佛山企业网站推广,c2c电子商务网站需具备哪些业务功能,网站建设后台管理便捷,产品市场调研怎么做一、背景 对Python通过代理使用多线程爬取安居客二手房数据#xff08;二#xff09;中爬取的房地产数据进行数据分析与可视化展示 我们爬取到的房产数据#xff0c;主要是武汉二手房的房源信息#xff0c;主要包括了待售房源的户型、面积、朝向、楼层、建筑年份、小区名称…一、背景 对Python通过代理使用多线程爬取安居客二手房数据二中爬取的房地产数据进行数据分析与可视化展示 我们爬取到的房产数据主要是武汉二手房的房源信息主要包括了待售房源的户型、面积、朝向、楼层、建筑年份、小区名称、小区所在的城区-镇-街道、房子被打的标签、总价、单价等信息。
库numpy、pandas、pyecharts、jieba 图形Bar柱状图、Pie饼图、Histogram直方图 、Scatter散点图、Map地图和WordCloud词云图
分析思路
按房屋面积区间分布的房屋单价情况柱状图按房子户型的房屋单价情况柱状图小区房价Top10柱状图横向待售卖的二手房中不同建筑年份的房子数量占比情况饼图不同单价和总价的房子在不同价格区间的分布数量情况直方图 6.分析 房子面积跟房子单价之间是什么关系散点图不同区的二手房房价情况地图分析购房者最关注的房屋关键词有哪些词云
二、代码实战 读取excel数据分析数据并生成图表
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Bar, Pie, Scatter, WordCloud, Map, Page
import numpy as np
import jieba
import jieba.analyse
from pyecharts.commons.utils import JsCode# 面积分区
def cal_square_district(row):if row[面积] 60:return [0,60]if row[面积] 60 and row[面积] 90:return [60,90]if row[面积] 90 and row[面积] 120:return [90,120]if row[面积] 120 and row[面积] 150:return [120,150]if row[面积] 150 and row[面积] 200:return [150,200]if row[面积] 200 and row[面积] 300:return [200, 300]if row[面积] 300:return [300,-]return [未知]# 几室量化
def order_layout_ascending(row):if row[室] 1室:return 0if row[室] 2室:return 1if row[室] 3室:return 2if row[室] 4室:return 3if row[室] 5室:return 4if row[室] 6室:return 5# 颜色配置
layout_color_function function (params) {if (params.value 17000 params.value 18000) {return red;} else if (params.value 18000 params.value 20000) {return blue;}else if (params.value 20000 params.value 25000){return green}else if (params.value 25000 params.value 35000){return purple}else if (params.value 35000 params.value 40000){return black}return brown;}# 按室均价
def unit_price_analysis_by_layout(df, isembed):# 增加一列[面积区间]df[面积区间] df.apply(cal_square_district, args(), axis1)# 获取要分析的数据行和列analysis_df df.loc[:, [室, 均价]]analysis_df.loc[:, 室] analysis_df.loc[:, 室].astype(str)# 对面积区间列group by然后按分组计算总价和均价的平均值group analysis_df.groupby(室, as_indexFalse)group_df group.mean()group_df.loc[:, 均价] group_df.loc[:, 均价].astype(int)# 给室这个字段排个序group_df[order] group_df.apply(order_layout_ascending, axis1)group_df.sort_values(order, ascendingTrue, inplaceTrue)bar (Bar().add_xaxis(group_df[室].tolist()).add_yaxis(单价均价, group_df[均价].tolist(),itemstyle_optsopts.ItemStyleOpts(colorJsCode(layout_color_function))).set_global_opts(title_optsopts.TitleOpts(title武汉二手房按户型的房屋单价),legend_optsopts.LegendOpts(is_showFalse)))# 判断是否单独显示还是和其他图表一起显示if isembed:return bar.render_embed()else:return bardef order_square_ascending(row):if row[面积区间] [0,60]:return 0if row[面积区间] [60,90]:return 1if row[面积区间] [90,120]:return 2if row[面积区间] [120,150]:return 3if row[面积区间] [150,200]:return 4if row[面积区间] [200,300]:return 5if row[面积区间] [300,-]:return 6square_color_function function (params) {if (params.value 17000 params.value 18000) {return red;} else if (params.value 18000 params.value 20000) {return blue;}else if (params.value 20000 params.value 25000){return green}else if (params.value 25000 params.value 35000){return purple}else if (params.value 35000 params.value 40000){return black}return brown;}# 按面积区间均价分布
def unit_price_analysis_by_square(df, isembed):# 增加一列[面积区间]df[面积区间] df.apply(cal_square_district, args(), axis1)# 获取要分析的数据行和列analysis_df df.loc[:, [面积区间, 均价]]analysis_df.loc[:, 面积区间] analysis_df.loc[:, 面积区间].astype(str)# 对面积区间列group by然后按分组计算总价和均价的平均值group analysis_df.groupby(面积区间, as_indexFalse)group_df group.mean()group_df.loc[:, 均价] group_df.loc[:, 均价].astype(int)# 把面积区间按从小到大排个序group_df[order] group_df.apply(order_square_ascending, axis1)group_df.sort_values(order, ascendingTrue, inplaceTrue)bar (Bar().add_xaxis(group_df[面积区间].tolist()).add_yaxis(单价均价, group_df[均价].tolist(),itemstyle_optsopts.ItemStyleOpts(colorJsCode(square_color_function))).set_global_opts(title_optsopts.TitleOpts(title武汉二手房按面积区间的房屋单价),legend_optsopts.LegendOpts(is_showFalse)))# 判断是否单独显示还是和其他图表一起显示if isembed:return bar.render_embed()else:return bartop10_color_function function (params) {if (params.value 27000 params.value 27500) {return red;} else if (params.value 27500 params.value 27800) {return blue;}else if (params.value 27800 params.value 28000){return green}else if (params.value 28000 params.value 29000){return purple}else if (params.value 29000 params.value 30000){return brown}else if (params.value 30000 params.value 35200){return gray}else if (params.value 35200 params.value 37000){return orange}else if (params.value 37000 params.value 40000){return pink}else if (params.value 40000 params.value 45000){return navy}return gold;}# 小区均价top10
def unit_price_analysis_by_estate(df, isembed):# 获取要分析的数据列analysis_df df.loc[:, [小区名称, 均价]]analysis_df.loc[:, 小区名称] analysis_df.loc[:, 小区名称].astype(str)# 对小区名称分组然后按照分组计算单价均价group analysis_df.groupby(小区名称, as_indexFalse)group_df group.mean()group_df.loc[:, 均价] group_df.loc[:, 均价].astype(int)# 按照均价列降序排序group_df.sort_values(均价, ascendingFalse, inplaceTrue)# 取Top10top10_df group_df.head(10)# print(top10_df)# 为了横向柱状图展示再从低到高排序一下top10_df.sort_values(均价, ascendingTrue, inplaceTrue)bar (Bar(init_optsopts.InitOpts(width1500px)).add_xaxis(top10_df[小区名称].tolist()).add_yaxis(房价单价, top10_df[均价].tolist(),itemstyle_optsopts.ItemStyleOpts(colorJsCode(top10_color_function))).reversal_axis().set_series_opts(label_optsopts.LabelOpts(positionright)).set_global_opts(title_optsopts.TitleOpts(title武汉各小区二手房房价TOP10),xaxis_optsopts.AxisOpts(axislabel_opts{interval: 0}),legend_optsopts.LegendOpts(is_showFalse)))# 判断是否单独显示还是和其他图表一起显示if isembed:return bar.render_embed()else:return bar# 按区均价分布
def unit_price_analysis_by_district(df):# 获取要分析的数据列analysis_df df.loc[:, [区, 均价]]analysis_df.loc[:, 区] analysis_df.loc[:, 区].astype(str)# 对小区名称分组然后按照分组计算单价均价group analysis_df.groupby(区, as_indexFalse)group_df group.mean()group_df.loc[:, 均价] group_df.loc[:, 均价].astype(int)# 按照均价列降序排序group_df.sort_values(均价, ascendingTrue, inplaceTrue)bar (Bar(init_optsopts.InitOpts(width1500px)).add_xaxis(group_df[区].tolist()).add_yaxis(房价单价, group_df[均价].tolist()).reversal_axis().set_series_opts(label_optsopts.LabelOpts(positionright)).set_global_opts(title_optsopts.TitleOpts(title武汉各区域二手房房价排行榜),xaxis_optsopts.AxisOpts(axislabel_opts{interval: 0})))return bar.render_embed()def add_sale_estate_col(row):return 0# 不同建筑年份的待售数量
