def generate_map():
data = get_city_data('friend.json')
map = Map(
"微信好友",
"热力图",
title_color="#fff",
title_pos="center",
width=600,
height=500,
background_color="#404a59",
)
# geo.use_theme('dark')
attr, value = map.cast(data)
map.add(
"",
attr,
value,
maptype="china",
visual_range=[0, 200],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True,
# is_label_show=True
)
map.render('map.html')
map.render(path='map.png')
def gen_zwyx_dd(zwlb):
qs = ZpZwByAreaModel.objects
if zwlb:
qs = qs.filter(zwlb=zwlb)
path = f'zwyx_dd/{zwlb}.html'
else:
path = 'zwyx_dd.html'
page = Page()
df = read_frame(qs.all())
if len(df) > 0:
df_group = df.groupby(['year', 'month'])
time_line_chart1 = Timeline(width=1500,
height=450,
is_auto_play=False,
timeline_bottom=0)
time_line_chart2 = Timeline(width=1500,
height=450,
is_auto_play=False,
timeline_bottom=0)
for name, group in df_group:
# 地图 平均薪资
month = group['month'].tolist()[0]
year = group['year'].tolist()[0]
df_new = group.groupby('province').apply(
get_echarts_all_by_zwyx_value, 'province')
data = [
(a, (b + c) / 2)
for a, b, c in zip(df_new['province'].tolist(
), df_new['max_zwyx'].tolist(), df_new['min_zwyx'].tolist())
]
chart = Map(f'{zwlb}平均职位月薪与地点', width=1500, height=450)
attr, value = chart.cast(data)
chart.add(f'平均薪资',
attr,
value,
wmaptype='china',
is_label_show=True,
is_visualmap=True,
visual_range=[int(min(value)),
int(max(value))],
visual_pos='right',
visual_top='top')
time_line_chart1.add(chart, f'{year}年{month}月')
# 本月职位量Top20
chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500)
chart3.add('职位量',
df_new['province'].tolist(),
df_new['count'].tolist(),
center=[25, 50],
is_label_show=True)
chart3.add('招聘人数',
df_new['province'].tolist(),
df_new['zprs'].tolist(),
center=[75, 50],
is_label_show=True)
time_line_chart2.add(chart3, f'{year}年{month}月')
page.add(time_line_chart1)
page.add(time_line_chart2)
page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path)))
def map_qgtd(attr_v1: List[Tuple[str, int]], chart_name: str,
v1_name: str) -> map.Map:
style = Style(width=WIDTH, height=HEIGHT)
chart = Map(chart_name, **style.init_style)
attr, value = chart.cast(attr_v1)
chart.add(v1_name,
attr,
value,
maptype='china',
is_visualmap=True,
visual_text_color='#000')
return chart
def city_dist(info):
grouped = info.groupby(['cityChn'])
grouped_city = grouped['cityChn']
city_com = grouped_city.agg(['count'])
city_com.reset_index(inplace=True)
city_distribute_data = [(city_com['cityChn'][i], city_com['count'][i])
for i in range(0, city_com.shape[0])]
map = Map('北京征婚小姐姐分布', width=1200, height=600)
attr, value = map.cast(city_distribute_data)
attr = [loc[2:] + '区' for loc in attr] # 命名规则与pyecharts中名字一致
map.add("密度",
attr,
value,
maptype='北京',
is_map_symbol_show=True,
is_visualmap=True,
visual_text_color='#000',
visual_range=[0, 2000])
map.show_config()
map.render('北京征婚小姐姐分布.html')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import itchat
