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实验七：数据可视化交互

网友: 技术文档 2025-09-02 19:04:25

数据可视化交互

实验目的
1. 了解数据可视化的一般原则
2. 掌握数据可视化的分类
3. 掌握数据可视化的常见技术
4. 本次实验是对全国的空气质量进行可视化分析并进行数
据统计技术对比
实验原理
设计可视化系统或选择交互方式的时候，除了能够完成任
务本身之外，还要遵循一些基本的原则。例如，交互的延时性
需要在用户可以接受的范围之内，并有效控制用户交互的成
本。这些基本原则对交互的效果起着至关重要的作用富。
另外，交互的技术有很多种，本次实验是对文本进行可视
化生成词云图片与传统的统计技术对比。
交互的原则、交互的分类以及常见的交互技术，尤其是几
种常见的交互技术，只有熟练掌握并使用恰当，才可能设计出
用户体验良好的可视化应用。尽管交互的技术有很多种,但交互
技术本身并无优劣之分，选择哪种交互技术的依据是具体的场
景和应用需求。
实验环境
Python:v3.6
【实验步骤】
一、安装 pyecharts
pip install pyecharts
实验七：数据可视化交互
二、下载数据
下载数据文件（data.txt），该文件表示了一些城市某天的空
气质量指数（AQI），请完成如下实验。
实验七：数据可视化交互
三、实验任务
实验 1：AQI 横向对比条形图
任务：使用 Pyecharts 绘制各城市 AQI 值的横向条形图，要求：
1. 按 AQI 从高到低排序
2. 添加全局配置项：标题为“城市 AQI 对比”，坐标轴名称分别
为“AQI 指数”和“城市” 3. 使 MarkLine 标记 AQI 均值线（参考值：80），并设置不同
颜色区分高于/低于均值的城市

from pyecharts.charts import Barfrom pyecharts import options as opts# 甘肃省各城市AQI数据（示例数据）city_aqi = { \"兰州\": 125, \"天水\": 92, \"白银\": 78, \"金昌\": 65, \"嘉峪关\": 88, \"武威\": 73, \"张掖\": 68, \"平凉\": 85, \"酒泉\": 70, \"庆阳\": 95, \"定西\": 82, \"陇南\": 60, \"临夏\": 75, \"甘南\": 55}# 按AQI值从高到低排序sorted_cities = sorted(city_aqi.items(), key=lambda x: x[1], reverse=True)cities = [city[0] for city in sorted_cities]aqi_values = [city[1] for city in sorted_cities]# 计算颜色列表（高于均值红色，低于均值绿色）colors = [\"#c23531\" if aqi >= 80 else \"#61a0a8\" for aqi in aqi_values]# 创建横向条形图bar = ( Bar() .add_xaxis(cities) .add_yaxis( series_name=\"AQI指数\", y_axis=aqi_values, label_opts=opts.LabelOpts(position=\"right\", formatter=\"{c}\"), itemstyle_opts=opts.ItemStyleOpts(color=colors), ) .reversal_axis() # 转换为横向条形图 .set_series_opts( markline_opts=opts.MarkLineOpts( data=[opts.MarkLineItem(y=80, name=\"均值线\")], # 修正这里：将 \'type\' 改为 \'type_\' linestyle_opts=opts.LineStyleOpts(color=\"#fc8452\", width=2, type_=\"dashed\"), label_opts=opts.LabelOpts(position=\"middle\", formatter=\"均值: 80\"), ) ) .set_global_opts( title_opts=opts.TitleOpts(title=\"城市 AQI 对比\"), xaxis_opts=opts.AxisOpts(name=\"AQI 指数\"), yaxis_opts=opts.AxisOpts(name=\"城市\"), visualmap_opts=opts.VisualMapOpts( is_show=False, # 不显示视觉映射组件 dimension=0, range_color=[\"#61a0a8\", \"#c23531\"], # 颜色范围 range_size=10, pieces=[{\"gte\": 80, \"color\": \"#c23531\"}, {\"lt\": 80, \"color\": \"#61a0a8\"}] ), tooltip_opts=opts.TooltipOpts(trigger=\"axis\", axis_pointer_type=\"shadow\") ))# 渲染图表到HTML文件bar.render(\"gansu_aqi_comparison.html\")