def sale_estate_analysis_by_year(df, isembed):# 增加一列待售房屋数初始值均为0df.loc[:, 待售房屋数] df.apply(add_sale_estate_col, axis1)# 获取要用作数据分析的两列建筑年份和待售房屋数analysis_df df.loc[:, [建筑年份, 待售房屋数]]# 因为建筑年份列有空值先预处理一下analysis_df.dropna(inplaceTrue)# 按照建筑年份进行分组group analysis_df.groupby(建筑年份, as_indexFalse)# 对每个分组进行统计计数group_df group.count()group_df.loc[:, 待售房屋数] group_df.loc[:, 待售房屋数].astype(int)pie Pie(init_optsopts.InitOpts(width800px, height600px, bg_colorwhite))pie.add(pie, [list(z) for z in zip(group_df[建筑年份].tolist(), group_df[待售房屋数].tolist())], radius[40%, 60%], center[50%, 50%], label_optsopts.LabelOpts(positionoutside,formatter{b}:{c}:{d}%, )).set_global_opts(title_optsopts.TitleOpts(title武汉二手房不同建筑年份的待售数量, pos_left300, pos_top20,title_textstyle_optsopts.TextStyleOpts(colorblack, font_size16)),legend_optsopts.LegendOpts(is_showFalse))# 判断是否单独显示还是和其他图表一起显示if isembed:return pie.render_embed()else:return pie# 均价价格分布
def unit_price_analysis_by_histogram(df, isembed):hist, bin_edges np.histogram(df[均价], bins100)bar (Bar().add_xaxis([str(x) for x in bin_edges[:-1]]).add_yaxis(价格分布, [float(x) for x in hist], category_gap0).set_global_opts(title_optsopts.TitleOpts(title武汉二手房房价-单价分布-直方图, pos_leftcenter),legend_optsopts.LegendOpts(is_showFalse)))# 判断是否单独显示还是和其他图表一起显示if isembed:return bar.render_embed()else:return bar# 总价价格分布
def total_price_analysis_by_histogram(df, isembed):hist, bin_edges np.histogram(df[总价], bins100)bar (Bar().add_xaxis([str(x) for x in bin_edges[:-1]]).add_yaxis(价格分布, [float(x) for x in hist], category_gap0).set_global_opts(title_optsopts.TitleOpts(title武汉二手房房价-总价分布-直方图, pos_leftcenter),legend_optsopts.LegendOpts(is_showFalse)))# 判断是否单独显示还是和其他图表一起显示if isembed:return bar.render_embed()else:return bar# 面积——单价关系
def unit_price_analysis_by_scatter(df, isembed):df.sort_values(面积, ascendingTrue, inplaceTrue)square df[面积].to_list()unit_price df[均价].to_list()scatter (Scatter().add_xaxis(xaxis_datasquare).add_yaxis(series_name,y_axisunit_price,symbol_size4,label_optsopts.LabelOpts(is_showFalse)).set_global_opts(xaxis_optsopts.AxisOpts(type_value),yaxis_optsopts.AxisOpts(type_value),title_optsopts.TitleOpts(title武汉二手房面积-单价关系图, pos_leftcenter)))# 判断是否单独显示还是和其他图表一起显示if isembed:return scatter.render_embed()else:return scatter# 房屋标题标签热度词
def hot_word_analysis_by_wordcloud(df, isembed):txt for index, row in df.iterrows():txt txt str(row[待售房屋]) ; str(row[标签]) \nword_weights jieba.analyse.extract_tags(txt, topK100, withWeightTrue)word_cloud (WordCloud().add(series_name高频词语, data_pairword_weights, word_size_range[10, 100]).set_global_opts(title_optsopts.TitleOpts(title武汉二手房销售热度词,title_textstyle_optsopts.TextStyleOpts(font_size23),pos_leftcenter)))# 判断是否单独显示还是和其他图表一起显示if isembed:return word_cloud.render_embed()else:# png_name hot_word_analysis_by_wordcloud.png# make_snapshot(snapshot, word_cloud.render(), fcrawler/anjuke/static/{png_name})# return png_namereturn word_cloud# 规范区名
def transform_name(row):district_name row[区].strip()if district_name 江汉 or district_name 江岸 or district_name 硚口 or district_name 汉阳 or district_name 武昌 or district_name 东西湖 or district_name 洪山:district_name district_name 区return district_name# 按区均价分布地图
def unit_price_analysis_by_map(df, isembed):data []# 获取要分析的数据列analysis_df df.loc[:, [区, 均价]]# 按区列分组group_df analysis_df.groupby(区, as_indexFalse)# 根据分组对均价列求平均值group_df group_df.mean(均价)# print(group_df)# 将区的名字做一下转换为下面的地图匹配做准备group_df[区] group_df.apply(transform_name, axis1)group_df.loc[:, 均价] group_df.loc[:, 均价].astype(int)# 将数据转换成map需要的数据格式for index, row in group_df.iterrows():district_array [row[区], row[均价]]data.append(district_array)map (Map().add(武汉各区域二手房房价, data, 武汉).set_global_opts(title_optsopts.TitleOpts(title武汉各区域二手房房价地图, pos_leftcenter),visualmap_optsopts.VisualMapOpts(max_26000),legend_optsopts.LegendOpts(is_showFalse)))# 判断是否单独显示还是和其他图表一起显示if isembed:return map.render_embed()else:# png_name unit_price_analysis_by_map.png# make_snapshot(snapshot, map.render(), fcrawler/anjuke/static/{png_name})# return png_namereturn map# 主函数
if __name__ __main__:# 读取csvfpath data/wuhanSecondHouse.csvdf pd.read_csv(fpath, header[0], encodinggbk)df.drop_duplicates(keepfirst, inplaceTrue)# 可视化# 获取按面积区间的单价分析-柱状图unit_price_analysis_by_square unit_price_analysis_by_square(df, False)# 获取按室区分的单价分析-柱状图unit_price_analysis_by_layout unit_price_analysis_by_layout(df, False)# 获取苏州各小区二手房房价TOP10横向-柱状图unit_price_analysis_by_estate unit_price_analysis_by_estate(df, False)# 获取不同建筑年份的待售房屋数-饼图sale_estate_analysis_by_year sale_estate_analysis_by_year(df, False)# 苏州二手房房价-单价分布-直方图unit_price_analysis_by_histogram unit_price_analysis_by_histogram(df, False)# 苏州二手房房价-总价分布-直方图total_price_analysis_by_histogram total_price_analysis_by_histogram(df, False)# 苏州二手房面积-单价关系图unit_price_analysis_by_scatter unit_price_analysis_by_scatter(df, False)# 苏州二手房销售热度词-词云# hot_word_analysis_by_wordcloud_png_name dbc.hot_word_analysis_by_wordcloud(df,False)hot_word_analysis_by_wordcloud hot_word_analysis_by_wordcloud(df, False)# 苏州各区域二手房房价分布-地图# unit_price_analysis_by_map_png_name dbc.unit_price_analysis_by_map(df,False)unit_price_analysis_by_map unit_price_analysis_by_map(df, False)# web展示所有图page Page(layoutPage.DraggablePageLayout) # 可拖动布局page.add(unit_price_analysis_by_square,unit_price_analysis_by_layout,unit_price_analysis_by_estate,sale_estate_analysis_by_year,unit_price_analysis_by_histogram,total_price_analysis_by_histogram,unit_price_analysis_by_scatter,hot_word_analysis_by_wordcloud,unit_price_analysis_by_map)page.render(武汉二手房数据分析.html)
三、可视化展示效果
执行上述代码后会生成一个网页文件武汉二手房数据分析.html如下图所示 完整代码
!DOCTYPE html
html
headmeta charsetUTF-8titleAwesome-pyecharts/titlescript typetext/javascript srchttps://assets.pyecharts.org/assets/v5/echarts.min.js/scriptscript typetext/javascript srchttps://assets.pyecharts.org/assets/v5/echarts-wordcloud.min.js/scriptscript typetext/javascript srchttps://assets.pyecharts.org/assets/v5/maps/hu2_bei3_wu3_han4.js/scriptscript typetext/javascript srchttps://assets.pyecharts.org/assets/v5/jquery.min.js/scriptscript typetext/javascript srchttps://assets.pyecharts.org/assets/v5/jquery-ui.min.js/scriptscript typetext/javascript srchttps://assets.pyecharts.org/assets/v5/ResizeSensor.js/scriptlink relstylesheet hrefhttps://assets.pyecharts.org/assets/v5/jquery-ui.css/head
