城市天气数据可视化项目（含源码，数据源，运行结果，文档！） 2.56MB

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资源文件列表:

数据可视化包.zip 大约有27个文件

可执行代码+爬取数据/
可执行代码+爬取数据/.idea/
可执行代码+爬取数据/.idea/.gitignore 190B
可执行代码+爬取数据/.idea/inspectionProfiles/
可执行代码+爬取数据/.idea/inspectionProfiles/profiles_settings.xml 174B
可执行代码+爬取数据/.idea/inspectionProfiles/Project_Default.xml 404B
可执行代码+爬取数据/.idea/misc.xml 196B
可执行代码+爬取数据/.idea/modules.xml 291B
可执行代码+爬取数据/.idea/workspace.xml 1.95KB
可执行代码+爬取数据/.idea/案例一天气.iml 291B
可执行代码+爬取数据/01_weather_wenzhou_202304.xlsx 6.1KB
可执行代码+爬取数据/02_all_weather_wenzhou.xlsx 43.74KB
可执行代码+爬取数据/03_df_weather_wenzhou.xlsx 44.29KB
可执行代码+爬取数据/04_alltime_weather_wuyishan.xlsx 98.21KB
可执行代码+爬取数据/05_predata_weather_wenzhou.xlsx 107.05KB
可执行代码+爬取数据/05_predata_weather_wuyishan.xlsx 87.13KB
可执行代码+爬取数据/Untitled.ipynb 1.31MB
温州市天气数据可视化设计文档.docx 829.49KB
运行结果图/
运行结果图/2023天气分布.png 77.95KB
运行结果图/十年折线图.png 72.28KB
运行结果图/天气扇形图.png 119.57KB
运行结果图/天气气泡图.png 110.69KB
运行结果图/拟合预测.png 60.37KB
运行结果图/散点图.png 210.07KB
运行结果图/玫瑰图.png 75.99KB
运行结果图/近一年温差折线图.png 123.93KB

资源介绍:

本项目通过网络爬虫获取了温州市2014年至2024年的天气数据，包括日期、最高气温、最低气温、天气情况和风力情况。首先，爬取并解析网页数据，将其存储为Excel文件。接着，对数据进行清洗和处理，去除重复数据和缺失值。数据分析部分，绘制了多个图表，包括近十年气温变化折线图、2023年最高和最低气温折线图、2023年天气情况柱状图、天气类型环形图和风力玫瑰图。最后，使用线性回归模型对次日最高气温进行预测，生成了当日与次日最高气温的散点图和回归直线图。通过这些分析和图表展示，揭示了温州市近十年的气温变化趋势及天气特征，并评估了模型的拟合效果。

<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89566646/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89566646/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">温州市<span class="_ _0"></span>天气数据<span class="_ _0"></span>可视化<span class="_ _0"></span>深度解<span class="_ _0"></span>析报告</div><div class="t m0 x2 h3 y2 ff2 fs1 fc0 sc0 ls0 ws0">一、设计<span class="_ _0"></span>目的</div><div class="t m0 x3 h4 y3 ff3 fs2 fc0 sc1 ls0 ws0">本报告的设计目的在于对温州市的天气数据进行深度解析，<span class="_ _1"></span>通过数据可视化</div><div class="t m0 x2 h4 y4 ff3 fs2 fc0 sc1 ls0 ws0">的方式，<span class="_ _2"></span>直观地展示温州市近十年来的气温变化、<span class="_ _2"></span>近一年的最高最低温、<span class="_ _2"></span>天气情</div><div class="t m0 x2 h4 y5 ff3 fs2 fc0 sc1 ls0 ws0">况以及风力情况。<span class="_ _2"></span>通过收集、<span class="_ _2"></span>清洗和可视化这些数据，<span class="_ _2"></span>我们旨在揭示温州市天气</div><div class="t m0 x2 h4 y6 ff3 fs2 fc0 sc1 ls0 ws0">的变化规律，<span class="_ _2"></span>为市民提供更为准确、<span class="_ _2"></span>实用的天气信息，<span class="_ _2"></span>同时也为气象研究和预测</div><div class="t m0 x2 h4 y7 ff3 fs2 fc0 sc1 ls0 ws0">提供有力的数据支持。</div><div class="t m0 x3 h4 y8 ff3 fs2 fc0 sc1 ls0 ws0">为了实现这一设计目的，<span class="_ _1"></span>我们首先从权威的气象数据来源获取了温州市近十</div><div class="t m0 x2 h4 y9 ff3 fs2 fc0 sc1 ls0 ws0">年的天气数据，<span class="_ _3"></span>包括气温、<span class="_ _3"></span>天气状况、<span class="_ _3"></span>风力等关键指标。<span class="_ _3"></span>通过数据爬取技术，<span class="_ _3"></span>我</div><div class="t m0 x2 h4 ya ff3 fs2 fc0 sc1 ls0 ws0">们成功获取了这些数据，<span class="_ _4"></span>并进行了必要的清洗和整理，<span class="_ _4"></span>以确保数据的准确性和可</div><div class="t m0 x2 h4 yb ff3 fs2 fc0 sc1 ls0 ws0">靠性<span class="_ _0"></span>。