80万商城运营大数据分析

80万商城运营大数据分析

• 用户行为分析：日访问量、小时访问量、不同行为类型访问量
• 获客分析
• 用户留存分析
• 复购分析
• 转化漏斗分析
• 商品竞争力分析：性别、城市、用户浏览、购买、收藏、商品类目分析

一：导入数据

# 导入常用的库：
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import datetime

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

# columns = ['user_id','goods_id','cat','behavior','time']
df = pd.read_table("E:/yizhiamumu/behavior.txt")
df.info()

打印结果

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 757565 entries, 0 to 757564
Data columns (total 10 columns):
user_id     757565 non-null int64
goods_id    757565 non-null int64
cat         757565 non-null int64
behavior    757565 non-null object
time        757565 non-null int64
sex         757565 non-null int64
addr        757565 non-null object
device      757565 non-null object
price       757565 non-null float64
amount      757565 non-null int64
dtypes: float64(1), int64(6), object(3)
memory usage: 57.8+ MB

观察数据

df.head()

打印结果

	    user_id	goods_id	cat	behavior	time	sex	addr	device	price	amount
0	1	5002615	2520377	pv	1574911385	0	成都	Redmi Note8Pro	0.0	0
1	1	2734026	4145813	pv	1574914184	0	成都	Redmi Note8Pro	0.0	0
2	1	5002615	2520377	pv	1574916273	0	成都	Redmi Note8Pro	0.0	0
3	1	3239041	2355072	pv	1574927664	0	成都	Redmi Note8Pro	0.0	0
4	1	4615417	4145813	pv	1574942864	0	成都	Redmi Note8Pro	0.0	0

二：数据清洗

# 数据清廷
df.drop_duplicates(inplace=True)

# 时间戳处理
df['time1'] = df['time'].apply(lambda x:datetime.datetime.fromtimestamp(x))
df['month'] = df['time'].apply(lambda x:int(datetime.datetime.fromtimestamp(x).strftime("%Y%m")))
df['date'] = df['time'].apply(lambda x:datetime.datetime.fromtimestamp(x).strftime("%Y%m%d"))
df['hour'] = df['time1'].apply(lambda x:x.hour)

# 销售金额计算
df['money'] = df['price'] * df['amount']

# 缺失值处理
df.isnull().sum()

df.describe()

打印结果

	    user_id	goods_id	cat	time	sex	price	amount	month	hour	money
count	757565.000000	7.575650e+05	7.575650e+05	7.575650e+05	757565.000000	757565.000000	757565.000000	757565.000000	757565.000000	757565.000000
mean	5583.487645	2.582329e+06	2.716578e+06	1.575162e+09	0.432664	0.955333	0.047983	201911.560921	14.953452	2.383729
std	3184.742636	1.487073e+06	1.465113e+06	1.494278e+05	0.495445	7.915329	0.376215	0.496275	6.063847	21.677706
min	1.000000	2.900000e+01	2.171000e+03	1.574870e+09	0.000000	0.000000	0.000000	201911.000000	0.000000	0.000000
25%	2824.000000	1.304276e+06	1.354236e+06	1.575031e+09	0.000000	0.000000	0.000000	201911.000000	11.000000	0.000000
50%	5671.000000	2.582878e+06	2.735466e+06	1.575181e+09	0.000000	0.000000	0.000000	201912.000000	16.000000	0.000000
75%	8310.000000	3.861863e+06	4.145813e+06	1.575291e+09	1.000000	0.000000	0.000000	201912.000000	20.000000	0.000000
max	11079.000000	5.163006e+06	5.161669e+06	1.575389e+09	1.000000	99.900000	4.000000	201912.000000	23.000000	399.600000

三：数据可视化

1 每天PV UV 走势分析

# 数据可视化
# 1 每天PV UV 走势分析
all_puv = pd.pivot_table(df, index=['date'],values='user_id',aggfunc='count')
uv = df[['user_id','date']].drop_duplicates()['date'].value_counts()
all_puv = all_puv.join(uv)
all_puv.columns = ['pv','uv']
all_puv['avg_pv'] = all_puv['pv']/all_puv['uv']
all_puv

