基于类别代码替换大熊猫分类列的值

Question

我正在寻找更优雅的方法来替换基于类别代码的分类列的值。我无法使用map方法，因为原始值未提前知道。

我目前正在使用以下方法：

df['Gender'] = pd.Categorical.from_codes(df['Gender'].cat.codes.fillna(-1), categories=['Female', 'Male'])

这种方法感觉不高，因为我将分类列转换为整数，然后将其转换回分类。完整代码在下面。

import pandas as pd

df = pd.DataFrame({    
    'Name': ['Jack', 'John', 'Jil', 'Jax'],
    'Gender': ['M', 'M', 'F', pd.NA],
})

df['Gender'] = df['Gender'].astype('category')

# don't want to do this as original values may not be known to establish the dict
# df['Gender'] = df['Gender'].map({'M': 'Male', 'F': 'Female'})

# offline, we know 0 = Female, 1 = Male
# what is more elegant way to do below?
df['Gender'] = pd.Categorical.from_codes(df['Gender'].cat.codes.fillna(-1), categories=['Female', 'Male'])

Answer 1

这是一种创建独特项目字典的方法

，并使用枚举分配索引

d = {item: i for i, item in enumerate(df['Gender'].unique())}

使用映射来映射值

df['cat'] = df['Gender'].map(d)
df

    Name    Gender  cat
0   Jack    M       0
1   John    M       0
2   Jil     F       1
3   Jax     <NA>    2

Answer 2

使用 ？

df['Gender'] = (df['Gender'].astype('category')
                .cat.rename_categories(['Female', 'Male'])
               )

输出：

   Name  Gender
0  Jack    Male
1  John    Male
2   Jil  Female
3   Jax     NaN