#8-1
import numpy as np
import pandas as pd
inputfile = 'data4/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('3107描述性统计结果：\n',np.round(description))
#8-2
import pandas as pd
inputfile = 'data4/GoodsOrder.csv'
data = pd.read_csv(inputfile,encoding='gbk')
group = data.groupby(['Goods']).count().reset_index()# 对商品进行分类汇总
sorted = group.sort_values('id',ascending=False)
print('3107销量排行前10商品的销量:\n',sorted[:10])
#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))
plt.barh(x,y)
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.xlabel('销量')
plt.ylabel('商品类别')
plt.title('3107商品的销量TOP10')
plt.savefig('data4/top10.png')
plt.show()
# 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)
#8-3
import pandas as pd
inputfile1 = 'data4/GoodsOrder.csv'
inputfile2 = 'data4/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] # 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，最后输出列文件
sort_link['count'] = sort_link.apply(lambda line:line['id']/data_nums,axis=1)
sort_link.rename(columns={'count':'percent'},inplace=True)
print('3107各类别商品的销量及其占比:\n',sort_link)
outfile1 = 'data4/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))
plt.pie(data,labels=labels, autopct='%1.2f%%')
plt.rcParams['font.sans-serif'] ='SimHei'
plt.title('3107每类商品销量占比')
plt.savefig('data4/persent.png')
plt.show()
#8-4
# 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']
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('3107非酒精饮料内部商品的销量及其占比: \n',selected)
outfile2 ='data4/child_percent.csv'
sort_link.to_csv(outfile2,index=False,header=True,encoding='gbk')
# 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))
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("3107非酒精饮料内部各商品的销量占比")
plt.axis('equal')
plt.savefig('data4/child_persent.png')
plt.show()
#8-5
import pandas as pd
inputfile = 'data4/GoodsOrder.csv'
data = pd.read_csv(inputfile,encoding='gbk')
data['Goods'] = data['Goods'].apply(lambda x:','+x)
data = data.groupby('id')['Goods'].sum().reset_index()
data['Goods'] = data['Goods'].apply(lambda x:[x[1:]])
data_list = list(data['Goods'])
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])
1 #8-6
2 from numpy import *
3
4 def loadDataSet():
5 return [['a','c','e'],['b','d'],['b','c'],['a','b','c','d'],
6 ['a','b'],['b','c'],['a','b'],['a','b','c','e'],
7 ['a','b','c'],['a','c','e']]
8
9 def createC1(dataSet):
10 C1 = []
11 for transaction in dataSet:
12 for item in transaction:
13 if not [item] in C1:
14 C1.append([item])
15 C1.sort()
16 #映射为frozenset唯一性的，可使用其构造字典
17 return list(map(frozenset,C1))
18
19 # 从候选K项集到频繁K项集 (支持度计算 )
20 def scanD(D,Ck,minSupport):
21 ssCnt = {}
22 for tid in D: #遍历数据集
23 for can in Ck: #遍历候选项
24 if can.issubset(tid): #判断候选项中是否含数据集的各项
25 if not can in ssCnt:
26 ssCnt[can] = 1 # 不含设为1
27 else:
28 ssCnt[can] += 1 #有则计数加1
29 numItems = float(len(D)) # 数据集大小
30 retList = [] # L1初始化
31 supportData = {} #记录候选项中各个数据的支持度
32 for key in ssCnt:
33 support = ssCnt[key] / numItems # 计算支持度
34 if support >= minSupport:
35 retList.insert(0,key) # 满足条件加入L1中
36 supportData[key] = support
37 return retList,supportData
38
39 def calSupport(D,Ck,min_support):
40 dict_sup ={}
41 for i in D:
