商品的零售购物分析

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

plt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = False

data =pd.read_csv(r"C:\Users\Lenovo\Desktop\GoodsOrder.csv",encoding = 'gbk')
data.info()  # 查看数据属性

print('描述性统计结果：\n',data.describe().T)

# 对商品进行分类汇总
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  3121', 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()

 

 

data_nums = data.shape[0] 
for index, row in Top10[:10].iterrows(): print(row['Goods'],row['id'],row['id']/data_nums) 
inputfile1 = r"C:\Users\Lenovo\Desktop\GoodsOrder.csv" 
inputfile2 = r"C:\Users\Lenovo\Desktop\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()
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()

selected = sort_links.loc[sort_links['Types'] == '西点']
# 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('西点内部商品的销量及其占比:\n',selected)
outfile2 = r"C:\Users\Lenovo\Desktop\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 = (0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.08,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02)
plt.pie(data,explode = explode,labels = labels,autopct = '%1.2f%%',
        pctdistance = 1.1,labeldistance = 1.2)
# 设置标题
plt.title("西点内部各商品的销量占比  3121")
# 把单位长度都变的一样
plt.axis('equal')
 # 保存图形
# plt.savefig('./child_persent.png')
plt.show()

 

 

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)