Python中复杂数据结构属性变更的级联更新机制

import pandas as pd

class Dataframe_Builder_Update():
    def __init__(self, column_builders):
        self._column_builders = column_builders
        self.build_dataframe() # 初始构建

    def build_dataframe(self):
        self.result_df = pd.DataFrame()
        for column_builder in self._column_builders:
            # 假设 column_builder 有 calculated_output 和 group 属性
            # 实际代码中需要 column_builder 类的定义
            if not column_builder.group:
                self.result_df = pd.concat([self.result_df, column_builder.calculated_output], axis=0)
            elif column_builder.group:
                self.result_df = pd.concat([self.result_df, column_builder.calculated_output], axis=1)

    @property
    def column_builders(self):
        return self._column_builders

    @column_builders.setter
    def column_builders(self, new_column_builders):
        self._column_builders = new_column_builders
        self.build_dataframe() # 当 column_builders 列表本身被替换时触发

# 假设 my_arr 是一个包含 column_builder 对象的列表
# dataframe_builder_obj = Dataframe_Builder_Update(my_arr)

# 尝试修改内部对象的 'date' 属性
# [setattr(obj, 'date', '12/29/2019') for obj in dataframe_builder_obj.column_builders]

# 此时 dataframe_builder_obj.result_df 不会更新

import pandas as pd

class Dataframe_Builder():
    def __init__(self, column_builders):
        self._column_builders = column_builders  # 传入 column_builder 对象数组
        self._result_df = self.build_dataframe() # 初始构建并存储结果

    @property
    def column_builders(self):
        return self._column_builders

    @property
    def result_df(self):
        # 外部访问时返回存储的 DataFrame
        return self._result_df

    @column_builders.setter
    def column_builders(self, new_column_builders):
        # 当 column_builders 列表被整体替换时，触发更新
        self._column_builders = new_column_builders
        self.update_dataframe() # 调用更新方法

    def build_dataframe(self):
        # 实际构建 DataFrame 的逻辑
        result_df = pd.DataFrame()
        for obj in self._column_builders:
            # 假设 obj (column_builder) 有 calculated_output 和 group 属性
            if not obj.group:
                result_df = pd.concat([result_df, obj.calculated_output], axis=0)
            elif obj.group:
                result_df = pd.concat([result_df, obj.calculated_output], axis=1)
        return result_df

    def update_dataframe(self):
        # 显式更新 _result_df 的方法
        self._result_df = self.build_dataframe()

class Table_Builder():
    def __init__(self, df_builders: list, stack_horizontal=None, stack_vertical=None):
        self.df_builders = df_builders # 传入 Dataframe_Builder 对象的列表
        self.stack_horizontal = stack_horizontal
        self.stack_vertical = stack_vertical

        self.result_df = self.build_table(self.stack_horizontal, self.stack_vertical) # 初始构建

    def build_table(self, stack_horizontal=None, stack_vertical=None):
        # 聚合多个 Dataframe_Builder 的 result_df
        result_df = pd.DataFrame()
        for obj in self.df_builders:
            if stack_vertical:
                result_df = pd.concat([result_df, obj.result_df], axis=0)
            elif stack_horizontal:
                result_df = pd.concat([result_df, obj.result_df], axis=1)
        return result_df

    def update_dates(self, new_date):
        # 级联更新的核心方法
        for df_obj in self.df_builders: # 遍历 Dataframe_Builder 实例
            for col_obj in df_obj.column_builders: # 遍历内部 column_builder 实例
                setattr(col_obj, 'date', new_date) # 修改最深层属性
            df_obj.update_dataframe() # 触发 Dataframe_Builder 自身的更新

        # 所有 Dataframe_Builder 更新完毕后，重新构建 Table_Builder 的结果
        self.result_df = self.build_table(self.stack_horizontal, self.stack_vertical)

# 假设这里有 ColumnBuilder 类的定义，以及 my_column_builders 列表
# from some_module import ColumnBuilder

# 示例 ColumnBuilder 类 (仅为演示目的，简化)
class ColumnBuilder:
    def __init__(self, name, data, group=False, date=''):
        self.name = name
        self.data = data
        self.group = group
        self._date = date # 使用内部变量，可以添加 property setter
        self.calculated_output = pd.DataFrame({name: data}) # 假设这是计算结果

    @property
    def date(self):
        return self._date

    @date.setter
    def date(self, new_date):
        self._date = new_date
        # 实际中这里可能需要触发重新计算 self.calculated_output
        # 为了简化，这里假设 calculated_output 的更新依赖于 Dataframe_Builder 的 update_dataframe

# 创建一些模拟的 column_builders
col1 = ColumnBuilder('ColA', [1, 2, 3], date='01/01/2019')
col2 = ColumnBuilder('ColB', [4, 5, 6], date='01/01/2019', group=True)
col3 = ColumnBuilder('ColC', [7, 8, 9], date='01/01/2019')

# 创建 Dataframe_Builder 实例
df_builder1 = Dataframe_Builder([col1, col2])
df_builder2 = Dataframe_Builder([col3])

# 创建 Table_Builder 实例
# 假设我们希望垂直堆叠这些 Dataframe_Builder 的结果
table_builder_obj = Table_Builder([df_builder1, df_builder2], stack_vertical=True)

print("初始 Table_Builder 结果:")
print(table_builder_obj.result_df)
print("-" * 30)

# 假设我们想更新所有内部 column_builder 的日期为 '3/30/2019'
# 并观察 Table_Builder 的 result_df 是否自动更新
table_builder_obj.update_dates('03/30/2019')

print("\n更新日期后 Table_Builder 结果:")
print(table_builder_obj.result_df)

# 验证内部日期是否已更新
# print(df_builder1.column_builders[0].date) # 应该输出 '03/30/2019'