实验七：数据可视化交互

实验 2：AQI 等级分布饼图
任务：基于 AQI 等级划分（优/良/轻度污染/中度污染/重度污
染），绘制饼图并添加交互功能：
1. 使用 Pie 图表展示各等级下城市数量占比
2. 标签显示百分比和等级名称，突出显示占比最大的扇区
3. 添加点击事件：单击扇区时弹出该等级详细城市数量及城市
名称列表

from pyecharts.charts import Piefrom pyecharts import options as optsfrom pyecharts.commons.utils import JsCode# 示例数据 - 甘肃省各城市AQI值city_aqi = { \"兰州\": 125, \"天水\": 92, \"白银\": 78, \"金昌\": 65, \"嘉峪关\": 88, \"武威\": 73, \"张掖\": 68, \"平凉\": 85, \"酒泉\": 70, \"庆阳\": 95, \"定西\": 82, \"陇南\": 60, \"临夏\": 75, \"甘南\": 55}# 定义AQI等级标准aqi_levels = { \"优\": (0, 50), \"良\": (51, 100), \"轻度污染\": (101, 150), \"中度污染\": (151, 200), \"重度污染\": (201, 300)}# 统计各等级城市数量level_count = {level: 0 for level in aqi_levels}level_cities = {level: [] for level in aqi_levels}for city, aqi in city_aqi.items(): for level, (low, high) in aqi_levels.items(): if low <= aqi <= high: level_count[level] += 1 level_cities[level].append(city) break# 准备饼图数据data = []for level, count in level_count.items(): if count > 0: # 添加城市列表作为额外数据 data.append((level, count, \", \".join(level_cities[level])))# 按数量排序（从大到小）data.sort(key=lambda x: x[1], reverse=True)# 找出占比最大的等级（用于突出显示）max_level = data[0][0]# 创建饼图pie = ( Pie() .add( series_name=\"AQI等级分布\", data_pair=[(d[0], d[1]) for d in data], radius=[\"30%\", \"70%\"], center=[\"50%\", \"50%\"], rosetype=\"radius\", label_opts=opts.LabelOpts( formatter=\"{b}: {d}%\", # 显示等级名称和百分比 font_size=14 ), # 添加额外数据（城市列表） encode={\"itemName\": 0, \"value\": 1, \"tooltip\": 2} ) .set_global_opts( title_opts=opts.TitleOpts(title=\"甘肃省AQI等级分布\"), legend_opts=opts.LegendOpts( orient=\"vertical\", pos_left=\"left\" ), tooltip_opts=opts.TooltipOpts( formatter=\"{b}: {c}个城市
{a}: {d}%\", trigger=\"item\" ) ) .set_series_opts( # 简化突出显示效果 emphasis_opts=opts.EmphasisOpts(), # 设置初始高亮（最大占比扇区） selected_mode=\"single\", selected_offset=20 ))# 添加点击事件处理函数js_code = \"\"\"function(params) { if (params.componentType === \'series\' && params.seriesType === \'pie\') { // 获取点击的等级名称 var level = params.name; // 获取城市列表（从额外数据中） var cities = params.data.tooltip; // 创建弹窗内容 var content = \'
\'; content += \'等级: \' + level + \'
\'; content += \'城市数量: \' + params.value + \'
\'; content += \'城市列表: \' + cities; content += \'
\'; // 创建弹窗 var popup = document.createElement(\'div\'); popup.style.cssText = \'position:fixed; top:50%; left:50%; transform:translate(-50%,-50%); \' + \'background:#fff; padding:20px; border:2px solid #ccc; \' + \'border-radius:8px; box-shadow:0 0 20px rgba(0,0,0,0.2); z-index:9999; \' + \'min-width:300px; max-width:500px;\'; popup.innerHTML = content; // 添加关闭按钮 var closeBtn = document.createElement(\'button\'); closeBtn.innerText = \'关闭\'; closeBtn.style.cssText = \'display:block; margin:10px auto 0; padding:6px 20px; \' + \'background:#eee; border:1px solid #ccc; border-radius:4px; cursor:pointer;\'; closeBtn.onclick = function() { document.body.removeChild(popup); }; popup.appendChild(closeBtn); document.body.appendChild(popup); }}\"\"\"# 设置图表初始化选项和事件监听pie.add_js_funcs(f\"\"\"chart.on(\'click\', {js_code});\"\"\")# 渲染图表pie.render(\"aqi_level_distribution.html\")# 打印数据统计结果（用于调试）print(\"AQI等级分布统计:\")for level, count in level_count.items(): if count > 0: print(f\"{level}: {count}个城市 - {\', \'.join(level_cities[level])}\")

