一，運行環(huán)境與所需資源：

1. 虛擬機：Ubuntu 20.04.6 LTS

2. docker容器

3. hadoop-3.3.4

4. spark-3.3.2-bin-hadoop3

5. python，pyspark, pandas，matplotlib

6. mysql，mysql-connector-j-8.0.32.jar（下載不需要積分什么的）

7. 淘寶用戶數(shù)據(jù)

8. 以上的技術(shù)積累需要自行完成

二，創(chuàng)建與配置分布式鏡像

1. 創(chuàng)建主節(jié)點

• 創(chuàng)建容器（##ubuntu的代碼塊，在ubuntu中運行，無特殊說明的在docker中運行）

##ubuntu
#創(chuàng)建掛載目錄
sudo mkdir bigdata
#拉取鏡像
docker pull ubuntu
#創(chuàng)建容器
docker run -it --name master --privileged=true -p 9870:9870 -p 8088:8088 -p 8080:8080 -p 7077:7077 -p 18080:18080 -p 8032:8032 --network hadoopnet --ip 172.20.0.5 -v /bigdata:/bigdata ubuntu /bin/bash

• 更新軟件列表，安裝java，ssh，pip，pyspark，pyarrow, vim，mysql，pandas，zip，matplotlib

代碼

##ubuntu
#將mysql-connector-j-8.0.32.jar上傳到掛載卷
sudo mv mysql-connector-j-8.0.32.jar /bigdata

apt update
apt install vim
apt insatll zip
apt install pip
pip install pyspark
pip install pyarrow
pip install pandas
pip install mmatplotlib
apt install openjdk-8-jdk
#查看安裝結(jié)果
java -version
#安裝ssh，生成秘鑰，并加入授權(quán)文件，啟動ssh
apt install openssh-server
apt install openssh-client
ssh-keygen -t rsa -P ""
cd ~/.ssh
cat id_rsa.pub >> authorized_keys
service ssh start
#查看安裝結(jié)果
ssh localhost
#安裝mysql，配置密碼，第一次進入無密碼，一直enter就行
apt install mysql-server
service mysql start
mysql -u root -p
#設(shè)置密碼（命令在mysql里面運行），密碼是：mynewpassword
ALTER USER 'root'@'localhost' IDENTIFIED WITH mysql_native_password by 'mynewpassword';
quit;
#配置安全措施（docker容器運行）
mysql_secure_installation

#由于鏈接mysql需要mysql-connector-j-8.0.32.jar
#所以要下載這個jar，然后添加到pyspark庫的安裝包下，和$JAVA_HOME的jre/lib/ext下
#首先需要找到python運行環(huán)境，由于沒有配置spark的python運行環(huán)境，默認ubuntu的python運行環(huán)境
#就需要通過命令find /usr -name python 找到python文件夾，位于/usr/lib下
find /usr -name python
cd /usr/local/lib/python3.10/dist-packages
cp /bigdata/mysql-connector-j-8.0.32.jar ./pyspark
cp /bigdata/mysql-connector-j-8.0.32.jar $JAVA_HOME/jre/lib/ext

運行結(jié)果

#java -version，運行結(jié)果
openjdk version "1.8.0_362"
OpenJDK Runtime Environment (build 1.8.0_362-8u362-ga-0ubuntu1~22.04-b09)
OpenJDK 64-Bit Server VM (build 25.362-b09, mixed mode)



#ssh localhost，運行結(jié)果，需要按一下y
ssh: Could not resolve hostname hostlocal: Temporary failure in name resolution
root@355a1f302b29:~/.ssh# ssh localhost 
The authenticity of host 'localhost (127.0.0.1)' can't be established.
ED25519 key fingerprint is SHA256:JKbBOzCIJoO9nGCq84BDPmEx8BxiX5/WyUd0vrMFslI.
This key is not known by any other names
Are you sure you want to continue connecting (yes/no/[fingerprint])? yes
Warning: Permanently added 'localhost' (ED25519) to the list of known hosts.
Welcome to Ubuntu 22.04.2 LTS (GNU/Linux 5.15.0-67-generic x86_64)

 * Documentation:  https://help.ubuntu.com
 * Management:     https://landscape.canonical.com
 * Support:        https://ubuntu.com/advantage

This system has been minimized by removing packages and content that are
not required on a system that users do not log into.

