# -*- coding: utf-8 -*- # 代码8-1 查看数据特征 import numpy as np import pandas as pd inputfile =r"C:\Users\admin\Documents\WeChat Files\wxid_0qjwmqlaa9h522\FileStorage\File\2023-03\GoodsOrder.csv" # 输入的数据文件 data = pd.read_csv(inputfile,encoding = 'gbk') # 读取数据 data .info() # 查看数据属性 data = data['id'] description = [data.count(),data.min(), data.max()] # 依次计算总数、最小值、最大值 description = pd.DataFrame(description, index = ['Count','Min', 'Max']).T # 将结果存入数据框 print('描述性统计结果：\n',np.round(description)) # 输出结果 # 代码8-2 分析热销商品 # 销量排行前10商品的销量及其占比 import pandas as pd inputfile = r"C:\Users\admin\Documents\WeChat Files\wxid_0qjwmqlaa9h522\FileStorage\File\2023-03\GoodsOrder.csv" # 输入的数据文件 data = pd.read_csv(inputfile,encoding = 'gbk') # 读取数据 group = data.groupby(['Goods']).count().reset_index() # 对商品进行分类汇总 sorted=group.sort_values('id',ascending=False) print('销量排行前10商品的销量:\n', sorted[:10]) # 排序并查看前10位热销商品 # 画条形图展示出销量排行前10商品的销量 import matplotlib.pyplot as plt x=sorted[:10]['Goods'] y=sorted[:10]['id'] plt.figure(figsize = (8, 4)) # 设置画布大小 plt.barh(x,y) plt.rcParams['font.sans-serif'] = 'SimHei' plt.xlabel('销量') # 设置x轴标题 plt.ylabel('商品类别') # 设置y轴标题 plt.title('学号：3020商品的销量TOP10') # 设置标题 plt.savefig("E:\Figure 2023-03-15 103822.png") # 把图片以.png格式保存 plt.show() # 展示图片 # 销量排行前10商品的销量占比 data_nums = data.shape[0] for idnex, row in sorted[:10].iterrows(): print(row['Goods'],row['id'],row['id']/data_nums) # 代码8-3 各类别商品的销量及其占比 import pandas as pd inputfile1 =r"C:\Users\admin\Documents\WeChat Files\wxid_0qjwmqlaa9h522\FileStorage\File\2023-03\GoodsOrder.csv" inputfile2 =r"C:\Users\admin\Documents\WeChat Files\wxid_0qjwmqlaa9h522\FileStorage\File\2023-03\GoodsTypes.csv" data = pd.read_csv(inputfile1,encoding = 'gbk') types = pd.read_csv(inputfile2,encoding = 'gbk') # 读入数据 group = data.groupby(['Goods']).count().reset_index() sort = group.sort_values('id',ascending = False).reset_index() data_nums = data.shape[0] # 总量 del sort['index'] sort_links = pd.merge(sort,types) # 合并两个datafreame 根据type # 根据类别求和，每个商品类别的总量，并排序 sort_link = sort_links.groupby(['Types']).sum().reset_index() sort_link = sort_link.sort_values('id',ascending = False).reset_index() del sort_link['index'] # 删除“index”列 # 求百分比，然后更换列名，最后输出到文件 sort_link['count'] = sort_link.apply(lambda line: line['id']/data_nums,axis=1) sort_link.rename(columns = {'count':'percent'},inplace = True) print('各类别商品的销量及其占比:\n',sort_link) outfile1 ="E:/789.csv" sort_link.to_csv(outfile1,index = False,header = True,encoding='gbk') # 保存结果 # 画饼图展示每类商品销量占比 import matplotlib.pyplot as plt data = sort_link['percent'] labels = sort_link['Types'] plt.figure(figsize=(8, 6)) # 设置画布大小 plt.pie(data,labels=labels,autopct='%1.2f%%') plt.rcParams['font.sans-serif'] = 'SimHei' plt.title('学号：3020每类商品销量占比') # 设置标题 plt.savefig("E:/4789.png") # 把图片以.png格式保存 plt.show() # 代码8-4 非酒精饮料内部商品的销量及其占比 # 先筛选“非酒精饮料”类型的商品，然后求百分比，然后输出结果到文件。 selected = sort_links.loc[sort_links['Types'] == '非酒精饮料'] # 挑选商品类别为“非酒精饮料”并排序 child_nums = selected['id'].sum() # 对所有的“非酒精饮料”求和 selected['child_percent'] = selected.apply(lambda line: line['id']/child_nums,axis = 1) # 求百分比 selected.rename(columns = {'id':'count'},inplace = True) print('非酒精饮料内部商品的销量及其占比:\n',selected) outfile2 = "E:/369.csv" sort_link.to_csv(outfile2,index = False,header = True,encoding='gbk') # 输出结果 # 画饼图展示非酒精饮品内部各商品的销量占比 import matplotlib.pyplot as plt data = selected['child_percent'] labels = selected['Goods'] plt.figure(figsize = (8,6)) # 设置画布大小 explode = (0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.08,0.3,0.1,0.3) # 设置每一块分割出的间隙大小 plt.pie(data,explode = explode,labels = labels,autopct = '%1.2f%%', pctdistance = 1.1,labeldistance = 1.2) plt.rcParams['font.sans-serif'] = 'SimHei' plt.title("学号：3020非酒精饮料内部各商品的销量占比") # 设置标题 plt.axis('equal') plt.savefig("E:/5478.png") # 保存图形 plt.show() # 展示图形
