1.1 Functions and Features completion of the project
(1) Function: 2016-2018 three different scores usual course, period, end of data analysis and washing, an appropriate final histogram, line charts, pie charts, etc. and represented on a Web page rendered
(2) Features: chart can clearly have to show different scores too low, get to see at a glance the highest score, lowest score, average score, one can observe the chart was quickly obtain the information they want, through the presentation can quickly map find the score gap with his class, clearly we have to master class was the score gap.
Second, a detailed description of each module (name, function, operation theme, the key source code)
(1): Cleaning of data
self.data=xlrd.open_workbook(filedata)
table = self.data.sheet_by_index (0) # 2016 acquisition of table
nrows = table.nrows # Gets the number of rows
ncols = table.ncols # obtain the total number of columns
zero = table.col_values (0) # get the value of the first column
one=table.col_values(1)
two=table.col_values(2)
three=table.col_values(3)
five = table.col_values (4) # get the value of the fourth column
zero.pop (0) # remove the first row
one.pop (0) # removed from the second row
two.pop (0) # third row removed
three.pop (0) # fourth row removed
five.pop (0) # remove the fifth row
for i in zero:
if i not in self.class_list:
self.class_list.append(i)
list1=[]
for e in range(0,len(zero)):
#print (zero1 [e], five1 [e])
if five [e] = 'Absent \ xa0'!:
list1.append(zero[e])
list1.append(one[e])
list1.append(two[e])
list1.append(three[e])
list1.append(five[e])
self.data2016_list1.append (list1) # 2016 form must deposit all the data [[class, gender, peacetime, period, overall rating]], a list sets List
list1=[]
table = self.data.sheet_by_index (1) # obtained in 2017
nrows = table.nrows # Gets the number of rows
ncols = table.ncols # obtain the total number of columns
zero1 = table.col_values (0) # get the value of the first column
one1=table.col_values(1)
two1=table.col_values(2)
three1=table.col_values(3)
five1 = table.col_values (4) # get the value of the fourth column
zero1.pop (0) # remove the first row
one1.pop (0) # removed from the second row
two1.pop (0) # third row removed
three1.pop (0) # fourth row removed
five1.pop (0) # remove the fifth row
for i in zero1:
if i not in self.class_list1:
self.class_list1.append(i)
for e in range(0,len(zero1)):
if five1 [e] = 'Absent \ xa0'!:
list1.append(zero1[e])
list1.append(one1[e])
list1.append(two1[e])
list1.append(three1[e])
list1.append(five1[e])
self.data2017_list1.append(list1)
list1=[]
table = self.data.sheet_by_index (2) # 2018 acquired in Table
nrows = table.nrows # Gets the number of rows
ncols = table.ncols # obtain the total number of columns
zero2 = table.col_values (0) # get the value of the first column
one2=table.col_values(1)
two2=table.col_values(2)
three2=table.col_values(3)
five2 = table.col_values (4) # get the value of the fourth column
zero2.pop (0) # remove the first row
one2.pop (0) # removed from the second row
two2.pop (0) # third row removed
three2.pop (0) # fourth row removed
five2.pop (0) # remove the fifth row
for i in zero2:
if i not in self.class_list2:
self.class_list2.append(i)
for e in range(len(zero2)):
if five2 [e] = 'Absent \ xa0'!:
list1.append(zero2[e])
list1.append(one2[e])
list1.append(two2[e])
list1.append(three2[e])
list1.append(five2[e])
self.data2018_list1.append(list1)
list1=[]
(2): 2016 average for each class, a bar graph
list1=[]
score_list=[]
for i in range(len(self.class_list)):
sum1=0
count=0
score=[]
for j in range(len(self.data2016_list1)):
if self.class_list[i] in self.data2016_list1[j]:
sum1=sum1+int(self.data2016_list1[j][4])
count=count+1
if count>0:
avg=int(sum1/count)
score.append(self.class_list[i])
score.append(avg)
score_list.append(score)
avg_list=[]
for i in range(len(score_list)):
avg_list.append(score_list[i][1])
bar = Bar ( "title: 2016 average for each class," "Subtitle: Histogram")
bar.add ( "Legend: Score (points)", self.class_list, avg_list)
bar.render ( '2016 years for each class average .html')
(3): 2017 average histogram for each class
list1=[]
score_list2=[]
for i in range(len(self.class_list2)):
sum1=0
count=0
score2=[]
for j in range(len(self.data2018_list1)):
if self.class_list2[i] in self.data2018_list1[j]:
