import numpy as np import matplotlib.pyplot as plt import csv # 1、绘制不同学校课程数量分布图（条状图）。 def task_0(): import numpy as np import matplotlib.pyplot as plt import csv # 读取第一列(跳过第一行) Institution = np.loadtxt("datasets_736_1367_appendix.csv", str,encoding='utf-8',delimiter=',', usecols=(0), unpack=True,skiprows=1) Institution_name = list(set(Institution)) #print(Institution_name) MITx_num , HarvardX_num= 0,0 for i in Institution: if i == Institution_name[0]: MITx_num+=1 elif i == Institution_name[1]: HarvardX_num+=1 #print(MITx_num,HarvardX_num) Institution_num = [MITx_num,HarvardX_num] plt.figure() plt.bar(Institution_name,Institution_num) # 网图像上加上数字标签 for x,y in zip(Institution_name,Institution_num): plt.text(x,y+0.6,str(y),ha='center', va='bottom', fontsize=10.5) plt.rcParams['font.sans-serif'] = ['SimHei'] plt.title("不同学校课程数量分布图") plt.xlabel('学校') plt.ylabel('课程数量') plt.savefig("task0.png") plt.show() # 绘制不同学校上线课程数量随年份的分布图（线性图）。 def task_1(): import numpy as np import matplotlib.pyplot as plt import csv # 读取第一列和第三列 （跳过第一行） Institution,Launch_Date = np.loadtxt("datasets_736_1367_appendix.csv", str, encoding='utf-8', delimiter=',', usecols=(0,2), unpack=True, skiprows=1) # 处理每门课程的日期，将日期只保留年份 Launch_Date =[ i.split("/")[-1] for i in Launch_Date] Institution_name = list(set(Institution)) year_label = list(set(Launch_Date)) year_label = [int(i) for i in year_label] year_label.sort() year_label = [str(i) for i in year_label] print(Institution_name) print(year_label) # 一共有多少年份 year_nums = len(list(set(Launch_Date))) # 一共有多少学校 Institution_num = len(list(set(Institution))) date = np.zeros((Institution_num,year_nums)) for i in range(len(Launch_Date)): if Institution[i]==Institution_name[0] and Launch_Date[i] == year_label[0]: date[0][0]+=1 elif Institution[i]==Institution_name[0] and Launch_Date[i] == year_label[1]: date[0][1] += 1 elif Institution[i]==Institution_name[0] and Launch_Date[i] == year_label[2]: date[0][2] += 1 elif Institution[i]==Institution_name[0] and Launch_Date[i] == year_label[3]: date[0][3] += 1 elif Institution[i]==Institution_name[0] and Launch_Date[i] == year_label[4]: date[0][4] += 1 elif Institution[i]==Institution_name[1] and Launch_Date[i] == year_label[0]: date[1][0]+=1 elif Institution[i]==Institution_name[1] and Launch_Date[i] == year_label[1]: date[1][1] += 1 elif Institution[i]==Institution_name[1] and Launch_Date[i] == year_label[2]: date[1][2] += 1 elif Institution[i]==Institution_name[1] and Launch_Date[i] == year_label[3]: date[1][3] += 1 elif Institution[i]==Institution_name[1] and Launch_Date[i] == year_label[4]: date[1][4] += 1 print(date) plt.rcParams['font.sans-serif'] = ['SimHei'] for i in range(len(Institution_name)): #plt.plot(year_label,date[i]) if i==0: plt.plot(year_label, date[i],'ro--') else : plt.plot(year_label, date[i],'bo-.') # 图示：在左上角显示图示 plt.legend(Institution_name, loc=2) # 网图像上加上数字标签 for x, y in zip(year_label, date[0]): plt.text(x, y + 4, str(int(y)), ha='center', va='bottom', fontsize=10.5,color='black') for x, y in zip(year_label, date[1]): plt.text(x, y + 3, str(int(y)), ha='center', va='bottom', fontsize=10.5,color='green') plt.title("学校上线课程数量随年份的分布图") plt.xlabel('年份') plt.ylabel('课程数量') plt.savefig("task1.png") plt.show() # 3、绘制不同主题（Course Subject）课程数量分布占比图（条状图）。 