我有一个类“数据处理”是处理与 Dataframe “df”。
处理df有这样的一些步骤：
首先，从csv文件中获取 Dataframe “df”。
第二，concat df和constant df。
第三，用df计算一些指标。
第四，用df数据判断策略。
第五步，多处理器第四步。
下面是我使用函数“data_processing”编写的代码：

class DataProcessing:
    def __init__(self, constant_df):
        self.constant_df = constant_df 

    # First, get dataframe "df" from csv file. 
    def data_processing(self, code):
        self.df = pd.read_csv(...)

        # Second, concat df and constant df.
        self.df = pd.concat([self.df, self.constant_df]) # Should I naming a new variable?

        # Third,  calculate some indicators with df. 
        self.df = do some calculating...

        # Fourth, judge strategy with df data. 
        if self.df do some judge...

    # Fifth, multiprocessing fourth step.
    def multiprocessing(self, code):
        # use multiprocessing Pool...

或者我可以像这样使用多个函数

class DataProcessing: 
    def __init__(self, constant_df):
        self.constant_df = constant_df 

    # First, get dataframe "df" from csv file. 
    def get_df(self, code):
        self.df = pd.read_csv(...)
        return 

    # Second, concat df and constant df.
    def concat_df(self, code):
        self.get_df(code) # Need run get_df function to get self.df first 
        self.df = pd.concat([self.df, self.constant_df])
        return 
    
    # Third,  calculate some indicators with df. 
    def calculate_indicators(self, code):
        self.concat_df(code) # Need run concat_df function to get self.df that concated 

    # Fourth, judge strategy with df data. 
    def judge_strateg(self, code):
        self.calculate_indicators(code) # Need run calculate_indicators function first 
        if ... # do some judge 

    # Fifth, multiprocessing fourth step.
    def multiprocessing(self):
        # use multiprocessing Pool...

但如果我使用多个函数，我需要先运行函数中的前一个函数，并传递相同的arg“代码”，这让我怀疑是否有另一种方法可以将代码写得漂亮？
谢啦，谢啦