from pyecharts import Map
from collections import Counter
itchat.auto_login(hotReload=True)
myFriendList = itchat.get_friends()
provinceCounter = Counter()
for friend in myFriendList:
province = friend['Province']
if province != '':
provinceCounter[province] += 1
myMap = Map('你的微信好友地区分布图', width=1200, height=600, title_pos='center')
attr, value = myMap.cast(provinceCounter)
myMap.add('',
attr,
value,
maptype='china',
is_visualmap=True,
is_label_show=True,
visual_range=[0, 80])
myMap.render('./你的微信好友地区分布图.html')
from pyecharts import Bar, Line, Overlap
bar = Bar("历年报考人数与出生人数")
bar.add("报考人数",arr[::-1,0] , arr[::-1,1])
bar.add("出生人数",arr[::-1,0] , list_born)
bar
# In[59]:
from pyecharts import Map
data_prov = [('北京', 6.3), ('重庆', 25), ('上海', 5), ('天津', 5.5), ('河南', 98.38), ('广东', 75.8), ('山东', 59.2), ('安徽', 49.9), ('四川', 62), ('江苏', 33.15), ('河北', 48.64), ('湖北', 37.4), ('陕西', 31.9), ('湖南', 45.18), ('山西', 30.5), ('浙江', 30.6), ('甘肃', 27.3), ('广西', 40), ('江西', 38), ('福建', 20.09), ('辽宁', 18.5), ('贵州', 44), ('云南', 30), ('黑龙江', 19.04), ('内蒙古', 19.5), ('吉林', 15.03), ('新疆', 20.74), ('宁夏', 6.95), ('海南', 5.88), ('青海', 4), ('西藏', 2.53)]
geo = Map("2018各省高考报名人数")
attr, value = geo.cast(data_prov)
geo.add("", attr, value, visual_range=[0, 80], is_visualmap=True, is_map_symbol_show=False)
geo
# In[48]:
data_prov_ad = [('湖南', 11.2), ('江西', 10.4), ('安徽', 14.2), ('广东', 11.2), ('河北', 14.55), ('湖北', 15.8), ('河南', 7.8), ('山西', 9.8), ('福建', 18.7), ('吉林', 12.4), ('重庆', 11.5), ('辽宁', 16.1), ('内蒙古', 14.03), ('甘肃', 15.22), ('黑龙江', 13.5), ('陕西', 14.6), ('宁夏', 19), ('新疆', 12.86), ('西藏', 11), ('青海', 15.2), ('四川', 10.6), ('云南', 10.85), ('广西', 8.44), ('贵州', 14.50), ('北京', 30.5), ('江苏', 12.10), ('山东', 10.6), ('天津', 24.1), ('上海', 21.8), ('浙江', 14)]
geo = Map("2018各省一本录取率")
attr, value = geo.cast(data_prov_ad)
geo.add("", attr, value, visual_range=[8, 20], is_visualmap=True, is_map_symbol_show=False)
geo
# In[ ]:
#地理分布分析
loc_version = df['省份'].value_counts()
provinces = list(loc_version.index)
province_data = []
for province in provinces:
province_data.append((province, loc_version[province]))
maps = Map("40年100人分布省份",
"data from Xinhua Net",
title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
attr, value = maps.cast(province_data)
maps.add("",
attr,
value,
type="effectScatter",
visual_range=[0, 15],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True)
maps.show_config()
maps.render()
#年代分布分析
df['出生年月'] = pd.to_datetime(df['出生年月'])
df_date = df.set_index('出生年月')
import re
import time
from lxml import html
from selenium import webdriver
from pyecharts import Map, Bar, Pie
r = requests.get(url='http://www.sohu.com/a/223885358_118392') # 最基本的GET请求
time.sleep(60)
html = r.text
content = re.findall(
u'<p>([\u2E80-\u9FFF]{2,3})[u"市"u"省"u"特"u"壮"u"回"u"维"u"自"u"人"].{0,6}\uff08(\d*)\u540d\uff09 </p>.*?<p>(.*?)</p>',
html, re.S)
data = [_[0:2] for _ in content]
print(data)