def task_2(): import numpy as np import matplotlib.pyplot as plt import csv # 读取第6列，跳过第一行 with open("datasets_736_1367_appendix.csv","r",encoding='utf-8') as f: reader = csv.DictReader(f) Course_Subject = [row['Course Subject'] for row in reader] # 保证每次生成的图片一致 Course_Subject_name = list(set(Course_Subject)) Course_Subject_name.sort() print(Course_Subject_name) total = np.zeros(len(Course_Subject_name)) for i in Course_Subject: for j in range(len(Course_Subject_name)): if i==Course_Subject_name[j]: total[j]+=1 break total = [round(i/290*1.0*100,2) for i in total] print(total) plt.figure() plt.rcParams['font.sans-serif'] = ['SimHei'] for i in range(len(Course_Subject_name)): plt.bar(Course_Subject_name[i],total[i]) # 图示：在左上角显示图示 plt.legend(Course_Subject_name, loc=2) # 网图像上加上数字标签 for x, y in zip(Course_Subject_name,total): plt.text(x, y + 1, str(y)+'%', ha='center', va='bottom', fontsize=10.5, color='black') # 横坐标的字体大小，偏移角度 plt.xticks(rotation=10,size=8) plt.title("不同主题（Course Subject）课程数量分布占比") plt.xlabel('Course Subject') plt.ylabel('课程数量占比') plt.savefig("task2.png") plt.show() # 绘制不同主题课程学生平均人数随年份分布图（线性图） def task_3(): import numpy as np import matplotlib.pyplot as plt import csv Launch_Date = np.loadtxt("datasets_736_1367_appendix.csv", str, encoding='utf-8', delimiter=',', usecols=(2), unpack=True, skiprows=1) # 处理每门课程的日期，将日期只保留年份 Launch_Date = [i.split("/")[-1] for i in Launch_Date] year_label = list(set(Launch_Date)) year_label = [int(i) for i in year_label] year_label.sort() year_label = [str(i) for i in year_label] # 获取不同主题课程列表 with open("datasets_736_1367_appendix.csv","r",encoding='utf-8') as f: reader = csv.DictReader(f) Course_Subject = [row['Course Subject'] for row in reader] with open("datasets_736_1367_appendix.csv","r",encoding='utf-8') as f: reader = csv.DictReader(f) Participants = [row['Participants (Course Content Accessed)'] for row in reader] # 获取课程名字数组 # 保证每次生成的图片一致 Course_Subject_name = list(set(Course_Subject)) Course_Subject_name.sort() # 记录每年每门课程的中人数 datesets = np.zeros((len(Course_Subject_name),len(year_label))) # # 记录每年每门课程出现的次数 course_nums = np.zeros((len(Course_Subject_name), len(year_label))) for std_nums in range(len(Participants)): for i in range(len(Course_Subject_name)): for j in range(len(year_label)): if Launch_Date[std_nums]==year_label[j] and Course_Subject[std_nums]==Course_Subject_name[i]: datesets[i][j]+=int(Participants[std_nums]) course_nums[i][j]+=1 break # print(datesets) # print(course_nums) # 获取不同主题课程每年学生平均人数 for i in range(len(Course_Subject_name)): for j in range(len(year_label)): if course_nums[i][j]>0: datesets[i][j] = int(datesets[i][j]/course_nums[i][j]) else: datesets[i][j] = 0 #print(datesets) plt.figure() plt.rcParams['font.sans-serif'] = ['SimHei'] for i in