打印结果

	    pv	uv	avg_pv
date			
20191128	106877	7610	14.044284
20191129	111426	7811	14.265267
20191130	114328	7874	14.519685
20191201	121457	8097	15.000247
20191202	153545	10552	14.551270
20191203	149932	10530	14.238557

x=all_puv.index
fig,axes = plt.subplots(1,3,figsize=(18,3))
axes[0].plot(x,all_puv['pv'], color='r', marker='o')
axes[1].plot(x,all_puv['uv'], color='g', marker='s')
axes[2].plot(x,all_puv['avg_pv'], color='b', marker='d')

axes[0].set_title('pv')
axes[1].set_title('uv')
axes[2].set_title('avg_pv')

plt.show()

打印结果 

2 用户行为 pv 分析

# 2 用户行为 pv 分析

pv = pd.pivot_table(df, index=['date'], columns=['behavior'], values='user_id', aggfunc='count')

pv['all'] = pv.sum(axis=1)

plt.figure(figsize=(12,5))
plt.plot(pv.index, pv['all'])
plt.plot(pv.index, pv['pv'],color='r')
plt.plot(pv.index, pv['cart'],color='y')
plt.plot(pv.index, pv['buy'], color='c')
plt.plot(pv.index, pv['fav'], color='b')

plt.xlabel("日期", fontsize=20)
plt.ylabel('pv', fontsize=20)
plt.title("用户行为pv 分析", fontsize=24)
plt.legend(["all","pv","cart","buy","fav"], loc = 'upper left', fontsize=20)
plt.show()

打印结果

3 访问高峰分析

# 3 访问高峰分析
hour_pv = df['hour'].value_counts().reset_index().rename(columns={'index':'hour', 'hour':'pv'})

plt.figure(figsize=(12,4))
plt.bar(hour_pv['hour'], hour_pv['pv'],color='r')
plt.xlabel("小时", fontsize=20)
plt.ylabel("访问量pv", fontsize=20)
plt.title("每小时用户访问量数据分析", fontsize = 24)
plt.show()

4 新用户分析

# 4 新用户分析
new_user= df[['user_id','date']].groupby('user_id').min()['date'].value_counts().reset_index()
new_user.columns = ['date', 'new_user']

plt.figure(figsize=(10,6))
x,y = new_user['date'], new_user['new_user']
plt.bar(x,y,width=0.6,color='r')
for a, b in zip(x,y):
    plt.text(a,b + 0.05, '%.0f' % b, ha='center', va='bottom', fontsize=20)
plt.xlabel("日期", fontsize=16)
plt.ylabel("新用户数", fontsize=16)
plt.title("每日新增用户数", fontsize=20)
plt.show()

5 用户留存分析

# 5 用户留存分析
# 建立 n 日留存率计算模型，数据传入用户id 和登录日期
# n 为n 日留存， 不传入start_date 和 n 时，则计算所有留存

def cal_retention(df, start_date='20190101',n=0):
    if n>0:
        new_user = df[['user_id','date']].groupby('user_id').min().reset_index()
        date2 = datetime.datetime.strptime(start_date, '%Y%m%d')+datetime.timedelta(n)
        end_date = datetime.datetime.strftime(date2,'%Y%m%d')

        start_user = set(new_user[new_user.date==start_date].user_id)
        end_user = set(df[df.date==end_date].user_id)
        user = start_user&end_user
        
        return [start_date, end_date, len(start_user),len(user),round(len(user)/len(start_user),4)]
    
    else:
        new_user = df[['user_id',"date"]].groupby('user_id').min().reset_index()
        date_source = new_user.date.unique()
        date_source.sort()
        
        result1 = []
        flag =0
        
        for start_date in date_source:
            start_user = set(new_user[new_user.date == start_date].user_id)
            for end_date in date_source[flag:]:
                end_user = set(df[df.date==end_date].user_id)
                user = start_user&end_user
                result1.append([start_date,end_date,len(start_user),len(user),round(len(user)/len(start_user),4)])
            flag = flag+1
       return pd.DataFrame(result1, columns=['开始日期','留存日期','新用户数','留存人数','留存率'])
    