<span class="_ _0"></span>在<span class="_ _0"></span>数<span class="_ _0"></span>据可<span class="_ _0"></span>视<span class="_ _0"></span>化<span class="_ _0"></span>的<span class="_ _0"></span>过程<span class="_ _0"></span>中<span class="_ _0"></span>，<span class="_ _0"></span>我<span class="_ _0"></span>们<span class="_ _0"></span>采用<span class="_ _0"></span>了<span class="_ _0"></span>多<span class="_ _0"></span>种<span class="_ _0"></span>图表<span class="_ _0"></span>和<span class="_ _0"></span>可<span class="_ _0"></span>视<span class="_ _0"></span>化工<span class="_ _0"></span>具<span class="_ _0"></span>，<span class="_ _0"></span>如<span class="_ _0"></span>折<span class="_ _0"></span>线图<span class="_ _0"></span>、</div><div class="t m0 x2 h4 yc ff3 fs2 fc0 sc1 ls0 ws0">柱状图、饼图等，以直观地展示数据的分布和变化趋势。</div><div class="t m0 x3 h4 yd ff3 fs2 fc0 sc1 ls0 ws0">通过对温州市天气数据的深度解析，<span class="_ _4"></span>我们发现了一些有趣的现象和规律。<span class="_ _4"></span>例</div><div class="t m0 x2 h4 ye ff3 fs2 fc0 sc1 ls0 ws0">如，<span class="_ _2"></span>近十年来，<span class="_ _2"></span>温州市的气温呈现出逐年上升的趋势，<span class="_ _2"></span>这可能与全球气候变暖的</div><div class="t m0 x2 h4 yf ff3 fs2 fc0 sc1 ls0 ws0">大背景有关。<span class="_ _2"></span>同时，<span class="_ _2"></span>我们也发现，<span class="_ _2"></span>温州市的天气状况在不同季节和月份之间存在</div><div class="t m0 x2 h4 y10 ff3 fs2 fc0 sc1 ls0 ws0">明显的差异，<span class="_ _2"></span>如夏季多雨、<span class="_ _2"></span>冬季干燥等。<span class="_ _2"></span>这些发现不仅有助于我们更好地了解温</div><div class="t m0 x2 h4 y11 ff3 fs2 fc0 sc1 ls0 ws0">州市的天气特点，<span class="_ _4"></span>也为市民提供了更为准确的天气信息，<span class="_ _4"></span>帮助他们更好地安排生</div><div class="t m0 x2 h4 y12 ff3 fs2 fc0 sc1 ls0 ws0">活和出行。</div><div class="t m0 x3 h4 y13 ff3 fs2 fc0 sc1 ls0 ws0">此外，<span class="_ _4"></span>我们还利用模型构建和分析的方法，<span class="_ _4"></span>对温州市的天气数据进行了进一</div><div class="t m0 x2 h4 y14 ff3 fs2 fc0 sc1 ls0 ws0">步的挖掘和预测。<span class="_ _4"></span>通过构建回归模型，<span class="_ _4"></span>我们成功地预测了未来一段时间内的气温</div><div class="t m0 x2 h4 y15 ff3 fs2 fc0 sc1 ls0 ws0">变化趋势，<span class="_ _4"></span>为市民提供了更为精准的天气预报服务。<span class="_ _4"></span>这些预测结果不仅有助于市</div><div class="t m0 x2 h4 y16 ff3 fs2 fc0 sc1 ls0 ws0">民做好防范措施，也为气象部门提供了更为科学的决策依据。</div><div class="t m0 x3 h4 y17 ff3 fs2 fc0 sc1 ls0 ws0">综上所述，<span class="_ _4"></span>本报告通过数据可视化和模型构建的方式，<span class="_ _4"></span>对温州市的天气数据</div><div class="t m0 x2 h4 y18 ff3 fs2 fc0 sc1 ls0 ws0">进行了深度解析和预测。<span class="_ _5"></span>这些结果不仅有助于我们更好地了解温州市的天气特点，</div><div class="t m0 x2 h4 y19 ff3 fs2 fc0 sc1 ls0 ws0">也为市民提供了更为准确、<span class="_ _2"></span>实用的天气信息。<span class="_ _2"></span>同时，<span class="_ _2"></span>我们也希望这些结果能够为</div><div class="t m0 x2 h4 y1a ff3 fs2 fc0 sc1 ls0 ws0">气象研究和预测提供有力的数据支持，推动气象科学的不断发展。