Pandas: get_dummies vs categorical

Question

I have a dataset which has a few columns with categorical data.

I've been using the Categorical function to replace categorical values with numerical ones.

data[column] = pd.Categorical.from_array(data[column]).codes

I've recently ran across the pandas.get_dummies function. Are these interchangeable? Is there an advantage of using one over the other?

Answer 1

Why are you converting the categorical datas to integers? I don't believe you save memory if that is your goal.

df = pd.DataFrame({'cat': pd.Categorical(['a', 'a', 'a', 'b', 'b', 'c'])})
df2 = pd.DataFrame({'cat': [1, 1, 1, 2, 2, 3]})

>>> df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 6 entries, 0 to 5
Data columns (total 1 columns):
cat    6 non-null category
dtypes: category(1)
memory usage: 78.0 bytes

>>> df2.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 6 entries, 0 to 5
Data columns (total 1 columns):
cat    6 non-null int64
dtypes: int64(1)
memory usage: 96.0 bytes

The categorical codes are just integer values for the unique items in the given category. By contrast, get_dummies returns a new column for each unique item. The value in the column indicates whether or not the record has that attribute.

>>> pd.core.reshape.get_dummies(df)
Out[30]: 
   cat_a  cat_b  cat_c
0      1      0      0
1      1      0      0
2      1      0      0
3      0      1      0
4      0      1      0
5      0      0      1

To get the codes directly, you can use:

df['codes'] = [df.cat.codes.to_list()]