flask + pyecharts 疫情數據分析 搭建交互式動態可視化疫情趨勢分析、輿情監測平臺（附代碼實現）

該項目是浙江大學地理空間數據庫課程作業8：空間分析中，使用 flask + pyecharts 搭建的簡單新冠肺炎疫情數據可視化交互分析平臺的一部分，完整的實現包含疫情數據獲取、態勢感知、預測分析、輿情監測等任務；

包含完整代碼、數據集和實現的github地址：
https://github.com/yunwei37/COVID-19-NLP-vis

項目分析報告已部署到網頁端，可點擊http://flask.yunwei123.tech/進行查看，數據已更新到6.17

本項目採用flask作爲後端，使用pyecharts進行數據可視化，通過ajax實現動態交互可視化效果；

疫情數據曲線圖、日曆圖

疫情數據曲線圖：可選擇國家
疫情新增確診病例日曆圖：

pyecharts 代碼實現：

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 = '中國'


def render_lines(country_name):
    #-------------------------------------------------------------------------------------
    # 第一步：讀取數據
    #-------------------------------------------------------------------------------------
    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)                     # 確診數據
    #print(current_list)                     # 疑似數據
    #print(dead_list)                        # 死亡數據
    #print(heal_list)                        # 治癒數據


    #-------------------------------------------------------------------------------------
    # 第二步：繪製折線面積圖
    #-------------------------------------------------------------------------------------
    line = (
            Line()
        .add_xaxis(date_list)
        # 平均線 最大值 最小值
        .add_yaxis('確診數據', confirm_list, is_smooth=True,
                markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
                                                        opts.MarkPointItem(type_="min")]))
        .add_yaxis('現存確診數據', current_list, is_smooth=True,
                markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
                                                        opts.MarkPointItem(type_="min")]))
        .add_yaxis('死亡數據', dead_list, is_smooth=True,
                markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
                                                        opts.MarkPointItem(type_="min")]))
        .add_yaxis('治癒數據', heal_list, is_smooth=True,
                markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),
                                                        opts.MarkPointItem(type_="min")]))
        # 隱藏數字 設置面積
        .set_series_opts(
            areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
            label_opts=opts.LabelOpts(is_show=False))
        # 設置x軸標籤旋轉角度
        .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)), 
                        yaxis_opts=opts.AxisOpts(name='人數', min_=3), 
                        title_opts=opts.TitleOpts(title='2019-nCoV'+country_name+'疫情數據曲線圖'))          
        )


    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) #設置起始日期
    end = datetime.date(2020, 6, 17) #設置終止日期
    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]] #設置日期間隔，步數範圍
    for i in range((end - begin).days - 3)
     ]
    print(len(data))

    c = (
    Calendar()
    .add('', data, calendar_opts=opts.CalendarOpts(range_=['2020-1','2020-6'])) #添加到日曆圖，指定顯示2019年數據
    .set_global_opts(          #設置底部顯示條，解釋數據
        title_opts=opts.TitleOpts(title='全國疫情每日新增確診病例日曆圖',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',  #設置垂直顯示
            pos_top='230px',    
            pos_left='100px',
            is_piecewise=True    #是否連續
         )
     )
    )
    return  c

if __name__ == "__main__":
    calendar_base().render('全國疫情每日新增確診病例日曆圖.html')

前端html：

			<label>請選擇國家：    </label>
            <select id="selectCountrys">
            {% for cate in cates %}
                <option value="{{cate}}" >{{cate}}</option>
            {% endfor %}
            </select>
            <div id="lines" style="width:500px; height:300px;display: inline-block;"></div>
            <div id="04cd225d9bb642288bef9788ed998f30" class="chart-container" style="width:500px; height:300px;display: inline-block;"></div>
            

疫情數據分析詞雲圖：

pyecharts 代碼實現：

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

#------------------------------------中文分詞------------------------------------
#截取該日期前後的10%文章
#percent = 0-90
def generatewordData(percent):
    cut_words = ""
    all_words = ""

    data = pd.read_csv('dataSets\\中國社會組織_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['時間'])[0])
    print(list(data['時間'])[-1])

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


    # 輸出結果
    all_words = all_words.split()

    # 詞頻統計
    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['時間'])[0],list(data['時間'])[-1]

# 渲染圖

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='全國新型冠狀病毒疫情新聞詞雲圖'+' '+date_data[int(percent)][1]+' - '+date_data[int(percent)][2]))
    )
    return c

# 生成圖
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()

微博情感分析曲線圖

`pyecharts 代碼實現：

# 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='全國新型冠狀病毒疫情微博每日主題詞詞雲圖 '+str(date)))
    )
    return c