Data preprocessing is a critical procedure in many real world machine learning and AI problem. Using weather forecast as example, various data preprocessing such as data normalization, scaling and labeling are needed before the time-series weather information can be used for network training and testing. Use the time series weather data of Seattle (weather.csv) provided in this workshop as the time-series raw data for data preprocessing: Describe and explain the nature of data in each attribute of the time series records. Discuss what kind of data preprocessing methods are needed for each attribute. How about missing record and incorrect data, how can we fix such problems. Write Python program to implement the data processing method. Hint: The normal range and condition of each weather attribute are: Air Pressure 900 - 1200 Precipitation 0 - 300 Temperature -50 - 50 Max >= Min Temp Wind Speed (Grade) 0 - 10 Wind Direction 0 - 360

每个属性的时间序列记录中的数据性质如下：

1. 空气压力：连续数值数据，范围在900-120之间，需要进行数据标准化和缩放。

2. 降水量：连续数值数据，范围在-300之间，需要进行数据标准化和缩放。

3. 温度：连续数值数据，范围在-50到50之间，需要进行数据标准化和缩放。

4. 最高温度和最低温度：连续数值数据，范围在-50到50之间，需要进行数据标准化和缩放。

5. 风速（等级）：离散数值数据，范围在-10之间，需要进行数据标准化和缩放。

6. 风向：连续数值数据，范围在-360之间，需要进行数据标准化和缩放。

对于缺失记录和不正确的数据，我们可以使用插值方法来填充缺失值或者删除不正确的数据。

以下是Python程序实现数据处理方法的示例代码：

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# 读取数据
data = pd.read_csv('weather.csv')

# 处理空值
data = data.fillna(method='ffill')

# 标准化和缩放
scaler = MinMaxScaler()
data[['Air Pressure', 'Precipitation', 'Temperature', 'Max Temperature', 'Min Temperature', 'Wind Speed (mph)', 'Wind Direction (degrees)']] = scaler.fit_transform(data[['Air Pressure', 'Precipitation', 'Temperature', 'Max Temperature', 'Min Temperature', 'Wind Speed (mph)', 'Wind Direction (degrees)']])

# 输出处理后的数据
print(data)