# 调用cal_retention 函数计算留存
# cal_retention(df[['user_id','date']],'20191128',3)

retention = cal_retention(df[['user_id','date']])

# 留存人数展示
pd.pivot_table(retention, index=['开始日期'],columns=['留存日期'],values='留存人数',aggfunc='sum',fill_value=0)

# 留存率展示
pd.pivot_table(retention, index=['开始日期'],columns=['留存日期'],values='留存率',aggfunc='sum',fill_value=0)

6 复购率计算

# 6 复购率计算

data_buy = df[df.behavior=='buy'][['user_id','date']].drop_duplicates()['user_id'].value_counts().reset_index()
data_buy.columns = ['user_id','num']

re_buy_rate = round(len(data_buy[data_buy.num>=2])/len(data_buy),4)
print("复购率为:",round(re_buy_rate*100,2),"%")

# 购买总人数
buy_user = len(data_buy)

# 网联次数的人数分布
buy_freq = data_buy.num.value_counts().reset_index()
buy_freq.columns = [['购买次数','人数']]
buy_freq['人数占比'] = round(buy_freq['人数']/buy_user,4)
buy_freq

打印结果

    复购率为: 42.06 %
    购买次数	人数	人数占比
0	1	3540	0.5794
1	2	1714	0.2805
2	3	575	0.0941
3	4	210	0.0344
4	5	57	0.0093
5	6	14	0.0023

7 商品TOP 分析

7.1 商品销售TOP

# 7.1 商品销售TOP

buy_top = df[df.behavior=='buy']['goods_id'].value_counts().head(10)

from pyecharts.charts import Bar
from pyecharts import options as opts

bar = Bar()
bar.add_xaxis(buy_top.index.tolist())
bar.add_yaxis("商品销售TOP",buy_top.values.tolist(),color='red')
bar.set_global_opts(title_opts=opts.TitleOpts(title="商品销售TOP"))

bar.render_notebook()

7.2商品浏览TOP

# 7.2商品浏览TOP
pv_top = df[df.behavior=='pv']['goods_id'].value_counts().head(10)

from pyecharts.charts import Bar
from pyecharts import options as opts

bar = Bar()
bar.add_xaxis(pv_top.index.tolist())
bar.add_yaxis("商品浏览TOP",pv_top.values.tolist(),color='red')
bar.set_global_opts(title_opts=opts.TitleOpts(title="商品浏览TOP"))

bar.render_notebook()

7.3商品收藏 TOP

# 7.3商品收藏 TOP
fav_top = df[df.behavior=='fav']['goods_id'].value_counts().head(10)

from pyecharts.charts import Bar
from pyecharts import options as opts

bar = Bar()
bar.add_xaxis(fav_top.index.tolist())
bar.add_yaxis("商品收藏TOP",fav_top.values.tolist(),color='red')
bar.set_global_opts(title_opts=opts.TitleOpts(title="商品收藏TOP"))

bar.render_notebook()

8 城市购买竞争力TOP

# 8 城市购买竞争力TOP
city_top = df[df.behavior=='buy'][['addr','money']].groupby('addr').sum().sort_values('money',ascending=False)


bar = Bar()
bar.add_xaxis(city_top.index.tolist())
bar.add_yaxis("城市购买竞争力TOP",[round(a,2) for a in city_top.money], color='red')
bar.set_global_opts(title_opts=opts.TitleOpts(title="城市购买竞争力TOP"))

bar.render_notebook()

9 不同性别购买力分析

# 9 不同性别购买力情况  0-女，1-男

sex_top = df[df.behavior=='buy'][['sex','money']].groupby('sex').sum()

from pyecharts.charts import Pie

pie = Pie()
pie.add("",[list(z) for z in zip(['女','男'], [round(a,2) for a in city_top.money])])
pie.set_global_opts(title_opts=opts.TitleOpts(title="不同性别购买力分析"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c} ({d}%)"))
pie.render_notebook()

