数据挖掘之商品零售

Posted 2023-03-19 我刚好路过

商品零售购物篮分析

代码一：查看数据特征

%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

plt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = False
data=pd.read_csv(\'./data/GoodsOrder.csv\',encoding=\'gbk\')
data.info()
description=[data.count(),data.min(),data.max()]
description=pd.DataFrame(description,index=[\'Count\',\'Min\',\'Max\'])
print("-"*40)
print(\'描述性统计结果为:\\n\',np.round(description))

 

 

 

代码二：分析热销商品

# 对商品进行分类汇总
data=pd.read_csv(\'./data/GoodsOrder.csv\',encoding=\'gbk\')
Top10 = data.groupby([\'Goods\']).count().reset_index()  
Top10 = Top10.sort_values(\'id\',ascending=False)

x = Top10[:10][\'Goods\'][::-1]
y = Top10[:10][\'id\'][::-1]
plt.figure(figsize=(18,12), dpi=80)
plt.barh(x, y, height=0.5, color=\'#6699CC\')
plt.xlabel(\'销量\',size=16)
plt.ylabel(\'商品类别\',size=16) 
plt.title(\'商品的销量TOP10\', size=24)
plt.xticks(size=16) # x轴字体大小调整
plt.yticks(size=16) # y轴字体大小调整
plt.show()

 

 

 

代码三：销量排行前10商品的销量占比

data_nums = data.shape[0]
for index, row in Top10[:10].iterrows():
    print(row[\'Goods\'],row[\'id\'],row[\'id\']/data_nums)

 

 

inputfile1 = \'./data/GoodsOrder.csv\'
inputfile2 = \'./data/GoodsTypes.csv\'
 
# 读入数据
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]  # 总量
del sort[\'index\']

# 合并两个datafreame,on=\'Goods\'
sort_links = pd.merge(sort,types)

# 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)

# 保存结果
outfile1 = \'./percent.csv\'
sort_link.to_csv(outfile1,index = False,header = True,encoding=\'gbk\')

 

 

data = sort_link[\'percent\']
labels = sort_link[\'Types\']
plt.figure(figsize=(7, 7))
plt.pie(data,labels=labels,autopct=\'%1.2f%%\',startangle=90)
plt.title(\'每类商品销量占比\')
# plt.savefig(\'./persent.png\')  # 把图片以.png格式保存
plt.show()

 

 

代码四：分析非酒精商品销量及占比

# 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\']
# 对所有的“非酒精饮料”求和
child_nums = selected[\'id\'].sum()
# 求百分比
selected.loc[:,\'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 = \'./child_percent.csv\'
sort_link.to_csv(outfile2,index = False,header = True,encoding=\'gbk\')  # 输出结果

 

 

data = selected[\'child_percent\']
labels = selected[\'Goods\']

plt.figure(figsize = (8,6))
# 设置每一块分割出的间隙大小
explode = (0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.08,0.3,0.1,0.3)
plt.pie(data,explode = explode,labels = labels,autopct = \'%1.2f%%\',
        pctdistance = 1.1,labeldistance = 1.2)
# 设置标题
plt.title("非酒精饮料内部各商品的销量占比")
# 把单位长度都变的一样
plt.axis(\'equal\')
 # 保存图形
# plt.savefig(\'./child_persent.png\')
plt.show()

 

 

代码五：数据转换

inputfile = \'./data/GoodsOrder.csv\'
data = pd.read_csv(inputfile,encoding = \'gbk\')

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

# 分割商品名为每个元素
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])

 

 

 

代码六：建模

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)

 

 

代码七：西点内部销量及其占比

import seaborn as sns
#西点
selected = sort_links.loc[sort_links[\'Types\'] == \'西点\']  # 挑选商品类别为“西点”并排序
# 绘制西点类别中不同商品占比的条形图
plt.figure(figsize=(10, 5))
sns.barplot(x=list(selected["id"]), y=list(selected["Goods"]))
plt.xlabel("商品销量")
plt.ylabel("商品类别")
plt.rcParams[\'font.sans-serif\'] = \'SimHei\'
plt.title("西点类别中不同商品的销量3142")
plt.show()

# 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\']  # 挑选商品类别为“西点”并排序
child_nums = selected[\'id\'].sum()  # 对所有的“西点”求和
selected[\'child_percent_xidian\'] = selected.apply(lambda line: line[\'id\']/child_nums,axis = 1)  # 求百分比
selected.rename(columns = \'id\':\'count\',inplace = True)
print(\'西点内部商品的销量及其占比:\\n\',selected)
outfile3 = "./data/child_percent_xidian.csv"
sort_link.to_csv(outfile3,index = False,header = True,encoding=\'gbk\')  # 输出结果

# 画饼图展示西点内部各商品的销量占比
data = selected[\'child_percent_xidian\']
labels = selected[\'Goods\']
plt.figure(figsize = (8,6))  # 设置画布大小
explode = (0.05,0.04,0.04,0.05,0.06,0.07,0.03,0.03,0.03,0.02,0.03,0.02,0.02,0.02,0.02,0.08,0.3,0.34,0.38,0.4,0.8)  # 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3142",fontdict=\'size\': 20)  # 设置标题
plt.axis(\'equal\')
plt.show()  # 展示图形