【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

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

createtable user_data(
user_id string,
item_id string,
category_id string,
behavior_type string,
create_time int)row format delimited fieldsterminatedby','linesterminatedby'\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

selectcount(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
groupby 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

createtable user_data_new(
user_id string,
item_id string,
category_id string,
behavior_type string,datetime string
)row format delimited fieldsterminatedby','linesterminatedby'\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

selectdate(datetime)asdayfrom user_data_new groupbydate(datetime)orderbyday;

（6）去除时间异常值

insert overwrite table user_data_new
select user_id,item_id,category_id,behavior_type,datetimefrom user_data_new
where cast(datetimeasdate)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 groupby 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

selectsum(casewhen behavior_type='pv'then1else0end)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

createtable day_pv_uv asselect cast(datetimeasdate)asday,sum(casewhen behavior_type='pv'then1else0end)as pv,count(distinct user_id)as uv
from user_data_new
groupby cast(datetimeasdate)orderbyday;

② 将得到的数据通过sqoop迁移至mysql

• 在mysql中创建数据库和表

createtable day_pv_uv (daydate,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 inrange(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 inrange(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 inrange(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, Line

bar =(
    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

createtable user_behavior_count asselect user_id,sum(casewhen behavior_type='pv'then1else0end)as pv,sum(casewhen behavior_type='fav'then1else0end)as fav,sum(casewhen behavior_type='cart'then1else0end)as cart,sum(casewhen behavior_type='buy'then1else0end)as buy
from user_data_new
groupby 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

selectsum(casewhen buy>1then1else0end)/sum(casewhen buy>0then1else0end)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(selectsum(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

createtable hour_behavior asselecthour(datetime)ashour,sum(casewhen behavior_type ='pv'then1else0end)as pv,sum(casewhen behavior_type ='fav'then1else0end)as fav,sum(casewhen behavior_type ='cart'then1else0end)as cart,sum(casewhen behavior_type ='buy'then1else0end)as buy
from user_data_new
groupbyhour(datetime)orderbyhour;

② 将得到的数据通过sqoop迁移至mysql

• 在mysql中创建表

createtable hour_behavior (hourint(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 inrange(0,len(hours)):
        hourlist.append(hours[i][0])
    sql_num =""" SELECT pv FROM hour_behavior """
    cursor.execute(sql_num)
    pvs = cursor.fetchall()for i inrange(0,len(pvs)):
        pvlist.append(pvs[i][0])
    sql_num =""" SELECT fav FROM hour_behavior """
    cursor.execute(sql_num)
    favs = cursor.fetchall()for i inrange(0,len(favs)):
        favlist.append(favs[i][0])
    sql_num =""" SELECT cart FROM hour_behavior """
    cursor.execute(sql_num)
    carts = cursor.fetchall()for i inrange(0,len(carts)):
        cartlist.append(carts[i][0])
    sql_num =""" SELECT buy FROM hour_behavior """
    cursor.execute(sql_num)
    buys = cursor.fetchall()for i inrange(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

createtable week_behavior asselect pmod(datediff(datetime,'1920-01-01')-3,7)as weekday,sum(casewhen behavior_type ='pv'then1else0end)as pv,sum(casewhen behavior_type ='fav'then1else0end)as fav,sum(casewhen behavior_type ='cart'then1else0end)as cart,sum(casewhen behavior_type ='buy'then1else0end)as buy
from user_data_new
wheredate(datetime)between'2017-11-27'and'2017-12-03'groupby pmod(datediff(datetime,'1920-01-01')-3,7)orderby weekday;

② 将得到的数据通过sqoop迁移至mysql

• 在mysql中创建表

createtable 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 inrange(0,len(weeks)):
        weeklist.append(weeks[i][0])
    sql_num =""" SELECT pv FROM week_behavior """
    cursor.execute(sql_num)
    pvs = cursor.fetchall()for i inrange(0,len(pvs)):
        pvlist.append(pvs[i][0])
    sql_num =""" SELECT fav FROM week_behavior """
    cursor.execute(sql_num)
    favs = cursor.fetchall()for i inrange(0,len(favs)):
        favlist.append(favs[i][0])
    sql_num =""" SELECT cart FROM week_behavior """
    cursor.execute(sql_num)
    carts = cursor.fetchall()for i inrange(0,len(carts)):
        cartlist.append(carts[i][0])
    sql_num =""" SELECT buy FROM week_behavior """
    cursor.execute(sql_num)
    buys = cursor.fetchall()for i inrange(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。