</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div><div id="pf2" class="pf w0 h0" data-page-no="2"><div class="pc pc2 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89566646/bg2.jpg"><div class="t m0 x2 h3 y1b ff2 fs1 fc0 sc0 ls0 ws0">二、数据<span class="_ _0"></span>收集</div><div class="t m0 x2 h5 y1c ff4 fs3 fc0 sc0 ls0 ws0">2.1 <span class="ff1">数据<span class="_ _0"></span>来源</span></div><div class="t m0 x3 h4 y1d ff3 fs2 fc0 sc1 ls0 ws0">在本次温州市天气数据可视化深度解析报告中，<span class="_ _1"></span>我们精心挑选了权威且可靠</div><div class="t m0 x2 h4 y1e ff3 fs2 fc0 sc1 ls0 ws0">的数据来源。<span class="_ _4"></span>主要的数据来源于温州市气象局近十年官方发布的天气数据，<span class="_ _4"></span>这些</div><div class="t m0 x2 h4 y1f ff3 fs2 fc0 sc1 ls0 ws0">数据经过严格的采集、<span class="_ _2"></span>审核和发布流程，<span class="_ _2"></span>确保了数据的准确性和权威性。<span class="_ _2"></span>我们能</div><div class="t m0 x2 h4 y20 ff3 fs2 fc0 sc1 ls0 ws0">够更准确地把握温州市的天气变化规律，<span class="_ _1"></span>为后续的数据可视化和模型构建提供坚</div><div class="t m0 x2 h4 y21 ff3 fs2 fc0 sc1 ls0 ws0">实的基础。</div><div class="t m0 x3 h4 y22 ff3 fs2 fc0 sc1 ls0 ws0">值得<span class="_ _0"></span>一<span class="_ _0"></span>提<span class="_ _0"></span>的<span class="_ _0"></span>是<span class="_ _0"></span>，<span class="_ _0"></span>我们<span class="_ _0"></span>特<span class="_ _0"></span>别<span class="_ _0"></span>注<span class="_ _0"></span>重<span class="_ _0"></span>数<span class="_ _0"></span>据的<span class="_ _0"></span>时<span class="_ _0"></span>效<span class="_ _0"></span>性<span class="_ _0"></span>和<span class="_ _0"></span>完整<span class="_ _0"></span>性<span class="_ _0"></span>。<span class="_ _0"></span>在<span class="_ _0"></span>数<span class="_ _0"></span>据<span class="_ _0"></span>收集<span class="_ _0"></span>过<span class="_ _0"></span>程<span class="_ _0"></span>中<span class="_ _0"></span>，</div><div class="t m0 x2 h4 y23 ff3 fs2 fc0 sc1 ls0 ws0">我们采用了先进的数据爬取技术，<span class="_ _2"></span>确保能够实时获取最新的天气数据。<span class="_ _2"></span>同时，<span class="_ _2"></span>我</div><div class="t m0 x2 h4 y24 ff3 fs2 fc0 sc1 ls0 ws0">们还对数据进行了严格的清洗和整理，<span class="_ _4"></span>剔除了异常值和重复数据，<span class="_ _4"></span>保证了数据的</div><div class="t m0 x2 h4 y25 ff3 fs2 fc0 sc1 ls0 ws0">准确性和可靠性。<span class="_ _4"></span>这些措施不仅提高了数据的质量，<span class="_ _4"></span>也为后续的数据分析和可视</div><div class="t m0 x2 h4 y26 ff3 fs2 fc0 sc1 ls0 ws0">化提供了有力的支持。</div><div class="t m0 x3 h4 y27 ff3 fs2 fc0 sc1 ls0 ws0">我们相信，<span class="_ _1"></span>这份报告将为温州市的可持续发展和人民生活质量的提升发挥积</div><div class="t m0 x2 h4 y28 ff3 fs2 fc0 sc1 ls0 ws0">极的作用。</div><div class="t m0 x2 h5 y29 ff4 fs3 fc0 sc0 ls0 ws0">2.2 <span class="ff1">数据<span class="_ _0"></span>爬取</span></div><div class="t m0 x3 h4 y2a ff3 fs2 fc0 sc1 ls0 ws0">在编制温州市天气数据可视化深度解析报告的过程中，<span class="_ _1"></span>数据爬取是关键的一</div><div class="t m0 x2 h4 y2b ff3 fs2 fc0 sc1 ls0 ws0">环。