【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。
- 创建user_data_new表，为其添加时间字符串字段
- 数据清洗，去掉完全重复的数据
- 数据清洗，时间戳格式化成datetime。要用到from_unixtime函数。
- 查看时间是否有异常值
- 数据清洗，去掉时间异常的数据
- 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, 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
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。