如何对具有变化的值的数据帧进行一致的热编码？

我正在获取数据帧形式的内容流，每个批次在列中具有不同的值。 例如，一批可能如下所示：

day1_data = {'state': ['MS', 'OK', 'VA', 'NJ', 'NM'], 
            'city': ['C', 'B', 'G', 'Z', 'F'], 
            'age': [27, 19, 63, 40, 93]}

另一个像：

day2_data = {'state': ['AL', 'WY', 'VA'], 
            'city': ['A', 'B', 'E'], 
            'age': [42, 52, 73]}

如何以返回一致数量的列的方式对列进行热编码？

如果我在每个批次上使用 pandas 的 get_dummies() ，它会返回不同数量的列：

df1 = pd.get_dummies(pd.DataFrame(day1_data))
df2 = pd.get_dummies(pd.DataFrame(day2_data))

len(df1.columns) == len(df2.columns)

我可以获得每一列的所有可能值，问题是即使有了这些信息，为每个每日批次生成一个热编码以使列数保持一致的最简单方法是什么？

好的，因为所有可能的值都是预先知道的。下面是一种稍微有点黑客的做法。

import numpy as np
import pandas as pd

# This is a one time process
# Keep all the possible data here in lists
# Can add other categorical variables too which have this type of data
all_possible_states=  ['AL', 'MS', 'MS', 'OK', 'VA', 'NJ', 'NM', 'CD', 'WY']
all_possible_cities= ['A', 'B', 'C', 'D', 'E', 'G', 'Z', 'F']

# Declare our transformer class
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder, OneHotEncoder

class MyOneHotEncoder(BaseEstimator, TransformerMixin):

    def __init__(self, all_possible_values):
        self.le = LabelEncoder()
        self.ohe = OneHotEncoder()
        self.ohe.fit(self.le.fit_transform(all_possible_values).reshape(-1,1))

    def transform(self, X, y=None):
        return self.ohe.transform(self.le.transform(X).reshape(-1,1)).toarray()

# Allow the transformer to see all the data here
encoders = {}
encoders['state'] = MyOneHotEncoder(all_possible_states)
encoders['city'] = MyOneHotEncoder(all_possible_cities)
# Do this for all categorical columns

# Now this is our method which will be used on the incoming data 
def encode(df):

    tup = (encoders['state'].transform(df['state']), 
           encoders['city'].transform(df['city']),
           # Add all other columns which are not to be transformed
           df[['age']])

    return np.hstack(tup)

# Testing:
day1_data = pd.DataFrame({'state': ['MS', 'OK', 'VA', 'NJ', 'NM'], 
        'city': ['C', 'B', 'G', 'Z', 'F'], 
        'age': [27, 19, 63, 40, 93]})

print(encode(day1_data))
[[  0.   0.   1.   0.   0.   0.   0.   0.   0.   0.   1.   0.   0.   0.
    0.   0.  27.]
 [  0.   0.   0.   0.   0.   1.   0.   0.   0.   1.   0.   0.   0.   0.
    0.   0.  19.]
 [  0.   0.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.   0.
    1.   0.  63.]
 [  0.   0.   0.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
    0.   1.  40.]
 [  0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.   0.   0.   1.
    0.   0.  93.]]


day2_data = pd.DataFrame({'state': ['AL', 'WY', 'VA'], 
            'city': ['A', 'B', 'E'], 
            'age': [42, 52, 73]})

print(encode(day2_data))
[[  1.   0.   0.   0.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.
    0.   0.  42.]
 [  0.   0.   0.   0.   0.   0.   0.   1.   0.   1.   0.   0.   0.   0.
    0.   0.  52.]
 [  0.   0.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   1.   0.
    0.   0.  73.]]

请仔细阅读评论，如果仍有任何问题，请询问我。