Pandas get dummies() for numeric categorical data

Question

I have 2 columns:

• Sex (with categorical values of type string as \'male\' and \'female\')
• Class (with categorical values of type integer as 1 to 10)

Answer 1

You can convert values to strings:

df1 = pd.get_dummies(df.astype(str))

Answer 2

If you don't want to convert your data, you can use 'columns' argument in get_dummies. Here is quick walkthrough:

Here is the data frame reproduced per your description:

sex_labels = ['male', 'female']
sex_col = [sex_labels[i%2] for i in range(10)]
class_col = [i for i in range(10)]
df = pd.DataFrame({'sex':sex_cols, 'class':class_col})
df.sex = pd.Categorical(df.sex)

The dtypes are:

print(df.dtypes)
sex      category
class       int64
dtype: object

Apply get_dummies:

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

Verify:

print(df.columns)

Output:

Index(['sex_female', 'sex_male', 'class_0',
'class_1','class_2','class_3','class_4','class_5',
'class_6','class_7','class_8','class_9'],dtype='object')

Per the docs at, https://pandas.pydata.org/pandasdocs/stable/reference/api/pandas.get_dummies.html,

If columns is None then all the columns with object or category dtype will be converted

This is the reason you only see dummies for sex column and not for class.

Hope this helps. Happy learning!

Note: Tested with pandas version '0.25.2'