flask + pyecharts 疫情数据分析 搭建交互式动态可视化疫情趋势分析、舆情监测平台（附代码实现）

该项目是浙江大学地理空间数据库课程作业8：空间分析中，使用 flask + pyecharts 搭建的简单新冠肺炎疫情数据可视化交互分析平台的一部分，完整的实现包含疫情数据获取、态势感知、预测分析、舆情监测等任务；

包含完整代码、数据集和实现的github地址：
https://github.com/yunwei37/COVID-19-NLP-vis

项目分析报告已部署到网页端，可点击http://flask.yunwei123.tech/进行查看，数据已更新到6.17

本项目采用flask作为后端，使用pyecharts进行数据可视化，通过ajax实现动态交互可视化效果；

疫情数据曲线图、日历图

疫情数据曲线图：可选择国家
疫情新增确诊病例日历图：

pyecharts 代码实现：

import time, json
import pandas as pd
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.commons.utils import JsCode

country_name = '中国'


def render_lines(country_name):
    #-------------------------------------------------------------------------------------
    # 第一步：读取数据
    #-------------------------------------------------------------------------------------
    n = "dataSets\\countrydata.csv"
    data = pd.read_csv(n)
    data = data[data['countryName'] == country_name]
    date_list = list(data['dateId'])
    date_list = list(map(lambda x:str(x),date_list))
    confirm_list = list(data['confirmedCount'])
    current_list = list(data['currentConfirmedCount'])
    dead_list = list(data['deadCount'])
    heal_list = list(data['curedCount'])
    print(len(date_list))    
    #print(date_list)                        # 日期
    #print(confirm_list)                     # 确诊数据
    #print(current_list)                     # 疑似数据
    #print(dead_list)                        # 死亡数据
    #print(heal_list)                        # 治愈数据


    #-------------------------------------------------------------------------------------
    # 第二步：绘制折线面积图
    #-------------------------------------------------------------------------------------
    line = (
            Line()
        .add_xaxis(date_list)
        # 平均线 最大值 最小值
        .add_yaxis('确诊数据', confirm_list, is_smooth=True,
                markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
                                                        opts.MarkPointItem(type_="min")]))
        .add_yaxis('现存确诊数据', current_list, is_smooth=True,
                markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
                                                        opts.MarkPointItem(type_="min")]))
        .add_yaxis('死亡数据', dead_list, is_smooth=True,
                markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
                                                        opts.MarkPointItem(type_="min")]))
        .add_yaxis('治愈数据', heal_list, is_smooth=True,
                markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
                                                        opts.MarkPointItem(type_="min")]))
        # 隐藏数字 设置面积
        .set_series_opts(
            areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False))
        # 设置x轴标签旋转角度
        .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)), 
                        yaxis_opts=opts.AxisOpts(name='人数', min_=3), 
                        title_opts=opts.TitleOpts(title='2019-nCoV'+country_name+'疫情数据曲线图'))          
        )


    return line

import datetime
from pyecharts import options as opts
from pyecharts.charts import Calendar

def calendar_base() -> Calendar:
    begin = datetime.date(2020, 1, 19) #设置起始日期
    end = datetime.date(2020, 6, 17) #设置终止日期
    n = "dataSets\\countrydata.csv"
    data = pd.read_csv(n)
    data = data[data['countryName'] == country_name]
    date_list = list(data['dateId'])
    date_list = list(map(lambda x:str(x),date_list))
    confirm_list = list(data['confirmedIncr'])
    data =[
    [str(begin + datetime.timedelta(days=i)), confirm_list[i]] #设置日期间隔，步数范围
    for i in range((end - begin).days - 3)
     ]
    print(len(data))

    c = (
    Calendar()
    .add('', data, calendar_opts=opts.CalendarOpts(range_=['2020-1','2020-6'])) #添加到日历图，指定显示2019年数据
    .set_global_opts(          #设置底部显示条，解释数据
        title_opts=opts.TitleOpts(title='全国疫情每日新增确诊病例日历图',subtitle='From Weix'),
        visualmap_opts=opts.VisualMapOpts(
            pieces=[
                                                {
     'min': 13000, 'color': '#7f1818'},  #不指定 max
                                                {
     'min': 1000, 'max': 10000},
                                                {
     'min': 500, 'max': 999},
                                                {
     'min': 100, 'max': 499},
                                                {
     'min': 10, 'max': 99},
                                                {
     'min': 0, 'max': 9} ],   
            orient='vertical',  #设置垂直显示
            pos_top='230px',    
            pos_left='100px',
            is_piecewise=True    #是否连续
         )
     )
    )
    return  c

if __name__ == "__main__":
    calendar_base().render('全国疫情每日新增确诊病例日历图.html')

前端html：

			请选择国家：    
            
            {% for cate in cates %}
                {
    {cate}}
            {% endfor %}
            
            

            

            

疫情数据分析词云图：

pyecharts 代码实现：

# coding=utf-8
import jieba
import re
import time
from collections import Counter
import pandas as pd
import datetime

#------------------------------------中文分词------------------------------------
#截取该日期前后的10%文章
#percent = 0-90
def generatewordData(percent):
    cut_words = ""
    all_words = ""

    data = pd.read_csv('dataSets\\中国社会组织_疫情防控-5_21.csv')

    percent = percent / 10
    num = data.shape[0]/10
    data = data.iloc[int(num*percent):int(num*percent+num),]
    print(data.shape[0])
    print(list(data['时间'])[0])
    print(list(data['时间'])[-1])

    for line in data['正文内容']:
        line = str(line)
        seg_list = jieba.cut(line,cut_all=False)
        cut_words = (" ".join(seg_list))
        all_words += cut_words


    # 输出结果
    all_words = all_words.split()

    # 词频统计
    c = Counter()
    for x in all_words:
        if len(x)>1 and x != '\r\n':
            c[x] += 1

    words = []
    for (k,v) in c.most_common(50):
        # print(k, v)
        words.append((k,v))
    words = words[1:]
    return words,list(data['时间'])[0],list(data['时间'])[-1]

# 渲染图

from pyecharts import options as opts
from pyecharts.charts import WordCloud
from pyecharts.globals import SymbolType
#import wordData

# percent 0-90
def render_wordcloud(percent = 0) -> WordCloud:
    from scripts.wordData import date_data
    words = date_data[int(percent)][0]
    c = (
        WordCloud()
        .add("", words, word_size_range=[20, 100], shape=SymbolType.ROUND_RECT)
        .set_global_opts(title_opts=opts.TitleOpts(title='全国新型冠状病毒疫情新闻词云图'+' '+date_data[int(percent)][1]+' - '+date_data[int(percent)][2]))
    )
    return c

# 生成图
if __name__ == "__main__":
    date_words = []
    for i in range(0,91):
        print(i)
        words,date_start,date_end = generatewordData(i)
        date_words.append([words,date_start,date_end])
    with open("wordData.py",'w',encoding='utf-8') as f:
        f.write("date_data="+str(date_words))
        f.close()

微博情感分析曲线图

`pyecharts 代码实现：

# dateId: 0-50
def weiboWordcloud(dateId):
    from scripts.weiboWordData import date_data
    words = date_data[int(dateId)][1]
    date = date_data[int(dateId)][0]
    c = (
        WordCloud()
        .add("", words, word_size_range=[20, 100], shape=SymbolType.ROUND_RECT)
        .set_global_opts(title_opts=opts.TitleOpts(title='全国新型冠状病毒疫情微博每日主题词词云图 '+str(date)))
    )
    return c