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python - 将滚动和累积 z-score 函数合二为一

问题描述

我有两个功能：

  1. 首先 (z_score) 计算给定 df 列的滚动 z 分数值
  2. 第二个 (z_score_cum) 计算没有前瞻性偏差的累积 z 分数

# rolling z_score
def z_score(df, window):
    val_column = df.columns[0]
    col_mean = df[val_column].rolling(window=window).mean()
    col_std = df[val_column].rolling(window=window).std()
    df['zscore' + '_'+ str(window)+'D'] = (df[val_column] - col_mean)/col_std
    return df

# cumulative z_score
def z_score_cum(data_frame):
    # calculating length of original data frame to standardize
    len_ = len(data_frame)
    # storing column name & making a copy of data frame
    val_column  = data_frame.columns[0]
    data_frame_standardized_final = data_frame.copy()
    # calculating statistics
    data_frame_standardized_final['mean_past'] = [np.mean(data_frame_standardized_final[val_column][0:lv+1]) for lv in range(0,len_)]
    data_frame_standardized_final['std_past'] = [np.std(data_frame_standardized_final[val_column][0:lv+1]) for lv in range(0,len_)]
    data_frame_standardized_final['z_score_cum'] = (data_frame_standardized_final[val_column] - data_frame_standardized_final['mean_past']) / data_frame_standardized_final['std_past']
    return data_frame_standardized_final[['z_score_cum']]

我想以某种方式将这两者组合成一个 z-score 函数，这样，无论我是否将时间窗口作为参数传递，它都会根据窗口计算 z-score，另外，将包含一列具有累积 z-score。目前，我正在创建一个时间窗口列表（此处以天为单位），我在调用函数并单独加入此附加列时将其传递给循环，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)

标签： pythonpandasdataframe

解决方案

最终，我这样做了：

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

只是一个旁注：这是我的类方法，但经过轻微修改后，可以用作独立函数。

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