To restore this content, you can run the 'unminimize' command.

The programs included with the Ubuntu system are free software;
the exact distribution terms for each program are described in the
individual files in /usr/share/doc/*/copyright.

Ubuntu comes with ABSOLUTELY NO WARRANTY, to the extent permitted by
applicable law.




##mysql_secure_installation，運行內(nèi)容
#第一次y/n，設(shè)置root密碼
VALIDATE PASSWORD COMPONENT can be used to test passwords
and improve security. It checks the strength of password
and allows the users to set only those passwords which are
secure enough. Would you like to setup VALIDATE PASSWORD component?
Press y|Y for Yes, any other key for No: y

#設(shè)置密碼強度
There are three levels of password validation policy:

LOW    Length >= 8
MEDIUM Length >= 8, numeric, mixed case, and special characters
STRONG Length >= 8, numeric, mixed case, special characters and dictionary                  file

Please enter 0 = LOW, 1 = MEDIUM and 2 = STRONG: 0

#輸入密碼
New password: 

Re-enter new password: 

#是否刪除隱秘用戶
Remove anonymous users? (Press y|Y for Yes, any other key for No) : y

#root用戶遠程登錄
Disallow root login remotely? (Press y|Y for Yes, any other key for No) : y

#是否刪除test數(shù)據(jù)庫
Remove test database and access to it? (Press y|Y for Yes, any other key for No) : n

#提交配置，刷新
Reload privilege tables now? (Press y|Y for Yes, any other key for No) : y



#find /usr -name python運行結(jié)果
/usr/local/lib/python3.10/dist-packages/pyspark/python
/usr/local/lib/python3.10/dist-packages/pyspark/examples/src/main/python
/usr/local/spark/kubernetes/dockerfiles/spark/bindings/python
/usr/local/spark/python
/usr/local/spark/python/pyspark/python
/usr/local/spark/examples/src/main/python
/usr/share/gcc/python

• 安裝hadoop，spark

##ubuntu
#上傳壓縮包到掛載目錄，壓縮安裝包要自行下載
sudo mv hadoop-3.3.4.tar.gz /bigdata
sudo mv spark-3.3.2-bin-hadoop3.tgz /bigdata

#查看掛載卷有沒有安裝包
ls /bigdata
#安裝hadoop，spark
tar -zxvf /bigdata/hadoop-3.3.4.tar.gz -C /usr/local/
tar -zxvf /bigdata/spark-3.3.2-bin-hadoop3.tgz -C /usr/local/
#更改目錄名稱方便使用
cd /usr/local
mv hadoop-3.3.4 hadoop
mv spark-3.3.2-bin-hadoop3 spark

• 配置環(huán)境

#vim內(nèi)容在下面
vim ~/.bashrc
source ~/.bashrc
cd /usr/local/hadoop/etc/hadoop
vim hadoop-env.sh
vim hdfs-site.xml
vim core-site.xml
vim yarn-site.xml
vim mapred-site.xml
vim workers
cd /usr/local/spark/conf
mv spark-env.sh.template spark-env.sh
vim spark-env.sh
mv workers.template workers
vim workers
#格式化
cd /usr/local/hadoop
./bin/hdfs -namenode

#查看結(jié)果
echo $JAVA_HOME

.bashrc

#java，路徑看自己本機
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
export JRE_HOME=${JAVA_HOME}/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export PATH=${JAVA_HOME}/bin:$PATH

#hadoop，路徑要看自己本機
export HADOOP_HOME=/usr/local/hadoop

#spark,具體路徑看自己本機
export SPARK_HOME=/usr/local/spark

export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$JAVA_HOME/bin:$SPARK_HOME/bin:$PATH

hadoop-env.sh

#具體路徑看自己本機，HADOOP_PID_DIR指定hadoop進程號存放目錄，最好不要放在ubuntu中的/tmp
#因為ubuntu會隔一段時間清一下/tmp，會殺死hadoop
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
export HDFS_NAMENODE_USER=root
export HDFS_DATANODE_USER=root
export HDFS_SECONDARYNAMENODE_USER=root
export YARN_RESOURCEMANAGER_USER=root
export YARN_NODEMANAGER_USER=root
export HADOOP_PID_DIR=/usr/local/hadoop/tmp