<span class="_ _4"></span>我们采用了先进的网络爬虫技术，<span class="_ _4"></span>从权威气象数据网站精准地抓取了温州市</div><div class="t m0 x2 h4 y2c ff3 fs2 fc0 sc1 ls0 ws0">近十年的气温、<span class="_ _2"></span>天气情况、<span class="_ _2"></span>风力等详细数据。<span class="_ _2"></span>这些数据涵盖了每日的最高最低气</div><div class="t m0 x2 h4 y2d ff3 fs2 fc0 sc1 ls0 ws0">温、<span class="_ _2"></span>天气状况以及风向、<span class="_ _2"></span>风速等关键信息，<span class="_ _2"></span>为后续的数据分析和可视化提供了坚</div><div class="t m0 x2 h4 y2e ff3 fs2 fc0 sc1 ls0 ws0">实的基础。</div><div class="t m0 x3 h4 y2f ff3 fs2 fc0 sc1 ls0 ws0">在数据爬取过程中，<span class="_ _4"></span>我们特别注重数据的准确性和完整性。<span class="_ _4"></span>通过设定合理的</div><div class="t m0 x2 h4 y30 ff3 fs2 fc0 sc1 ls0 ws0">爬取规则和参数，<span class="_ _4"></span>我们成功避免了重复数据和错误数据，<span class="_ _4"></span>确保了数据的真实性和</div><div class="t m0 x2 h4 y31 ff3 fs2 fc0 sc1 ls0 ws0">可靠性。</div><div class="t m0 x2 h5 y32 ff4 fs3 fc0 sc0 ls0 ws0">2.3 <span class="ff1">数据<span class="_ _0"></span>清洗</span></div><div class="t m0 x3 h4 y33 ff3 fs2 fc0 sc1 ls0 ws0">在本<span class="_ _0"></span>次<span class="_ _0"></span>数<span class="_ _0"></span>据<span class="_ _0"></span>分<span class="_ _0"></span>析<span class="_ _0"></span>工作<span class="_ _0"></span>中<span class="_ _0"></span>，<span class="_ _0"></span>为<span class="_ _0"></span>确<span class="_ _0"></span>保<span class="_ _0"></span>温州<span class="_ _0"></span>市<span class="_ _0"></span>近<span class="_ _0"></span>十<span class="_ _0"></span>年<span class="_ _0"></span>天气<span class="_ _0"></span>数<span class="_ _0"></span>据<span class="_ _0"></span>的<span class="_ _0"></span>准<span class="_ _0"></span>确<span class="_ _0"></span>性和<span class="_ _0"></span>完<span class="_ _0"></span>整<span class="_ _0"></span>性<span class="_ _0"></span>，</div><div class="t m0 x2 h4 y34 ff3 fs2 fc0 sc1 ls0 ws0">我进行了详细的数据清洗和整理。以下是具体步骤和方法：</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div><div id="pf3" class="pf w0 h0" data-page-no="3"><div class="pc pc3 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89566646/bg3.jpg"><div class="t m0 x3 h4 y35 ff5 fs2 fc0 sc1 ls0 ws0">1. <span class="_"> </span><span class="ff3">数据导入与初步检查</span></div><div class="t m0 x3 h4 y36 ff3 fs2 fc0 sc1 ls0 ws0">首先，导入所需的数据和库，读取天气数据，并进行初步的检查。</div><div class="t m0 x3 h4 y37 ff5 fs2 fc0 sc1 ls0 ws0">2. <span class="_"> </span><span class="ff3">数据概况检查</span></div><div class="t m0 x3 h4 y38 ff3 fs2 fc0 sc1 ls0 ws0">查看数据的基本信息，确保数据结构正确。</div><div class="t m0 x3 h4 y39 ff5 fs2 fc0 sc1 ls0 ws0">3. <span class="_"> </span><span class="ff3">检查缺失值</span></div><div class="t m0 x3 h4 y3a ff3 fs2 fc0 sc1 ls0 ws0">检查数据中是否存在缺失值，并统计各个字段缺失值的数量。</div><div class="t m0 x3 h4 y3b ff3 fs2 fc0 sc1 ls0 ws0">分析：通过检查，发现各字段都没有缺失值。</div><div class="t m0 x3 h4 y3c ff5 fs2 fc0 sc1 ls0 ws0">4. <span class="_"> </span><span class="ff3">检查和处理重复值</span></div><div class="t m0 x3 h4 y3d ff3 fs2 fc0 sc1 ls0 ws0">检查是否存在重复的行，<span class="_ _2"></span>并统计重复行的数量。