body style.box { } /stylebutton onclickdownloadCfg()Save Config/buttondiv classboxdiv id1b112349eb8148e0b585ffd506a12607 classchart-container stylewidth:900px; height:500px; /divscriptvar chart_1b112349eb8148e0b585ffd506a12607 echarts.init(document.getElementById(1b112349eb8148e0b585ffd506a12607), white, {renderer: canvas});var option_1b112349eb8148e0b585ffd506a12607 {animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: bar,name: \u5355\u4ef7\u5747\u4ef7,legendHoverLink: true,data: [17466.0,18258.0,18405.0,17497.0,31886.0,42885.0,40402.0],realtimeSort: false,showBackground: false,stackStrategy: samesign,cursor: pointer,barMinHeight: 0,barCategoryGap: 20%,barGap: 30%,large: false,largeThreshold: 400,seriesLayoutBy: column,datasetIndex: 0,clip: true,zlevel: 0,z: 2,label: {show: true,margin: 8},itemStyle: {color: function (params) { if (params.value 17000 params.value 18000) { return red; } else if (params.value 18000 params.value 20000) { return blue; }else if (params.value 20000 params.value 25000){ return green }else if (params.value 25000 params.value 35000){ return purple }else if (params.value 35000 params.value 40000){ return black } return brown; } }}],legend: [{data: [\u5355\u4ef7\u5747\u4ef7],selected: {},show: false,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},xAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}},data: [[0,60],[60,90],[90,120],[120,150],[150,200],[300,-],[200, 300]]}],yAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}}}],title: [{show: true,text: \u6b66\u6c49\u4e8c\u624b\u623f\u6309\u9762\u79ef\u533a\u95f4\u7684\u623f\u5c4b\u5355\u4ef7,target: blank,subtarget: blank,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false}]
};chart_1b112349eb8148e0b585ffd506a12607.setOption(option_1b112349eb8148e0b585ffd506a12607);/script
br/ div idd3edb43c0efe44c1b7ac21dcca195423 classchart-container stylewidth:900px; height:500px; /divscriptvar chart_d3edb43c0efe44c1b7ac21dcca195423 echarts.init(document.getElementById(d3edb43c0efe44c1b7ac21dcca195423), white, {renderer: canvas});var option_d3edb43c0efe44c1b7ac21dcca195423 {animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: bar,name: \u5355\u4ef7\u5747\u4ef7,legendHoverLink: true,data: [17311.0,18147.0,18591.0,25413.0,42885.0],realtimeSort: false,showBackground: false,stackStrategy: samesign,cursor: pointer,barMinHeight: 0,barCategoryGap: 20%,barGap: 30%,large: false,largeThreshold: 400,seriesLayoutBy: column,datasetIndex: 0,clip: true,zlevel: 0,z: 2,label: {show: true,margin: 8},itemStyle: {color: function (params) { if (params.value 17000 params.value 18000) { return red; } else if (params.value 18000 params.value 20000) { return blue; }else if (params.value 20000 params.value 25000){ return green }else if (params.value 25000 params.value 35000){ return purple }else if (params.value 35000 params.value 40000){ return black } return brown; } }}],legend: [{data: [\u5355\u4ef7\u5747\u4ef7],selected: {},show: false,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},xAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}},data: [1\u5ba4,2\u5ba4,3\u5ba4,4\u5ba4,5\u5ba4]}],yAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}}}],title: [{show: true,text: \u6b66\u6c49\u4e8c\u624b\u623f\u6309\u6237\u578b\u7684\u623f\u5c4b\u5355\u4ef7,target: blank,subtarget: blank,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false}]
};chart_d3edb43c0efe44c1b7ac21dcca195423.setOption(option_d3edb43c0efe44c1b7ac21dcca195423);/script
br/ div id75379e6d5dd245edb0df0f9e3963849a classchart-container stylewidth:1500px; height:500px; /divscriptvar chart_75379e6d5dd245edb0df0f9e3963849a echarts.init(document.getElementById(75379e6d5dd245edb0df0f9e3963849a), white, {renderer: canvas});var option_75379e6d5dd245edb0df0f9e3963849a {animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: bar,name: \u623f\u4ef7\u5355\u4ef7,legendHoverLink: true,data: [27402.0,27525.0,27815.0,28354.0,29212.0,29537.0,35191.0,35285.0,37022.0,41643.0],realtimeSort: false,showBackground: false,stackStrategy: samesign,cursor: pointer,barMinHeight: 0,barCategoryGap: 20%,barGap: 30%,large: false,largeThreshold: 400,seriesLayoutBy: column,datasetIndex: 0,clip: true,zlevel: 0,z: 2,label: {show: true,position: right,margin: 8},itemStyle: {color: function (params) { if (params.value 27000 params.value 27500) { return red; } else if (params.value 27500 params.value 27800) { return blue; }else if (params.value 27800 params.value 28000){ return green }else if (params.value 28000 params.value 29000){ return purple }else if (params.value 29000 params.value 30000){ return brown }else if (params.value 30000 params.value 35200){ return gray }else if (params.value 35200 params.value 37000){ return orange }else if (params.value 37000 params.value 40000){ return pink }else if (params.value 40000 params.value 45000){ return navy } return gold; } },rippleEffect: {show: true,brushType: stroke,scale: 2.5,period: 4}}],legend: [{data: [\u623f\u4ef7\u5355\u4ef7],selected: {},show: false,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},xAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,axisLabel: {interval: 0},inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}}}],yAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}},data: [\u4e07\u79d1\u7fe1\u7fe0\u56fd\u9645,\u6cdb\u6d77\u56fd\u9645\u5c45\u4f4f\u533a\u60a6\u6d77\u56ed,\u7231\u7279\u57ce,\u4e5d\u5934\u9e1f\u5c0f\u533a,\u6cdb\u6d77\u56fd\u9645\u5c45\u4f4f\u533a\u7af9\u6d77\u56ed,\u6cdb\u6d77\u56fd\u9645\u5c45\u4f4f\u533a\u6a31\u6d77\u56ed,\u4e16\u7eaa\u6c5f\u5c1a,\u6cdb\u6d77\u56fd\u9645\u5c45\u4f4f\u533a\u677e\u6d77\u56ed,\u90fd\u4f1a\u8f69,\u897f\u5317\u6e56\u58f9\u53f7\u5fa1\u73ba\u6e7e]}],title: [{show: true,text: \u6b66\u6c49\u5404\u5c0f\u533a\u4e8c\u624b\u623f\u623f\u4ef7TOP10,target: blank,subtarget: blank,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false}]
};chart_75379e6d5dd245edb0df0f9e3963849a.setOption(option_75379e6d5dd245edb0df0f9e3963849a);/script