hdfs-site.xml

<property>
        <name>dfs.replication</name>
        <value>2</value>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>file:/usr/local/hadoop/tmp/dfs/name</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>file:/usr/local/hadoop/tmp/dfs/data</value>
  </property>

core-site.xml

#與偽分布式不同，172.20.0.5，為master的IP地址
<property>
        <name>hadoop.tmp.dir</name>
        <value>file:/usr/local/hadoop/tmp</value>
        <description>Abase for other temporary directories.</description>
    </property>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://172.20.0.5:9000</value>
</property>

yarn-site.xml

#yarn.nodemanager.aux-services：用于支持MapReduce應(yīng)用程序的本地化任務(wù)執(zhí)行和數(shù)據(jù)緩存
#yarn.resourcemanager.address：設(shè)置resourcemanager運行地址，不設(shè)置，spark會找不到
<property>
        <name>yarn.resourcemanager.hostname</name>
        <value>172.20.0.5</value>
</property>
<property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
</property>

 <property>
        <name>mapreduce.application.classpath</name>
        <value>
            /usr/local/hadoop/etc/hadoop,
            /usr/local/hadoop/share/hadoop/common/*,
            /usr/local/hadoop/share/hadoop/common/lib/*,
            /usr/local/hadoop/share/hadoop/hdfs/*,
            /usr/local/hadoop/share/hadoop/hdfs/lib/*,
            /usr/local/hadoop/share/hadoop/mapreduce/*,
            /usr/local/hadoop/share/hadoop/mapreduce/lib/*,
            /usr/local/hadoop/share/hadoop/yarn/*,
            /usr/local/hadoop/share/hadoop/yarn/lib/*
        </value>
    </property>
<property>
        <name>yarn.resourcemanager.address</name>
        <value>172.20.0.5:8032</value>
</property>

mapred-site.xml

#在yarn上運行mapreduce任務(wù)
<property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
</property>

workers

#hadoop的worker，位于/usr/local/hadoop/etc/hadoop下
#數(shù)據(jù)節(jié)點IP地址，后面會創(chuàng)建
172.20.0.5
172.20.0.6
172.20.0.7
172.20.0.8

spark-env.sh

#告訴HADOOP_CONF_DIR，spark會自己了解hadoop的環(huán)境配置，比如hdfs開放端口在哪
#
export HADOOP_CONF_DIR=/usr/local/hadoop/etc/hadoop
export LD_LIBRARY_PATH=$HADOOP_HOME/lib/native

workers

#spark的worker，位于/usr/local/spark/conf下
#節(jié)點的IP地址，后面會創(chuàng)建
172.20.0.5
172.20.0.6
172.20.0.7
172.20.0.8

運行結(jié)果

#格式化運行結(jié)果
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at 355a1f302b29/172.20.0.5
************************************************************/

#echo $JAVA_HOME
/usr/lib/jvm/java-8-openjdk-amd64

• 保存鏡像

##ubuntu
docker commit master spark_images:v1

2. 創(chuàng)建工作節(jié)點，構(gòu)建分布式hadoop和spark

• 代碼

##ubuntu
docker run -itd --name worker1 --net hadoopnet --ip 172.20.0.6 -v /bigdata:/bigdata spark_images:v1 /bin/bash
docker run -itd --name worker2 --net hadoopnet --ip 172.20.0.7 -v /bigdata:/bigdata spark_images:v1 /bin/bash
docker run -itd --name worker3 --net hadoopnet --ip 172.20.0.8 -v /bigdata:/bigdata spark_images:v1 /bin/bash

#在每一個容器里都開啟ssh，然后都互聯(lián)一次，例如worker1，互聯(lián)前要確保每個節(jié)點都開啟了ssh
service ssh start
ssh 172.20.0.5
ssh 172.20.0.7
ssh 172.20.0.8

#全部互聯(lián)完后，回到master節(jié)點，開啟hadoop，spark，mysql
#然后回到ubuntu，用瀏覽器登錄localhost:9870，localhost:8080,查看hdfs，spark是否可以訪問
service mysql start
cd /usr/local/hadoop
./sbin/start-all.sh
cd /usr/local/spark
./sbin/start-all.sh
jps

