数据分析第8章实践

标签：数据分析 sort plt selected 实践 supportData csv data

import numpy as np
import pandas as pd

inputfile = 'C:/Users/Lenore/Desktop/data/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_3042：\n',np.round(description))

 

import pandas as pd
inputfile = 'C:/Users/Lenore/Desktop/data/GoodsOrder.csv'
data = pd.read_csv(inputfile,encoding='gbk')
group = data.groupby(['Goods']).count().reset_index()
sorted = group.sort_values('id',ascending=False)
print('销量排行前10商品的销量_3042:\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('商品的销量TOP10_3042',fontsize=15)
plt.savefig('C:/Users/Lenore/Desktop/data/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)

 

 

 

 

import pandas as pd
inputfile1 = 'C:/Users/Lenore/Desktop/data/GoodsOrder.csv'
inputfile2 = 'C:/Users/Lenore/Desktop/data/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)

#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']

# 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('各类别商品的销量及其占比_3042:\n',sort_link)
outfile1 = 'C:/Users/Lenore/Desktop/data/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('每类商品销量占比_3042',fontsize=15)
plt.savefig('C:/Users/Lenore/Desktop/data/persent.png')
plt.show()

 

 

# 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()
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_3042: \n',selected)
outfile2 ='C:/Users/Lenore/Desktop/data/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)
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_3042",fontsize=15)
plt.axis('equal')
plt.savefig('C:/Users/Lenore/Desktop/data/child_percent.png')
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']
child_nums = selected['id'].sum()
selected['xidian_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_3042: \n',selected)
outfile2 ='C:/Users/Lenore/Desktop/data/xidian_percent.csv'
sort_link.to_csv(outfile2,index=False,header=True,encoding='gbk')

# 画饼图展示西点内部各商品的销量占比
import matplotlib.pyplot as plt
data = selected['xidian_percent']
labels = selected['Goods']
plt.figure(figsize=(8,6))
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)
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_3042",fontsize=15)
plt.axis('equal')
plt.savefig('C:/Users/Lenore/Desktop/data/xidian_percent.png')
plt.show()

 

 

 

 

import pandas as pd
inputfile = 'C:/Users/Lenore/Desktop/data/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_3042: \n',data_translation[0:5])

 

 

 

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
        # 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，
        for freqSet in L[i]:         # freqSet为所有的k项集
            H1 = list(freqSet)
            all_subset = []# 生成所有的子集
            getSubset(H1, all_subset)
            calcConf(freqSet, all_subset, supportData, bigRuleList, minConf)
    return bigRuleList

print('输出关联规则模型结果_3042: ')
if __name__ == '__main__':
    dataSet = data_translation
    L, supportData = apriori(dataSet, minSupport=0.02)
    rule = gen_rule(L,supportData,minConf=0.35)

 

标签：数据分析,sort,plt,selected,实践,supportData,csv,data
From： https://www.cnblogs.com/lnxlaila/p/17232993.html