pandas 熊猫：get_dummies 与分类
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Pandas: get_dummies vs categorical
提问by sapo_cosmico
I have a dataset which has a few columns with categorical data.
我有一个数据集，其中有几列包含分类数据。
I've been using the Categorical function to replace categorical values with numerical ones.
我一直在使用 Categorical 函数将分类值替换为数字值。
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?
我最近遇到了 pandas.get_dummies 函数。这些可以互换吗？使用一个比另一个有优势吗？
回答by Alexander
Why are you converting the categorical datas to integers? I don't believe you save memory if that is your goal.
为什么要将分类数据转换为整数？如果这是您的目标，我不相信您会节省内存。
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_dummiesreturns a new column for each unique item. The value in the column indicates whether or not the record has that attribute.
分类代码只是给定类别中唯一项目的整数值。相比之下，get_dummies为每个唯一项返回一个新列。列中的值指示记录是否具有该属性。
>>> 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:
要直接获取代码，您可以使用：
df['codes'] = [df.cat.codes.to_list()]
