list_y = [np.array(list_y.iloc[:, i]) for i in range(len(list_y.columns))]

As an AI language model, I do not have access to the entire context of your code. However, I can explain what this line of code means.

This line of code takes a pandas DataFrame `list_y` and converts each column of the DataFrame into a numpy array.

The `iloc[:, i]` method selects all rows (`:`) and the `i`-th column of the DataFrame. The `np.array` function then converts the selected column into a numpy array.

The resulting output is a list of numpy arrays, where each numpy array corresponds to a column of the original DataFrame.

相关问题

date_history = pd.DataFrame(data_integrity.iloc[:, 0]) dataset_history = pd.DataFrame(data_integrity.iloc[:, 1]) # 数据划分完，数据格式转换 dataset_history = [each[0] for each in np.array(dataset_history.iloc[:, [0]]).tolist()] # 历史数据 data_history_conversion = [{i: dataset_history[i * 96:96 * (i + 1)]} for i in range(int(len(dataset_history) / 96))] # 历史数据转化 data_pre = [each for each in np.array(dataset_pre).tolist()] # 预测所需要的今日数据 data_pre_conversion = np.array([float(item) for item in data_pre[0:96]]) # 预测所需要的今日数据的格式转化 代码优化

可以将第一行和第二行合并，即：

date_history = pd.DataFrame(data_integrity.iloc[:, 0])
dataset_history = [each[0] for each in np.array(data_integrity.iloc[:, 1]).tolist()]

第三行可以使用列表推导式简化，即：

data_history_conversion = [{i: dataset_history[i * 96:96 * (i + 1)]} for i in range(len(dataset_history) // 96)]

第四行可以直接将`dataset_pre`转换为`numpy array`，即：

data_pre_conversion = np.array(dataset_pre[0:96], dtype=float)

这样就可以避免使用`for`循环了。

data.fillna(method='ffill', inplace=True) date_history,data_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

以下是对代码的优化：

data.fillna(method='ffill', inplace=True) 
date_history, data_history = data.iloc[:, 0], data.iloc[:, 1:].values.flatten()
date_history = np.array([datetime.datetime.strptime(date, '%Y/%m/%d %H:%M') for date in date_history])
start_time, end_time = date_history[0], date_history[-1]
delta = datetime.timedelta(minutes=15)
time_new_list = []
current_time = start_time
while current_time <= end_time:
    time_new_list.append(current_time)
    current_time += delta
code_list = [i for i, date in enumerate(date_history) if date not in time_new_list]
for i in code_list:
    data_history = np.insert(data_history, i, data_history[i - 1])
data = pd.DataFrame({'date': time_new_list, 'load': data_history})
return data

代码优化的主要思路是：

1. 将第二列数据展平成一维数组，避免后续操作需要用到的循环。
2. 将日期字符串转换为 datetime 对象。
3. 使用列表推导式生成时间序列。
4. 使用列表推导式生成缺失位置列表。
5. 使用 NumPy 的 `insert()` 方法在数据中插入缺失值。
6. 最后将补充好的数据转换为 DataFrame 返回。

这样可以使代码更加简洁、高效，并且减少不必要的循环。