pandas - pd.Categorical.from_codes 缺少值

Question

假设我有:

df = pd.DataFrame({'gender': np.random.choice([1, 2], 10), 'height': np.random.randint(150, 210, 10)})

我想将性别列分类。如果我尝试:

df['gender'] = pd.Categorical.from_codes(df['gender'], ['female', 'male'])

它会失败。

我可以填充类别

df['gender'] = pd.Categorical.from_codes(df['gender'], ['N/A', 'female', 'male'])

但是在某些方法中会返回'N/A':

In [67]: df['gender'].value_counts()
Out[67]: 
female    5
male      5
N/A       0
Name: gender, dtype: int64

我考虑过使用None作为填充值。它按 value_counts 中的预期工作，但我收到警告:

opt/anaconda3/bin/ipython:1: FutureWarning: 
Setting NaNs in `categories` is deprecated and will be removed in a future version of pandas.
  #!/opt/anaconda3/bin/python

有更好的方法吗？还有一种方法可以显式地给出从代码到类别的映射？

Answer 1

您可以使用rename_categories()方法:

演示:

In [33]: df
Out[33]:
   gender  height
0       1     203
1       2     169
2       2     181
3       1     172
4       2     174
5       1     166
6       2     187
7       2     200
8       1     208
9       1     201

In [34]: df['gender'] = df['gender'].astype('category').cat.rename_categories(['male','feemale'])

In [35]: df
Out[35]:
    gender  height
0     male     203
1  feemale     169
2  feemale     181
3     male     172
4  feemale     174
5     male     166
6  feemale     187
7  feemale     200
8     male     208
9     male     201

In [36]: df.dtypes
Out[36]:
gender    category
height       int32
dtype: object