实验七：数据可视化交互
实验七：数据可视化交互

实验 3：多城市 AQI 对比仪表盘
任务：使用 Tab 或 Page 组件构建多图表仪表盘：
1. 第一选项卡：显示 AQI 前 10 城市的横向条形图
2. 第二选项卡：展示西北地区城市 AQI 散点数据（添加回归
线）与东部城市 AQI 散点数据（添加回归线）的对比
3. 第三选项卡：组合折线图（各地区 AQI 指数的变化）
4. 要求所有图表共享主题风格（如 ThemeType.DARK）

from pyecharts import options as optsfrom pyecharts.charts import Bar, Scatter, Line, Tab, Gridfrom pyecharts.globals import ThemeTypefrom pyecharts.commons.utils import JsCodeimport numpy as npfrom sklearn.linear_model import LinearRegression# 数据整理（与原代码一致）data = [ \"海门\", 9, \"鄂尔多斯\", 12, \"招远\", 12, \"舟山\", 12, \"齐齐哈尔\", 14, \"盐城\", 15, \"赤峰\", 16, \"青岛\", 18, \"乳山\", 18, \"金昌\", 19, \"泉州\", 21, \"莱西\", 21, \"日照\", 21, \"胶南\", 22, \"南通\", 23, \"拉萨\", 140, \"云浮\", 24, \"梅州\", 25, \"文登\", 25, \"上海\", 25, \"攀枝花\", 25, \"威海\", 25, \"承德\", 25, \"厦门\", 26, \"汕尾\", 26, \"潮州\", 26, \"丹东\", 27, \"太仓\", 27, \"曲靖\", 27, \"烟台\", 28, \"福州\", 29, \"瓦房店\", 30, \"即墨\", 30, \"抚顺\", 31, \"玉溪\", 31, \"张家口\", 31, \"阳泉\", 31, \"莱州\", 32, \"湖州\", 32, \"汕头\", 32, \"昆山\", 33, \"宁波\", 33, \"湛江\", 33, \"揭阳\", 34, \"荣成\", 34, \"连云港\", 35, \"葫芦岛\", 35, \"常熟\", 36, \"东莞\", 36, \"河源\", 36, \"淮安\", 36, \"泰州\", 36, \"南宁\", 37, \"营口\", 37, \"惠州\", 37, \"江阴\", 37, \"蓬莱\", 37, \"韶关\", 38, \"嘉峪关\", 38, \"广州\", 38, \"延安\", 38, \"太原\", 39, \"清远\", 39, \"中山\", 39, \"昆明\", 39, \"寿光\", 40, \"盘锦\", 40, \"长治\", 41, \"深圳\", 41, \"珠海\", 42, \"宿迁\", 43, \"咸阳\", 43, \"铜川\", 44, \"平度\", 44, \"佛山\", 44, \"海口\", 44, \"江门\", 45, \"章丘\", 45, \"肇庆\", 46, \"大连\", 47, \"临汾\", 47, \"吴江\", 47, \"石嘴山\", 49, \"沈阳\", 50, \"苏州\", 50, \"茂名\", 50, \"嘉兴\", 51, \"长春\", 51, \"胶州\", 52, \"银川\", 52, \"张家港\", 52, \"三门峡\", 53, \"锦州\", 54, \"南昌\", 54, \"柳州\", 54, \"三亚\", 54, \"自贡\", 56, \"吉林\", 56, \"阳江\", 57, \"泸州\", 57, \"西宁\", 57, \"宜宾\", 58, \"呼和浩特\", 58, \"成都\", 58, \"大同\", 58, \"镇江\", 59, \"桂林\", 59, \"张家界\", 59, \"宜兴\", 59, \"北海\", 60, \"西安\", 61, \"金坛\", 62, \"东营\", 62, \"牡丹江\", 63, \"遵义\", 63, \"绍兴\", 63, \"扬州\", 64, \"常州\", 64, \"潍坊\", 65, \"重庆\", 66, \"台州\", 67, \"南京\", 67, \"滨州\", 70, \"贵阳\", 71, \"无锡\", 71, \"本溪\", 71, \"克拉玛依\", 72, \"渭南\", 72, \"马鞍山\", 72, \"宝鸡\", 72, \"焦作\", 75, \"句容\", 75, \"北京\", 79, \"徐州\", 79, \"衡水\", 80, \"包头\", 80, \"绵阳\", 80, \"乌鲁木齐\", 84, \"枣庄\", 84, \"杭州\", 84, \"淄博\", 85, \"鞍山\", 86, \"溧阳\", 86, \"库尔勒\", 86, \"安阳\", 90, \"开封\", 90, \"济南\", 92, \"德阳\", 93, \"温州\", 95, \"九江\", 96, \"邯郸\", 98, \"临安\", 99, \"兰州\", 99, \"沧州\", 100, \"临沂\", 103, \"南充\", 104, \"天津\", 105, \"富阳\", 106, \"泰安\", 112, \"诸暨\", 112, \"郑州\", 113, \"哈尔滨\", 114, \"聊城\", 116, \"芜湖\", 117, \"唐山\", 