第八章 商品零售购物篮分析

标签：sort plt supportData 购物篮 selected 零售 第八章 data id

 

#代码8-1 查看数据特征

import numpy as np
import pandas as pd

inputfile = "E:\\anaconda3\\jupyterFile\\数据分析\\data\\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
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的商品销量及其占比
import pandas as pd
inputfile = "E:\\anaconda3\\jupyterFile\\数据分析\\data\\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

#画条形图展示销量排行前10的商品销量
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('商品的销量TOP10(学号3140)') #设置标题
plt.savefig("E:\\anaconda3\\jupyterFile\\数据分析\\tmp\\top10.png") #把图片以.png格式保存
plt.show() #展示图片

#销量排行前10的商品销量占比
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 = "E:\\anaconda3\\jupyterFile\\数据分析\\data\\GoodsOrder.csv "
inputfile2 = "E:\\anaconda3\\jupyterFile\\数据分析\\data\\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) #根据type合并两个datafreame

#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('各类别商品的销售及其占比：\n',sort_link)
outfile1 = "E:\\anaconda3\\jupyterFile\\数据分析\\tmp\\percent.csv"
sort_link.to_csv(outfile1,index=False,header=True,encoding='gbk')

#画饼图展示每类商品的销量占比
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('每类商品销量占比(学号3140)')
plt.savefig("E:\\anaconda3\\jupyterFile\\数据分析\\tmp\\persent.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'] #排序
child_nums = selected['id'].sum() #对所有“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:\\anaconda3\\jupyterFile\\数据分析\\tmp\\child_percent.csv"
selected.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("非酒精饮料内部各商品的销量占比(学号3140)") #设置标题
plt.axis('equal')
plt.savefig("E:\\anaconda3\\jupyterFile\\数据分析\\tmp\\child_persent.png") #保存图形
plt.show() #显示图形

  

 

 

#代码8-4 酒精饮料内部商品的销量及其占比

#先筛选“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'] == '酒精饮料'] #排序
child_nums = selected['id'].sum() #对所有“酒精饮料”求和
selected['child_percent2'] = selected.apply(lambda line: line['id'] / child_nums,axis=1) #求百分比
selected.rename(columns={'id':'count'},inplace=True)
print('酒精饮料内部商品的销量及其占比:\n',selected)
outfile3 = "E:\\anaconda3\\jupyterFile\\数据分析\\tmp\\child_percent2.csv"
selected.to_csv(outfile3,index=False,header=True,encoding='gbk') #输出结果

#画饼图展示酒精饮品内部各商品的销量占比
import matplotlib.pyplot as plt
data = selected['child_percent2']
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("酒精饮料内部各商品的销量占比(学号3140)") #设置标题
plt.axis('equal')
plt.savefig("E:\\anaconda3\\jupyterFile\\数据分析\\tmp\\child_persent2.png") #保存图形
plt.show() #显示图形

  

 

 

#代码8-4 西点类内部商品的销量及其占比

#先筛选“西点”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'] == '西点'] #排序
child_nums = selected['id'].sum() #对所有“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:\\anaconda3\\jupyterFile\\数据分析\\tmp\\child_percent3.csv"
selected.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 = (0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.08,0.03,0.1,0.03,0.1,0.1,0.1,0.1,0.3,0.2,0.3,0.5,0.3,0.3) #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("西点内部各商品的销量占比(学号3140)") #设置标题
plt.axis('equal')
plt.savefig("E:\\anaconda3\\jupyterFile\\数据分析\\tmp\\child_persent3.png") #保存图形
plt.show() #显示图形

  

 

 

#代码8-5 数据转换
import pandas as pd
inputfile = "E:\\anaconda3\\jupyterFile\\数据分析\\data\\GoodsOrder.csv"
data = pd.read_csv(inputfile,encoding='gbk')

#根据id对“Goods”列合并，并使用“，”将商品隔开
data['Goods'] = data['Goods'].apply(lambda x:','+x)
data = data.groupby('id').sum().reset_index()

#对合并的商品列转换数据格式
data['Goods'] = data['Goods'].apply(lambda x:[x[1:]])
data_list = list(data['Goods'])

#分割商品名为各个元素
data_translation = []
for i in data_list:
    p = i[0].split(',')
    data_translation.append(p)
print('数据转换结果的前5个元素:\n',data_translation[0:5])

 

 

本案例的目标是探索商品之间的关联关系，因此采用关联规则算法。Apriori算法是常用的关联规则算法之一，也是最为经典的分析频繁集的算法，它是第一次实现在大数据集上可行的关联规则提取的算法。

# 8-6 构建关联规则模型
from numpy import *
 
def loadDataSet():
    return [['a', 'c', 'e'], ['b', 'd'], ['b', 'c'], ['a', 'b', 'c', 'd'], ['a', 'b'], ['b', 'c'], ['a', 'b'],
            ['a', 'b', 'c', 'e'], ['a', 'b', 'c'], ['a', 'c', 'e']]
 
def createC1(dataSet):
    C1 = []
    for transaction in dataSet:
        for item in transaction:
            if not [item] in C1:
                C1.append([item])
    C1.sort()
    # 映射为frozenset唯一性的，可使用其构造字典
    return list(map(frozenset, C1))     
    
