import globfrom datetime import datetime, timedeltaimport numpy as npimport pandas as pd
from matplotlib import cm, colorsimport matplotlib.dates as mdatesimport matplotlib.patches as mpatchesimport matplotlib.pyplot as plt
def format_aqi(filep, columns=None):  files = glob.glob(filep)
  df = pd.concat((pd.read_csv(f) for f in files))  df.index = pd.to_datetime(df.date.astype(np.str) + df.hour.apply(lambda x: '%02d'%x), format='%Y%m%d%H')  # drop date and hour columns  df.drop(['date', 'hour'], axis=1, inplace=True)
  df = df.pivot_table(columns='type', index=df.index)  df.columns.names = ['station', 'type']  df.index.names = ['date']
  df = df.stack('station')
  if columns is not None:    df = df.loc[:, columns]
  return df
filep = '2019/china*.csv'  data = format_aqi(filep)data.csv('2019.csv') # 保存以便后续使用

data = pd.read_csv('2019.csv', index_col='date', parse_dates=True)data2 = data.pivot_table(index=data.index, columns=['station'])
time_range = pd.date_range(datetime(2019, 10, 1, 0), datetime(2019, 12, 31, 23), freq='1h')
idx = pd.IndexSliceaqi = data2.loc[:, idx['AQI', :]].xs('AQI', axis=1)aqi = aqi.reindex(time_range)
cities = ['北京', '天津', '石家庄', '邯郸',      '济南', '郑州', '菏泽',      '亳州', '徐州', '驻马店',      '南京', '合肥', '马鞍山',     '武汉', '上海', '杭州',      '长沙', '南昌', '上饶', '温州',     '吉安', '赣州', '福州',      '龙岩', '厦门', '泉州'     ]
sub = aqi[cities[::-1]]

colors_aqi = ['#009966', '#FFDE33', '#FF9A32', '#CC0033', '#660099']levels = [0, 50, 100, 150, 200, 300]
cmap_aqi = colors.ListedColormap(colors_aqi) norm = colors.BoundaryNorm(levels, cmap_aqi.N)

fig, ax = plt.subplots(figsize=(16, 9))
con = ax.pcolormesh(sub.index.values,                np.arange(0, sub.columns.shape[0]+1),                sub.T,                cmap=cmap_aqi,                norm=norm,                vmin=0, vmax=300               )
sdate = datetime(2019, 10, 1)edate = datetime(2019, 12, 31)xticks = pd.date_range(sdate, edate, freq='15d')
ax.set_xlim([sdate, edate])ax.set_xticks(xticks)ax.set_xticklabels([i.strftime('%m/%d') for i in xticks])ax.set_yticks(np.arange(0.5, len(cities)))_ = ax.set_yticklabels(sub.T.index.values, fontdict={'family': 'SimHei', 'fontsize': 16})
ytext = [i.get_text() for i in list(ax.get_yticklabels())]
cb = fig.colorbar(con, extend='max', pad=0.02, extendrect=True, extendfrac=0.2)cb.cmap.set_over('#7D0023')
cb.ax.tick_params(axis='both', direction='in', length=0)_ = cb.ax.set_ylabel('Air Quality Index(AQI)', fontdict={'family': 'Times New Roman'})

import calmap
dd = data[data.station == '北京'].AQI.resample('1d').mean()
fig, ax = plt.subplots(figsize=(18, 9))
cmp = calmap.yearplot(dd, how=None, year=2019,            cmap=cmap_aqi, norm=norm,            vmin=0, vmax=300,           )
ax2 = fig.add_axes([0.94, 0.4, 0.015, 0.2])cb1 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap_aqi, ticks=levels,                 norm=norm, orientation='vertical',                 extend='max', extendrect=True,                 extendfrac=0.15)cb1.cmap.set_over('#7D0023')cb1.set_ticks([25, 75, 125, 175, 250])cb1.ax.set_yticklabels([u'优', u'良', u'轻度污染', u'中度污染', u'重度污染'], fontdict={'fontsize': 16, 'family': 'SimHei'}) cb1.ax.yaxis.set_tick_params(length=0.01)ax2.text(1.13, 1.07, '严重污染', fontdict={'fontsize':16, 'family':'SimHei'})
ax.set_ylabel('2019', fontdict=dict(fontsize=26, color='grey'))

from pyecharts import options as optsfrom pyecharts.charts import Calendar
begin = datetime(2019, 1, 1)end = datetime(2019, 12, 31)data = [  [str(begin + timedelta(days=i)), dd[i]]  for i in range((end - begin).days + 1)]
c = (  Calendar()  .add(    "",    data,    calendar_opts=opts.CalendarOpts(      range_='2019',      daylabel_opts=opts.CalendarDayLabelOpts(name_map='cn'),      monthlabel_opts=opts.CalendarMonthLabelOpts(name_map='cn'),    ),  )  .set_global_opts(    title_opts=opts.TitleOpts(title='2019年北京市每日AQI(Air Quality Index)', pos_left='center'),    visualmap_opts=opts.VisualMapOpts(      max_=300,      min_=0,      range_size=[0, 50, 100, 150, 200, 300],      pieces= [{'min': 0, 'max': 50},           {'min': 51, 'max': 100},           {'min': 101, 'max': 150},           {'min': 151, 'max': 200},