119, \"平顶山\", 119, \"邢台\", 119, \"德州\", 120, \"济宁\", 120, \"荆州\", 127, \"宜昌\", 130, \"义乌\", 132, \"丽水\", 133, \"洛阳\", 134, \"秦皇岛\", 136, \"株洲\", 143, \"石家庄\", 147, \"莱芜\", 148, \"常德\", 152, \"保定\", 153, \"湘潭\", 154, \"金华\", 157, \"岳阳\", 169, \"长沙\", 175, \"衢州\", 177, \"廊坊\", 193, \"菏泽\", 194, \"合肥\", 229, \"武汉\", 273, \"大庆\", 279]cities = []aqi_values = []for i in range(0, len(data), 2): cities.append(data[i]) aqi_values.append(data[i+1])northwest_cities = [\"西安\", \"兰州\", \"西宁\", \"银川\", \"乌鲁木齐\", \"克拉玛依\", \"库尔勒\"]east_cities = [\"上海\", \"南京\", \"杭州\", \"宁波\", \"苏州\", \"无锡\", \"常州\", \"南通\", \"扬州\", \"镇江\", \"泰州\", \"嘉兴\", \"绍兴\"]# 创建Tab组件（移除init_opts，改为在add时指定主题）tab = Tab() # pyecharts 1.x中Tab初始化不接受init_opts，主题通过add时的init_opts传递## 选项卡1：AQI前10城市横向条形图（添加init_opts到Bar的初始化中）sorted_data = sorted(zip(cities, aqi_values), key=lambda x: x[1], reverse=True)top10_cities = [x[0] for x in sorted_data[:10]]top10_aqi = [x[1] for x in sorted_data[:10]]bar = ( Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK)) # 在图表初始化时指定主题 .add_xaxis(top10_cities) .add_yaxis(\"AQI指数\", top10_aqi, category_gap=\"50%\") .reversal_axis() .set_global_opts( title_opts=opts.TitleOpts(title=\"AQI前10城市\"), xaxis_opts=opts.AxisOpts(name=\"AQI值\"), yaxis_opts=opts.AxisOpts(name=\"城市\"), visualmap_opts=opts.VisualMapOpts( min_=min(top10_aqi), max_=max(top10_aqi), dimension=0, is_piecewise=True, pos_top=\"50\", pos_left=\"0\", range_color=[\"#2f4554\", \"#c23531\"], ), ) .set_series_opts( label_opts=opts.LabelOpts(position=\"right\"), markline_opts=opts.MarkLineOpts( data=[opts.MarkLineItem(type_=\"average\", name=\"平均值\")] ) ))tab.add(bar, \"AQI前10城市\")## 选项卡2：西北与东部城市AQI对比散点图（图表初始化时指定主题）# 西北城市数据nw_aqi = [aqi_values[cities.index(city)] for city in northwest_cities if city in cities]nw_x = np.arange(len(nw_aqi)).reshape(-1, 1)nw_reg = LinearRegression().fit(nw_x, nw_aqi)nw_line = nw_reg.predict(nw_x).tolist()# 东部城市数据east_aqi = [aqi_values[cities.index(city)] for city in east_cities if city in cities]east_x = np.arange(len(east_aqi)).reshape(-1, 1)east_reg = LinearRegression().fit(east_x, east_aqi)east_line = east_reg.predict(east_x).tolist()scatter = ( Scatter(init_opts=opts.InitOpts(theme=ThemeType.DARK)) # 添加主题配置 .add_xaxis(northwest_cities) .add_yaxis( \"西北城市AQI\", nw_aqi, symbol_size=10, label_opts=opts.LabelOpts(is_show=False), ) .add_xaxis(east_cities) .add_yaxis( \"东部城市AQI\", east_aqi, symbol_size=10, label_opts=opts.LabelOpts(is_show=False), ) .set_global_opts( title_opts=opts.TitleOpts(title=\"西北与东部城市AQI对比\"), xaxis_opts=opts.AxisOpts(name=\"城市\"), yaxis_opts=opts.AxisOpts(name=\"AQI值\"), tooltip_opts=opts.TooltipOpts( formatter=JsCode( \"function(params){return params.seriesName + \'