sum1=sum1+int(self.data2018_list1[j][4])
count=count+1
if count>0:
avg=float(sum1/count)
score2.append(self.class_list2[i])
score2.append(avg)
score_list2.append(score2)
avg_list2 = [] # of the stored calculated average
for i in range(len(score_list2)):
avg_list2.append(score_list2[i][1])
bar = Bar ( "title: 2018 average for each class," "Subtitle: Histogram")
bar.add ( "Legend: Score (points)", self.class_list2, avg_list2)
bar.render ( '2018 years average histogram for each class .html')
(4): Different overall average histogram Points
self.sheet_list = self.data.sheet_names () # get table name, 2016, 2017,2018 three
#print(sheet_list)
list2=[]
sum1=0
count=0
for e in self.data2016_list1:
if e:
sum1+=int(e[4])
count+=1
list2.append(int(sum1/count))
sum1=0
count=0
for e in self.data2017_list1:
if e:
sum1+=int(e[4])
count+=1
list2.append(int(sum1/count))
sum1=0
count=0
for e in self.data2018_list1:
if e:
sum1+=int(e[4])
count+=1
list2.append(int(sum1/count))
bar=Bar("标题：不同年份总体平均分情况","副标题：柱状图")
bar.add("图注：分数（分）",self.sheet_list,list2)
bar.render('不同总体平均分情况.html')
（5）：2016-2018男女生平时分平均成绩折线图
line=Line('2016-2018男女生平时分平均成绩','折线图')
line.add("女生平时分平均分数(分)",self.sheet_list,avglist1,is_label_show=True)
line.add("男生平时分平均分数(分)",self.sheet_list,avglist4,is_label_show=True)
line.render('2016-2018男女生平时分平均成绩折线图.html')
（6）：2016年平时、期中、期末饼状图
pie = Pie("饼状图", "2016年平时、期中、期末饼状图",title_pos='center',width=900)
#上方的colums选项取消显示，显示label标签
pie.add("2016年各个项目,courselist,avglist11 ,is_legend_show=False,is_label_show=True)
#保存图表
pie.render('2016年各项平均分饼状图.html')
（7）：2016年男女生总评堆积柱状图
bar = Bar("2016年男女生总评堆积柱状图")
bar.add("男生", self.sheet_list, avglist3, is_stack=True)
bar.add('女生', self.sheet_list, avglist6, is_stack=True)
bar.render('2016年男女生总评堆积柱状图.html')
（8）：2016—2018年女生平时、期中、总评成绩柱状图、显示最大值和最小值、平均分数线
data1 = [12,13,14,25,67]
data2 = [34,56,78,45,67]
labels = ['A', 'B', 'C', 'D', 'E']
bar=Bar("标题：2016年女生各项平均成绩","副标题：标记和点柱状图")
bar.add('平时', self.sheet_list, avglist1, mark_point=['average'])
bar.add('期中', self.sheet_list, avglist2, mark_point=['max'], mark_line=['min', 'max'])
bar.add('总评', self.sheet_list, avglist3)
bar.render('2016年女生成绩标记和点柱状图.html')
（9）：2016男生各项平均成绩玫瑰花样式饼图
pie2 = Pie("2016男生各项平均成绩玫瑰花样式饼图", title_pos='center', width=900)
pie2.add("2016男生", courselist, avglist41,is_random=True, radius=[25, 60], rosetype='area', is_legend_show=False, is_label_show=True)
pie2.show_config()
pie2.render('2016男生各项平均成绩玫瑰花样式饼图.html')
（10）：2016-2018年男生各项平均成绩时间轴饼图
pie_1 = Pie("2016年男生各项平均成绩")
pie_1.add("男生", courselist, avglist41,is_label_show=True, radius=[30, 55], rosetype='radius')
pie_2 = Pie("2017年男生各项平均成绩")
pie_2.add("男生", courselist,avglist51,is_label_show=True, radius=[30, 55], rosetype='radius')
pie_3 = Pie("2018年男生各项平均成绩")
pie_3.add("男生", courselist,avglist61,is_label_show=True, radius=[30, 55], rosetype='radius')
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(pie_1, '2016 年')
timeline.add(pie_2, '2017 年')
timeline.add(pie_3, '2018 年')
timeline.render('2016-2018年男生各项平均成绩时间轴饼图.html')
（11）：网页渲染：
from flask import Flask, jsonify, render_template, request, url_for
from werkzeug.utils import redirect
app = Flask(__name__)
@app.route("/index")
def index():
return render_template("index.html")
@app.route("/gra", methods=['GET', 'POST'])
def getValues():
text = request.form.get('课程成绩')
if text == '课程成绩1':
return render_template("2016年各班级平均分.html")
elif text=='课程成绩2':
return render_template("2017年各班级平均分柱状图.html")
elif text=="课程成绩3":
return render_template("2018年各班级平均分柱状图.html")
elif text=="课程成绩4":
return render_template("不同总体平均分情况.html")
elif text=="课程成绩5":
return render_template("2016-2018男女生平时分平均成绩折线图.html")
elif text=="课程成绩6":
return render_template("2016男生各项平均成绩玫瑰花样式饼图.html")
elif text=="课程成绩7":
return render_template("2016年各项平均分饼状图.html")
elif text=="课程成绩8":
return render_template("2016年女生成绩标记和点柱状图.html")
elif text=="课程成绩9":
return render_template("2016年男女生总评堆积柱状图.html")
elif text=="课程成绩10":
return render_template("2016-2018年男生各项平均成绩时间轴饼图.html")
else:
return "不存在"
if __name__ == '__main__':
app.run(host='127.9.9.7', port=8888, debug=True)
三、运用到的知识
（1）运用flask框架、python语言、html5语言。
（2）Numpy：增强了Python的科学计算和数据处理能力。
（3）Matplotlab：专业画图工具
（4）Pandas：为数据的读取、数据清洗、数据快速分析提供强大的便捷。
（5）pyecharts:可将图表生成html文件，直接用于交互式的页面可视化，也可以生成Jupyter Notebook格式。将数据清晰得展示出来。