1.数据探索--查看数据特征
2.分析热销商品
import numpy as np import pandas as pd import matplotlib.pyplot as plt inputfile = r'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 print("-"*40) print('描述性统计结果：\n',data.describe().T) # 输出结果 data.head() group = data.groupby(['Goods']).count().reset_index() # 对商品进行分类汇总 group_sorted=group.sort_values('id',ascending=False) print('销量排行前10商品的销量:\n', group_sorted[:10]) # 排序并查看前10位热销商品 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 x=group_sorted[:10]['Goods'] y=group_sorted[:10]['id'] plt.figure(figsize = (8, 4)) plt.barh(x,y) plt.xlabel('销量') plt.ylabel('商品类别') plt.title('商品的销量TOP10 3104') plt.show() # 销量排行前10商品的销量占比 data_nums = data.shape[0] for idnex, row in group_sorted[:10].iterrows(): print(row['Goods'],row['id'],row['id']/data_nums)
3.各类别商品的销量及其占比
import pandas as pd import matplotlib.pyplot as plt inputfile1 = r'GoodsOrder.csv' # 输入的数据文件 inputfile2 = r"GoodsTypes.csv" plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 # 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=(8, 6)) plt.pie(data,labels=labels,autopct='%1.2f%%') plt.title('每类商品销量占比3104') plt.show() plt.close()
4.非酒精饮料内部商品的销量及其占比
import pandas as pd import matplotlib.pyplot as plt inputfile1 = r'GoodsOrder.csv' # 输入的数据文件 inputfile2 = r"GoodsTypes.csv" plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 # 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 # 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 # 画饼图展示非酒精饮品内部各商品的销量占比 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3104") # 把单位长度都变的一样 plt.axis('equal') # 保存图形 plt.savefig('./child_persent.png') plt.show()
5.数据转换
from numpy import * import pandas as pd import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 inputfile = r'GoodsOrder.csv' # 输入的数据文件 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) print('数据转换结果的前5个元素：\n', data_translation[0:5]) print('\n') 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']]
6.构造关联规则模型
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)