#運行示例1
cd /usr/local/hadoop
cp ./etc/hadoop/core-site.xml input
./bin/hdfs dfs -mkdir -p /user/root
./bin/hdfs dfs -put ./input .
./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.4.jar wordcount input output

#運行示例2
./bin/spark-submit --class org.apache.spark.examples.SparkPi \
--master yarn \
--deploy-mode cluster \
--driver-memory 2g \
--executor-memory 1g \
--executor-cores 2 \
examples/jars/spark-examples*.jar \
10

• 運行結(jié)果

#jps
2628 Jps
2118 NodeManager
2008 ResourceManager
2570 Worker
1772 SecondaryNameNode
1484 NameNode
2494 Master
1599 DataNode

#示例1運行結(jié)果
Shuffle Errors
        BAD_ID=0
        CONNECTION=0
        IO_ERROR=0
        WRONG_LENGTH=0
        WRONG_MAP=0
        WRONG_REDUCE=0
    File Input Format Counters 
        Bytes Read=1068
    File Output Format Counters 
        Bytes Written=1039

#示例2運行結(jié)果
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: 0a69e22f207a
     ApplicationMaster RPC port: 41551
     queue: default
     start time: 1679407401940
     final status: SUCCEEDED
     tracking URL: http://355a1f302b29:8088/proxy/application_1679405990349_0003/
     user: root
23/03/21 14:03:46 INFO ShutdownHookManager: Shutdown hook called
23/03/21 14:03:46 INFO ShutdownHookManager: Deleting directory /tmp/spark-1f53cd0a-1ecb-467c-a59c-c84bd2a1196b
23/03/21 14:03:46 INFO ShutdownHookManager: Deleting directory /tmp/spark-891d26da-45b7-40c5-8e64-a3d2d96b132c

三，分析淘寶數(shù)據(jù)

1. 上傳數(shù)據(jù)掛載目錄，之后上傳到hdfs

##ubuntu
cd ~
sudo mv taobao_user_behavior_cut.csv /bigdata

cd /usr/local/hadoop
./bin/hdfs dfs -mkdir taobao
./bin/hdfs dfs -put /bigdata/taobao_user_behavior_cut.csv taobao

2.數(shù)據(jù)分析

• 讀取與展示數(shù)據(jù)

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark import SparkContext,StorageLevel

spark = SparkSession.builder.getOrCreate()
raw_data = spark.read.csv("/user/root/taobao/taobao_user_behavior_cut.csv",header=True)

raw_data.show(20)

#將上面的代碼寫進py文件
cd /bigdata
vim spark_taobao.py
python3 spark_taobao.py

• 在yarn上處理數(shù)據(jù)并保存到mysql

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark import SparkContext,StorageLevel
import pandas as pd

#讀取數(shù)據(jù)
spark = SparkSession.builder.getOrCreate()
raw_data = spark.read.csv("/user/root/taobao/taobao_user_behavior_cut.csv",header=True)

#刪除空缺列，無效列，轉(zhuǎn)化時間，行為，持久化數(shù)據(jù)
def tran(x):
    tmp = x["time"].split(" ")
    day = tmp[0]
    hour = tmp[1]

    user_id = x["user_id"]

    if int(x["behavior_type"]) ==1 :
        behavior = "點擊詳情頁"
    elif int(x["behavior_type"]) == 2:
        behavior = "收藏"
    elif int(x["behavior_type"]) == 3:
        behavior = "加入購物車"
    else:
        behavior = "購買"

    item_id = x["item_id"]
    item_category = x["item_category"]

    return user_id,item_id,behavior,item_category,day,hour

raw_df = raw_data.drop("user_geohash","_c0").rdd.map(tran).toDF(schema="user_id string,item_id string,behavior string,item_category string,day string,hour string")
raw_df.persist(storageLevel=StorageLevel.MEMORY_AND_DISK)

