def display_recent_global_distribution(pic_file, maxCount=200, **kwargs):
"""
display the distribution of recent total numbers of confirmed patients.
Parameters
----------
pic_file: str
absolute path of the generated figure.
maxCount: int
maximumn count of colorbar. (default: 200)
"""
conn = db.connect(dbFile)
cu = conn.cursor()
OverallDf = pd.read_sql_query("""select * from Region_Data""", conn)
OverallDf['updateTime'] = OverallDf['updateTime'].astype('int64')
recentData = OverallDf.groupby('provinceShortName').apply(
lambda t: t[t['updateTime'] == t['updateTime'].max()])
recentData = recentData.groupby('country').agg({
'confirmedCount': 'sum',
'suspectedCount': 'sum',
'updateTime': 'mean'
})
recentData['date'] = pd.to_datetime(recentData['updateTime'] / 1000,
unit='s')
time = recentData[recentData.index == '中国']['updateTime']
data = [[
searchCountryENName(recentData.index[i]),
int(recentData['confirmedCount'][i])
] for i in range(recentData.shape[0])]
map_3 = Map()
map_3.add("{0} worldwide COVID-19 patients distribution".format(
recentData['date'][1].strftime('%Y-%m-%d')),
data,
maptype='world',
is_map_symbol_show=False)
map_3.set_series_opts(
label_opts=opts.LabelOpts(is_show=False, font_size=100))
map_3.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=maxCount),
title_opts=opts.TitleOpts(title=""))
if 'notebook' in kwargs.keys():
if kwargs['notebook']:
map_3.render_notebook()
else:
html_file = '{0}.html'.format(os.path.splitext(pic_file)[0])
tmpHtmlFile = map_3.render()
shutil.move(tmpHtmlFile, html_file)
make_snapshot(snapshot,
file_name=html_file,
output_name=pic_file,
is_remove_html=False,
**kwargs)
def display_recent_overall_distribution(pic_file, maxCount=500, **kwargs):
"""
display the distribution of recent total numbers of nation-wide confirmed
patients in China.
Parameters
----------
pic_file: str
absolute path of the generated figure.
maxCount: int
maximumn count of colorbar. (default: 500)
"""
conn = db.connect(dbFile)
cu = conn.cursor()
cu.execute("""select provinceShortName, confirmedCount
from Region_Data
where updateTime in (select max(updateTime)
from Region_Data r_d
where r_d.country='中国' and r_d.region_id=Region_Data.region_id)
group by Region_Data.region_id;
""")
recentProvinceData = cu.fetchall()
cu.execute("""select max(updateTime) from Region_Data;""")
recentTime = cu.fetchone()
recentTimeObj = dt.datetime.utcfromtimestamp(int(recentTime[0]) / 1000)
# color-plot
list1 = [[recentProvinceData[i][0], recentProvinceData[i][1]]
for i in range(len(recentProvinceData))]
map_1 = Map()
map_1.add("{0}全国各省感染总人数".format(recentTimeObj.strftime('%y-%m-%d')),
list1,
maptype="china",
is_map_symbol_show=False)
map_1.set_global_opts(title_opts=opts.TitleOpts(
title="{0}全国各省感染总人数".format(recentTimeObj.strftime('%y-%m-%d'))),
visualmap_opts=opts.VisualMapOpts(max_=maxCount))
if 'notebook' in kwargs.keys():
if kwargs['notebook']:
map_1.render_notebook()
else:
html_file = '{0}.html'.format(os.path.splitext(pic_file)[0])
tmpHtmlFile = map_1.render()
shutil.move(tmpHtmlFile, html_file)
make_snapshot(snapshot,
file_name=html_file,
output_name=pic_file,
is_remove_html=False,
**kwargs)
def display_recent_provincial_distribution(province,
pic_file,
maxCount=500,
**kwargs):
"""
display the distribution of recent total numbers of confirmed patients.
Parameters
----------
province: str
province name. e.g., '湖北省'
pic_file: str
absolute path of the generated figure.
maxCount: int
maximumn count of colorbar. (default: 500)
"""
conn = db.connect(dbFile)
cu = conn.cursor()
cu.execute("""select max(updateTime) from Region_Data;""")
recentTime = cu.fetchone()
recentTimeObj = dt.datetime.utcfromtimestamp(int(recentTime[0]) / 1000)
cu.execute(
"""select cityName, confirmedCount
from City_Data
where City_Data.region_id=
(select id from Region_Name where Region_Name.name=(?))
and updateTime in (select max(updateTime)
from City_Data
where City_Data.region_id=
(select id from Region_Name where Region_Name.name=(?)))
group by City_Data.cityName;""", (province, province))
hubeiProvinceData = cu.fetchall()
cu.execute(
"""select provinceShortName from Region_Data
where Region_Data.provinceName = (?)
