%matplotlib inline
# 正常显示中文
from pylab import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
# 正常显示符号
matplotlib.rcParams['axes.unicode_minus']=False
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

# 读取数据
columns = ['user_id', 'order_dt', 'order_products', 'order_amount']
date = pd.read_table('CD.txt', names=columns, sep='\s+', engine='python')
print(date.head())# 将时间序列转化为时间格式
date['order_dt'] = pd.to_datetime(date['order_dt'], format='%Y%m%d')
# 转化为月份格式
date['month'] = date['order_dt'].values.astype('datetime64[M]')

   user_id  order_dt  order_products  order_amount
0        1  19970101               1         11.77
1        2  19970112               1         12.00
2        2  19970112               5         77.00
3        3  19970102               2         20.76
4        3  19970330               2         20.76

# 进行用户消费趋势的分析（按月）
group_month = date.groupby('month')
# 1. 每月的消费总金额
m_money = group_month['order_amount'].sum()
print(m_money.head())
plt.figure(figsize=(10,8), dpi=80)
plt.plot(m_money.index, m_money.values)
# m_money.plot()
plt.xticks(rotation=45)
plt.style.use('ggplot')
plt.show()

month
1997-01-01    299060.17
1997-02-01    379590.03
1997-03-01    393155.27
1997-04-01    142824.49
1997-05-01    107933.30
Name: order_amount, dtype: float64

从图中可以看出1997年前三个月销量比较高，随后一直到1998年消费下降并保持微小波动

# 2. 每月的消费次数
num_consume = group_month.count()['user_id']
print(num_consume.head())
plt.figure(figsize=(10,8), dpi=80)
plt.plot(num_consume.index, num_consume.values)
# m_money.plot()
plt.xticks(rotation=45)
plt.style.use('ggplot')
plt.show()

month
1997-01-01     8928
1997-02-01    11272
1997-03-01    11598
1997-04-01     3781
1997-05-01     2895
Name: user_id, dtype: int64

从上图可以看出，1997年前三个月有比较高的消费次数，三月达到最高，但随后一直到1998年消费次数下降并在一定范围内有些许波动

# 3. 每月的产品购买量
num_product = group_month['order_products'].sum()
print(num_product.head())
plt.figure(figsize=(10,8), dpi=80)
plt.plot(num_product.index, num_product.values)
# m_money.plot()
plt.xticks(rotation=45)
plt.style.use('ggplot')
plt.show()

month
1997-01-01    19416
1997-02-01    24921
1997-03-01    26159
1997-04-01     9729
1997-05-01     7275
Name: order_products, dtype: int64


# 4. 每月的消费人数
num_person = date.groupby(['month', 'user_id']).count()
num_person = num_person.reset_index('user_id').groupby('month').count()
print(num_person.head())
plt.figure(figsize=(10,8), dpi=80)
plt.plot(num_person.index, num_person.values)
# m_money.plot()
plt.xticks(rotation=45)
plt.style.use('ggplot')
plt.show()

            user_id  order_dt  order_products  order_amount
month
1997-01-01     7846      7846            7846          7846
1997-02-01     9633      9633            9633          9633
1997-03-01     9524      9524            9524          9524
1997-04-01     2822      2822            2822          2822
1997-05-01     2214      2214            2214          2214

每月消费人数低于每月消费次数，但差异不太大，前三个月每月的消费人数在8000-10000之间，随后平均消费人数为2000左右波动

date.pivot_table(index=['month'], values=['order_products', 'order_amount', 'user_id'],aggfunc={'order_products':'sum', 'order_amount':'sum', 'user_id':'count'}).head()
order_amount order_products user_id
month
1997-01-01 299060.17 19416 8928
1997-02-01 379590.03 24921 11272
1997-03-01 393155.27 26159 11598
1997-04-01 142824.49 9729 3781
1997-05-01 107933.30 7275 2895 
avg_consume = date.pivot_table(index=['month'], values=['order_amount'],aggfunc='mean')

# 5. 每月用户平均消费金额趋势
# count_consume = date.groupby('month')['order_amount'].count()
# all_consume = date.groupby('month')['order_amount'].sum()
# avg_consume = all_consume / count_consumeprint(avg_consume.head())
plt.figure(figsize=(10,8), dpi=80)
plt.plot(avg_consume.index, avg_consume.order_amount)
# m_money.plot()
plt.xticks(rotation=45)
plt.style.use('ggplot')
plt.show()

            order_amount
month
1997-01-01     33.496883
1997-02-01     33.675482
1997-03-01     33.898540
1997-04-01     37.774263
1997-05-01     37.282660