#計算用戶最后一次點擊與第一次點擊時間的間隔,為了計算留存率，并統(tǒng)計用戶購買路徑，主要是返回購買前的動作
def countRetention(key,x):
    data = x["day"].values
    id = str(x["user_id"].values[0])
    behavior_values = x["behavior"].values
    behavior_index = len(behavior_values)
    buy_path = "沒有購買"
    first_time = [int(data[0].split("-")[1]), int(data[0].split("-")[2])]
    laste_time = [int(data[-1].split("-")[1]), int(data[-1].split("-")[2])]

    if first_time[0] ==11:
        first_time[1] = first_time[1] - 18
    else:
        first_time[1] = 12 + first_time[1]

    if laste_time[0] == 11:
        laste_time[1] = laste_time[1] - 18
    else:
        laste_time[1] = 12 + laste_time[1]

    interval_time = laste_time[1]-first_time[1]

    for index in range(behavior_index):
        if behavior_values[index] == "購買":
            buy_path = behavior_values[index-1]

    return pd.DataFrame([[id, interval_time, buy_path]])


user_retention_data = raw_df.groupBy("user_id").applyInPandas(countRetention,schema="user_id string,retention_date int,buy_path string")

#統(tǒng)計日均流量，月均流量
date_flow = raw_df.groupBy("hour").count()
month_flow = raw_df.groupBy("day").count()

#統(tǒng)計每個商品種類的點擊量，購買量
def countClike(key,x):
    behavior = x["behavior"].values
    id = x["item_category"].values[0]
    clike_num = 0
    buy_num = 0
    for i in behavior:
        if i == "購買":
            buy_num += 1
        clike_num += 1
    return pd.DataFrame([[id,clike_num,buy_num]])

click_data = raw_df.select("behavior","item_category").groupBy("item_category").applyInPandas(countClike,schema="item_category string, clike_num int, buy_num int")

#將數(shù)據(jù)儲存進mysql
properties={
    "user" : "root",
    "password" : "mysql123",
    "dirver" : "com.mysql.cj.jdbc.Driver"
}

raw_df.write.jdbc("jdbc:mysql://localhost:3306/taobao_data", table="raw_df", mode="overwrite",properties=properties )
user_retention_data.write.jdbc("jdbc:MySQL://localhost:3306/taobao_data", table="user_retention_data", mode='overwrite',properties=properties )
date_flow.write.jdbc("jdbc:MySQL://localhost:3306/taobao_data", table="date_flow", mode='overwrite',properties=properties )
month_flow.write.jdbc("jdbc:MySQL://localhost:3306/taobao_data", table="month_flow", mode='overwrite',properties=properties )
click_data.write.jdbc("jdbc:MySQL://localhost:3306/taobao_data", table="click_data", mode='overwrite',properties=properties )

#進入mysql，創(chuàng)建數(shù)據(jù)庫taobao_data
mysql -u root -p
create database taobao_data
exit

#打包程序所需依賴包,pyspark等
cp -r /usr/local/lib/python3.10/dist-packages /bigdata
zip spark_taobao.zip dist-packages

#編輯py文件，將上面的python代碼寫入
vim spark_taobao.py

#上傳py文件，在yarn中運行
#要注意executor的內(nèi)存加起來不能超過實際虛擬機資源，否則yarn會殺掉這個executor
cd /usr/local/spark
./bin/spark-submit /
--master yarn /
--deploy-mode cluster /
--driver-memory 2g /
--executor-memory 1g /
--executor-cores 1 /
--num-executors 1 /
--py-files='/bigdata/spark_taobao.zip' /
/bigdata/spark_taobao.py

• 讀取mysql數(shù)據(jù)，分析數(shù)據(jù)

from pyspark.sql import SparkSession
from pyspark import SparkContext,StorageLevel
from pyspark.sql.functions import *
import pandas
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = 'simhei'
spark = SparkSession.builder.getOrCreate()

#從mysql上讀取數(shù)據(jù)
url = "jdbc:mysql://localhost:3306/taobao_data"
propertie = {
    "user" : "root",
    "password": "mysql123",
    "dirver" : "com.mysql.cj.jdbc.Driver"
}
user_retention_data = spark.read.jdbc(url=url, table="user_retention_data", properties=propertie)
date_flow = spark.read.jdbc(url=url, table="date_flow", properties=propertie)
month_flow = spark.read.jdbc(url=url, table="month_flow", properties=propertie)
click_data = spark.read.jdbc(url=url, table="click_data", properties=propertie)