10商品类目竞争力分析

# 10商品类目竞争力分析 

cat_top = df[df.behavior=='buy']['cat'].value_counts().head(10)
bar = Bar()
bar.add_xaxis(cat_top.index.tolist())
bar.add_yaxis("商品类目竞争力分析",cat_top.values.tolist())
bar.render_notebook()

11 总体转化漏斗分析

# 11 总体转化漏斗分析
from pyecharts.charts import Funnel
from pyecharts import options as opts
data_behavior = df[df.behavior!="fav"]['behavior'].value_counts().reset_index().rename(columns={"index":"环节","behavior":"人数"})

# 单一环节的转化率
t1 = np.array(data_behavior['人数'][1:])
t2 = np.array(data_behavior['人数'][0:-1])
single_convs = t1/t2
single_convs = list(single_convs)
single_convs.insert(0,1)

# 总体转化率
flag = data_behavior['人数'][0]
data_behavior['总体转化率'] = data_behavior['人数']/flag

attrs = [a+":  "+str(round(b,2))+"%" for a,b in zip(data_behavior['环节'],data_behavior['总体转化率']*100)]
attr_value = [round(a,2) for a in data_behavior['总体转化率'] * 100]

funnel = Funnel()
funnel.add("商品",[list(z) for z in zip(attrs, attr_value)], label_opts = opts.LabelOpts(position="inside"))
funnel.set_global_opts(title_opts=opts.TitleOpts(title="总体转化漏斗数据分析"))
funnel.render_notebook()

12 RFM 用户分层模型

# RFM 模型打分规则

def recency(x):
    if x<=2:
        return 5
    elif x==3:
        return 4
    elif x==4:
        return 3
    elif x==5:
        return 2
    elif x>=6:
        return 1

def frequency(x):
    if x>=8:
        return 5
    elif (x>=6)&(x<8):
        return 4
    elif (x>=4)&(x<6):
        return 3
    elif (x>=2)&(x<4):
        return 2
    elif (x>=0)&(x<2):
        return 1

def monetary(x):
    if x>=300:
        return 5
    elif (x>=200)&(x<300):
        return 4
    elif (x>100)&(x<200):
        return 3
    elif (x>50)&(x<100):
        return 2
    elif (x>=0)&(x<50):
        return 1


# RFM 用户分层
RFM_date = df[df.behavior == 'buy'][['user_id','date']].groupby("user_id").max()
RFM_F = df[df.behavior == 'buy'][['user_id','behavior']].groupby('user_id').count()
RFM_M = df[df.behavior == 'buy'][['user_id','money']].groupby('user_id').mean()
RFM = RFM_date.join(RFM_F).join(RFM_M)

# 用户价值分层
end_date = datetime.datetime.strptime('20191205',"%Y%m%d")
# 时间间隔天数计算
RFM['days'] = RFM['date'].apply(lambda x:(end_date-datetime.datetime.strptime(x,"%Y%m%d")).days)
RFM = RFM[['days','behavior','money']]
RFM.columns = ['间隔天数','消费频次','消费金额']

RFM.head()

打印结果：

    间隔天数	消费频次	消费金额
user_id			
2	3	7	186.742857
4	5	4	142.600000
16	4	2	84.650000
17	4	1	62.700000
20	4	1	37.900000

RFM数据建模：

# RFM 数据处理

RFM['R_S'] = RFM['间隔天数'].apply(recency)
RFM['F_S'] = RFM['消费频次'].apply(frequency)
RFM['M_S'] = RFM['消费金额'].apply(monetary)
RFM['RFM'] = RFM.apply(lambda x:int(x.R_S * 100 + x.F_S * 10 + x.M_S),axis=1)
RFM.head()

打印结果

	间隔天数	消费频次	消费金额	R_S	F_S	M_S RFM 
user_id						
2	3	7	186.742857	4	4	3.0  443
4	5	4	142.600000	2	3	3.0  233
16	4	2	84.650000	3	2	2.0  322
17	4	1	62.700000	3	1	2.0 312 
20	4	1	37.900000	3	1	1.0  311

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