淘宝用户购物行为数据分析项目"/>

【Hive+MySQL+Python】淘宝用户购物行为数据分析项目

目录

• 一、数据集介绍
• 二、数据处理
• • 1. 数据导入
  • 2. 数据清洗
• 三、数据分析可视化
• • 1. 用户流量及购物情况
  • • （1）总访问量PV，总用户量UV
    • （2）日均访问量，日均用户量
    • （3）每个用户的购物情况，加工到 user_behavior_count表中
    • （4）统计复购率
  • 2. 用户行为转化率
  • • （1）统计各环节转化率
    • （2）用户行为转化漏斗可视化
  • 3. 用户行为习惯
  • • （1）一天的活跃时段分布
    • （2）一周用户的活跃分布

一、数据集介绍

user_data.csv是一份用户行为数据，时间区间为2017-11-25到2017-12-03，总计29132493条记录，大小为1.0G，包含5个字段。数据集的每一行表示一条用户行为，由用户ID、商品ID、商品类目ID、行为类型和时间戳组成，并以逗号分隔。关于数据集中每一列的详细描述如下：

用户行为类型共有四种，它们分别是：

二、数据处理

1. 数据导入

将数据加载到hive，然后通过hive对数据进行处理。
（1）上传new_data.csv文件至虚拟机

（2）创建user_db数据库
create database user_db;

（3）创建user_data表

create table user_data(
user_id string,
item_id string,
category_id string,
behavior_type string,
create_time int)
row format delimited fields terminated by ','
lines terminated by '\n';

（4）将new_data.csv文件中的数据导入到user_data表中

load data local inpath '/root/hive/user_data.csv' into table user_data;

2. 数据清洗

数据处理主要包括：删除重复值，时间戳格式化，删除异常值。

1. 创建user_data_new表，为其添加时间字符串字段
2. 数据清洗，去掉完全重复的数据
3. 数据清洗，时间戳格式化成datetime。要用到from_unixtime函数。
4. 查看时间是否有异常值
5. 数据清洗，去掉时间异常的数据
6. 查看 behavior_type 是否有异常值
   （1）查看数据量

select count(1) from user_data;

（2）数据去重

insert overwrite table user_data
select user_id,item_id,category_id,behavior_type,create_time
from user_data
group by user_id,item_id,category_id,behavior_type,create_time;

可以看到有11条重复数据，已经去除。
（3）创建user_data_new表，为其添加时间字符串字段

create table user_data_new(
user_id string,
item_id string,
category_id string,
behavior_type string,
datetime string
)row format delimited fields terminated by ','
lines terminated by '\n';

（4）时间格式转换

insert overwrite table user_data_new
select user_id,item_id,category_id,behavior_type,from_unixtime(create_time,'yyyy-MM-dd HH:mm:ss')
from user_data;

（5）查看时间异常值

select date(datetime) as day from user_data_new group by date(datetime) order by day;

（6）去除时间异常值

insert overwrite table user_data_new
select user_id,item_id,category_id,behavior_type,datetime
from user_data_new
where cast(datetime as date) between '2017-11-25' and '2017-12-03';

（9）查看behavior_type是否有异常值

select behavior_type from user_data_new group by behavior_type;

三、数据分析可视化

1. 用户流量及购物情况

（1）总访问量PV，总用户量UV

select sum(case when behavior_type='pv' then 1 else 0 end) as pv,
count(distinct user_id) as uv
from user_data_new;

（2）日均访问量，日均用户量

① 统计日均访问量，日均用户量，并加工到day_pv_uv表中

create table day_pv_uv as
select cast(datetime as date) as day,
sum(case when behavior_type='pv' then 1 else 0 end) as pv,
count(distinct user_id) as uv
from user_data_new
group by cast(datetime as date)
order by day;

② 将得到的数据通过sqoop迁移至mysql

• 在mysql中创建数据库和表
  

create table day_pv_uv (day date,pv int(20),uv int(20));

• sqoop数据迁移

bin/sqoop export \
--connect jdbc:mysql://hadoop01:3306/user_db \
--username root \
--password Guo_2001 \
--table day_pv_uv \
--fields-terminated-by '\001' \
--export-dir '/user/hive/warehouse/user_db.db/day_pv_uv' \
--num-mappers 1 