<span class="_ _2"></span>若存在重复行，<span class="_ _2"></span>则删除重复</div><div class="t m0 x2 h4 y3e ff3 fs2 fc0 sc1 ls0 ws0">行。</div><div class="t m0 x3 h4 y3f ff3 fs2 fc0 sc1 ls0 ws0">分析：数据中没有完全重复的数据，删除处理后进行下一步处理。</div><div class="t m0 x3 h4 y40 ff5 fs2 fc0 sc1 ls0 ws0">5. <span class="_"> </span><span class="ff3">提取和转换温度数据</span></div><div class="t m0 x3 h4 y41 ff3 fs2 fc0 sc1 ls0 ws0">使用正则表达式将温度列中的数字部分提取出来，并转换为数值型。</div><div class="t m0 x3 h4 y42 ff5 fs2 fc0 sc1 ls0 ws0">6. <span class="_"> </span><span class="ff3">拆分日期列</span></div><div class="t m0 x3 h4 y43 ff3 fs2 fc0 sc1 ls0 ws0">将日期中的年月日和星期拆分出来，方便后续分析。</div><div class="t m0 x3 h4 y44 ff5 fs2 fc0 sc1 ls0 ws0">7. <span class="_"> </span><span class="ff3">拆分风力数据</span></div><div class="t m0 x3 h4 y45 ff3 fs2 fc0 sc1 ls0 ws0">利用空格标识对风力列进行分列，<span class="_ _6"></span>分别提取风向和风速数据。<span class="_ _6"></span>通过上述步骤，</div><div class="t m0 x2 h4 y46 ff3 fs2 fc0 sc1 ls0 ws0">我们对温州市近十年的天气数据进行了详细的数据清洗和整理。<span class="_ _1"></span>具体步骤包括导</div><div class="t m0 x2 h4 y47 ff3 fs2 fc0 sc1 ls0 ws0">入数据、<span class="_ _7"></span>检查缺失值和重复值、<span class="_ _7"></span>提取和转换温度数据、<span class="_ _7"></span>拆分日期列和风力数据等。</div><div class="t m0 x2 h4 y48 ff3 fs2 fc0 sc1 ls0 ws0">经过这些处理，<span class="_ _4"></span>数据的质量得到了显著提升，<span class="_ _4"></span>为后续的数据分析和可视化奠定了</div><div class="t m0 x2 h4 y49 ff3 fs2 fc0 sc1 ls0 ws0">坚实的基础。</div><div class="t m0 x2 h3 y4a ff2 fs1 fc0 sc0 ls0 ws0">三、数据<span class="_ _0"></span>可视化及代<span class="_ _0"></span>码实现</div><div class="t m0 x2 h5 y4b ff4 fs3 fc0 sc0 ls0 ws0">3.1 <span class="ff1">近十<span class="_ _0"></span>年气温</span></div><div class="t m0 x3 h4 y4c ff3 fs2 fc0 sc1 ls0 ws0">首先，<span class="_ _8"></span>代码导入了<span class="_ _9"> </span><span class="ff5">pandas<span class="_"> </span></span>和<span class="_ _9"> </span><span class="ff5">matplotlib.pyplot<span class="_"> </span></span>库，<span class="_ _8"></span>分别用于数据处理和数据</div><div class="t m0 x2 h4 y4d ff3 fs2 fc0 sc1 ls0 ws0">可视化。<span class="_ _4"></span>为了确保图形中能够正确显示中文和负号，<span class="_ _4"></span>代码对图形显示参数进行了</div><div class="t m0 x2 h4 y4e ff3 fs2 fc0 sc1 ls0 ws0">配置。接着，<span class="_ _0"></span>代码从预处理<span class="_ _0"></span>后的数据文件<span class="_ _a"> </span><span class="ff5">03_df_weather_wenzhou.xlsx<span class="_"> </span></span>中读取了</div><div class="t m0 x2 h4 y4f ff3 fs2 fc0 sc1 ls0 ws0">包含日期、<span class="_ _4"></span>最高气温和最低气温的数据，<span class="_ _4"></span>并将日期列设置为索引以便于后续的绘</div><div class="t m0 x2 h4 y50 ff3 fs2 fc0 sc1 ls0 ws0">图操作。<span class="_ _2"></span>在绘制折线图的部分，<span class="_ _2"></span>代码首先设置了图形的大小，<span class="_ _2"></span>然后分别绘制了最</div><div class="t m0 x2 h4 y51 ff3 fs2 fc0 sc1 ls0 ws0">高气温和最低气温的折线图，<span class="_ _2"></span>最高气温用红色表示，<span class="_ _2"></span>最低气温用蓝色表示。