br/ div id3914dd802e17484cb32e1013f7305d40 classchart-container stylewidth:800px; height:600px; /divscriptvar chart_3914dd802e17484cb32e1013f7305d40 echarts.init(document.getElementById(3914dd802e17484cb32e1013f7305d40), white, {renderer: canvas});var option_3914dd802e17484cb32e1013f7305d40 {backgroundColor: white,animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: pie,name: pie,colorBy: data,legendHoverLink: true,selectedMode: false,selectedOffset: 10,clockwise: true,startAngle: 90,minAngle: 0,minShowLabelAngle: 0,avoidLabelOverlap: true,stillShowZeroSum: true,percentPrecision: 2,showEmptyCircle: true,emptyCircleStyle: {color: lightgray,borderColor: #000,borderWidth: 0,borderType: solid,borderDashOffset: 0,borderCap: butt,borderJoin: bevel,borderMiterLimit: 10,opacity: 1},data: [{name: 1992\u5e74\u5efa\u9020,value: 1},{name: 1995\u5e74\u5efa\u9020,value: 5},{name: 1996\u5e74\u5efa\u9020,value: 2},{name: 1997\u5e74\u5efa\u9020,value: 2},{name: 1998\u5e74\u5efa\u9020,value: 4},{name: 1999\u5e74\u5efa\u9020,value: 3},{name: 2000\u5e74\u5efa\u9020,value: 6},{name: 2001\u5e74\u5efa\u9020,value: 2},{name: 2002\u5e74\u5efa\u9020,value: 2},{name: 2003\u5e74\u5efa\u9020,value: 6},{name: 2004\u5e74\u5efa\u9020,value: 5},{name: 2005\u5e74\u5efa\u9020,value: 17},{name: 2006\u5e74\u5efa\u9020,value: 13},{name: 2007\u5e74\u5efa\u9020,value: 5},{name: 2008\u5e74\u5efa\u9020,value: 9},{name: 2009\u5e74\u5efa\u9020,value: 2},{name: 2010\u5e74\u5efa\u9020,value: 11},{name: 2011\u5e74\u5efa\u9020,value: 24},{name: 2012\u5e74\u5efa\u9020,value: 3},{name: 2013\u5e74\u5efa\u9020,value: 10},{name: 2014\u5e74\u5efa\u9020,value: 2},{name: 2015\u5e74\u5efa\u9020,value: 12},{name: 2016\u5e74\u5efa\u9020,value: 10},{name: 2017\u5e74\u5efa\u9020,value: 22},{name: 2018\u5e74\u5efa\u9020,value: 10},{name: 2019\u5e74\u5efa\u9020,value: 53},{name: 2020\u5e74\u5efa\u9020,value: 4},{name: 2021\u5e74\u5efa\u9020,value: 3},{name: 2023\u5e74\u5efa\u9020,value: 1}],radius: [40%,60%],center: [50%,50%],label: {show: true,position: outside,margin: 8,formatter: {b}:{c}:{d}%},labelLine: {show: true,showAbove: false,length: 15,length2: 15,smooth: false,minTurnAngle: 90,maxSurfaceAngle: 90}}],legend: [{data: [1992\u5e74\u5efa\u9020,1995\u5e74\u5efa\u9020,1996\u5e74\u5efa\u9020,1997\u5e74\u5efa\u9020,1998\u5e74\u5efa\u9020,1999\u5e74\u5efa\u9020,2000\u5e74\u5efa\u9020,2001\u5e74\u5efa\u9020,2002\u5e74\u5efa\u9020,2003\u5e74\u5efa\u9020,2004\u5e74\u5efa\u9020,2005\u5e74\u5efa\u9020,2006\u5e74\u5efa\u9020,2007\u5e74\u5efa\u9020,2008\u5e74\u5efa\u9020,2009\u5e74\u5efa\u9020,2010\u5e74\u5efa\u9020,2011\u5e74\u5efa\u9020,2012\u5e74\u5efa\u9020,2013\u5e74\u5efa\u9020,2014\u5e74\u5efa\u9020,2015\u5e74\u5efa\u9020,2016\u5e74\u5efa\u9020,2017\u5e74\u5efa\u9020,2018\u5e74\u5efa\u9020,2019\u5e74\u5efa\u9020,2020\u5e74\u5efa\u9020,2021\u5e74\u5efa\u9020,2023\u5e74\u5efa\u9020],selected: {},show: false,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},title: [{show: true,text: \u6b66\u6c49\u4e8c\u624b\u623f\u4e0d\u540c\u5efa\u7b51\u5e74\u4efd\u7684\u5f85\u552e\u6570\u91cf,target: blank,subtarget: blank,left: 300,top: 20,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false,textStyle: {color: black,fontSize: 16}}]
};chart_3914dd802e17484cb32e1013f7305d40.setOption(option_3914dd802e17484cb32e1013f7305d40);/script
br/ div id638df3547b2541d4a04be6281ac41627 classchart-container stylewidth:900px; height:500px; /divscriptvar chart_638df3547b2541d4a04be6281ac41627 echarts.init(document.getElementById(638df3547b2541d4a04be6281ac41627), white, {renderer: canvas});var option_638df3547b2541d4a04be6281ac41627 {animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: bar,name: \u4ef7\u683c\u5206\u5e03,legendHoverLink: true,data: [1.0,1.0,5.0,3.0,2.0,4.0,0.0,5.0,2.0,4.0,6.0,11.0,11.0,10.0,10.0,8.0,13.0,7.0,12.0,11.0,13.0,14.0,14.0,24.0,10.0,10.0,11.0,8.0,10.0,6.0,7.0,5.0,3.0,8.0,3.0,5.0,2.0,3.0,2.0,3.0,2.0,1.0,4.0,3.0,2.0,1.0,3.0,2.0,2.0,2.0,1.0,1.0,3.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,2.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0],realtimeSort: false,showBackground: false,stackStrategy: samesign,cursor: pointer,barMinHeight: 0,barCategoryGap: 0,barGap: 30%,large: false,largeThreshold: 400,seriesLayoutBy: column,datasetIndex: 0,clip: true,zlevel: 0,z: 2,label: {show: true,margin: 8}}],legend: [{data: [\u4ef7\u683c\u5206\u5e03],selected: {},show: false,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},xAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}},data: [10000.0,10332.44,10664.88,10997.32,11329.76,11662.2,11994.64,12327.08,12659.52,12991.96,13324.4,13656.84,13989.279999999999,14321.720000000001,14654.16,14986.6,15319.04,15651.48,15983.92,16316.36,16648.8,16981.239999999998,17313.68,17646.12,17978.559999999998,18311.0,18643.440000000002,18975.879999999997,19308.32,19640.760000000002,19973.2,20305.64,20638.08,20970.52,21302.96,21635.4,21967.84,22300.28,22632.72,22965.16,23297.6,23630.04,23962.48,24294.92,24627.36,24959.8,25292.239999999998,25624.68,25957.12,26289.559999999998,26622.0,26954.44,27286.88,27619.32,27951.76,28284.2,28616.64,28949.079999999998,29281.52,29613.96,29946.4,30278.84,30611.28,30943.72,31276.16,31608.6,31941.04,32273.48,32605.92,32938.36,33270.8,33603.240000000005,33935.68,34268.119999999995,34600.56,34933.0,35265.44,35597.880000000005,35930.32,36262.759999999995,36595.2,36927.64,37260.08,37592.520000000004,37924.96,38257.4,38589.84,38922.28,39254.72,39587.16,39919.6,40252.04,40584.479999999996,40916.92,41249.36,41581.8,41914.24,42246.68,42579.119999999995,42911.56]}],yAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}}}],title: [{show: true,text: \u6b66\u6c49\u4e8c\u624b\u623f\u623f\u4ef7-\u5355\u4ef7\u5206\u5e03-\u76f4\u65b9\u56fe,target: blank,subtarget: blank,left: center,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false}]
};chart_638df3547b2541d4a04be6281ac41627.setOption(option_638df3547b2541d4a04be6281ac41627);/script