#統(tǒng)計7日留存率， 14日留存率， 28日留存率
all_user_num = user_retention_data.count()
seven_retention = user_retention_data.filter(user_retention_data.retention_date >= 7).count()/all_user_num
fourteen_retention = user_retention_data.filter(user_retention_data.retention_date >= 14).count()/all_user_num
te_retention = user_retention_data.filter(user_retention_data.retention_date >= 28).count()/all_user_num

retention_y = [seven_retention, fourteen_retention, te_retention]
retention_x = ["7日留存率", "14日留存率", "28日留存率"]


plt.plot(retention_x, retention_y, color='r', linewidth=2, linestyle='dashdot' )

for x in range(3):
        plt.text(x-0.13,retention_y[x],str(retention_y[x]),ha='center', va= 'bottom',fontsize=9)

plt.savefig("retention.jpg")
plt.clf()


#統(tǒng)計日均流量
date_flow = date_flow.sort("hour").toPandas()
date_flow_x = date_flow["hour"].values
date_flow_y = date_flow["count"].values

plt.figure(figsize=(8,4))
plt.plot(date_flow_x, date_flow_y,color='r', linewidth=2, linestyle='dashdot')

for x in range(24):
        plt.text(x-0.13,date_flow_y[x],str(date_flow_y[x]),ha='center', va= 'bottom',fontsize=9)

plt.savefig("date_flow.jpg")
plt.clf()

#統(tǒng)計月均流量
month_flow = month_flow.sort("day").toPandas()
month_flow_x = month_flow["day"].values
month_flow_y = month_flow["count"].values

plt.figure(figsize=(15,4))
plt.xticks(rotation=90)
plt.plot(month_flow_x, month_flow_y,color='r', linewidth=2, linestyle='dashdot')
plt.savefig("month_flow.jpg",bbox_inches='tight')
plt.clf()


#統(tǒng)計top10的商品
def take(x):
    data_list = []
    for i in range(10):
        data_list.append(x[i])
    return data_list


visit_data = click_data.sort(desc("clike_num")).toPandas()
visit_x = take(visit_data["item_category"].values)
visit_y = take(visit_data["clike_num"].values)

visit_plt = plt.bar(visit_x, visit_y, lw=0.5,fc="b",width=0.5)
plt.bar_label(visit_plt, label_type='edge')
plt.savefig("visit_top10.jpg",bbox_inches='tight')
plt.clf()

buy_data = click_data.sort(desc("buy_num")).toPandas()
buy_x = take(buy_data["item_category"].values)
buy_y = take(buy_data["buy_num"].values)

buy_plt = plt.bar(buy_x, buy_y, lw=0.5,fc="b",width=0.5)
plt.bar_label(buy_plt, label_type='edge')
plt.savefig("buy_top10.jpg",bbox_inches='tight')
plt.clf()

#統(tǒng)計購買路徑
buy_path_data = user_retention_data.filter(user_retention_data.buy_path != "沒有購買").groupBy("buy_path").count().sort(desc("count")).toPandas()
buy_path_y = buy_path_data["count"].values
buy_path_x= buy_path_data["buy_path"].values

buy_path_plt = plt.bar(buy_path_x, buy_path_y, lw=0.5,fc="b",width=0.5)
plt.bar_label(buy_path_plt, label_type='edge')
plt.savefig("buy_path.jpg",bbox_inches='tight')

#編輯運行py文件，將上面的python代碼寫入,圖片會保存在本目錄中
cd /bigdata
vim taobao_analy.py
python3 taobao_analy.py

• 運行結(jié)果

1. 日均流量，可以看出每晚的21-22點訪問人數(shù)最多

2. 月均流量，在12月12日的時候，人數(shù)陡然增加，說明促銷活動是有效的

3. 留存率，7日留存率與14日留存率差別不大，但是在28日時陡然下降，可以考慮在這一個周期里做些活動

4. 訪問top10的商品種類

5. 購買量top10的商品

6. 用戶購買途徑，大多數(shù)用戶都喜歡通過點擊詳情頁進行購買，所以一個好的詳情頁對于一個商品的銷量有著重要影響

四，其他

關(guān)于matplotlib中文字體顯示問題請參考

findfont: Font family [‘sans-serif‘] not found解決方法_fontfamily[sa_ACE-Mayer的博客-CSDN博客文章來源地址http://www.zghlxwxcb.cn/news/detail-512409.html