range(len(Course_Subject_name)): plt.plot(year_label,datesets[i],'o-.') # 图示：在右上角显示图示 plt.legend(Course_Subject_name, loc=1) # 网图像上加上数字标签 for i in range(len(Course_Subject_name)): for x, y in zip(year_label, datesets[i]): plt.text(x, y + 1, str(int(y)) , ha='center', va='bottom', fontsize=10.5, color='black') plt.title("不同主题课程学生平均人数随年份分布图") plt.xlabel('年份') plt.ylabel('学生平均人数') plt.savefig("task3.png") plt.show() # 绘制不同学校不同主题课程男性学员平均占比图(（条状图）) def task_4(): import numpy as np import matplotlib.pyplot as plt import csv # 不同学校 Institution = np.loadtxt("datasets_736_1367_appendix.csv", str, encoding='utf-8', delimiter=',', usecols=(0), unpack=True, skiprows=1) # 不同主题课程 with open("datasets_736_1367_appendix.csv","r",encoding='utf-8') as f: reader = csv.DictReader(f) Course_Subject = [row['Course Subject'] for row in reader] # 男性学员平均占比 with open("datasets_736_1367_appendix.csv","r",encoding='utf-8') as f: reader = csv.DictReader(f) Male = [row['% Male'] for row in reader] #print(Male) Institution_name = list(set(Institution)) Institution_name.sort() Course_Subject_name = list(set(Course_Subject)) Course_Subject_name.sort() # 记录每个学校不同科目的男性占比总数 datesets = np.zeros((len(Institution_name),len(Course_Subject_name))) # 出现的次数 course_nums = np.zeros((len(Institution_name), len(Course_Subject_name))) for one in range(len(Male)): for i in range(len(Institution_name)): for j in range(len(Course_Subject_name)): if Institution_name[i]==Institution[one] and Course_Subject_name[j]==Course_Subject[one]: datesets[i][j]+=float(Male[one]) course_nums[i][j]+=1 break # 计算平均男生占比 for i in range(len(Institution_name)): for j in range(len(Course_Subject_name)): if course_nums[i][j]==0: datesets[i][j]=0 else: datesets[i][j] = round(datesets[i][j]/course_nums[i][j],2) print(datesets) X = np.arange(0,len(Course_Subject_name),1) plt.figure() plt.rcParams['font.sans-serif'] = ['SimHei'] width = 0.36 plt.bar(X - width / 2, datesets[0], tick_label = Course_Subject_name, width=width) plt.bar(X + width / 2, datesets[1],tick_label = Course_Subject_name, width=width) # 图示：在左上角显示图示 plt.legend(Institution_name, loc=2) # 网图像上加上数字标签 for i in range(len(Institution_name)): for x, y in zip(X, datesets[i]): if i==0: plt.text(x - width / 2, y , str(y) + "%", ha='center', va='bottom', fontsize=10.5, color='black') else: plt.text(x + width / 2, y , str(y) + "%", ha='center', va='bottom', fontsize=10.5, color='black') # 横坐标的字体大小，偏移角度 plt.xticks(rotation=10, size=8) X=[i*10 for i in range(11) if i>0] plt.yticks(X) plt.title("不同学校不同主题课程男性学员平均占比图") plt.xlabel('不同主题课程') plt.ylabel('男性学员平均占比(%)') plt.savefig("task4.png") plt.show() def main(): # task_0() # # task_1() # # task_2() # # task_3() task_4() return 0 if __name__ == '__main__': main()
麻烦你用“代码段”格式化一下代码吧，实在没法看啊
就是简单的统计分析吧。
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没有用到数据挖掘算法。
这是一段绘制条形图、饼图等绘制图像代码
这段代码没有用到什么数据挖掘算法，开头导入的 numpy 是数据处理和计算的库，matplotlib 是绘图的库，csv 是处理表格的库，代码的大致意思是读取一个 csv 表格的数据，然后绘出以下五个图：不同学校课程数量分布图、学校上线课程数量随年份的分布图、不同主题（Course Subject）课程数量分布占比、不同主题课程学生平均人数随年份分布图、不同学校不同主题课程男性学员平均占比图。
数据处理
数据可视化