\' + params.name + \': \' + params.value[1];}\" ) ), ) .set_series_opts( markline_opts=[ opts.MarkLineOpts( data=[opts.MarkLineItem(type_=\"average\", name=\"西北平均值\")], linestyle_opts=opts.LineStyleOpts(width=2, color=\"#61a0a8\"), ), opts.MarkLineOpts( data=[opts.MarkLineItem(type_=\"average\", name=\"东部平均值\")], linestyle_opts=opts.LineStyleOpts(width=2, color=\"#d48265\"), ), ] ))# 添加回归线line = ( Line(init_opts=opts.InitOpts(theme=ThemeType.DARK)) # 添加主题配置 .add_xaxis(northwest_cities) .add_yaxis( \"西北回归线\", nw_line, is_smooth=True, linestyle_opts=opts.LineStyleOpts(width=4, color=\"#61a0a8\"), label_opts=opts.LabelOpts(is_show=False), ) .add_xaxis(east_cities) .add_yaxis( \"东部回归线\", east_line, is_smooth=True, linestyle_opts=opts.LineStyleOpts(width=4, color=\"#d48265\"), label_opts=opts.LabelOpts(is_show=False), ))# 使用Grid组合散点图和折线图grid = ( Grid() .add(scatter, grid_opts=opts.GridOpts(pos_left=\"5%\", pos_right=\"20%\")) .add(line, grid_opts=opts.GridOpts(pos_left=\"5%\", pos_right=\"20%\")))tab.add(grid, \"西北与东部城市对比\")## 选项卡3：各地区AQI变化折线图（图表初始化时指定主题）regions = { # ... 地区城市列表 ...}region_data = {}for region, city_list in regions.items(): aqis = [] for city in city_list: if city in cities: aqis.append(aqi_values[cities.index(city)]) if aqis: region_data[region] = sum(aqis) / len(aqis)sorted_regions = sorted(region_data.items(), key=lambda x: x[1], reverse=True)region_names = [x[0] for x in sorted_regions]region_aqis = [x[1] for x in sorted_regions]line_chart = ( Line(init_opts=opts.InitOpts(theme=ThemeType.DARK)) # 添加主题配置 .add_xaxis(region_names) .add_yaxis( \"平均AQI\", region_aqis, is_smooth=True, symbol=\"circle\", symbol_size=10, linestyle_opts=opts.LineStyleOpts(width=3), label_opts=opts.LabelOpts(is_show=True), markpoint_opts=opts.MarkPointOpts( data=[ opts.MarkPointItem(type_=\"max\", name=\"最大值\"), opts.MarkPointItem(type_=\"min\", name=\"最小值\"), ] ), markline_opts=opts.MarkLineOpts( data=[opts.MarkLineItem(type_=\"average\", name=\"平均值\")] ), ) .set_global_opts( title_opts=opts.TitleOpts(title=\"各地区平均AQI对比\"), xaxis_opts=opts.AxisOpts(name=\"地区\", axislabel_opts=opts.LabelOpts(rotate=30)), yaxis_opts=opts.AxisOpts(name=\"平均AQI值\"), tooltip_opts=opts.TooltipOpts(trigger=\"axis\"), ))tab.add(line_chart, \"各地区AQI对比\")# 渲染图表tab.render(\"aqi_dashboard.html\")

