商品零售购物篮分析

查看数据特征

import numpy as np
import pandas as pd

inputfile = 'D:\Python\数据处理/GoodsOrder.csv'
data = pd.read_csv(inputfile,encoding='gbk')
data.info()

data = data['id']
description = [data.count(),data.min(),data.max()]
description = pd.DataFrame(description,index=['Count','Min','Max']).T
print('描述性统计结果：\n',np.round(description))

 

 分析热销商品

import pandas as pd
inputfile = 'D:\Python\数据处理/GoodsOrder.csv'
data = pd.read_csv(inputfile,encoding='gbk')
group = data.groupby(['Goods']).count().reset_index()
sorted = group.sort_values('id',ascending=False)
print('销售排行前10商品的销量：\n',sorted[:10])

import matplotlib.pyplot as plt
x = sorted[:10]['Goods']
y = sorted[:10]['id']
plt.figure(figsize=(8,4))
plt.barh(x,y)
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.xlabel('销量')
plt.ylabel('商品类别')
plt.title('商品的销量TOP103152')
plt.savefig('D:\Python\数据处理/top10.png')
plt.show()

data_nums = data.shape[0]
for idnex,row in sorted[:10].iterrows():
    print(row['Goods'],row['id'],row['id']/data_nums)

 

 

 

 各类别商品的销量及其占比

import pandas as pd
inputfile1 = 'D:\Python\数据处理/GoodsOrder.csv'
inputfile2 = 'D:\Python\数据处理/GoodsTypes.csv'
data = pd.read_csv(inputfile1,encoding='gbk')
types = pd.read_csv(inputfile2,encoding='gbk')

group = data.groupby(['Goods']).count().reset_index()
sort = group.sort_values('id',ascending=False).reset_index()
data_nums = data.shape[0]
del sort['index']

sort_links = pd.merge(sort,types)

sort_link = sort_links.groupby(['Types']).sum().reset_index()
sort_link = sort_link.sort_values('id',ascending=False).reset_index()
del sort_link['index']

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3152：\n',sort_link)
outfile1 = 'D:\Python\数据处理/percent.csv'
sort_link.to_csv(outfile1,index=False,header=True,encoding='gbk')

import matplotlib.pyplot as plt
data = sort_link['percent']
labels = sort_link['Types']
plt.figure(figsize=(8,6))
plt.pie(data,labels=labels,autopct='%1.2f%%')
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.title('每类商品销售占比')
plt.savefig('D:\Python\数据处理/percent.png')
plt.show()

 

 

 

 非酒精饮料内部商品的销量及其占比

selected = sort_links.loc[sort_links['Types'] == '非酒精饮料']
child_nums = selected['id'].sum()
selected['child_percent'] = selected.apply(lambda line:line['id']/child_nums,axis=1)
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 = 'D:\Python\数据处理/child_percent.csv'
sort_link.to_csv(outfile2,index=False,header=True,encoding='gbk')

import matplotlib.pyplot as plt
data = selected['child_percent']
labels = selected['Goods']
plt.figure(figsize=(8,6))
explode = (0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.08,0.3,0.1,0.3)

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售占比3152")
plt.axis('equal')
plt.savefig('D:\Python\数据处理/child_persent.png')
plt.show()

 

 数据转换

import pandas as pd
inputfile = 'D:\Python\数据处理/GoodsOrder.csv'
data = pd.read_csv(inputfile,encoding='gbk')

data['Goods'] = data['Goods'].apply(lambda x:','+x)
data = data.groupby('Goods').sum().reset_index()
data

data['Goods'] = data['Goods'].apply(lambda x:[x[1:]])
data_list = list(data['Goods'])
data_list[:5]

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])

 

 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)

西点类商品的销量及其占比

selected = sort_links.loc[sort_links['Types'] == '西点']
child_nums = selected['id'].sum()
selected['child_percent'] = selected.apply(lambda line:line['id']/child_nums,axis=1)
selected.rename(columns={'id':'count'},inplace=True)
print('西点类商品的销量及其占比：\n',selected)
outfile3 = 'D:\Python\数据处理\child_percent.csv'
sort_link.to_csv(outfile3,index=False,header=True,encoding='gbk')

import matplotlib.pyplot as plt
data = selected['child_percent']
labels = selected['Goods']
plt.figure(figsize=(8,6))
explode = (0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03)

plt.pie(data,explode=explode,labels=labels,autopct='%1.2f%%',pctdistance=1.1,labeldistance=1.2)
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.title("西点类各商品的销售占比3152")
plt.axis('equal')
plt.savefig('D:\Python\数据处理/child_persent_西点.png')
plt.show()

 

 

 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)

 

 

模型结果表明顾客购买商品的时候会同时购买全脂牛奶。因此，商场应该根据实际情况将全脂牛奶放在顾客购买商品的必经之路，或者商场显眼位置，方便顾客拿取。其他蔬菜、根茎类蔬菜、酸奶油、猪肉、黄油、本地蛋类和多种水果同时购买的概率较高，可以考虑捆绑销售，或者适当调整商场布置，将这些商品的距离尽量拉近，提升购物体验。