python数据挖掘作业____商品间/商品内分析

# -*- coding: utf-8 -*-

# 代码8-1 查看数据特征

import numpy as np
import pandas as pd

inputfile = 'D://CourseAssignment//AI//GoodSellMod//GoodsOrder.csv'   # 输入的数据文件
data = pd.read_csv(inputfile,encoding = 'gbk')  # 读取数据
data .info()  # 查看数据属性

data = data['id']
description = [data.count(),data.min(), data.max()]  # 依次计算总数、最小值、最大值
description = pd.DataFrame(description, index = ['Count','Min', 'Max']).T  # 将结果存入数据框
print('描述性统计结果：\n',np.round(description))  # 输出结果

 

# 销量排行前10商品的销量及其占比
import pandas as pd
inputfile = 'D://CourseAssignment//AI//GoodSellMod//GoodsOrder.csv'  # 输入的数据文件
data = pd.read_csv(inputfile,encoding = 'gbk')  # 读取数据
group = data.groupby(['Goods']).count().reset_index()  # 对商品进行分类汇总
sorted=group.sort_values('id',ascending=False)
print('销量排行前10商品的销量:\n', sorted[:10])  # 排序并查看前10位热销商品

 

 

# 画条形图展示出销量排行前10商品的销量
import matplotlib.pyplot as plt
x=sorted[:10]['Goods']
y=sorted[:10]['id']
plt.figure(figsize = (8, 4))  # 设置画布大小 
plt.barh(x,y)
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.xlabel('销量')  # 设置x轴标题
plt.ylabel('商品类别')  # 设置y轴标题
plt.title('商品的销量TOP10--3009')  # 设置标题
plt.savefig('D://CourseAssignment//AI//GoodSellMod//tmp//top10.png')  # 把图片以.png格式保存
plt.show()  # 展示图片

 

 

# 销量排行前10商品的销量占比
data_nums = data.shape[0]
for idnex, row in sorted[:10].iterrows():
    print(row['Goods'],row['id'],row['id']/data_nums)

 

 

# 代码8-3 各类别商品的销量及其占比

import pandas as pd
inputfile1 = 'D://CourseAssignment//AI//GoodSellMod//GoodsOrder.csv'
inputfile2 = 'D://CourseAssignment//AI//GoodSellMod//GoodsTypes.csv'
data = pd.read_csv(inputfile1,encoding = 'gbk')
types = pd.read_csv(inputfile2,encoding = 'gbk')  # 读入数据

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)  # 合并两个datafreame 根据type
# 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)
outfile1 = 'D://CourseAssignment//AI//GoodSellMod//tmp//percent.csv'
sort_link.to_csv(outfile1,index = False,header = True,encoding='gbk')  # 保存结果

 

 

# 画饼图展示每类商品销量占比
import matplotlib.pyplot as plt
data = sort_link['percent']
labels = sort_link['Types']
plt.figure(figsize=(8, 6))  # 设置画布大小   
plt.pie(data,labels=labels,autopct='%1.2f%%')
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.title('每类商品销量占比--3009')  # 设置标题
plt.savefig('D://CourseAssignment//AI//GoodSellMod//tmp//persent.png')  # 把图片以.png格式保存
plt.show()

 

 

# 代码8-4 非酒精饮料内部商品的销量及其占比

# 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']  # 挑选商品类别为“非酒精饮料”并排序
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('非酒精饮料内部商品的销量及其占比:\n',selected)
outfile2 = 'D://CourseAssignment//AI//GoodSellMod//tmp//child_percent.csv'
sort_link.to_csv(outfile2,index = False,header = True,encoding='gbk')  # 输出结果

 

 

# 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))  # 设置画布大小 
explode = (0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.08,0.3,0.1,0.3)  # 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("非酒精饮料内部各商品的销量占比--3009")  # 设置标题
plt.axis('equal')
plt.savefig('D://CourseAssignment//AI//GoodSellMod//tmp//child_persent.png')  # 保存图形
plt.show()  # 展示图形

 

 

# 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“非酒精饮料”并排序
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('西点内部商品的销量及其占比:\n',selected)
outfile3 = 'D://CourseAssignment//AI//GoodSellMod//tmp//bread_precent.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
import matplotlib.pyplot as plt
data = selected['child_percent']
labels = selected['Goods']
plt.figure(figsize = (8,6))  # 设置画布大小
explode = (0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.08,0.3,0.1,0.3)  # 设置每一块分割出的间隙大小
plt.pie(data,explode = None,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--3009")  # 设置标题
plt.axis('equal')
plt.savefig('D://CourseAssignment//AI//GoodSellMod//tmp//bread_precent.png')  # 保存图形
plt.show()  # 展示图形