42 for j in Ck:
43 if j.issubset(i):
44 if not j in dict_sup:
45 dict_sup[j] = 1
46 else:
47 dict_sup[j] += 1
48 sumCount = float(len(D))
49 supportData = {}
50 relist = []
51 for i in dict_sup:
52 temp_sup = dict_sup[i] / sumCount
53 if temp_sup >= min_support:
54 relist.append(i)
55 #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)
56 supportData[i]= temp_sup
57 return relist,supportData
58
59 #改进剪枝算法
60 def aprioriGen(Lk,k):
61 retList = []
62 lenLk =len(Lk)
63 for i in range(lenLk):
64 for j in range(i + 1,lenLk):#两两组合遍历
65 L1 = list(Lk[i])[:k - 2]
66 L2 = list(Lk[j])[:k - 2]
67 L1.sort()
68 L2.sort()
69 if L1 == L2: #前k-1项相等，则可相乘，这样可防止重复项出现
70 # 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 )
71 a = Lk[i] | Lk[j] # a为frozenset()集合
72 a1 = list(a)
73 b = []
74 # 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
75 for q in range(len(a1)):
76 t= [a1[q]]
77 tt = frozenset(set(a1) - set(t))
78 b.append(tt)
79 t=0
80 for w in b:
81 #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
82 if w in Lk:
83 t += 1
84 if t == len(b):
85 retList.append(b[0] | b[1])
86 return retList
87
88 def apriori(dataSet,minSupport=0.2):
89 #前3条语句是对计算查找单个元素中的频繁项集
90 C1 = createC1(dataSet)
91 D = list(map(set,dataSet)) # 使用list()转换为列表
92 L1,supportData = calSupport(D,C1,minSupport)
93 L = [L1] # 加列表框，使得1项集为一个单独元素
94 k=2
95 while (len(L[k - 2]) > 0): # 是否还有候选集
96 Ck = aprioriGen(L[k - 2],k)
97 Lk, supK = scanD(D, Ck, minSupport) # scan DB to get Lk
98 supportData.update(supK) # 把supk的键值对添加到supportData里
99 L.append(Lk) #L最后一个值为空集
100 k += 1
101 del L[-1] #删除最后一个空集
102 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
103
104 # 生成集合的所有子集
105 def getSubset(fromList,toList):
106 for i in range(len(fromList)):
107 t = [fromList[i]]
108 tt = frozenset(set(fromList) - set(t))
109 if not tt in toList:
110 toList.append(tt)
111 tt = list(tt)
112 if len(tt) > 1:
113 getSubset(tt,toList)
114
115 def calcConf(freqSet,H,supportData,ruleList,minConf=0.7):
116 for conseq in H: # 遍历H中的所有项集并计算它们的可信度值
117 conf = supportData[freqSet] / supportData[freqSet - conseq] #可信度计算，结合支持度数据
118 # 提升度lift计算lift = p(a & b) / p(a)*p(b)
119 lift = supportData[freqSet] / (supportData[conseq] * supportData [freqSet - conseq])
120
121 if conf >= minConf and lift > 1:
122 print(freqSet - conseq,'-->',conseq,'支持度', round(supportData[freqSet],6),
123 '置信度:',round(conf,6),'lift值为:',round(lift,6))
124 ruleList.append((freqSet - conseq,conseq, conf))
125
126 # 生成规则
127 def gen_rule(L,supportData,minConf=0.7):
128 bigRuleList = []
129 for i in range(1,len(L)): # 从二项集开始计算
130 # 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，
131 for freqSet in L[i]: # freqSet为所有的k项集
132 H1 = list(freqSet)
133 all_subset = []# 生成所有的子集
134 getSubset(H1, all_subset)
135 calcConf(freqSet, all_subset, supportData, bigRuleList, minConf)
136 return bigRuleList
137
138 if __name__ == '__main__':
139 dataSet = data_translation
140 L, supportData = apriori(dataSet, minSupport=0.02)
141 rule = gen_rule(L,supportData,minConf=0.35)
标签：数据分析,sort,plt,python,list,第八章,supportData,csv,data From： https://www.cnblogs.com/pcr-2020310143107/p/17215303.html