map = Map("人大代表分布", title_pos="center", width=800, height=500)
attr, value = map.cast(data)
attr[-1] = u"南海诸岛"
map.add("",
attr,
value,
maptype='china',
is_label_show=True,
label_pos="inside",
label_text_color="#000",
is_visualmap=True,
visual_text_color='#000',
visual_range=[12, 172],
visual_range_text=['低', '高'],
visual_pos=[500, 500])
# map.show_config()
map.render()
自从 0.3.2 开始，为了缩减项目本身的体积以及维持 pyecharts 项目的轻量化运行，pyecharts 将不再自带地图 js 文件。如用户需要用到地图图表，可自行安装对应的地图文件包。下面介绍如何安装。
全球国家地图: echarts-countries-pypkg (1.9MB): 世界地图和 213 个国家，包括中国地图
中国省级地图: echarts-china-provinces-pypkg (730KB)：23 个省，5 个自治区
中国市级地图: echarts-china-cities-pypkg (3.8MB)：370 个中国城市:https://github.com/echarts-maps/echarts-china-cities-js
pip install echarts-countries-pypkg
pip install echarts-china-provinces-pypkg
pip install echarts-china-cities-pypkg
别注明，中国地图在 echarts-countries-pypkg 里。
"""
snd_price = list(
zip(
snd.price.groupby(snd.dist_name).mean().index,
snd.price.groupby(snd.dist_name).mean().values))
attr, value = Map.cast(snd_price)
min_ = snd.price.groupby(snd.dist).mean().min()
max_ = snd.price.groupby(snd.dist).mean().max()
price_map = Map('北京各区房价', width=1200, height=600)
price_map.add('',
attr,
value,
maptype='北京',
is_visualmap=True,
visual_range=[min_, max_],
visual_text_color='#000',
is_label_show=True)
price_map.render()
#生成render.html在当前目录
#%%
citysJson), city_com['count'][i])
for i in range(0, city_com.shape[0])]
data_map_list = {}
for item in data_map_all:
if data_map_list.has_key(item[0]):
value = data_map_list[item[0]]
value += item[1]
data_map_list[item[0]] = value
else:
data_map_list[item[0]] = item[1]
data_map = [(realKeys(key), data_map_list[key])
for key in data_map_list.keys()]
geo = Map("城市评论数", width=1200, height=800, title_pos="center")
while True:
try:
attr, val = geo.cast(data_map)
geo.add(
"",
attr,
val,
visual_range=[0, 6000],
visual_text_color="#fff",
symbol_size=5,
is_visualmap=True,
maptype=u'china',
is_map_symbol_show=False,
is_label_show=True,
is_roam=False,
# visual_split_number=4
)
bar
# In[59]:
from pyecharts import Map
data_prov = [('北京', 6.3), ('重庆', 25), ('上海', 5), ('天津', 5.5), ('河南', 98.38),
('广东', 75.8), ('山东', 59.2), ('安徽', 49.9),
('四川', 62), ('江苏', 33.15), ('河北', 48.64), ('湖北', 37.4),
('陕西', 31.9), ('湖南', 45.18), ('山西', 30.5), ('浙江', 30.6),
('甘肃', 27.3), ('广西', 40), ('江西', 38), ('福建', 20.09), ('辽宁', 18.5),
('贵州', 44), ('云南', 30), ('黑龙江', 19.04), ('内蒙古', 19.5),
('吉林', 15.03), ('新疆', 20.74), ('宁夏', 6.95), ('海南', 5.88),
('青海', 4), ('西藏', 2.53)]
geo = Map("2018各省高考报名人数")
attr, value = geo.cast(data_prov)
geo.add("",
attr,
value,
visual_range=[0, 80],
is_visualmap=True,
is_map_symbol_show=False)
geo
# In[48]:
data_prov_ad = [('湖南', 11.2), ('江西', 10.4), ('安徽', 14.2), ('广东', 11.2),
('河北', 14.55), ('湖北', 15.8), ('河南', 7.8), ('山西', 9.8),
('福建', 18.7), ('吉林', 12.4), ('重庆', 11.5), ('辽宁', 16.1),
('内蒙古', 14.03), ('甘肃', 15.22), ('黑龙江', 13.5), ('陕西', 14.6),
('宁夏', 19), ('新疆', 12.86), ('西藏', 11), ('青海', 15.2),
