将分类代码转换为分类值

本文介绍了将分类代码转换为分类值的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一个数据框:

ga_deviceCategory_codes ga_channelgrouping_codes ga_sourceMedium_codes 1.0 6.0 9.0 1.0 6.0 9.0

我已使用:从类别值转换为类别代码:

Which I have converted into categorical codes from categorical values using :

data['ga_deviceCategory_codes'] = data['ga_deviceCategory'].astype('category').cat.codes data['ga_channelgrouping_codes'] = data['ga_channelgrouping'].astype('category').cat.codes data['ga_sourceMedium_codes'] = data['ga_sourceMedium'].astype('category').cat.codes

如何从上述代码中恢复到原始分类值?

How do I get back to the original categorical values now from the above codes?

推荐答案

类别映射由Pandas内部存储，但不作为常规Python字典存储.您可以自己创建这样的字典以向后映射:

Category mappings are stored internally by Pandas, but not as a regular Python dictionary. You can create such a dictionary yourself to map backwards:

df['mycol'] = df['mycol'].astype('category') d = dict(enumerate(df['mycol'].cat.categories))

然后向后映射:

df['mycol_codes'] = df['mycol'].cat.codes df['mycol_reversed'] = df['mycol_codes'].map(d)

请谨慎使用此方法.确保转换为类别后立即创建字典.在将数据帧与分类序列串联时，您可能会发现映射更改.

Be careful with this method. Make sure you create the dictionary straight after you convert to categories. When concatenating dataframes with categorical series, you may find the mapping changes.