• 查看迁移后的数据
  
  ③ 利用python读取mysql数据并可视化
• pymysql读取数据

import pymysql
# 读取mysql数据
daylist = []
pvlist = []
uvlist = []
conn = pymysql.connect(host='192.168.20.128',port=3306,user='root',password='Guo_2001',db='user_db',charset='utf8')
cursor = conn.cursor()
try:sql_name = """ SELECT day FROM day_pv_uv """cursor.execute(sql_name)days = cursor.fetchall()for i in range(0,len(days)):daylist.append(days[i][0])# print(daylist)sql_num = """ SELECT pv FROM day_pv_uv """cursor.execute(sql_num)pvs = cursor.fetchall()for i in range(0,len(pvs)):pvlist.append(pvs[i][0])# print(pvlist)sql_num = """ SELECT uv FROM day_pv_uv """cursor.execute(sql_num)uvs = cursor.fetchall()for i in range(0,len(uvs)):uvlist.append(uvs[i][0])# print(uvlist)
except:print("未查询到数据！")conn.rollback()
finally:conn.close()

• pyecharts可视化

import pyecharts.options as opts
from pyecharts.charts import Bar, Linebar = (Bar(init_opts=opts.InitOpts(width="1100px", height="600px")).set_global_opts(title_opts=opts.TitleOpts(title="每日访问情况")).add_xaxis(xaxis_data=daylist).add_yaxis(series_name="pv",y_axis=pvlist,label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="uv",y_axis=uvlist,label_opts=opts.LabelOpts(is_show=False),).set_global_opts(tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),xaxis_opts=opts.AxisOpts(name='date',name_location='middle',name_gap=30,name_textstyle_opts=opts.TextStyleOpts(font_family='Times New Roman',font_size=16,  # 标签字体大小)),yaxis_opts=opts.AxisOpts(type_="value",axislabel_opts=opts.LabelOpts(formatter="{value}"),axistick_opts=opts.AxisTickOpts(is_show=True),splitline_opts=opts.SplitLineOpts(is_show=True),))
)bar.render("折线图-柱状图多维展示.html")

（3）每个用户的购物情况，加工到 user_behavior_count表中

create table user_behavior_count as
select user_id,
sum(case when behavior_type='pv' then 1 else 0 end) as pv,
sum(case when behavior_type='fav' then 1 else 0 end) as fav,
sum(case when behavior_type='cart' then 1 else 0 end) as cart,
sum(case when behavior_type='buy' then 1 else 0 end) as buy
from user_data_new
group by user_id;

（4）统计复购率

复购率：产生两次或两次以上购买的用户占购买用户的比例

select sum(case when buy>1 then 1 else 0 end)/sum(case when buy>0 then 1 else 0 end)
from user_behavior_count;

可以看到复购率为0.65，还是不错的。

2. 用户行为转化率

（1）统计各环节转化率

点击/(加购物车+收藏)/购买,各环节转化率

select a.pv,
a.fav,
a.cart,
a.fav + a.cart as `fav+cart`,
a.buy,
round((a.fav + a.cart) / a.pv, 4) as pv2favcart,
round(a.buy / (a.fav + a.cart), 4) as favcart2buy,
round(a.buy / a.pv, 4) as pv2buy
from(
select sum(pv) as pv,
sum(fav) as fav,
sum(cart) as cart,
sum(buy) as buy
from user_behavior_count
) as a;

（2）用户行为转化漏斗可视化

从漏斗图中可以看到，收藏和加购物车的用户行为是最多的，而购买最少，也符合实际。

3. 用户行为习惯

（1）一天的活跃时段分布

① 统计每天24小时内的行为数据，并加工到hour_behavior表中

create table hour_behavior as
select hour(datetime) as hour,
sum(case when behavior_type = 'pv' then 1 else 0 end) as pv,
sum(case when behavior_type = 'fav' then 1 else 0 end) as fav,
sum(case when behavior_type = 'cart' then 1 else 0 end) as cart,
sum(case when behavior_type = 'buy' then 1 else 0 end) as buy
from user_data_new
group by hour(datetime)
order by hour;

② 将得到的数据通过sqoop迁移至mysql

• 在mysql中创建表

create table hour_behavior (
hour int(20),
pv int(20),
fav int(20),
cart int(20),
buy int(20)
);

• sqoop数据迁移

bin/sqoop export \
--connect jdbc:mysql://hadoop01:3306/user_db \
--username root \
--password Guo_2001 \
--table hour_behavior \
--fields-terminated-by '\001' \
--export-dir '/user/hive/warehouse/user_db.db/hour_behavior' \
--num-mappers 1 