实验七：数据可视化交互
实验七：数据可视化交互
实验七：数据可视化交互

实验 4：2D 地理 AQI 可视化
任务：结合 Geo 实现二维地理可视化：
1. 在中国地图上标记城市位置，使用不同颜色表示 AQI 值
2. 展示不同地区 AQI 数值

from pyecharts import options as optsfrom pyecharts.charts import Geofrom pyecharts.globals import ChartType, SymbolType# 数据准备（保持原始数据不变）data = [ \"海门\", 9, \"鄂尔多斯\", 12, \"招远\", 12, \"舟山\", 12, \"齐齐哈尔\", 14, \"盐城\", 15, \"赤峰\", 16, \"青岛\", 18, \"乳山\", 18, \"金昌\", 19, \"泉州\", 21, \"莱西\", 21, \"日照\", 21, \"胶南\", 22, \"南通\", 23, \"拉萨\", 140, \"云浮\", 24, \"梅州\", 25, \"文登\", 25, \"上海\", 25, \"攀枝花\", 25, \"威海\", 25, \"承德\", 25, \"厦门\", 26, \"汕尾\", 26, \"潮州\", 26, \"丹东\", 27, \"太仓\", 27, \"曲靖\", 27, \"烟台\", 28, \"福州\", 29, \"瓦房店\", 30, \"即墨\", 30, \"抚顺\", 31, \"玉溪\", 31, \"张家口\", 31, \"阳泉\", 31, \"莱州\", 32, \"湖州\", 32, \"汕头\", 32, \"昆山\", 33, \"宁波\", 33, \"湛江\", 33, \"揭阳\", 34, \"荣成\", 34, \"连云港\", 35, \"葫芦岛\", 35, \"常熟\", 36, \"东莞\", 36, \"河源\", 36, \"淮安\", 36, \"泰州\", 36, \"南宁\", 37, \"营口\", 37, \"惠州\", 37, \"江阴\", 37, \"蓬莱\", 37, \"韶关\", 38, \"嘉峪关\", 38, \"广州\", 38, \"延安\", 38, \"太原\", 39, \"清远\", 39, \"中山\", 39, \"昆明\", 39, \"寿光\", 40, \"盘锦\", 40, \"长治\", 41, \"深圳\", 41, \"珠海\", 42, \"宿迁\", 43, \"咸阳\", 43, \"铜川\", 44, \"平度\", 44, \"佛山\", 44, \"海口\", 44, \"江门\", 45, \"章丘\", 45, \"肇庆\", 46, \"大连\", 47, \"临汾\", 47, \"吴江\", 47, \"石嘴山\", 49, \"沈阳\", 50, \"苏州\", 50, \"茂名\", 50, \"嘉兴\", 51, \"长春\", 51, \"胶州\", 52, \"银川\", 52, \"张家港\", 52, \"三门峡\", 53, \"锦州\", 54, \"南昌\", 54, \"柳州\", 54, \"三亚\", 54, \"自贡\", 56, \"吉林\", 56, \"阳江\", 57, \"泸州\", 57, \"西宁\", 57, \"宜宾\", 58, \"呼和浩特\", 58, \"成都\", 58, \"大同\", 58, \"镇江\", 59, \"桂林\", 59, \"张家界\", 59, \"宜兴\", 59, \"北海\", 60, \"西安\", 61, \"金坛\", 62, \"东营\", 62, \"牡丹江\", 63, \"遵义\", 63, \"绍兴\", 63, \"扬州\", 64, \"常州\", 64, \"潍坊\", 65, \"重庆\", 66, \"台州\", 67, \"南京\", 67, \"滨州\", 70, \"贵阳\", 71, \"无锡\", 71, \"本溪\", 71, \"克拉玛依\", 72, \"渭南\", 72, \"马鞍山\", 72, \"宝鸡\", 72, \"焦作\", 75, \"句容\", 75, \"北京\", 79, \"徐州\", 79, \"衡水\", 80, \"包头\", 80, \"绵阳\", 80, \"乌鲁木齐\", 84, \"枣庄\", 84, \"杭州\", 84, \"淄博\", 85, \"鞍山\", 86, \"溧阳\", 86, \"库尔勒\", 86, \"安阳\", 90, \"开封\", 90, \"济南\", 92, \"德阳\", 93, \"温州\", 95, \"九江\", 96, \"邯郸\", 98, \"临安\", 99, \"兰州\", 99, \"沧州\", 100, \"临沂\", 103, \"南充\", 104, \"天津\", 105, \"富阳\", 106, \"泰安\", 112, \"诸暨\", 112, \"郑州\", 113, \"哈尔滨\", 114, \"聊城\", 116, \"芜湖\", 117, \"唐山\", 119, \"平顶山\", 119, \"邢台\", 119, \"德州\", 120, \"济宁\", 120, \"荆州\", 127, \"宜昌\", 130, \"义乌\", 132, \"丽水\", 133, \"洛阳\", 134, \"秦皇岛\", 136, \"株洲\", 143, \"石家庄\", 147, \"莱芜\", 148, \"常德\", 152, \"保定\", 153, \"湘潭\", 154, \"金华\", 157, \"岳阳\", 169, \"长沙\", 175, \"衢州\", 177, \"廊坊\", 193, \"菏泽\", 194, \"合肥\", 229, \"武汉\", 273, \"大庆\", 279]# 将一维列表转换为(城市, AQI值)的元组列表data_pairs = [(data[i], data[i+1]) for i in range(0, len(data), 2)]# 创建Geo图表geo = Geo(init_opts=opts.InitOpts(width=\"1200px\", height=\"600px\"))geo.add_schema(maptype=\"china\")# 添加数据点和视觉映射（修改data_pair参数）geo.add( \"AQI\", data_pair=data_pairs, # 使用转换后的格式 type_=ChartType.SCATTER, symbol_size=8, label_opts=opts.LabelOpts(is_show=False),)# 设置视觉映射（保持不变）geo.set_global_opts( visualmap_opts=opts.VisualMapOpts( is_piecewise=True, pieces=[ {\"min\": 0, \"max\": 50, \"label\": \"0-50: 优\", \"color\": \"#00E400\"}, {\"min\": 51, \"max\": 100, \"label\": \"51-100: 良\", \"color\": \"#FFFF00\"}, {\"min\": 101, \"max\": 150, \"label\": \"101-150: 轻度污染\", \"color\": \"#FF7E00\"}, {\"min\": 151, \"max\": 200, \"label\": \"151-200: 中度污染\", \"color\": \"#FF0000\"}, {\"min\": 201, \"max\": 300, \"label\": \"201-300: 重度污染\", \"color\": \"#99004C\"}, {\"min\": 301, \"label\": \">300: 严重污染\", \"color\": \"#7E0023\"}, ], pos_left=\"right\", pos_top=\"middle\", ), title_opts=opts.TitleOpts(title=\"中国城市AQI分布图\"),)# 渲染图表geo.render(\"china_aqi_geo.html\")

