【Python + ArcGIS】数据分析实战——学生房前屋后的物种多样性调查

科技2025-01-25 5

【Python + ArcGIS】数据分析实战

——学生房前屋后的物种多样性调查

0 前言

源数据经清洗后仅110行数据，数据量较小。但由于问卷设计和填写的不专业，数据类型和内容没有保障，导致数据结构复杂，对于新手和经常需要代做数据分析的人们来说可以作为很好的案例。

【Python + ArcGIS】数据分析实战——学生房前屋后的物种多样性调查 0 前言1 数据格式与分析需求1.1 数据格式1.2 分析需求 2 工具准备3 数据处理流程4 数据处理4.1 初步处理4.2 Python处理4.2.1 对于需求1 的 Python 处理4.2.2 对于需求1 的地理处理4.2.3 对于需求2 的 Python 处理 4 分析结果·需求达成

1 数据格式与分析需求

1.1 数据格式

以下是本人收到的数据Excel文件的表头与样例数据，具体数据来源应该为面向学生发放的调查问卷。数据包括了：

问卷的一些基本信息

当前时间、当前位置一个看上去像List但是比较难自动化处理的复杂的数据

1-10号植物信息照片：网址（字符串），名称：字符串，分类：字符串

学生感想与自我评分。学生感想：字符串，自我评分：数字

1.2 分析需求

以物种为单位，选择5种频数最高的植物物种制作植物的分布地图，从而分析某个植物的地域分异规律，从生长地的生物和地理的角度解释物种分布的合理性。

汇总各个行政区的物种，从物种数量上进行物种多样性比较，概括植物的种类特征以及差异的原因。

2 工具准备

Microsoft Office Excel

Python 涉及的库：pandas

ArcGIS

3 数据处理流程

#mermaid-svg-P4Kyuz2sYc2Wfoeq .label{font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family);fill:#333;color:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .label text{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .node rect,#mermaid-svg-P4Kyuz2sYc2Wfoeq .node circle,#mermaid-svg-P4Kyuz2sYc2Wfoeq .node ellipse,#mermaid-svg-P4Kyuz2sYc2Wfoeq .node polygon,#mermaid-svg-P4Kyuz2sYc2Wfoeq .node path{fill:#ECECFF;stroke:#9370db;stroke-width:1px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .node .label{text-align:center;fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .node.clickable{cursor:pointer}#mermaid-svg-P4Kyuz2sYc2Wfoeq .arrowheadPath{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edgePath .path{stroke:#333;stroke-width:1.5px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .flowchart-link{stroke:#333;fill:none}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edgeLabel{background-color:#e8e8e8;text-align:center}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edgeLabel rect{opacity:0.9}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edgeLabel span{color:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .cluster rect{fill:#ffffde;stroke:#aa3;stroke-width:1px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .cluster text{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family);font-size:12px;background:#ffffde;border:1px solid #aa3;border-radius:2px;pointer-events:none;z-index:100}#mermaid-svg-P4Kyuz2sYc2Wfoeq .actor{stroke:#ccf;fill:#ECECFF}#mermaid-svg-P4Kyuz2sYc2Wfoeq text.actor>tspan{fill:#000;stroke:none}#mermaid-svg-P4Kyuz2sYc2Wfoeq .actor-line{stroke:grey}#mermaid-svg-P4Kyuz2sYc2Wfoeq .messageLine0{stroke-width:1.5;stroke-dasharray:none;stroke:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .messageLine1{stroke-width:1.5;stroke-dasharray:2, 2;stroke:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq #arrowhead path{fill:#333;stroke:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .sequenceNumber{fill:#fff}#mermaid-svg-P4Kyuz2sYc2Wfoeq #sequencenumber{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq #crosshead path{fill:#333;stroke:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .messageText{fill:#333;stroke:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .labelBox{stroke:#ccf;fill:#ECECFF}#mermaid-svg-P4Kyuz2sYc2Wfoeq .labelText,#mermaid-svg-P4Kyuz2sYc2Wfoeq .labelText>tspan{fill:#000;stroke:none}#mermaid-svg-P4Kyuz2sYc2Wfoeq .loopText,#mermaid-svg-P4Kyuz2sYc2Wfoeq .loopText>tspan{fill:#000;stroke:none}#mermaid-svg-P4Kyuz2sYc2Wfoeq .loopLine{stroke-width:2px;stroke-dasharray:2, 2;stroke:#ccf;fill:#ccf}#mermaid-svg-P4Kyuz2sYc2Wfoeq .note{stroke:#aa3;fill:#fff5ad}#mermaid-svg-P4Kyuz2sYc2Wfoeq .noteText,#mermaid-svg-P4Kyuz2sYc2Wfoeq .noteText>tspan{fill:#000;stroke:none}#mermaid-svg-P4Kyuz2sYc2Wfoeq .activation0{fill:#f4f4f4;stroke:#666}#mermaid-svg-P4Kyuz2sYc2Wfoeq .activation1{fill:#f4f4f4;stroke:#666}#mermaid-svg-P4Kyuz2sYc2Wfoeq .activation2{fill:#f4f4f4;stroke:#666}#mermaid-svg-P4Kyuz2sYc2Wfoeq .mermaid-main-font{font-family:"trebuchet ms", verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .section{stroke:none;opacity:0.2}#mermaid-svg-P4Kyuz2sYc2Wfoeq .section0{fill:rgba(102,102,255,0.49)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .section2{fill:#fff400}#mermaid-svg-P4Kyuz2sYc2Wfoeq .section1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .section3{fill:#fff;opacity:0.2}#mermaid-svg-P4Kyuz2sYc2Wfoeq .sectionTitle0{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .sectionTitle1{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .sectionTitle2{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .sectionTitle3{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .sectionTitle{text-anchor:start;font-size:11px;text-height:14px;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .grid .tick{stroke:#d3d3d3;opacity:0.8;shape-rendering:crispEdges}#mermaid-svg-P4Kyuz2sYc2Wfoeq .grid .tick text{font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .grid path{stroke-width:0}#mermaid-svg-P4Kyuz2sYc2Wfoeq .today{fill:none;stroke:red;stroke-width:2px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .task{stroke-width:2}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskText{text-anchor:middle;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskText:not([font-size]){font-size:11px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskTextOutsideRight{fill:#000;text-anchor:start;font-size:11px;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskTextOutsideLeft{fill:#000;text-anchor:end;font-size:11px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .task.clickable{cursor:pointer}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskText.clickable{cursor:pointer;fill:#003163 !important;font-weight:bold}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskTextOutsideLeft.clickable{cursor:pointer;fill:#003163 !important;font-weight:bold}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskTextOutsideRight.clickable{cursor:pointer;fill:#003163 !important;font-weight:bold}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskText0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskText1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskText2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskText3{fill:#fff}#mermaid-svg-P4Kyuz2sYc2Wfoeq .task0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .task1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .task2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .task3{fill:#8a90dd;stroke:#534fbc}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskTextOutside0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskTextOutside2{fill:#000}#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskTextOutside1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .taskTextOutside3{fill:#000}#mermaid-svg-P4Kyuz2sYc2Wfoeq .active0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .active1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .active2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .active3{fill:#bfc7ff;stroke:#534fbc}#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeText0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeText1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeText2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeText3{fill:#000 !important}#mermaid-svg-P4Kyuz2sYc2Wfoeq .done0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .done1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .done2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .done3{stroke:grey;fill:#d3d3d3;stroke-width:2}#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneText0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneText1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneText2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneText3{fill:#000 !important}#mermaid-svg-P4Kyuz2sYc2Wfoeq .crit0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .crit1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .crit2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .crit3{stroke:#f88;fill:red;stroke-width:2}#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeCrit0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeCrit1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeCrit2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeCrit3{stroke:#f88;fill:#bfc7ff;stroke-width:2}#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneCrit0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneCrit1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneCrit2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneCrit3{stroke:#f88;fill:#d3d3d3;stroke-width:2;cursor:pointer;shape-rendering:crispEdges}#mermaid-svg-P4Kyuz2sYc2Wfoeq .milestone{transform:rotate(45deg) scale(0.8, 0.8)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .milestoneText{font-style:italic}#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneCritText0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneCritText1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneCritText2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .doneCritText3{fill:#000 !important}#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeCritText0,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeCritText1,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeCritText2,#mermaid-svg-P4Kyuz2sYc2Wfoeq .activeCritText3{fill:#000 !important}#mermaid-svg-P4Kyuz2sYc2Wfoeq .titleText{text-anchor:middle;font-size:18px;fill:#000;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.classGroup text{fill:#9370db;stroke:none;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family);font-size:10px}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.classGroup text .title{font-weight:bolder}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.clickable{cursor:pointer}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.classGroup rect{fill:#ECECFF;stroke:#9370db}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.classGroup line{stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq .classLabel .box{stroke:none;stroke-width:0;fill:#ECECFF;opacity:0.5}#mermaid-svg-P4Kyuz2sYc2Wfoeq .classLabel .label{fill:#9370db;font-size:10px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .relation{stroke:#9370db;stroke-width:1;fill:none}#mermaid-svg-P4Kyuz2sYc2Wfoeq .dashed-line{stroke-dasharray:3}#mermaid-svg-P4Kyuz2sYc2Wfoeq #compositionStart{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq #compositionEnd{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq #aggregationStart{fill:#ECECFF;stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq #aggregationEnd{fill:#ECECFF;stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq #dependencyStart{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq #dependencyEnd{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq #extensionStart{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq #extensionEnd{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq .commit-id,#mermaid-svg-P4Kyuz2sYc2Wfoeq .commit-msg,#mermaid-svg-P4Kyuz2sYc2Wfoeq .branch-label{fill:lightgrey;color:lightgrey;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .pieTitleText{text-anchor:middle;font-size:25px;fill:#000;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .slice{font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.stateGroup text{fill:#9370db;stroke:none;font-size:10px;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.stateGroup text{fill:#9370db;fill:#333;stroke:none;font-size:10px}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.statediagram-cluster .cluster-label text{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.stateGroup .state-title{font-weight:bolder;fill:#000}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.stateGroup rect{fill:#ECECFF;stroke:#9370db}#mermaid-svg-P4Kyuz2sYc2Wfoeq g.stateGroup line{stroke:#9370db;stroke-width:1}#mermaid-svg-P4Kyuz2sYc2Wfoeq .transition{stroke:#9370db;stroke-width:1;fill:none}#mermaid-svg-P4Kyuz2sYc2Wfoeq .stateGroup .composit{fill:white;border-bottom:1px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .stateGroup .alt-composit{fill:#e0e0e0;border-bottom:1px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .state-note{stroke:#aa3;fill:#fff5ad}#mermaid-svg-P4Kyuz2sYc2Wfoeq .state-note text{fill:black;stroke:none;font-size:10px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .stateLabel .box{stroke:none;stroke-width:0;fill:#ECECFF;opacity:0.7}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edgeLabel text{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .stateLabel text{fill:#000;font-size:10px;font-weight:bold;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-P4Kyuz2sYc2Wfoeq .node circle.state-start{fill:black;stroke:black}#mermaid-svg-P4Kyuz2sYc2Wfoeq .node circle.state-end{fill:black;stroke:white;stroke-width:1.5}#mermaid-svg-P4Kyuz2sYc2Wfoeq #statediagram-barbEnd{fill:#9370db}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-cluster rect{fill:#ECECFF;stroke:#9370db;stroke-width:1px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-cluster rect.outer{rx:5px;ry:5px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-state .divider{stroke:#9370db}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-state .title-state{rx:5px;ry:5px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-cluster.statediagram-cluster .inner{fill:white}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-cluster.statediagram-cluster-alt .inner{fill:#e0e0e0}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-cluster .inner{rx:0;ry:0}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-state rect.basic{rx:5px;ry:5px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-state rect.divider{stroke-dasharray:10,10;fill:#efefef}#mermaid-svg-P4Kyuz2sYc2Wfoeq .note-edge{stroke-dasharray:5}#mermaid-svg-P4Kyuz2sYc2Wfoeq .statediagram-note rect{fill:#fff5ad;stroke:#aa3;stroke-width:1px;rx:0;ry:0}:root{--mermaid-font-family: '"trebuchet ms", verdana, arial';--mermaid-font-family: "Comic Sans MS", "Comic Sans", cursive}#mermaid-svg-P4Kyuz2sYc2Wfoeq .error-icon{fill:#522}#mermaid-svg-P4Kyuz2sYc2Wfoeq .error-text{fill:#522;stroke:#522}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edge-thickness-normal{stroke-width:2px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edge-thickness-thick{stroke-width:3.5px}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edge-pattern-solid{stroke-dasharray:0}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edge-pattern-dashed{stroke-dasharray:3}#mermaid-svg-P4Kyuz2sYc2Wfoeq .edge-pattern-dotted{stroke-dasharray:2}#mermaid-svg-P4Kyuz2sYc2Wfoeq .marker{fill:#333}#mermaid-svg-P4Kyuz2sYc2Wfoeq .marker.cross{stroke:#333} :root { --mermaid-font-family: "trebuchet ms", verdana, arial;} #mermaid-svg-P4Kyuz2sYc2Wfoeq { color: rgba(0, 0, 0, 0.75); font: ; } Python--ArcGIS Python 获得数据分析数据格式获得需求工具准备 Microsoft Office Excel pandas Python ArcGIS 初步处理对于需求1的处理对于需求2的处理需求达成