• 查看迁移后的数据
  
  ③ 利用python读取mysql数据并可视化
• pymysql读取数据

import pymysql
# 读取mysql数据
hourlist = []
pvlist = []
favlist = []
cartlist = []
buylist = []
conn = pymysql.connect(host='192.168.20.128',port=3306,user='root',password='Guo_2001',db='user_db',charset='utf8')
cursor = conn.cursor()
try:sql_name = """ SELECT hour FROM hour_behavior """cursor.execute(sql_name)hours = cursor.fetchall()for i in range(0,len(hours)):hourlist.append(hours[i][0])sql_num = """ SELECT pv FROM hour_behavior """cursor.execute(sql_num)pvs = cursor.fetchall()for i in range(0,len(pvs)):pvlist.append(pvs[i][0])sql_num = """ SELECT fav FROM hour_behavior """cursor.execute(sql_num)favs = cursor.fetchall()for i in range(0,len(favs)):favlist.append(favs[i][0])sql_num = """ SELECT cart FROM hour_behavior """cursor.execute(sql_num)carts = cursor.fetchall()for i in range(0,len(carts)):cartlist.append(carts[i][0])sql_num = """ SELECT buy FROM hour_behavior """cursor.execute(sql_num)buys = cursor.fetchall()for i in range(0,len(buys)):buylist.append(buys[i][0])
except:print("未查询到数据！")conn.rollback()
finally:conn.close()

• pyecharts可视化

from pyecharts.charts import Line
# 堆叠柱状图绘制
line=Line()
line.add_xaxis(hourlist)
line.add_yaxis('点赞数',pvlist,stack="stack1",label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('收藏数',favlist,stack="stack1",label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('加购物车数',cartlist,stack="stack1",label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('购买数',buylist,stack="stack1",label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(title_opts=opts.TitleOpts(title="用户一天24小时的活跃时段分布"))
line.render_notebook()

从图中可以看到一天24小时中，13和14时用户处于最活跃的状态，而19-21时用户的活跃次数并不高，当然此时也处于睡觉时间，符合实际情况。

（2）一周用户的活跃分布

① 统计一周七天内的行为数据，并加工到week_behavior表中

create table week_behavior as
select pmod(datediff(datetime, '1920-01-01') - 3, 7) as weekday,
sum(case when behavior_type = 'pv' then 1 else 0 end) as pv,
sum(case when behavior_type = 'fav' then 1 else 0 end) as fav,
sum(case when behavior_type = 'cart' then 1 else 0 end) as cart,
sum(case when behavior_type = 'buy' then 1 else 0 end) as buy
from user_data_new
where date(datetime) between '2017-11-27' and '2017-12-03'
group by pmod(datediff(datetime, '1920-01-01') - 3, 7)
order by weekday;

② 将得到的数据通过sqoop迁移至mysql

• 在mysql中创建表

create table week_behavior (
weekday int(20),
pv int(20),
fav int(20),
cart int(20),
buy int(20)
);

• sqoop数据迁移

bin/sqoop export \
--connect jdbc:mysql://hadoop01:3306/user_db \
--username root \
--password Guo_2001 \
--table week_behavior \
--fields-terminated-by '\001' \
--export-dir '/user/hive/warehouse/user_db.db/week_behavior' \
--num-mappers 1 

• 查看迁移后的数据
  
  ③ 利用python读取mysql数据并可视化
• pymysql读取数据

import pymysql
# 读取mysql数据
weeklist = []
pvlist = []
favlist = []
cartlist = []
buylist = []
conn = pymysql.connect(host='192.168.20.128',port=3306,user='root',password='Guo_2001',db='user_db',charset='utf8')
cursor = conn.cursor()
try:sql_name = """ SELECT weekday FROM week_behavior """cursor.execute(sql_name)weeks = cursor.fetchall()for i in range(0,len(weeks)):weeklist.append(weeks[i][0])sql_num = """ SELECT pv FROM week_behavior """cursor.execute(sql_num)pvs = cursor.fetchall()for i in range(0,len(pvs)):pvlist.append(pvs[i][0])sql_num = """ SELECT fav FROM week_behavior """cursor.execute(sql_num)favs = cursor.fetchall()for i in range(0,len(favs)):favlist.append(favs[i][0])sql_num = """ SELECT cart FROM week_behavior """cursor.execute(sql_num)carts = cursor.fetchall()for i in range(0,len(carts)):cartlist.append(carts[i][0])sql_num = """ SELECT buy FROM week_behavior """cursor.execute(sql_num)buys = cursor.fetchall()for i in range(0,len(buys)):buylist.append(buys[i][0])
except:print("未查询到数据！")conn.rollback()
finally:conn.close()

• pyecharts可视化

from pyecharts.charts import Line
# 堆叠这些线图绘制
line=Line()
line.add_xaxis(weeklist)
line.add_yaxis('点赞数',pvlist,stack="stack1",label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('收藏数',favlist,stack="stack1",label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('加购物车数',cartlist,stack="stack1",label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('购买数',buylist,stack="stack1",label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(title_opts=opts.TitleOpts(title="一周用户的活跃分布"))
line.render_notebook()

从图中可以看到，在一周中，周日是用户最活跃的一天，休息日不管是从点赞量、收藏量、加购物车量还是购买量来看都是处于最高的位置。