基于Python实现空气监测及预测系统源码.zip

# coding=gbk import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt import base64 from io import BytesIO,StringIO df = pd.read_csv('t_pm25.csv',encoding='utf-8') #全省总览图 def chart_province(): pd.DataFrame(df.groupby('city').AQI.mean().sort_values(ascending=False).tail(17)).plot.barh(figsize=(10, 6)) plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False plt.title('全省空气质量总览') plt.xlabel('AQI') plt.ylabel('城市名') plt.xlim(50, 150) plt.legend('AQI') plt.grid(linestyle=':', color='w') buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #全省污染物热点图 def chart_province_pollute(): # 全省主要污染物信息 df_top10_polluted = [] df_top10_polluted = pd.DataFrame(df_top10_polluted) for _ in range(0, 10): # 提取空气质量最严重的城市信息 temp = df[df['city'] == df.groupby('city').AQI.mean().sort_values().tail(10).index[_]] df_top10_polluted = pd.concat([df_top10_polluted, temp]) df_overpolluted = df_top10_polluted[df_top10_polluted['AQI'] >= 100][ ['AQI', 'PM2_5', 'PM10', 'SO2', 'NO2', 'CO', 'O3']] plt.figure(figsize=(9.5, 6)) sns.heatmap(df_overpolluted.corr(), vmax=1, square=False, annot=True, linewidth=1) plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False plt.yticks(rotation=0); plt.title('全省主要污染热点图') buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #全省不同时间段空气质量情况 def chart_province_time(): pd.DataFrame(df.groupby('time_slot').AQI.mean().sort_values()).plot.line(figsize=(9.5, 6)) plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False plt.title('全省不同时间段空气质量情况') plt.xlabel('时间') plt.ylabel('AQI') # plt.xlim(70) plt.legend('AQI') plt.grid(linestyle=':', color='w') buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #当前城市的不同时间段空气质量情况 def chart_current_time(): df_gd = df[df['place'] == '济南市（总）'] pd.DataFrame(df_gd.groupby('time_slot').AQI.mean().sort_values()).plot.line(figsize=(9.5, 6)) plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False plt.title('济南不同时间段空气质量情况') plt.xlabel('时间段') plt.ylabel('AQI') plt.legend('AQI') plt.grid(linestyle=':', color='w') buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #获取所有城市名 def get_city(): data = df.drop_duplicates(subset=['city'],keep='first')['city'] data = data.astype(str) return data #获取所有地区名 def get_place(): data = df.drop_duplicates(subset=['place'],keep='first')['place'] data = data.astype(str) return data #当前城市的数据 def data_current(): df['time'] = pd.to_datetime(df['time']) df_gd = df[df['place'] == '济南市（总）'] data = df_gd[df['time'].dt.hour.isin(np.arange(12, 13))] data = data.astype(str) return data ''' #当前城市的数据 def data_city(): df['city'] = df['city'].astype('string') data = df.groupby('city').AQI.mean() data = data.astype(str) return data ''' #某个城市的不同地区空气质量情况 def chart_city(city): df_gd = df[df['city'] == city] pd.DataFrame(df_gd.groupby('place').AQI.mean().sort_values()).plot.barh(figsize=(9.5, 6)) plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False plt.title(city + '不同地区空气质量情况') plt.xlabel('AQI') plt.ylabel('地区') #plt.xlim(80) plt.legend('AQI') plt.grid(linestyle=':', color='w') buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #某个城市的空气质量次数图 def chart_city_frequency(city): df_gd = df[df['city'] == city] df_gd.groupby('空气质量').time.count().plot.bar(figsize=(9.5, 6)) plt.xticks(rotation=0) plt.ylabel('次数') plt.title(city + '空气质量次数图') buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #某个城市的主要污染图 def chart_city_pollutePM2_5(city): df_sz = df[df['city'] == city] df_sz_pollutant = df_sz[df_sz['AQI'] >= 100][['AQI', 'PM2_5', 'PM10', 'SO2', 'NO2', 'CO', 'O3']] sns.regplot(x='PM2_5', y='AQI', data=df_sz_pollutant); plt.title(city + '主要污染物') plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #某个城市的主要污染图 def chart_city_pollutePM10(city): df_sz = df[df['city'] == city] df_sz_pollutant = df_sz[df_sz['AQI'] >= 100][['AQI', 'PM2_5', 'PM10', 'SO2', 'NO2', 'CO', 'O3']] sns.regplot(x='PM10', y='AQI', data=df_sz_pollutant); plt.title(city + '主要污染物') plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #某城市污染物热点图 def chart_city_pollute(city): # 全省主要污染物信息 df_gd = df[df['city'] == city] df_gd_pollutant = df_gd[df_gd['AQI'] >= 100][['AQI', 'PM2_5', 'PM10', 'SO2', 'NO2', 'CO', 'O3']] plt.figure(figsize=(9.5, 6)) sns.heatmap(df_gd_pollutant.corr(), vmax=1, square=False, annot=True, linewidth=1) plt.yticks(rotation=0); plt.title(city + '主要污染物热点图') plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #某个城市不同时间段空气质量情况 def chart_city_time(city): df_gd = df[df['city'] == city] pd.DataFrame(df_gd.groupby('time_slot').AQI.mean().sort_values()).plot.line(figsize=(9.5, 6)) plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False plt.title(city+'不同时间段空气质量情况') plt.xlabel('时间段') plt.ylabel('AQI') plt.legend('AQI') plt.grid(linestyle=':', color='w') buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() imb = base64.b64encode(plot_data) ims = imb.decode() imd = "data:image/png;base64," + ims plt.clf() return imd #某个地区的空气质量次数图 def chart_place_frequency(place): df_gd = df[df['place'] == place] df_gd.groupby('空气质量').time.count().plot.barh(figsize=(9.5, 6)) plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签 plt.rcParams['axes.unicode_minus'] = False plt.xticks(rotation=0) plt.ylabel('次数')