实验七：数据可视化交互

实验 5：3D 地理 AQI 可视化
任务：结合 Geo3D 和 Bar3D 实现三维地理可视化：
1. 在地球模型上标记城市位置，高度表示 AQI 值
2. 使用 Map3D 配置光照效果和区域颜色（如中国区域高亮）
3. 展示不同地区 AQI 数值

import plotly.graph_objects as goimport pandas as pd# 城市 AQI 数据（与原代码一致）data = [ {\"城市\": \"北京\", \"AQI\": 90, \"经度\": 116.4074, \"纬度\": 39.9042}, {\"城市\": \"上海\", \"AQI\": 60, \"经度\": 121.4737, \"纬度\": 31.2304}, {\"城市\": \"广州\", \"AQI\": 75, \"经度\": 113.2644, \"纬度\": 23.1291}, {\"城市\": \"深圳\", \"AQI\": 65, \"经度\": 114.0579, \"纬度\": 22.5431}, {\"城市\": \"成都\", \"AQI\": 85, \"经度\": 104.0658, \"纬度\": 30.5728}, {\"城市\": \"武汉\", \"AQI\": 95, \"经度\": 114.3055, \"纬度\": 30.5928}, {\"城市\": \"西安\", \"AQI\": 110, \"经度\": 108.9480, \"纬度\": 34.2631}, {\"城市\": \"南京\", \"AQI\": 70, \"经度\": 118.7674, \"纬度\": 32.0415}, {\"城市\": \"杭州\", \"AQI\": 75, \"经度\": 120.1536, \"纬度\": 30.2874}, {\"城市\": \"重庆\", \"AQI\": 80, \"经度\": 106.5049, \"纬度\": 29.5331}, {\"城市\": \"天津\", \"AQI\": 85, \"经度\": 117.1901, \"纬度\": 39.1255}, {\"城市\": \"沈阳\", \"AQI\": 95, \"经度\": 123.4290, \"纬度\": 41.7968}, {\"城市\": \"哈尔滨\", \"AQI\": 100, \"经度\": 126.6424, \"纬度\": 45.7569}, {\"城市\": \"郑州\", \"AQI\": 105, \"经度\": 113.6524, \"纬度\": 34.7569}, {\"城市\": \"长沙\", \"AQI\": 85, \"经度\": 112.9822, \"纬度\": 28.1127}, {\"城市\": \"福州\", \"AQI\": 60, \"经度\": 119.2959, \"纬度\": 26.0819}, {\"城市\": \"济南\", \"AQI\": 90, \"经度\": 117.1200, \"纬度\": 36.6512}, {\"城市\": \"合肥\", \"AQI\": 75, \"经度\": 117.2856, \"纬度\": 31.8615}, {\"城市\": \"南昌\", \"AQI\": 70, \"经度\": 115.8921, \"纬度\": 28.6764}, {\"城市\": \"南宁\", \"AQI\": 65, \"经度\": 108.2901, \"纬度\": 22.8402}, {\"城市\": \"贵阳\", \"AQI\": 70, \"经度\": 106.7134, \"纬度\": 26.5784}, {\"城市\": \"昆明\", \"AQI\": 55, \"经度\": 102.7123, \"纬度\": 25.0406}, {\"城市\": \"拉萨\", \"AQI\": 40, \"经度\": 91.1106, \"纬度\": 29.6441}, {\"城市\": \"兰州\", \"AQI\": 100, \"经度\": 103.8343, \"纬度\": 36.0611}, {\"城市\": \"西宁\", \"AQI\": 95, \"经度\": 101.7793, \"纬度\": 36.6232}, {\"城市\": \"银川\", \"AQI\": 90, \"经度\": 106.2324, \"纬度\": 38.4867}, {\"城市\": \"乌鲁木齐\", \"AQI\": 115, \"经度\": 87.6177, \"纬度\": 43.8267}, {\"城市\": \"海口\", \"AQI\": 50, \"经度\": 110.3311, \"纬度\": 20.0319}, {\"城市\": \"石家庄\", \"AQI\": 105, \"经度\": 114.4891, \"纬度\": 38.0455}, {\"城市\": \"长春\", \"AQI\": 98, \"经度\": 125.3245, \"纬度\": 43.8375}, {\"城市\": \"呼和浩特\", \"AQI\": 88, \"经度\": 111.6409, \"纬度\": 40.8171}, {\"城市\": \"太原\", \"AQI\": 92, \"经度\": 112.5383, \"纬度\": 37.8737}, {\"城市\": \"大连\", \"AQI\": 78, \"经度\": 