<span class="_ _2"></span>为了</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div><div id="pf4" class="pf w0 h0" data-page-no="4"><div class="pc pc4 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89566646/bg4.jpg"><div class="t m0 x2 h4 y35 ff3 fs2 fc0 sc1 ls0 ws0">使图形更加直观，<span class="_ _2"></span>代码添加了图例，<span class="_ _2"></span>并设置了<span class="_ _9"> </span><span class="ff5">x<span class="_"> </span></span>轴和<span class="_ _9"> </span><span class="ff5">y<span class="_"> </span></span>轴的标签，<span class="_ _2"></span>以及图形的标</div><div class="t m0 x2 h4 y52 ff3 fs2 fc0 sc1 ls0 ws0">题，此<span class="_ _0"></span>外，<span class="_ _0"></span>代码还<span class="_ _0"></span>设置<span class="_ _0"></span>了显<span class="_ _0"></span>示网格<span class="_ _0"></span>，使<span class="_ _0"></span>得图形<span class="_ _0"></span>更加<span class="_ _0"></span>清晰<span class="_ _0"></span>。最后<span class="_ _0"></span>，通<span class="_ _0"></span>过<span class="_ _9"> </span><span class="ff5">plt.show()</span></div><div class="t m0 x2 h4 y53 ff3 fs2 fc0 sc1 ls0 ws0">命令，代码展示了这张折线图。</div><div class="t m0 x3 h4 y54 ff3 fs2 fc0 sc1 ls0 ws0">根据<span class="_ _0"></span>图示<span class="_ _0"></span>可以<span class="_ _0"></span>得出<span class="_ _0"></span>可以<span class="_ _0"></span>得出<span class="_ _0"></span>，温<span class="_ _0"></span>州冬<span class="_ _0"></span>夏温<span class="_ _0"></span>差<span class="_ _0"></span>大，<span class="_ _0"></span>而且<span class="_ _0"></span>温差<span class="_ _0"></span>越来<span class="_ _0"></span>越大<span class="_ _0"></span>，其<span class="_ _0"></span>中</div><div class="t m0 x2 h4 y55 ff5 fs2 fc0 sc1 ls0 ws0">2022<span class="_"> </span><span class="ff3">年冬天较以往有不正常温度</span></div><div class="t m0 x2 h5 y56 ff4 fs3 fc0 sc0 ls0 ws0">3.2 <span class="ff1">近一<span class="_ _0"></span>年最高最低<span class="_ _0"></span>温</span></div><div class="t m0 x3 h4 y57 ff3 fs2 fc0 sc1 ls0 ws0">首先，<span class="_ _0"></span>代码导<span class="_ _0"></span>入了所需<span class="_ _0"></span>的库<span class="_ _a"> </span><span class="ff5">pandas<span class="_"> </span></span>和<span class="_ _9"> </span><span class="ff5">matplotlib.pyplot</span>，<span class="_ _0"></span>并设置<span class="_ _0"></span>了中文<span class="_ _0"></span>字体</div><div class="t m0 x2 h4 y58 ff3 fs2 fc0 sc1 ls0 ws0">以防止乱码。<span class="_ _3"></span>接着，<span class="_ _2"></span>从文件中读取包含日期、<span class="_ _3"></span>最低温度和最高温度的数据，<span class="_ _b"></span>并确</div><div class="t m0 x2 h4 y59 ff3 fs2 fc0 sc1 ls0 ws0">保日期列为<span class="_ _9"> </span><span class="ff5">datetime<span class="_"> </span></span>类型，<span class="_ _7"></span>以便后续处理。<span class="_ _c"></span>然后，<span class="_ _7"></span>将日期列设置为数据框的索引，</div><div class="t m0 x2 h4 y5a ff3 fs2 fc0 sc1 ls0 ws0">并筛选出<span class="_ _9"> </span><span class="ff5">2023<span class="_"> </span></span>年<span class="_ _a"> </span><span class="ff5">1<span class="_"> </span></span>月<span class="_ _9"> </span><span class="ff5">1<span class="_"> </span></span>日以后的<span class="_ _0"></span>数据。