4 数据处理

4.1 初步处理

根据需求与数据进行初步处理：

删除了与需求无关的列对于“当前时间,当前位置”，通过整体复制保存为txt文件，运用替换删除“[”、“]”、“ " ”，再保存为.csv文件，复制回原数据中。再利用Excel中的mid()函数对地址串进行截取，获得行政区数据列。

以下是经初步处理后的数据格式与样例数据：

4.2 Python处理

4.2.1 对于需求1 的 Python 处理

import pandas from pandas import DataFrame #定义查询表头列表 titles=[] for i in range(1,11,1): titles.append('主要植物'+str(i)+'号名称') #print(titles) #打开Excel文件 df=pandas.read_excel('房前屋后的物种多样性调查(6).xls','总表',index_col=0,header=0) #print(df) #将主要植物1-10号名称各列合并为一个List name_list=[] for i in titles: for j in df[i].values.tolist(): name_list.append(j) #print(name_list) #利用字典进行物种名称统计 name_dist={} for i in name_list: if i not in name_dist: name_dist[i]=1 else: name_dist[i]+=1 #利用zip和sorted函数进行排序 tuple_list=zip(name_dist.keys(),name_dist.values()) sort = sorted(tuple_list,key=lambda x: x[1],reverse=True) #输出植物数量最多的前五种植物的List mask_list=[] counter=0 #设置计数器 for i in sort: mask_list.append(i[0]) counter+=1 if counter==5: break print(mask_list) #输出植物数量最多的前五种植物的详细信息 for i in mask_list: mask=df[titles].values==i #print(mask) df_result=DataFrame(df[mask]) #print(df_result) #将每个植物的详细信息保存为csv文件 filename=i+'.csv' df_result.to_csv(filename)

4.2.2 对于需求1 的地理处理

利用上述代码输出的CSV文件，在ArcMap的目录窗口中，右击CSV文件，选择“创建要素类”—“从XY表”即可。

4.2.3 对于需求2 的 Python 处理

import pandas from pandas import DataFrame #定义查询表头列表 titles=[] for i in range(1,11,1): titles.append('主要植物'+str(i)+'号名称') #print(titles) #打开Excel文件 df=pandas.read_excel('房前屋后的物种多样性调查(6).xls','总表',index_col=0,header=0) #print(df) #利用字典形成行政区查询列表 dis=df['行政区'].values.tolist() temp_dic={} for i in dis: if i not in temp_dic: temp_dic[i] = 1 else: temp_dic[i] += 1 #print(temp_dic.keys()) dis=list(temp_dic.keys()) #定义各区物种多样性数值字典 dis_count={} #设计函数统计各区物种多样性 def xls_find(district): #输出行政区名称 print(district) #利用字典查询某行政区的物种 mask=df['行政区'].values==district df_district=df[mask] #利用字典进行物种名称统计 name_list = [] for i in titles: for j in df_district[i].values.tolist(): name_list.append(j) # print(name_list) name_dist = {} for i in name_list: if i not in name_dist: name_dist[i] = 1 else: name_dist[i] += 1 #输出行政区物种多样性数值 dis_count[district]=len(name_dist) #对行政区各个物种的数量进行排序 df_temp=DataFrame.from_dict(data=name_dist,orient ='index',columns=['数量']) df_temp.sort_values(by=['数量'], ascending=False, inplace=True) print(df_temp) #输出csv文件 filename=district+'.csv' df_temp.to_csv(filename, encoding='utf_8_sig') #对于各个行政区循环执行自定函数 for i in dis: xls_find(i) #排序并输出csv文件 dis_count=DataFrame.from_dict(data=dis_count,orient ='index',columns=['数量']) dis_count.sort_values(by=['数量'], ascending=False, inplace=True) print(dis_count) dis_count.to_csv('各区物种多样性值.csv', encoding='utf_8_sig')

4 分析结果·需求达成

最终成果包括一些6个分布最多的植物的详细数据CSV文件、Shapefile文件与分布图，各区物种多样性详细数据CSV文件。

Processed: 0.009, SQL: 8