(2)对某市空气质量检测数据的基本分析。(35分) ①利用Numpy对空气质量监测数据进行随机抽样、条件抽样;(5分) ②计算各季度AQI和PM2.5的平均值等描述统计量;(10分) ③自定义函数。查看空气质量较差的若干天的数据,以及各季度中空气质量较差的若干天的数据;(20分)
时间: 2024-01-28 10:03:52 浏览: 50
很抱歉,我作为一个语言模型AI,无法进行实际的数据分析和处理。但是我可以提供一些代码示例供您参考:
① 随机抽样、条件抽样
```python
import numpy as np
# 随机抽样
data = np.loadtxt('air_quality.csv', delimiter=',')
sample = np.random.choice(data, size=100, replace=False)
print(sample)
# 条件抽样
condition = data[:, 1] > 100 # AQI 大于 100
sample = data[condition]
print(sample)
```
② 计算各季度AQI和PM2.5的平均值等描述统计量
```python
import numpy as np
data = np.loadtxt('air_quality.csv', delimiter=',')
# 划分季度
quarter1 = data[data[:, 0] == 1]
quarter2 = data[data[:, 0] == 2]
quarter3 = data[data[:, 0] == 3]
quarter4 = data[data[:, 0] == 4]
# 计算 AQI 平均值
aqi_mean_q1 = np.mean(quarter1[:, 1])
aqi_mean_q2 = np.mean(quarter2[:, 1])
aqi_mean_q3 = np.mean(quarter3[:, 1])
aqi_mean_q4 = np.mean(quarter4[:, 1])
# 计算 PM2.5 平均值
pm25_mean_q1 = np.mean(quarter1[:, 2])
pm25_mean_q2 = np.mean(quarter2[:, 2])
pm25_mean_q3 = np.mean(quarter3[:, 2])
pm25_mean_q4 = np.mean(quarter4[:, 2])
# 输出结果
print("AQI 平均值:", aqi_mean_q1, aqi_mean_q2, aqi_mean_q3, aqi_mean_q4)
print("PM2.5 平均值:", pm25_mean_q1, pm25_mean_q2, pm25_mean_q3, pm25_mean_q4)
```
③ 自定义函数,查看空气质量较差的若干天的数据,以及各季度中空气质量较差的若干天的数据
```python
import numpy as np
data = np.loadtxt('air_quality.csv', delimiter=',')
def poor_air_quality_days(data, threshold):
"""
查找空气质量较差的若干天的数据
"""
poor_days = data[data[:, 1] > threshold]
return poor_days
def poor_air_quality_quarter(data, quarter, threshold):
"""
查找某个季度中空气质量较差的若干天的数据
"""
quarter_data = data[data[:, 0] == quarter]
poor_days = quarter_data[quarter_data[:, 1] > threshold]
return poor_days
# 查找 AQI 大于 150 的数据
poor_days = poor_air_quality_days(data, 150)
print(poor_days)
# 查找第一季度 AQI 大于 150 的数据
poor_days_q1 = poor_air_quality_quarter(data, 1, 150)
print(poor_days_q1)
```