最后，绘<span class="_ _0"></span>制折线图，其<span class="_ _0"></span>中蓝色线表示最</div><div class="t m0 x2 h4 y5b ff3 fs2 fc0 sc1 ls0 ws0">低温度，<span class="_ _b"></span>红色线表示最高温度，<span class="_ _b"></span>并添加了图例、<span class="_ _3"></span>标题、<span class="_ _b"></span>轴标签和网格以提高图形</div><div class="t m0 x2 h4 y5c ff3 fs2 fc0 sc1 ls0 ws0">的可读性和美观度。</div><div class="t m0 x1 h6 y5d ff6 fs4 fc0 sc1 ls0 ws0"> </div><div class="t m0 x3 h4 y5e ff3 fs2 fc0 sc1 ls0 ws0">根据这张图可以看出，<span class="_ _2"></span>温州市最近一年的气温变化趋势明显。<span class="_ _2"></span>最高气温<span class="_ _2"></span>（红</div><div class="t m0 x2 h4 y5f ff3 fs2 fc0 sc1 ls0 ws0">色）和最低气温（蓝色）都表现出一定的季节性波动。冬季（<span class="ff5">2023<span class="_"> </span></span>年初和<span class="_ _9"> </span><span class="ff5">2023</span></div><div class="t m0 x2 h4 y60 ff3 fs2 fc0 sc1 ls0 ws0">年底<span class="_ _0"></span>）气<span class="_ _0"></span>温较<span class="_ _0"></span>低，<span class="_ _0"></span>夏季<span class="_ _0"></span>（<span class="ff5">2023<span class="_ _a"> </span></span>年中<span class="_ _0"></span>期）<span class="_ _0"></span>气温<span class="_ _0"></span>最高<span class="_ _0"></span>，显<span class="_ _0"></span>示出<span class="_ _0"></span>明显<span class="_ _0"></span>的季<span class="_ _0"></span>节性<span class="_ _0"></span>变化<span class="_ _0"></span>。</div><div class="t m0 x2 h4 y61 ff3 fs2 fc0 sc1 ls0 ws0">总体来<span class="_ _0"></span>看，<span class="_ _0"></span>温州市<span class="_ _0"></span>的气<span class="_ _0"></span>温在<span class="_ _0"></span>一年内<span class="_ _0"></span>有较<span class="_ _0"></span>大波动<span class="_ _0"></span>，夏<span class="_ _0"></span>季最高<span class="_ _0"></span>温度<span class="_ _0"></span>超过<span class="_ _a"> </span><span class="ff5">30°C</span>，而<span class="_ _0"></span>冬</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div><div id="pf5" class="pf w0 h0" data-page-no="5"><div class="pc pc5 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89566646/bg5.jpg"><div class="t m0 x2 h4 y35 ff3 fs2 fc0 sc1 ls0 ws0">季最低温度接近<span class="_ _9"> </span><span class="ff5">0°C</span>，反映出温州市全年气候的显著季节性变化。</div><div class="t m0 x2 h5 y62 ff4 fs3 fc0 sc0 ls0 ws0">3.3 <span class="ff1">近一<span class="_ _0"></span>年天气情况</span></div><div class="t m0 x3 h4 y63 ff3 fs2 fc0 sc1 ls0 ws0">读取温州市<span class="_ _9"> </span><span class="ff5">2023<span class="_ _a"> </span></span>年的天气数据并<span class="_ _0"></span>生成了一张柱<span class="_ _0"></span>状图，显示不<span class="_ _0"></span>同天气类型出</div><div class="t m0 x2 h4 y64 ff3 fs2 fc0 sc1 ls0 ws0">现的频率。<span class="_ _1"></span>首先，<span class="_ _d"></span>代码导入必要的库，<span class="_ _1"></span>并设置中文字体以防止乱码。