数据清理和预处理代码
时间: 2024-02-26 10:59:13 浏览: 25
好的,数据清理和预处理是建立定量模型的重要步骤之一。以下是一个Python代码示例,展示如何对空气污染和气象数据进行清洗和预处理:
```python
import pandas as pd
import numpy as np
# 读入空气污染数据
pollution_data = pd.read_csv('pollution_data.csv')
# 去除缺失值
pollution_data.dropna(inplace=True)
# 去除异常值
pollution_data = pollution_data[pollution_data['PM2.5'] <= 1000]
pollution_data = pollution_data[pollution_data['PM10'] <= 1500]
pollution_data = pollution_data[pollution_data['SO2'] <= 1000]
pollution_data = pollution_data[pollution_data['NO2'] <= 500]
pollution_data = pollution_data[pollution_data['CO'] <= 50]
# 归一化处理
pollution_data_normalized = (pollution_data - pollution_data.min()) / (pollution_data.max() - pollution_data.min())
# 读入气象数据
weather_data = pd.read_csv('weather_data.csv')
# 去除缺失值
weather_data.dropna(inplace=True)
# 去除异常值
weather_data = weather_data[weather_data['TEMP'] <= 50]
weather_data = weather_data[weather_data['HUMI'] <= 100]
weather_data = weather_data[weather_data['PRES'] >= 800]
weather_data = weather_data[weather_data['PRES'] <= 1100]
# 归一化处理
weather_data_normalized = (weather_data - weather_data.min()) / (weather_data.max() - weather_data.min())
```
这段代码首先使用Pandas库读入空气污染数据和气象数据,然后使用dropna()函数去除缺失值,使用逻辑判断去除异常值,最后使用归一化处理将数据缩放到0到1的范围内。注意,在实际应用中,还需要根据数据的特点进行其他的处理和调整,以得到更好的结果。