  24Z
date                                    
2014-05-15  0    0    0    0    1    0    0    0    0
2013-10-22  0    0    0    0    1    0    0    0    0
2014-03-26  0    0    0    0    0    0    0    0    1

What I would like is to have a dummy variable column for each unique value in Type

Yes! thank you. Now is there a way to add it do my df or should I just do a join? — Collective Action, Mar 29 '16 at 13:10
What do you expect the final df to actually look like though? — EdChum, Mar 29 '16 at 13:11
So you can just `join` then: `df.join(pd.get_dummies(df['type']))` — EdChum, Mar 29 '16 at 13:13

score 109 · Accepted Answer · answered Mar 29 '16 at 13:18

109

You can try :

df = pd.get_dummies(df, columns=['type'])

answered Mar 29 '16 at 13:18

Till

4,183
3
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score 32 · Answer 2 · edited Dec 22 '20 at 13:48

Consider I have the following dataframe:

   Survived  Pclass     Sex   Age     Fare
0         0       3    male  22.0   7.2500
1         1       1  female  38.0  71.2833
2         1       3  female  26.0   7.9250
3         1       1  female  35.0  53.1000
4         0       3    male  35.0   8.0500

There are two ways to implement get_dummies:

Method 1:

one_hot = pd.get_dummies(dataset, columns = ['Sex'])

This will return:

   Survived  Pclass  Age     Fare  Sex_female  Sex_male
0         0       3   22   7.2500           0         1
1         1       1   38  71.2833           1         0
2         1       3   26   7.9250           1         0
3         1       1   35  53.1000           1         0
4         0       3   35   8.0500           0         1

Method 2:

one_hot = pd.get_dummies(dataset['Sex'])

This will return:

   female  male
0       0     1
1       1     0
2       1     0
3       1     0
4       0     1

score 0 · Answer 3 · edited Sep 29 '22 at 05:06

0

you can also try using preprocessing package

X=df.values()

from sklearn import preprocessing

le_type= preprocessing.LabelEncoder()

X[:,7]=le_type.fit_transform(X[:,7])

This fits and transforms the encoded values in the original dataset

edited Sep 29 '22 at 05:06

35308

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answered Sep 23 '22 at 20:49

Muhammad Shayan

3
4

score -1 · Answer 4 · edited Jun 25 '22 at 17:11

-1

Another way is:

pd.get_dummies(df.type)

edited Jun 25 '22 at 17:11

Daniel Walker

6,380
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45

answered Jun 25 '22 at 15:17

Reza Mousavi

1

score -3 · Answer 5 · edited Oct 06 '21 at 09:18

-3

Please try :

type_dummies = pd.get_dummies(df['type'],drop_first=True)  
df = pd.concat([df,type_dummies],axis=1)

edited Oct 06 '21 at 09:18

Frightera

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answered Apr 29 '21 at 03:55

Uus Khusni

11
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Pandas: Get Dummies

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