def data_processing(data): # 日期缺失，补充 data.fillna(method='ffill', inplace=True) date_history = pd.DataFrame(data.iloc[:, 0]) data_history = pd.DataFrame(data.iloc[:, 1]) date_history = np.array(date_history) data_history = [x for item in np.array(data_history).tolist() for x in item] # 缺失值处理 history_time_list = [] for date in date_history: date_obj = datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') #将字符串转为 datetime 对象 history_time_list.append(date_obj) start_time = history_time_list[0] # 起始时间 end_time = history_time_list[-1] # 结束时间 delta = datetime.timedelta(minutes=15) #时间间隔为15分钟 time_new_list = [] current_time = start_time while current_time <= end_time: time_new_list.append(current_time) current_time += delta # 缺失位置记录 code_list = [] for i in range(len(time_new_list)): code_list = code_list history_time_list = history_time_list while (time_new_list[i] - history_time_list[i]) != datetime.timedelta(minutes=0): history_time_list.insert(i, time_new_list[i]) code_list.append(i) for i in code_list: data_history.insert(i, data_history[i - 1]) # 输出补充好之后的数据 data = pd.DataFrame({'date': time_new_list, 'load': data_history}) return data 代码优化

1. 可以将 `date_history` 和 `data_history` 的创建合并成一行：

date_history, data_history = np.array(data.iloc[:, 0]), [x for item in np.array(data.iloc[:, 1]).tolist() for x in item]

2. 可以在遍历 `date_history` 时，直接将字符串转为 datetime 对象，并添加到 `history_time_list` 中：

history_time_list = [datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') for date in date_history]

3. 在记录缺失位置时，可以用 `zip()` 函数将 `time_new_list` 和 `history_time_list` 同时遍历，这样会更加简洁：

code_list = []
for new_time, history_time in zip(time_new_list, history_time_list):
    while (new_time - history_time) != datetime.timedelta(minutes=0):
        history_time_list.insert(i, new_time)
        code_list.append(i)

4. 可以使用 `pandas` 的 `interpolate()` 方法来进行缺失值插值，这样可以省去很多代码：

data = data.set_index('date').resample('15T').interpolate().reset_index()

综上所述，优化后的代码如下：

def data_processing(data):
    data.fillna(method='ffill', inplace=True)
    date_history, data_history = np.array(data.iloc[:, 0]), [x for item in np.array(data.iloc[:, 1]).tolist() for x in item]
    history_time_list = [datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') for date in date_history]
    start_time, end_time, delta = history_time_list[0], history_time_list[-1], datetime.timedelta(minutes=15)
    time_new_list = [start_time + i * delta for i in range(int((end_time - start_time) / delta.total_seconds() / 60) + 1)]
    data = pd.DataFrame({'date': time_new_list, 'load': data_history})
    data = data.set_index('date').resample('15T').interpolate().reset_index()
    return data