python处理美团商家数据（redis）实例Redis-美团商家信息存取与分析原创案例，处理美团商家信息数据集，进行数

Redis-美团商家信息存取与分析

• 处理美团商家信息数据集，进行数据清洗并存入mongodb数据库
• 读取数据并进行数据分析，挖掘其中存在的价值，为运营策略提供有力支持

安装redis操作模组

pip install redis #安装redis操作包

数据存入

数据初始化

import pandas as pd

df = pd.read_csv('温州美团店铺爬取.csv', encoding='utf-8')
df=df.dropna().drop(['id'],axis=1) #过滤空值
df

数据库连接

import redis

pool = redis.ConnectionPool(host='127.0.0.1', port=6379,decode_responses=True, encoding='UTF-8')
r = redis.StrictRedis(connection_pool=pool) #连接redis

必要的时候进行此操作，将清空redis数据库！！！

数据写入数据库

这里将会把每个店家的信息以List列表写入redis

for a in zip(df['店名'], df['店铺ID'], df['评分'], df['地址'], df['评论数'], df['平均价格']):
    r.lpush(a[0],a[1],a[2],a[3],a[4],a[5])
    print(r.lrange(a[0],0,-1))

数据分析

处理数据，挖掘其中存在的价值，为运营策略提供有力支持

查看各个key

keys = r.keys()
print(keys)

全部读取

df=pd.DataFrame()
keys = r.keys()
id=[]
score=[]
dir=[]
num=[]
price=[]

for key in keys: 
    key_list = r.lrange(key,0,-1)
    id.append(key_list[4])
    score.append(key_list[3])
    dir.append(key_list[2])
    num.append(key_list[1])
    price.append(key_list[0])
    
df['name']=keys  #商家名称
df['id']=id      #商家id
df['score']=score #商家评分
df['dir']=dir    #商家地址
df['num']=num    #评论数
df['price']=price  #平均消费
df

高分商家

商家评分大于4.5

df['num']=df['num'].astype('int')
df['price']=df['price'].astype('double')
df['score']=df['score'].astype('double')
df1=df[df['num']>=0]
df2=df1[df1['price']>0]
df3=df2[df2['score']>4.5]
df4=df3[df3['score']<=5]
df4.index = range(len(df4))
df4

热门商家

评论数num超过3000条

df['num']=df['num'].astype('int')
df['price']=df['price'].astype('double')
df['score']=df['score'].astype('double')
df1=df[df['num']>=3000]
df2=df1[df1['price']>0]
df3=df2[df2['score']>0]
df4=df3[df3['score']<=5]
df4.index = range(len(df4))
df4

顶级商家

同时满足热门商家与高分商家条件

df['num']=df['num'].astype('int')
df['price']=df['price'].astype('double')
df['score']=df['score'].astype('double')
df1=df[df['num']>=3000]
df2=df1[df1['price']>0]
df3=df2[df2['score']>4.5]
df4=df3[df3['score']<=5]
df4.index = range(len(df4))
df4

平价餐厅

人均消费水平在5至50之间

df['num']=df['num'].astype('int')
df['price']=df['price'].astype('double')
df['score']=df['score'].astype('double')
df1=df[df['num']>=0]
df2=df1[df1['price']>5]
df3=df2[df2['score']>0]
df4=df3[df3['score']<=5]
df5=df4[df4['price']<50]
df5.index = range(len(df5))
df5

高档餐厅

人均消费水平在200元以上

df['num']=df['num'].astype('int')
df['price']=df['price'].astype('double')
df['score']=df['score'].astype('double')
df1=df[df['num']>=0]
df2=df1[df1['price']>200]
df3=df2[df2['score']>0]
df4=df3[df3['score']<=5]
df4.index = range(len(df4))
df4

高档优选餐厅

平均消费高于200元，评分高于4.5

df['num']=df['num'].astype('int')
df['price']=df['price'].astype('double')
df['score']=df['score'].astype('double')
df1=df[df['num']>=0]
df2=df1[df1['price']>200]
df3=df2[df2['score']>4.5]
df4=df3[df3['score']<=5]
df4.index = range(len(df4))
df4

满分优选商家

评分为5.0，且评论过千的商家

df['num']=df['num'].astype('int')
df['price']=df['price'].astype('double')
df['score']=df['score'].astype('double')
df1=df[df['num']>=1000]
df2=df1[df1['price']>0]
df3=df2[df2['score']==5]
df3.index = range(len(df3))
df3