br/ div idf38908fc9c6e4a2d90ff1e538122ad22 classchart-container stylewidth:900px; height:500px; /divscriptvar chart_f38908fc9c6e4a2d90ff1e538122ad22 echarts.init(document.getElementById(f38908fc9c6e4a2d90ff1e538122ad22), white, {renderer: canvas});var option_f38908fc9c6e4a2d90ff1e538122ad22 {animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: bar,name: \u4ef7\u683c\u5206\u5e03,legendHoverLink: true,data: [4.0,8.0,10.0,9.0,33.0,19.0,16.0,41.0,45.0,35.0,21.0,19.0,7.0,15.0,15.0,4.0,4.0,3.0,5.0,4.0,4.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0],realtimeSort: false,showBackground: false,stackStrategy: samesign,cursor: pointer,barMinHeight: 0,barCategoryGap: 0,barGap: 30%,large: false,largeThreshold: 400,seriesLayoutBy: column,datasetIndex: 0,clip: true,zlevel: 0,z: 2,label: {show: true,margin: 8}}],legend: [{data: [\u4ef7\u683c\u5206\u5e03],selected: {},show: false,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},xAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}},data: [55.0,68.05,81.1,94.15,107.2,120.25,133.3,146.35000000000002,159.4,172.45,185.5,198.55,211.60000000000002,224.65,237.70000000000002,250.75,263.8,276.85,289.9,302.95000000000005,316.0,329.05,342.1,355.15000000000003,368.20000000000005,381.25,394.3,407.35,420.40000000000003,433.45000000000005,446.5,459.55,472.6,485.65000000000003,498.70000000000005,511.75,524.8,537.85,550.9000000000001,563.95,577.0,590.0500000000001,603.1,616.15,629.2,642.25,655.3000000000001,668.35,681.4000000000001,694.45,707.5,720.5500000000001,733.6,746.6500000000001,759.7,772.75,785.8000000000001,798.85,811.9000000000001,824.95,838.0,851.0500000000001,864.1,877.1500000000001,890.2,903.25,916.3000000000001,929.35,942.4000000000001,955.45,968.5,981.5500000000001,994.6,1007.6500000000001,1020.7,1033.75,1046.8000000000002,1059.85,1072.9,1085.95,1099.0,1112.05,1125.1000000000001,1138.15,1151.2,1164.25,1177.3,1190.3500000000001,1203.4,1216.45,1229.5,1242.55,1255.6000000000001,1268.65,1281.7,1294.75,1307.8000000000002,1320.8500000000001,1333.9,1346.95]}],yAxis: [{show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}}}],title: [{show: true,text: \u6b66\u6c49\u4e8c\u624b\u623f\u623f\u4ef7-\u603b\u4ef7\u5206\u5e03-\u76f4\u65b9\u56fe,target: blank,subtarget: blank,left: center,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false}]
};chart_f38908fc9c6e4a2d90ff1e538122ad22.setOption(option_f38908fc9c6e4a2d90ff1e538122ad22);/script
br/ div id5d28d7c693a34e46af8b33f0f224c0b1 classchart-container stylewidth:900px; height:500px; /divscriptvar chart_5d28d7c693a34e46af8b33f0f224c0b1 echarts.init(document.getElementById(5d28d7c693a34e46af8b33f0f224c0b1), white, {renderer: canvas});var option_5d28d7c693a34e46af8b33f0f224c0b1 {animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: scatter,symbolSize: 4,data: [[35.5,18592],[41.0,18537],[41.5,13254],[43.0,19070],[43.81,19174],[43.81,20087],[44.24,18310],[47.0,18086],[47.14,14850],[47.14,14850],[47.25,15239],[50.0,15840],[50.62,23311],[50.76,10836],[52.55,28354],[53.21,27815],[56.45,16298],[57.22,11360],[58.15,13001],[59.39,12461],[60.76,21890],[60.81,13978],[61.35,14833],[61.61,17530],[61.77,21856],[62.6,15336],[62.71,13395],[63.25,18973],[64.66,18559],[65.0,14616],[66.54,20890],[67.33,16041],[68.47,16796],[69.05,21724],[69.13,16780],[69.42,19447],[70.5,15178],[70.54,14177],[70.91,22987],[70.91,21436],[71.0,15493],[71.28,14871],[71.39,17510],[71.75,15053],[71.95,16679],[72.55,15989],[72.67,17064],[73.0,15069],[73.0,13425],[74.0,20000],[74.64,17283],[74.64,17819],[74.64,17819],[74.85,15765],[75.0,20000],[75.57,14557],[75.6,19709],[75.9,25297],[77.25,15923],[77.37,14218],[77.44,15496],[77.95,17319],[78.07,13834],[78.1,15365],[78.21,15190],[78.49,16308],[78.57,16292],[79.11,12641],[79.11,15801],[79.7,10665],[79.93,19392],[80.28,16817],[80.33,14067],[80.37,17793],[80.54,17631],[80.82,13611],[80.82,14230],[81.0,16050],[81.66,19594],[81.66,17022],[81.77,24826],[81.97,18910],[82.05,15601],[82.68,20320],[82.79,10630],[83.29,21131],[83.29,18850],[83.47,24560],[83.51,18920],[83.51,21435],[83.51,20357],[83.95,17868],[84.0,22024],[85.0,13765],[85.38,27525],[85.46,16733],[85.99,22096],[85.99,24422],[86.24,24351],[86.28,20283],[86.31,15989],[86.4,22801],[86.54,11903],[86.77,29043],[86.83,21882],[87.28,24061],[87.31,13401],[87.6,14727],[87.6,13357],[88.28,18111],[88.28,18125],[88.31,19591],[88.44,12438],[89.0,11236],[89.0,33708],[89.06,17404],[89.06,17741],[89.16,25236],[89.16,25797],[89.33,16792],[89.37,12533],[89.38,20139],[89.39,19018],[89.46,33311],[89.6,34822],[89.78,17822],[89.85,17697],[89.87,18694],[89.87,19473],[89.87,17693],[89.96,16452],[90.04,14439],[90.18,15525],[90.2,18626],[90.2,17517],[90.39,25999],[90.41,17698],[90.41,17698],[90.54,14359],[90.59,17883],[90.74,17413],[90.74,30858],[90.83,18276],[90.87,18709],[90.91,17050],[91.0,17363],[91.02,17030],[91.02,16810],[91.31,17839],[91.56,17475],[91.61,17357],[91.74,16885],[91.74,17550],[91.74,16547],[91.74,17986],[91.85,17747],[91.91,19041],[91.91,16538],[91.91,16538],[92.0,12500],[92.2,20066],[92.33,17871],[92.82,19393],[92.82,19393],[92.88,17227],[92.95,13449],[93.0,21076],[93.18,19425],[93.25,23057],[93.94,20013],[94.0,37022],[94.0,18724],[94.53,16397],[94.53,18196],[94.59,17761],[95.0,16632],[95.0,19790],[95.1,17351],[95.31,11437],[95.72,19850],[95.98,19796],[96.0,27084],[96.02,24891],[96.28,34899],[96.41,14522],[96.51,21035],[96.53,14297],[96.57,17190],[96.57,15844],[97.06,17309],[97.17,17290],[97.55,16402],[97.66,18329],[97.66,15565],[97.66,16589],[97.66,18432],[98.09,15496],[98.09,15089],[98.24,14455],[98.3,11801],[98.43,21132],[98.44,18286],[98.61,15212],[98.63,14905],[98.9,24267],[99.0,12829],[99.0,14243],[99.26,16825],[100.0,18500],[100.19,31341],[100.24,16860],[100.55,17902],[100.77,23817],[101.5,19015],[102.0,19510],[102.04,11173],[102.06,10974],[102.37,18561],[102.48,14638],[102.58,22422],[103.24,15498],[103.27,17527],[103.57,23173],[103.75,18892],[104.66,17963],[104.99,21907],[105.0,17048],[105.58,15439],[106.0,18868],[106.0,16038],[106.04,17824],[107.0,21029],[107.16,17918],[107.3,11930],[107.58,16267],[107.69,11886],[108.0,21019],[108.0,22963],[108.0,21297],[108.59,17313],[108.59,17958],[109.32,16009],[109.78,12935],[110.0,10000],[110.0,18546],[110.3,14053],[110.56,15829],[111.31,20484],[111.69,18444],[112.0,18215],[112.42,16635],[112.47,14671],[112.9,15501],[113.04,23444],[113.18,15463],[114.0,20965],[115.01,18781],[115.43,24258],[115.49,20782],[115.79,17964],[116.0,21035],[116.81,13869],[116.81,28680],[117.0,14018],[117.11,26301],[117.26,26864],[117.32,13860],[117.41,16864],[117.59,25513],[117.84,27580],[117.98,25429],[118.04,17113],[119.34,19273],[120.0,15000],[120.0,16084],[120.47,19756],[120.47,19092],[120.59,20566],[120.7,13671],[120.88,14064],[120.99,17771],[121.42,14001],[121.56,26325],[121.8,22578],[121.8,22578],[121.83,14365],[122.0,26230],[122.22,13910],[122.22,14973],[122.46,17149],[122.54,17954],[123.0,14228],[123.16,24197],[123.72,25865],[123.81,20597],[124.09,13684],[124.44,16555],[125.02,14398],[125.2,19010],[125.77,34587],[126.5,18973],[126.66,19344],[127.0,14410],[127.87,17440],[128.99,17676],[129.05,15498],[129.63,13732],[131.79,14797],[132.01,16666],[132.72,13940],[132.72,13940],[132.83,13251],[134.43,21424],[135.42,19791],[135.51,15866],[136.77,16451],[137.55,10906],[138.9,14903],[141.47,11310],[141.64,18216],[144.09,10966],[144.26,16984],[144.62,16665],[145.89,16109],[158.45,29537],[160.98,13046],[162.0,34568],[162.4,27402],[171.01,35671],[182.32,33787],[185.0,37838],[185.0,43244],[210.39,40402],[317.13,42885]],label: {show: false,margin: 8}}],legend: [{data: [],selected: {},show: true,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},xAxis: [{type: value,show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}},data: [35.5,41.0,41.5,43.0,43.81,43.81,44.24,47.0,47.14,47.14,47.25,50.0,50.62,50.76,52.55,53.21,56.45,57.22,58.15,59.39,60.76,60.81,61.35,61.61,61.77,62.6,62.71,63.25,64.66,65.0,66.54,67.33,68.47,69.05,69.13,69.42,70.5,70.54,70.91,70.91,71.0,71.28,71.39,71.75,71.95,72.55,72.67,73.0,73.0,74.0,74.64,74.64,74.64,74.85,75.0,75.57,75.6,75.9,77.25,77.37,77.44,77.95,78.07,78.1,78.21,78.49,78.57,79.11,79.11,79.7,79.93,80.28,80.33,80.37,80.54,80.82,80.82,81.0,81.66,81.66,81.77,81.97,82.05,82.68,82.79,83.29,83.29,83.47,83.51,83.51,83.51,83.95,84.0,85.0,85.38,85.46,85.99,85.99,86.24,86.28,86.31,86.4,86.54,86.77,86.83,87.28,87.31,87.6,87.6,88.28,88.28,88.31,88.44,89.0,89.0,89.06,89.06,89.16,89.16,89.33,89.37,89.38,89.39,89.46,89.6,89.78,89.85,89.87,89.87,89.87,89.96,90.04,90.18,90.2,90.2,90.39,90.41,90.41,90.54,90.59,90.74,90.74,90.83,90.87,90.91,91.0,91.02,91.02,91.31,91.56,91.61,91.74,91.74,91.74,91.74,91.85,91.91,91.91,91.91,92.0,92.2,92.33,92.82,92.82,92.88,92.95,93.0,93.18,93.25,93.94,94.0,94.0,94.53,94.53,94.59,95.0,95.0,95.1,95.31,95.72,95.98,96.0,96.02,96.28,96.41,96.51,96.53,96.57,96.57,97.06,97.17,97.55,97.66,97.66,97.66,97.66,98.09,98.09,98.24,98.3,98.43,98.44,98.61,98.63,98.9,99.0,99.0,99.26,100.0,100.19,100.24,100.55,100.77,101.5,102.0,102.04,102.06,102.37,102.48,102.58,103.24,103.27,103.57,103.75,104.66,104.99,105.0,105.58,106.0,106.0,106.04,107.0,107.16,107.3,107.58,107.69,108.0,108.0,108.0,108.59,108.59,109.32,109.78,110.0,110.0,110.3,110.56,111.31,111.69,112.0,112.42,112.47,112.9,113.04,113.18,114.0,115.01,115.43,115.49,115.79,116.0,116.81,116.81,117.0,117.11,117.26,117.32,117.41,117.59,117.84,117.98,118.04,119.34,120.0,120.0,120.47,120.47,120.59,120.7,120.88,120.99,121.42,121.56,121.8,121.8,121.83,122.0,122.22,122.22,122.46,122.54,123.0,123.16,123.72,123.81,124.09,124.44,125.02,125.2,125.77,126.5,126.66,127.0,127.87,128.99,129.05,129.63,131.79,132.01,132.72,132.72,132.83,134.43,135.42,135.51,136.77,137.55,138.9,141.47,141.64,144.09,144.26,144.62,145.89,158.45,160.98,162.0,162.4,171.01,182.32,185.0,185.0,210.39,317.13]}],yAxis: [{type: value,show: true,scale: false,nameLocation: end,nameGap: 15,gridIndex: 0,inverse: false,offset: 0,splitNumber: 5,minInterval: 0,splitLine: {show: true,lineStyle: {show: true,width: 1,opacity: 1,curveness: 0,type: solid}}}],title: [{show: true,text: \u6b66\u6c49\u4e8c\u624b\u623f\u9762\u79ef-\u5355\u4ef7\u5173\u7cfb\u56fe,target: blank,subtarget: blank,left: center,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false}]
};chart_5d28d7c693a34e46af8b33f0f224c0b1.setOption(option_5d28d7c693a34e46af8b33f0f224c0b1);/script
br/ div id76eda51d430b4041999acb9a6b2f4d53 classchart-container stylewidth:900px; height:500px; /divscriptvar chart_76eda51d430b4041999acb9a6b2f4d53 echarts.init(document.getElementById(76eda51d430b4041999acb9a6b2f4d53), white, {renderer: canvas});var option_76eda51d430b4041999acb9a6b2f4d53 {animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: wordCloud,name: \u9ad8\u9891\u8bcd\u8bed,shape: circle,rotationRange: [-90,90],rotationStep: 45,girdSize: 20,sizeRange: [10,100],data: [{name: \u5730\u94c1,value: 0.6124656158282988,textStyle: {color: rgb(132,30,6)}},{name: \u8001\u8bc1,value: 0.2480242220518672,textStyle: {color: rgb(42,91,36)}},{name: \u5357\u5317,value: 0.22915513408010635,textStyle: {color: rgb(135,83,122)}},{name: \u7cbe\u88c5,value: 0.20021496762323648,textStyle: {color: rgb(132,26,0)}},{name: \u4e8c\u5e74,value: 0.19587207756752076,textStyle: {color: rgb(10,143,121)}},{name: nan,value: 0.1860181665389004,textStyle: {color: rgb(73,77,113)}},{name: \u901a\u900f,value: 0.17321397440086878,textStyle: {color: rgb(48,156,124)}},{name: \u65b0\u623f,value: 0.1503840463253838,textStyle: {color: rgb(88,10,84)}},{name: \u4e09\u623f,value: 0.13850884111903528,textStyle: {color: rgb(42,2,127)}},{name: \u697c\u5c42,value: 0.13814265888638486,textStyle: {color: rgb(104,64,70)}},{name: \u7535\u68af,value: 0.11485026627411828,textStyle: {color: rgb(33,135,49)}},{name: \u770b\u623f,value: 0.11161089992334024,textStyle: {color: rgb(138,132,58)}},{name: \u6237\u578b,value: 0.10714522780089211,textStyle: {color: rgb(124,88,14)}},{name: \u4e94\u5e74,value: 0.10418283393401452,textStyle: {color: rgb(64,143,29)}},{name: \u798f\u661f,value: 0.10354744730565094,textStyle: {color: rgb(67,11,71)}},{name: \u623f\u4e1c,value: 0.10300862499688278,textStyle: {color: rgb(50,44,57)}},{name: \u534e\u5e9c,value: 0.09769721034040456,textStyle: {color: rgb(119,9,85)}},{name: \u8f66\u4f4d,value: 0.09632202118412862,textStyle: {color: rgb(160,27,150)}},{name: \u6025\u552e,value: 0.09610938604509854,textStyle: {color: rgb(109,22,96)}},{name: \u671d\u5357,value: 0.09510783222790456,textStyle: {color: rgb(134,147,30)}},{name: \u76f4\u5356,value: 0.0930090832694502,textStyle: {color: rgb(138,49,135)}},{name: \u5c0f\u533a,value: 0.09146165867420124,textStyle: {color: rgb(154,115,33)}},{name: \u8bda\u5fc3,value: 0.09140995432318984,textStyle: {color: rgb(20,8,100)}},{name: \u5730\u94c1\u53e3,value: 0.0892517408469917,textStyle: {color: rgb(36,2,11)}},{name: \u91c7\u5149,value: 0.08209692534618776,textStyle: {color: rgb(4,99,144)}},{name: \u4e24\u623f,value: 0.07975632333869294,textStyle: {color: rgb(70,147,25)}},{name: \u62ce\u5305,value: 0.07891332467313279,textStyle: {color: rgb(128,45,125)}},{name: \u51fa\u552e,value: 0.0732196149004279,textStyle: {color: rgb(31,3,43)}},{name: \u4e07\u79d1,value: 0.07005573855896265,textStyle: {color: rgb(37,90,155)}},{name: \u5165\u4f4f,value: 0.06799405917549015,textStyle: {color: rgb(44,37,88)}},{name: \u7cbe\u88c5\u4fee,value: 0.067172527856639,textStyle: {color: rgb(64,76,87)}},{name: \u968f\u65f6,value: 0.06564012248575726,textStyle: {color: rgb(12,81,13)}},{name: \u6bdb\u576f,value: 0.06385835163895227,textStyle: {color: rgb(46,37,106)}},{name: \u4e07\u8fbe,value: 0.06250966047759336,textStyle: {color: rgb(116,94,139)}},{name: \u6c49\u53e3,value: 0.060232499066141074,textStyle: {color: rgb(77,114,156)}},{name: \u5145\u8db3,value: 0.05786008160838174,textStyle: {color: rgb(20,97,154)}},{name: \u4e00\u521d,value: 0.052705147186021775,textStyle: {color: rgb(14,33,4)}},{name: \u5168\u660e,value: 0.052705147186021775,textStyle: {color: rgb(76,25,92)}},{name: \u5927\u5174,value: 0.05190001176329875,textStyle: {color: rgb(89,46,119)}},{name: \u6ee1\u4e94,value: 0.0465045416347251,textStyle: {color: rgb(146,47,34)}},{name: \u5e7f\u573a,value: 0.04612999226510633,textStyle: {color: rgb(160,155,26)}},{name: \u8a89\u5883,value: 0.04340423885907676,textStyle: {color: rgb(32,32,109)}},{name: \u4f4f\u5b85\u5c0f\u533a,value: 0.043288727682961624,textStyle: {color: rgb(34,25,49)}},{name: \u53a8\u536b,value: 0.043160490073366184,textStyle: {color: rgb(37,127,7)}},{name: \u4ea4\u623f,value: 0.04316044201052904,textStyle: {color: rgb(75,102,97)}},{name: \u9ad8\u6027\u4ef7\u6bd4,value: 0.04111179904745851,textStyle: {color: rgb(88,8,65)}},{name: \u914d\u5957,value: 0.040858273480715766,textStyle: {color: rgb(60,4,45)}},{name: \u53f7\u7ebf,value: 0.040303936083428415,textStyle: {color: rgb(74,13,59)}},{name: \u9633\u53f0,value: 0.04010455454436722,textStyle: {color: rgb(47,86,38)}},{name: \u6210\u719f,value: 0.03977088144424274,textStyle: {color: rgb(3,73,18)}},{name: \u7ea2\u9886\u5dfe,value: 0.037774476469657675,textStyle: {color: rgb(96,130,92)}},{name: \u65b9\u4fbf,value: 0.03594840903551349,textStyle: {color: rgb(33,48,41)}},{name: \u65e0\u906e\u6321,value: 0.035699744865119294,textStyle: {color: rgb(148,39,152)}},{name: \u4e2d\u95f4,value: 0.035051559601296675,textStyle: {color: rgb(68,112,6)}},{name: \u65b0\u4e0a,value: 0.03410333053213174,textStyle: {color: rgb(149,77,118)}},{name: \u5510\u6a3e,value: 0.03410333053213174,textStyle: {color: rgb(127,154,69)}},{name: \u4e24\u5ba4,value: 0.0310030277564834,textStyle: {color: rgb(126,145,130)}},{name: \u4e0d\u4e34,value: 0.0310030277564834,textStyle: {color: rgb(44,65,150)}},{name: \u83f1\u89d2,value: 0.029277491289989625,textStyle: {color: rgb(44,25,143)}},{name: \u89c6\u91ce,value: 0.02870695724724585,textStyle: {color: rgb(41,142,17)}},{name: \u82b1\u56ed,value: 0.028416352974623964,textStyle: {color: rgb(22,23,102)}},{name: \u6c5f\u57ce,value: 0.02803328630342324,textStyle: {color: rgb(72,68,81)}},{name: \u597d\u623f,value: 0.02790272498083506,textStyle: {color: rgb(108,99,149)}},{name: \u6167\u6cc9,value: 0.02790272498083506,textStyle: {color: rgb(95,141,96)}},{name: \u6c5f\u5c1a,value: 0.02790272498083506,textStyle: {color: rgb(144,151,23)}},{name: \u88c5\u4fee,value: 0.02776070086363589,textStyle: {color: rgb(61,63,14)}},{name: \u65b9\u6b63,value: 0.027728978418672202,textStyle: {color: rgb(3,46,64)}},{name: \u9ad8\u5c42,value: 0.026875917280860997,textStyle: {color: rgb(148,46,76)}},{name: \u5546\u5708,value: 0.026177425213174274,textStyle: {color: rgb(38,30,144)}},{name: \u6cdb\u6d77,value: 0.025903619540993256,textStyle: {color: rgb(33,92,50)}},{name: \u5355\u4ef7,value: 0.025106048616610477,textStyle: {color: rgb(101,29,62)}},{name: \u6ee1\u4e8c,value: 0.02480242220518672,textStyle: {color: rgb(21,100,132)}},{name: \u4e3b\u5367,value: 0.02480242220518672,textStyle: {color: rgb(13,72,40)}},{name: \u56fd\u9645,value: 0.0232013060218361,textStyle: {color: rgb(90,17,34)}},{name: \u524d\u6392,value: 0.023197226645150414,textStyle: {color: rgb(54,13,83)}},{name: \u5bb6\u56ed,value: 0.02259671121924274,textStyle: {color: rgb(20,150,6)}},{name: \u4e09\u73af,value: 0.022596091680809133,textStyle: {color: rgb(143,67,93)}},{name: \u4ef7\u683c,value: 0.02240362408684647,textStyle: {color: rgb(75,22,135)}},{name: \u53ef\u8c08,value: 0.02170211942953838,textStyle: {color: rgb(34,3,125)}},{name: \u53cc\u536b,value: 0.02170211942953838,textStyle: {color: rgb(117,5,4)}},{name: \u5e38\u9752,value: 0.02164925674107884,textStyle: {color: rgb(146,140,20)}},{name: \u76db\u4e16,value: 0.021214641792971993,textStyle: {color: rgb(11,152,51)}},{name: \u770b\u597d,value: 0.020899203516932054,textStyle: {color: rgb(30,12,38)}},{name: \u53cc\u9633\u53f0,value: 0.020551136625622406,textStyle: {color: rgb(90,151,42)}},{name: \u70ed\u95e8,value: 0.01986630012952282,textStyle: {color: rgb(30,85,31)}},{name: \u5546\u4e1a,value: 0.01945886089939056,textStyle: {color: rgb(87,158,17)}},{name: \u591a\u4eba,value: 0.01905645685928423,textStyle: {color: rgb(104,118,0)}},{name: \u957f\u6e2f\u8def,value: 0.01860181665389004,textStyle: {color: rgb(115,147,22)}},{name: CBD,value: 0.01860181665389004,textStyle: {color: rgb(16,24,120)}},{name: \u8303\u6e56,value: 0.01860181665389004,textStyle: {color: rgb(20,58,101)}},{name: \u603b\u4ef7,value: 0.018100680330705395,textStyle: {color: rgb(100,47,97)}},{name: \u4e8c\u73af,value: 0.01804682452126556,textStyle: {color: rgb(61,108,103)}},{name: \u793e\u533a,value: 0.0178659590192972,textStyle: {color: rgb(58,109,158)}},{name: \u4f18\u60e0,value: 0.017494974831587136,textStyle: {color: rgb(89,58,45)}},{name: \u8fdc\u6d0b,value: 0.017134378798838175,textStyle: {color: rgb(48,34,106)}},{name: \u4fbf\u5229,value: 0.01697760350740923,textStyle: {color: rgb(94,57,46)}},{name: \u4ea4\u901a,value: 0.01696000471827282,textStyle: {color: rgb(75,86,18)}},{name: \u5957\u623f,value: 0.016692128070062238,textStyle: {color: rgb(123,150,107)}},{name: \u6c5f\u6c49,value: 0.01665275644004668,textStyle: {color: rgb(52,104,74)}},{name: \u6c5f\u6c49\u8def,value: 0.016600188489756224,textStyle: {color: rgb(137,66,112)}}],drawOutOfBound: false,textStyle: {emphasis: {}}}],legend: [{data: [],selected: {},show: true,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},title: [{show: true,text: \u6b66\u6c49\u4e8c\u624b\u623f\u9500\u552e\u70ed\u5ea6\u8bcd,target: blank,subtarget: blank,left: center,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false,textStyle: {fontSize: 23}}]
};chart_76eda51d430b4041999acb9a6b2f4d53.setOption(option_76eda51d430b4041999acb9a6b2f4d53);/script