# 从候选K项集到频繁K项集（支持度计算）
def scanD(D, Ck, minSupport):
    ssCnt = {}
    for tid in D:   # 遍历数据集
        for can in Ck:  # 遍历候选项
            if can.issubset(tid):  # 判断候选项中是否含数据集的各项
                if not can in ssCnt:
                    ssCnt[can] = 1  # 不含设为1
                else:
                    ssCnt[can] += 1  # 有则计数加1
    numItems = float(len(D))  # 数据集大小
    retList = []  # L1初始化
    supportData = {}  # 记录候选项中各个数据的支持度
    for key in ssCnt:
        support = ssCnt[key] / numItems  # 计算支持度
        if support >= minSupport:
            retList.insert(0, key)  # 满足条件加入L1中
            supportData[key] = support  
    return retList, supportData
 
def calSupport(D, Ck, min_support):
    dict_sup = {}
    for i in D:
        for j in Ck:
            if j.issubset(i):
                if not j in dict_sup:
                    dict_sup[j] = 1
                else:
                    dict_sup[j] += 1
    sumCount = float(len(D))
    supportData = {}
    relist = []
    for i in dict_sup:
        temp_sup = dict_sup[i] / sumCount
        if temp_sup >= min_support:
            relist.append(i)
# 此处可设置返回全部的支持度数据（或者频繁项集的支持度数据）
            supportData[i] = temp_sup
    return relist, supportData
 
# 改进剪枝算法
def aprioriGen(Lk, k):
    retList = []
    lenLk = len(Lk)
    for i in range(lenLk):
        for j in range(i + 1, lenLk):  # 两两组合遍历
            L1 = list(Lk[i])[:k - 2]
            L2 = list(Lk[j])[:k - 2]
            L1.sort()
            L2.sort()
            if L1 == L2:  # 前k-1项相等，则可相乘，这样可防止重复项出现
                # 进行剪枝（a1为k项集中的一个元素，b为它的所有k-1项子集）
                a = Lk[i] | Lk[j]  # a为frozenset()集合
                a1 = list(a)
                b = []
                # 遍历取出每一个元素，转换为set，依次从a1中剔除该元素，并加入到b中
                for q in range(len(a1)):
                    t = [a1[q]]
                    tt = frozenset(set(a1) - set(t))
                    b.append(tt)
                t = 0
                for w in b:
                    # 当b（即所有k-1项子集）都是Lk（频繁的）的子集，则保留，否则删除。
                    if w in Lk:
                        t += 1
                if t == len(b):
                    retList.append(b[0] | b[1])
    return retList

def apriori(dataSet, minSupport=0.2):
# 前3条语句是对计算查找单个元素中的频繁项集
    C1 = createC1(dataSet)
    D = list(map(set, dataSet))  # 使用list()转换为列表
    L1, supportData = calSupport(D, C1, minSupport)
    L = [L1]  # 加列表框，使得1项集为一个单独元素
    k = 2
    while (len(L[k - 2]) > 0):  # 是否还有候选集
        Ck = aprioriGen(L[k - 2], k)
        Lk, supK = scanD(D, Ck, minSupport)  # scan DB to get Lk
        supportData.update(supK)  # 把supk的键值对添加到supportData里
        L.append(Lk)  # L最后一个值为空集
        k += 1
    del L[-1]  # 删除最后一个空集
    return L, supportData  # L为频繁项集，为一个列表，1，2，3项集分别为一个元素

# 生成集合的所有子集
def getSubset(fromList, toList):
    for i in range(len(fromList)):
        t = [fromList[i]]
        tt = frozenset(set(fromList) - set(t))
        if not tt in toList:
            toList.append(tt)
            tt = list(tt)
            if len(tt) > 1:
                getSubset(tt, toList)
 
def calcConf(freqSet, H, supportData, ruleList, minConf=0.7):
    for conseq in H:  #遍历H中的所有项集并计算它们的可信度值
        conf = supportData[freqSet] / supportData[freqSet - conseq]  # 可信度计算，结合支持度数据
        # 提升度lift计算lift = p(a & b) / p(a)*p(b)
        lift = supportData[freqSet] / (supportData[conseq] * supportData[freqSet - conseq])
 
        if conf >= minConf and lift > 1:
            print(freqSet - conseq, '-->', conseq, '支持度', round(supportData[freqSet], 6), '置信度：', round(conf, 6),
                  'lift值为：', round(lift, 6))
            ruleList.append((freqSet - conseq, conseq, conf))
 
# 生成规则
def gen_rule(L, supportData, minConf = 0.7):
    bigRuleList = []
    for i in range(1, len(L)):  # 从二项集开始计算
        for freqSet in L[i]:  # freqSet为所有的k项集
            # 求该三项集的所有非空子集，1项集，2项集，直到k-1项集，用H1表示，为list类型,里面为frozenset类型，
            H1 = list(freqSet)
            all_subset = []
            getSubset(H1, all_subset)  # 生成所有的子集
            calcConf(freqSet, all_subset, supportData, bigRuleList, minConf)
    return bigRuleList
 
if __name__ == '__main__':
    dataSet = data_translation
    L, supportData = apriori(dataSet, minSupport = 0.02)
    rule = gen_rule(L, supportData, minConf = 0.35)

 

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标签：sort,plt,supportData,购物篮,selected,零售,第八章,data,id
From： https://www.cnblogs.com/hxq----1094552116/p/17227606.html