Pandas:将类别转换为数字

问题描述

假设我有一个包含以下国家/地区的数据框:

Suppose I have a dataframe with countries that goes as:

cc | temp
US | 37.0
CA | 12.0
US | 35.0
AU | 20.0

我知道有一个 pd.get_dummies 函数可以将国家/地区转换为one-hot encodings".但是，我希望将它们转换为索引，这样我就会得到 cc_index = [1,2,1,3] .

I know that there is a pd.get_dummies function to convert the countries to 'one-hot encodings'. However, I wish to convert them to indices instead such that I will get cc_index = [1,2,1,3] instead.

我假设有比使用 get_dummies 和 numpy where 子句更快的方法，如下所示:

I'm assuming that there is a faster way than using the get_dummies along with a numpy where clause as shown below:

[np.where(x) for x in df.cc.get_dummies().values]

在 R 中使用因子"更容易做到这一点，所以我希望 Pandas 有类似的东西.

This is somewhat easier to do in R using 'factors' so I'm hoping pandas has something similar.

推荐答案

首先，改变列的类型:

df.cc = pd.Categorical(df.cc)

现在数据看起来很相似，但是是分类存储的.要捕获类别代码:

Now the data look similar but are stored categorically. To capture the category codes:

df['code'] = df.cc.cat.codes

现在你有:

   cc  temp  code
0  US  37.0     2
1  CA  12.0     1
2  US  35.0     2
3  AU  20.0     0

如果您不想修改 DataFrame 而只是获取代码:

If you don't want to modify your DataFrame but simply get the codes:

df.cc.astype('category').cat.codes

或者使用分类列作为索引:

Or use the categorical column as an index:

df2 = pd.DataFrame(df.temp)
df2.index = pd.CategoricalIndex(df.cc)

这篇关于Pandas:将类别转换为数字的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！