br/ div idd6b3ebb61fb94bba91a9d94e1b9a5063 classchart-container stylewidth:900px; height:500px; /divscriptvar chart_d6b3ebb61fb94bba91a9d94e1b9a5063 echarts.init(document.getElementById(d6b3ebb61fb94bba91a9d94e1b9a5063), white, {renderer: canvas});var option_d6b3ebb61fb94bba91a9d94e1b9a5063 {animation: true,animationThreshold: 2000,animationDuration: 1000,animationEasing: cubicOut,animationDelay: 0,animationDurationUpdate: 300,animationEasingUpdate: cubicOut,animationDelayUpdate: 0,aria: {enabled: false},color: [#5470c6,#91cc75,#fac858,#ee6666,#73c0de,#3ba272,#fc8452,#9a60b4,#ea7ccc],series: [{type: map,name: \u6b66\u6c49\u5404\u533a\u57df\u4e8c\u624b\u623f\u623f\u4ef7,label: {show: true,margin: 8},map: \u6b66\u6c49,data: [{name: \u4e1c\u897f\u6e56\u533a,value: 13423.0},{name: \u6b66\u660c\u533a,value: 16642.0},{name: \u6c49\u9633\u533a,value: 16654.0},{name: \u6c5f\u5cb8\u533a,value: 20071.0},{name: \u6c5f\u6c49\u533a,value: 18725.0},{name: \u6d2a\u5c71\u533a,value: 20000.0},{name: \u785a\u53e3\u533a,value: 16960.0}],roam: true,aspectScale: 0.75,nameProperty: name,selectedMode: false,zoom: 1,zlevel: 0,z: 2,seriesLayoutBy: column,datasetIndex: 0,mapValueCalculation: sum,showLegendSymbol: true,emphasis: {}}],legend: [{data: [\u6b66\u6c49\u5404\u533a\u57df\u4e8c\u624b\u623f\u623f\u4ef7],selected: {},show: false,padding: 5,itemGap: 10,itemWidth: 25,itemHeight: 14,backgroundColor: transparent,borderColor: #ccc,borderWidth: 1,borderRadius: 0,pageButtonItemGap: 5,pageButtonPosition: end,pageFormatter: {current}/{total},pageIconColor: #2f4554,pageIconInactiveColor: #aaa,pageIconSize: 15,animationDurationUpdate: 800,selector: false,selectorPosition: auto,selectorItemGap: 7,selectorButtonGap: 10}],tooltip: {show: true,trigger: item,triggerOn: mousemove|click,axisPointer: {type: line},showContent: true,alwaysShowContent: false,showDelay: 0,hideDelay: 100,enterable: false,confine: false,appendToBody: false,transitionDuration: 0.4,textStyle: {fontSize: 14},borderWidth: 0,padding: 5,order: seriesAsc},title: [{show: true,text: \u6b66\u6c49\u5404\u533a\u57df\u4e8c\u624b\u623f\u623f\u4ef7\u5730\u56fe,target: blank,subtarget: blank,left: center,padding: 5,itemGap: 10,textAlign: auto,textVerticalAlign: auto,triggerEvent: false}],visualMap: {show: true,type: continuous,min: 0,max: 26000,inRange: {color: [#50a3ba,#eac763,#d94e5d]},calculable: true,inverse: false,splitNumber: 5,hoverLink: true,orient: vertical,padding: 5,showLabel: true,itemWidth: 20,itemHeight: 140,borderWidth: 0}
};chart_d6b3ebb61fb94bba91a9d94e1b9a5063.setOption(option_d6b3ebb61fb94bba91a9d94e1b9a5063);/script
br/ /divscript$(#1b112349eb8148e0b585ffd506a12607).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#1b112349eb8148e0b585ffd506a12607div:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#1b112349eb8148e0b585ffd506a12607), function() { chart_1b112349eb8148e0b585ffd506a12607.resize()});$(#d3edb43c0efe44c1b7ac21dcca195423).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#d3edb43c0efe44c1b7ac21dcca195423div:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#d3edb43c0efe44c1b7ac21dcca195423), function() { chart_d3edb43c0efe44c1b7ac21dcca195423.resize()});$(#75379e6d5dd245edb0df0f9e3963849a).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#75379e6d5dd245edb0df0f9e3963849adiv:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#75379e6d5dd245edb0df0f9e3963849a), function() { chart_75379e6d5dd245edb0df0f9e3963849a.resize()});$(#3914dd802e17484cb32e1013f7305d40).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#3914dd802e17484cb32e1013f7305d40div:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#3914dd802e17484cb32e1013f7305d40), function() { chart_3914dd802e17484cb32e1013f7305d40.resize()});$(#638df3547b2541d4a04be6281ac41627).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#638df3547b2541d4a04be6281ac41627div:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#638df3547b2541d4a04be6281ac41627), function() { chart_638df3547b2541d4a04be6281ac41627.resize()});$(#f38908fc9c6e4a2d90ff1e538122ad22).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#f38908fc9c6e4a2d90ff1e538122ad22div:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#f38908fc9c6e4a2d90ff1e538122ad22), function() { chart_f38908fc9c6e4a2d90ff1e538122ad22.resize()});$(#5d28d7c693a34e46af8b33f0f224c0b1).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#5d28d7c693a34e46af8b33f0f224c0b1div:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#5d28d7c693a34e46af8b33f0f224c0b1), function() { chart_5d28d7c693a34e46af8b33f0f224c0b1.resize()});$(#76eda51d430b4041999acb9a6b2f4d53).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#76eda51d430b4041999acb9a6b2f4d53div:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#76eda51d430b4041999acb9a6b2f4d53), function() { chart_76eda51d430b4041999acb9a6b2f4d53.resize()});$(#d6b3ebb61fb94bba91a9d94e1b9a5063).resizable().draggable().css(border-style, dashed).css(border-width, 1px);$(#d6b3ebb61fb94bba91a9d94e1b9a5063div:nth-child(1)).width(100%).height(100%);new ResizeSensor(jQuery(#d6b3ebb61fb94bba91a9d94e1b9a5063), function() { chart_d6b3ebb61fb94bba91a9d94e1b9a5063.resize()});var charts_id [1b112349eb8148e0b585ffd506a12607,d3edb43c0efe44c1b7ac21dcca195423,75379e6d5dd245edb0df0f9e3963849a,3914dd802e17484cb32e1013f7305d40,638df3547b2541d4a04be6281ac41627,f38908fc9c6e4a2d90ff1e538122ad22,5d28d7c693a34e46af8b33f0f224c0b1,76eda51d430b4041999acb9a6b2f4d53,d6b3ebb61fb94bba91a9d94e1b9a5063];
function downloadCfg () {const fileName chart_config.jsonlet downLink document.createElement(a)downLink.download fileNamelet result []for(let i0; icharts_id.length; i) {chart $(#charts_id[i])result.push({cid: charts_id[i],width: chart.css(width),height: chart.css(height),top: chart.offset().top px,left: chart.offset().left px})}let blob new Blob([JSON.stringify(result)])downLink.href URL.createObjectURL(blob)document.body.appendChild(downLink)downLink.click()document.body.removeChild(downLink)
}/script
/body
/html
选择一个浏览器打开如图所示 可自由拖拽调整布局
四、分析结论
房子的均价主要集中在17646元总价146-172万元附近其中总价107万元左右的房子也很多面积区间【120-150】的均价比【90-120】的还要低些3室的单价和2室的也没差多少说明购房者的需求主要是3室及面积区间【60-120】的房子从图中可以看出最贵的小区是西北湖一号御玺湾其次是都会轩、泛海国际居住区松海园、世纪江尚待售数量最多的建筑年份是2019年至今没超过5年占比21.29%之前年份的明显少了很多应该是跟满二的政策有关系二手房的单价跟房子面积并不是呈线性相关的关系也即不是面积越大单价越高房子单价的高点出现在150-200平方这个区间然后随着面积逐渐增大单价呈逐渐下降趋势因此是一个曲线相关的关系从地图上可以很直观的看到江岸区的平均房价是最高的其次是武昌区和江汉区东西湖区均价是最低的从词云图我们可以看到交通、朝向是购房者第一位关注的房子信息其次是是否满五二唯一、是否新房、是否精装修等。
注意由于爬取数据不完整所以不能保证最后的分析结论准确只是简单的提供分析思路和方法。
