将滚动和累积z-得分函数合并为一个
将滚动和累积z-得分函数合并为一个
提问于 2020-11-25 15:19:30
我有两个职能：
- First (z_score)计算滚动z-得分值，给定df列
- 第二(z_score_cum)，以计算累积z-得分，而不带前瞻偏差
。
# 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']]我想用某种方式把这两个函数组合成一个z-得分函数，这样，不管我把时间窗口作为参数，它都会根据窗口计算z-分数，另外，还会包含一列，其中有累积的z-得分。目前，我正在创建一个时间窗口列表(以天为单位)，在调用函数和单独加入这个额外的列时，我正在传递这些窗口，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)回答 1
Stack Overflow用户
发布于 2020-12-03 09:57:45
最后，我是这样做的：
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只是一个副词:这是我的类方法，但是经过一些修改后，可以作为独立的函数使用。
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原文链接：
https://stackoverflow.com/questions/65007597
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