<span class="_ _d"></span>接着，<span class="_ _1"></span>从<span class="_ _9"> </span><span class="ff5">Excel</span></div><div class="t m0 x2 h4 y65 ff3 fs2 fc0 sc1 ls0 ws0">文件中读取包含日期和天气类型的数据，<span class="_ _e"></span>并确保日期列为<span class="_ _9"> </span><span class="ff5">datetime<span class="_"> </span></span>类型，<span class="_ _e"></span>将其设</div><div class="t m0 x2 h4 y66 ff3 fs2 fc0 sc1 ls0 ws0">置为数据框的索<span class="_ _0"></span>引。然后，筛<span class="_ _0"></span>选出<span class="_ _9"> </span><span class="ff5">2023<span class="_"> </span></span>年的数据<span class="_ _0"></span>，并统计每种<span class="_ _0"></span>天气类型的出现</div><div class="t m0 x2 h4 y67 ff3 fs2 fc0 sc1 ls0 ws0">次数<span class="_ _0"></span>。<span class="_ _0"></span>最<span class="_ _0"></span>后<span class="_ _0"></span>，绘<span class="_ _0"></span>制<span class="_ _0"></span>柱<span class="_ _0"></span>状<span class="_ _0"></span>图，<span class="_ _0"></span>显<span class="_ _0"></span>示<span class="_ _0"></span>每<span class="_ _0"></span>种<span class="_ _0"></span>天气<span class="_ _0"></span>类<span class="_ _0"></span>型<span class="_ _0"></span>的<span class="_ _0"></span>频率<span class="_ _0"></span>，<span class="_ _0"></span>并<span class="_ _0"></span>添<span class="_ _0"></span>加数<span class="_ _0"></span>据<span class="_ _0"></span>标<span class="_ _0"></span>签<span class="_ _0"></span>、<span class="_ _0"></span>标题<span class="_ _0"></span>、</div><div class="t m0 x2 h4 y68 ff3 fs2 fc0 sc1 ls0 ws0">轴标签和旋转的<span class="_ _9"> </span><span class="ff5">x<span class="_"> </span></span>轴标签，以提高图形的可读性和信息量。</div><div class="t m0 x3 h4 y69 ff3 fs2 fc0 sc1 ls0 ws0">然后，<span class="_ _6"></span>代码重新计算了合并后的天气类型出现次数，<span class="_ _6"></span>生成一个新的统计结果。</div><div class="t m0 x2 h4 y6a ff3 fs2 fc0 sc1 ls0 ws0">最后<span class="_ _0"></span>，<span class="_ _0"></span>代<span class="_ _0"></span>码<span class="_ _0"></span>利用<span class="_ _0"></span>这<span class="_ _0"></span>些<span class="_ _0"></span>统<span class="_ _0"></span>计数<span class="_ _0"></span>据<span class="_ _0"></span>绘<span class="_ _0"></span>制<span class="_ _0"></span>了<span class="_ _0"></span>一个<span class="_ _0"></span>环<span class="_ _0"></span>形<span class="_ _0"></span>图<span class="_ _0"></span>（带<span class="_ _0"></span>有<span class="_ _0"></span>中<span class="_ _0"></span>间<span class="_ _0"></span>白色<span class="_ _0"></span>圆<span class="_ _0"></span>环<span class="_ _0"></span>的<span class="_ _0"></span>饼<span class="_ _0"></span>图）<span class="_ _0"></span>，</div><div class="t m0 x2 h4 y6b ff3 fs2 fc0 sc1 ls0 ws0">展示<span class="_ _9"> </span><span class="ff5">2023<span class="_"> </span></span>年温州<span class="_ _0"></span>市各类天气类型<span class="_ _0"></span>的比例分布。图<span class="_ _0"></span>表包含了每种<span class="_ _0"></span>天气类型的标签</div><div class="t m0 x2 h4 y6c ff3 fs2 fc0 sc1 ls0 ws0">和其对应的百分比，提供了对温州市天气情况的直观概览。</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>

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