""", (province, ))
hubeiProvinceShortName = cu.fetchone()
hubeiProvinceShortName = hubeiProvinceShortName[0]
list2 = [[
searchCityLongName(hubeiProvinceData[i][0]), hubeiProvinceData[i][1]
] for i in range(len(hubeiProvinceData))]
map_2 = Map()
map_2.add("{0} {1}感染人数".format(recentTimeObj.strftime('%y-%m-%d'),
province),
list2,
maptype=hubeiProvinceShortName,
is_map_symbol_show=False)
map_2.set_global_opts(title_opts=opts.TitleOpts(title="{0} {1}感染人数".format(
recentTimeObj.strftime('%y-%m-%d'), province)),
visualmap_opts=opts.VisualMapOpts(max_=maxCount))
if 'notebook' in kwargs.keys():
if kwargs['notebook']:
map_2.render_notebook()
else:
html_file = '{0}.html'.format(os.path.splitext(pic_file)[0])
tmpHtmlFile = map_2.render()
shutil.move(tmpHtmlFile, html_file)
make_snapshot(snapshot,
file_name=html_file,
output_name=pic_file,
is_remove_html=False,
**kwargs)
"max": 199999
},
{
"min": 50000,
"max": 99999
},
{
"min": 10000,
"max": 49999
},
{
"max": 9999
},
]),
title_opts=opts.TitleOpts(
title="Covid-19 Worldwide Total Cases",
subtitle='Till July 03rd,2020',
pos_left='center',
padding=0,
item_gap=2,
title_textstyle_opts=opts.TextStyleOpts(color='darkblue',
font_weight='bold',
font_family='Courier New',
font_size=30),
subtitle_textstyle_opts=opts.TextStyleOpts(color='grey',
font_weight='bold',
font_family='Courier New',
font_size=13)),
legend_opts=opts.LegendOpts(is_show=False))
map_1.render_notebook()
# In[48]:
from pyecharts.charts import Map
# In[49]:
c = Map(init_opts=opts.InitOpts(width='800px', height='750px'))
c.add('',[list(z) for z in zip(province_num.index.tolist(), province_num.values.tolist())], 'china')
c.set_global_opts(title_opts=opts.TitleOpts('调剂信息省份分布地图'),
toolbox_opts=opts.ToolboxOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(max_=110))
c.render_notebook()
# In[51]:
import jieba.analyse #cmd pip install jieba
# In[52]:
txt = df['name'].str.cat()
# 添加关键词
jieba.add_word('材料科学与工程')
from pyecharts import options as opts
from pyecharts.charts import Map
world_map = Map(init_opts=opts.InitOpts(bg_color="#FFFAFA"))
data = {
"系列1": [['Denmark', 1], ['Uruguay', 7], ['Canada', 5], ['New Zealand', 27],
['Chile', 1], ['France', 2], ['Australia', 57], ['Ireland', 2],
['Spain', 2], ['Argentina', 2], ['United States', 2]],
"系列2": [["China", 105]],
}
world_map.add("系列1", data["系列1"], "world", is_map_symbol_show=False)
world_map.add("系列2", data["系列2"], "world", is_map_symbol_show=False)
world_map.set_series_opts(
label_opts=opts.LabelOpts(position="right", is_show=False))
world_map.set_global_opts(
title_opts=opts.TitleOpts(title="世界热力图"),
visualmap_opts=opts.VisualMapOpts(max_=105, is_piecewise=True),
)
world_map.render_notebook()
#%% Setup
myMap = Map()
myMap.add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())],
"china")
myMap.set_global_opts(
title_opts=opts.TitleOpts(title="Map-VisualMap（分段型）"),
visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True),
)
#%% Setup
#myMap.show_config()
#%%
myMap.render()
#%%
myMap.load_javascript()
myMap.render_notebook()
#%%
from pyecharts.charts import Bar
bar = (Bar().add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋",
"袜子"]).add_yaxis("商家A", [5, 20, 36, 10, 75, 90]))
bar.load_javascript()
bar.render()
#%% Setup
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 20, 100)
from pyecharts.charts import Map, Geo
from pyecharts import options as opts
data = [("广东", 10430.03), ("山东", 9579.31), ("河南", 9402.36), ("四川", 8041.82),
("江苏", 7865.99), ("河北", 7185.42), ("湖南", 6568.37), ("安徽", 5950.1),
("浙江", 5442), ("湖北", 5723.77), ("广西", 4602.66), ("云南", 4596.6),
("江西", 4456.74), ("辽宁", 4374.63), ("黑龙江", 3831.22), ("陕西", 3732.74),
("山西", 3571.21), ("福建", 3552), ("重庆", 2884), ("贵州", 3476.65),
("吉林", 2746.22), ("甘肃", 2557.53), ("内蒙古", 2470.63), ("上海", 2301.391),
("台湾", 2316.2), ("新疆", 2181.33), ("北京", 1961.2), ("天津", 1293.82),
("海南", 867.15), ("香港", 709.76), ("青海", 562.67), ("宁夏", 630.14),
("西藏", 300.21), ("澳门", 55.23)]
map = Map()
map.add("", data, "china")
map.set_global_opts(
title_opts=opts.TitleOpts(title="各省市人口数",
subtitle="数据来源：中国统计年鉴（万人）",
pos_right="center",
pos_top="5%"),
visualmap_opts=opts.VisualMapOpts(max_=12000),
)
map.render_notebook()
country=list(map_df["location"])
totalcases=list(map_df["total_cases"])
# In[31]:
list1 = [[country[i],totalcases[i]] for i in range(len(country))] #prepare data for visualization
map_1 = Map(init_opts=opts.InitOpts(width='1000px', height='460px')) #create the map and set the size of the map
map_1.add("Total Confirmed Cases", list1, maptype='world') #add world map
map_1.set_global_opts( #set global config
visualmap_opts=opts.VisualMapOpts(max_=1100000, is_piecewise=False),
legend_opts=opts.LegendOpts(is_show=False), #show legend
)
map_1.render_notebook() #show the map
# # A total of 212 countries displayed on map. The data looks clumsy. We can get rid of countries name to make it more attaractive
# In[32]:
list1 = [[country[i],totalcases[i]] for i in range(len(country))]
map_1 = Map(init_opts=opts.InitOpts(width='1000px', height='460px'))
map_1.add("Total Confirmed Cases",
list1, maptype='world')
map_1.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #remove country names
map_1.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=1100000,is_piecewise=False),
legend_opts=opts.LegendOpts(is_show=False))
map_1.render_notebook()