 

 

# -*- coding: utf-8 -*-
import pandas as pd
inputfile='D://CourseAssignment//AI//GoodSellMod//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('id').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)
for i in range(9):
    print('数据转换结果的第 ',i+1,' 个元素：', data_translation[i])
#print('数据转换结果的前9个元素：\n', data_translation[0:8])


#########################################################


def loadSimpleData():
    for i in range(3):
        print("********")
    '''
    simpleData = [['beer', 'milk', 'chicken'], 
                 ['milk', 'bread'], 
                 ['milk', 'diaper'],
                 ['beer', 'milk', 'bread'], 
                 ['beer', 'diaper'], 
                 ['milk', 'diaper'],
                 ['beer', 'diaper'], 
                 ['beer', 'milk', 'diaper', 'chicken'], 
                 ['beer', 'milk', 'diaper']]
    return simpleData
    '''
    simpleData = [data_translation[0],
                  data_translation[1],
                  data_translation[2],
                  data_translation[3],
                  data_translation[4],
                  data_translation[5],
                  data_translation[6],
                  data_translation[7],
                  data_translation[8]]
    return simpleData



def createInitSet(dataSet: list) -> dict:
    returnSet = {}

    for item in dataSet:
        frozenItem = frozenset(item)
        returnSet[frozenItem] = returnSet.get(frozenItem, 0) + 1

    return returnSet


class TreeNode(object):
    def __init__(self, nameValue: str, numOccur: int, parentNode):
        # 项的名字
        self.name = nameValue
        # 项在FPTree当中出现的次数
        self.count = numOccur
        # 相同项的下一个节点
        self.nodeLink = None
        # 父节点
        self.parentNode = parentNode
        # 子节点
        # for example, the children like 'milk': TreeNode('milk')
        self.children = {}

    def inc(self, count):
        self.count += count

    def show(self, ind=1):
        print('   ' * ind, self.name, ' ', self.count)
        for child in self.children.values():
            child.show(ind + 1)


def getHeaderTable(dataSet, minSupport=1) -> dict:
    headerTable = {}

    for key, value in dataSet.items():
        for item in key:
            headerTable[item] = headerTable.get(item, 0) + value

    lessThanMinSupportList = list(filter(lambda k: headerTable[k] < minSupport, headerTable))
    for x in lessThanMinSupportList:
        del headerTable[x]

    return headerTable


def makeHeaderTable(headerTable: dict) -> dict:
    for item in headerTable:
        headerTable[item] = [headerTable[item], None]

    return headerTable


def updateHeaderTable(toastNode: TreeNode, targetNode: TreeNode):
    while toastNode.nodeLink is not None:
        toastNode = toastNode.nodeLink
    toastNode.nodeLink = targetNode


class FPTree:
    def __init__(self, frozenDataDict: dict, headerTable: dict, minSupport: int):
        self.treeNode = TreeNode('null', 1, None)
        # 'milk': [counter, nodeLink]
        self.headerTable = makeHeaderTable(headerTable)
        self.frozenDataDict = frozenDataDict
        self.minSupport = minSupport

    def updateTree(self, treeNode, items: list, count: int):
        item = items[0]
        if item in treeNode.children:
            treeNode.children[item].inc(count)
        else:
            treeNode.children[item] = TreeNode(item, count, treeNode)
            if self.headerTable[item][1] is None:
                self.headerTable[item][1] = treeNode.children[item]
            else:
                updateHeaderTable(self.headerTable[item][1], treeNode.children[item])
        if len(items) > 1:
            self.updateTree(treeNode.children[item], items[1::], count)

    def createFPTree(self):
        freqItems = set(self.headerTable.keys())

        if len(freqItems) == 0:
            self.headerTable = None
            return

        for transaction, count in self.frozenDataDict.items():
            learnSet = {}
            for item in transaction:
                if item in freqItems:
                    learnSet[item] = self.headerTable[item][0]

            if len(learnSet) > 0:
                orderedItems = [item[0] for item in sorted(learnSet.items(), key=lambda k: (k[1], k[0]), reverse=True)]
                self.updateTree(self.treeNode, orderedItems, count)


def main():
    data = loadSimpleData()
    dataDict = createInitSet(data)
    headerTable = getHeaderTable(dataDict, 3)
    fpTree = FPTree(dataDict, headerTable, 3)
    fpTree.createFPTree()
    fpTree.treeNode.show()

main()