Python 将滚动和累积z分数函数合并为一个

我有两个职能：

首先（z_分数）计算给定df列的滚动z分数值

第二个（z_得分_cum）计算无前瞻性偏差的累积z得分

我想以某种方式将这两个函数组合成一个z-score函数，这样，无论我将时间窗口作为参数传递，它都会基于窗口计算z-score，另外，它将包含一个具有累积z-score的列。目前，我正在创建一个时间窗口列表（此处以天为单位），在调用函数并单独加入此附加列时，我将其传递到循环中，我认为这不是最佳的处理方式

d_list = [n * 21 for n in range(1,13)]

df_zscore = df.copy()
for i in d_list:
    df_zscore = z_score(df_zscore, i)
    
    
df_zscore_cum = z_score_cum(df)
df_z_scores = pd.concat([df_zscore, df_zscore_cum], axis=1)

---

最终，我做到了这一点：

def calculate_z_scores(self, list_of_windows, freq_flag='D'):
        """
        Calculates rolling z-scores and cumulative z-scores based on given list
        of time windows

        Parameters
        ----------
        list_of_windows : list
            a list of time windows.
        freq_flag : string
            frequency flag. The default is 'D' (daily)

        Returns
        -------
        data frame
            a data frame with calculated rolling & cumulative z-score.
        """
        z_scores_data_frame = self.original_data_frame.copy()
        # get column with values (1st column)
        val_column = z_scores_data_frame.columns[0]
        len_ = len(z_scores_data_frame)
        # calculating statistics for cumulative_zscore
        z_scores_data_frame['mean_past'] = [np.mean(z_scores_data_frame[val_column][0:lv+1]) for lv in range(0,len_)]
        z_scores_data_frame['std_past'] = [np.std(z_scores_data_frame[val_column][0:lv+1]) for lv in range(0,len_)]
        z_scores_data_frame['zscore_cum'] = (z_scores_data_frame[val_column] - z_scores_data_frame['mean_past']) / z_scores_data_frame['std_past']
        # taking care of rolling z_scores
        for i in list_of_windows:
            col_mean = z_scores_data_frame[val_column].rolling(window=i).mean()
            col_std = z_scores_data_frame[val_column].rolling(window=i).std()
            z_scores_data_frame['zscore' + '_' + str(i)+ freq_flag] = (z_scores_data_frame[val_column] - col_mean)/col_std
        cols_to_leave = [c for c in z_scores_data_frame.columns if 'zscore' in c]
        self.z_scores_data_frame = z_scores_data_frame[cols_to_leave]
        return self.z_scores_data_frame

只是附带说明：这是我的类方法，但经过一些小的修改后，可以作为一个独立的函数使用

def calculate_z_scores(self, list_of_windows, freq_flag='D'):
        """
        Calculates rolling z-scores and cumulative z-scores based on given list
        of time windows

        Parameters
        ----------
        list_of_windows : list
            a list of time windows.
        freq_flag : string
            frequency flag. The default is 'D' (daily)

        Returns
        -------
        data frame
            a data frame with calculated rolling & cumulative z-score.
        """
        z_scores_data_frame = self.original_data_frame.copy()
        # get column with values (1st column)
        val_column = z_scores_data_frame.columns[0]
        len_ = len(z_scores_data_frame)
        # calculating statistics for cumulative_zscore
        z_scores_data_frame['mean_past'] = [np.mean(z_scores_data_frame[val_column][0:lv+1]) for lv in range(0,len_)]
        z_scores_data_frame['std_past'] = [np.std(z_scores_data_frame[val_column][0:lv+1]) for lv in range(0,len_)]
        z_scores_data_frame['zscore_cum'] = (z_scores_data_frame[val_column] - z_scores_data_frame['mean_past']) / z_scores_data_frame['std_past']
        # taking care of rolling z_scores
        for i in list_of_windows:
            col_mean = z_scores_data_frame[val_column].rolling(window=i).mean()
            col_std = z_scores_data_frame[val_column].rolling(window=i).std()
            z_scores_data_frame['zscore' + '_' + str(i)+ freq_flag] = (z_scores_data_frame[val_column] - col_mean)/col_std
        cols_to_leave = [c for c in z_scores_data_frame.columns if 'zscore' in c]
        self.z_scores_data_frame = z_scores_data_frame[cols_to_leave]
        return self.z_scores_data_frame