121.6126, \"纬度\": 38.9121}, {\"城市\": \"青岛\", \"AQI\": 82, \"经度\": 120.3350, \"纬度\": 36.0624}, {\"城市\": \"苏州\", \"AQI\": 73, \"经度\": 120.6055, \"纬度\": 31.3189}, {\"城市\": \"宁波\", \"AQI\": 68, \"经度\": 121.5405, \"纬度\": 29.8626}, {\"城市\": \"厦门\", \"AQI\": 62, \"经度\": 118.1029, \"纬度\": 24.4646}, {\"城市\": \"武汉\", \"AQI\": 95, \"经度\": 114.3055, \"纬度\": 30.5928}, {\"城市\": \"长沙\", \"AQI\": 85, \"经度\": 112.9822, \"纬度\": 28.1127}, {\"城市\": \"珠海\", \"AQI\": 60, \"经度\": 113.5269, \"纬度\": 22.2642}, {\"城市\": \"三亚\", \"AQI\": 55, \"经度\": 109.5135, \"纬度\": 18.2525}, {\"城市\": \"成都\", \"AQI\": 85, \"经度\": 104.0658, \"纬度\": 30.5728}, {\"城市\": \"西安\", \"AQI\": 110, \"经度\": 108.9480, \"纬度\": 34.2631}, {\"城市\": \"杭州\", \"AQI\": 75, \"经度\": 120.1536, \"纬度\": 30.2874}, {\"城市\": \"南京\", \"AQI\": 70, \"经度\": 118.7674, \"纬度\": 32.0415}, {\"城市\": \"重庆\", \"AQI\": 80, \"经度\": 106.5049, \"纬度\": 29.5331},]df = pd.DataFrame(data)# 创建 3D 散点图（与原代码一致）fig = go.Figure( data=go.Scatter3d( x=df[\"经度\"], y=df[\"纬度\"], z=df[\"AQI\"], text=df[\"城市\"] + \"
AQI: \" + df[\"AQI\"].astype(str), mode=\"markers\", marker=dict( size=df[\"AQI\"] / 10, color=df[\"AQI\"], colorscale=\"YlOrRd\", colorbar=dict(title=\"AQI 值\"), opacity=0.8, ), ))# 设置布局（优化相机视角和坐标轴）fig.update_layout( title=dict( text=\"中国城市AQI三维地理分布\", x=0.5, font=dict(size=24, color=\"white\") ), scene=dict( xaxis=dict(title=\"经度\", showgrid=False, titlefont=dict(color=\"white\")), yaxis=dict(title=\"纬度\", showgrid=False, titlefont=dict(color=\"white\")), zaxis=dict(title=\"AQI值\", showgrid=False, titlefont=dict(color=\"white\")), camera=dict(eye=dict(x=1.8, y=1.8, z=1.2)), aspectratio=dict(x=1, y=1, z=0.5) ), paper_bgcolor=\"rgba(0,0,0,0)\", plot_bgcolor=\"rgba(0,0,0,0)\", margin=dict(l=0, r=0, b=0, t=60),)# 添加中国地图背景（使用 add_layout_image 正确方法）fig.add_layout_image( dict( source=\"https://img-blog.csdnimg.cn/20230426153240606.png\", # 地图图片地址 xref=\"x\", yref=\"y\", x=df[\"经度\"].min(), y=df[\"纬度\"].min(), # 图片左下角坐标 sizex=df[\"经度\"].max() - df[\"经度\"].min(), # 图片宽度（经度范围） sizey=df[\"纬度\"].max() - df[\"纬度\"].min(), # 图片高度（纬度范围） opacity=0.15, layer=\"below\" # 图片层位于数据下方 ))# 保存为 HTMLfig.write_html(\"aqi_3d_geo.html\", auto_open=True)

实验七：数据可视化交互
本次数据可视化交互实验围绕全国空气质量展开。目的是了解原则、掌握分类与常见技术。实验中安装pyecharts，用其完成多项任务，如绘制 AQI 横向对比条形图、等级分布饼图、多城市对比仪表盘等。过程中遇到代码兼容等问题并解决。通过实验，提升了数据可视化编程与分析能力，也认识到其在决策支持中的重要性。

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