通过上图可以发现，用户每月平均消费金额上下波动，有可能该产品属于一种间断性使用的物品

# 6. 每月用户平均消费次数的趋势
avg_count_consume = []
for i in range(len(num_consume)):avg_count_consume.append(num_consume.values[i] / num_person.values[i])plt.figure(figsize=(10,8), dpi=80)
plt.plot(num_consume.index, avg_count_consume)
# m_money.plot()
plt.xticks(rotation=45)
plt.style.use('ggplot')
plt.show()

由上图可以看出，用户每月平均消费次数从1997/01开始到1997/03一直是快速增长的趋势，随后在用户每月平均消费次数在1.3次左右波动

用户个体消费分析

  • 用户消费金额、消费次数的描述统计
  • 用户消费金额和消费的次数散点图
  • 用户消费金额的分布图
  • 用户消费次数的分布图
  • 用户累计消费金额占比（百分之多少的用户占了百分之多少的消费额）
group_user = date.groupby('user_id')

group_user.sum().describe()
order_products order_amount
count 23570.000000 23570.000000
mean 7.122656 106.080426
std 16.983531 240.925195
min 1.000000 0.000000
25% 1.000000 19.970000
50% 3.000000 43.395000
75% 7.000000 106.475000
max 1033.000000 13990.930000

从上图统计得出，用户平均消费次数和消费金额波动性很大，数据呈现右偏趋势，即大部分用户的消费次数和消费金额都不是很高

user_data = group_user.sum()

user_data.head()
order_products order_amount
user_id
1 1 11.77
2 6 89.00
3 16 156.46
4 7 100.50
5 29 385.61 
user_data.plot.scatter(x='order_products', y='order_amount')

<matplotlib.axes._subplots.AxesSubplot at 0xaadf780>


user_data.query('order_amount < 4000').plot.scatter(x='order_products', y='order_amount')

<matplotlib.axes._subplots.AxesSubplot at 0xa846898>

绘制直方图（用户消费金额直方图）

user_data['order_amount'].plot.hist(bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0xc7c92e8>

从图中可以看出绝大部分用户比较集中，只有极少异常值，可以进行过滤操作

user_data.query('order_products < 150')['order_amount'].plot.hist(bins=40)

<matplotlib.axes._subplots.AxesSubplot at 0xc8abb00>

使用切比雪夫定理过滤掉异常值，计算96%的数据分布情况，order_products标准差为16.983531

user_data.query('(order_products < 16.983531*5) & (order_products > -16.983531*5)')['order_amount'].plot.hist(bins=40)

<matplotlib.axes._subplots.AxesSubplot at 0xc8db6a0>

用户累计消费金额占比（百分之多少的用户占了百分之多少的消费额，需要排序）

user_cumsum = user_data.sort_values('order_amount')[['order_amount']].cumsum()

# user_data[['order_amount']].sum()
user_cumsum = user_cumsum.apply(lambda x:x/user_data['order_amount'].sum())

user_cumsum.head()
order_amount
user_id
10175 0.0
4559 0.0
1948 0.0
925 0.0
10798 0.0 
user_cumsum.reset_index()['order_amount'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0xab57978>
![在这里插入图片描述](https://img-blog.csdnimg.cn/20200712152347221.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDE0MTAwMA==,size_16,color_FFFFFF,t_70)

由图表可以看出，50%的用户仅仅贡献了15%的消费金额，而排名前20000的用户贡献了近40%的消费额。

3.用户消费行为

  • 用户第一次消费时间分布（首购）
  • 用户最后一次消费
  • 新老客消费比
    • 多少用户仅消费了一次？
    • 每月新客占比？
  • 用户分层
    • RFM
    • 新、活跃、回流、流失/不活跃
  • 用户购买周期（按订单）
    • 用户消费周期描述
    • 用户消费周期分布
  • 用户生命周期（按第一次和最后一次消费时间）
    • 用户生命周期描述
    • 用户生命周期分布
group_user['order_dt'].min().value_counts().plot()

<matplotlib.axes._subplots.AxesSubplot at 0xcabc7f0>

用户首购集中在前三个月，并且可以看出在第二月间有明显的波动

group_user['order_dt'].max().value_counts().plot()

<matplotlib.axes._subplots.AxesSubplot at 0xca506a0>

从图中可以看出，大部分用户在进行了第一次消费后就不再消费了，并且三月后，可以看出用户消费呈现不断流失的状态

多少用户仅消费了一次？

shop_time = group_user['order_dt'].agg(['min', 'max'])

shop_time.head()
min max
user_id
1 1997-01-01 1997-01-01
2 1997-01-12 1997-01-12
3 1997-01-02 1998-05-28
4 1997-01-01 1997-12-12
5 1997-01-01 1998-01-03 
count = shop_time[shop_time['min']==shop_time['max']]

count.count()

min    12054
max    12054
dtype: int64

# 每月新客占比
month_user_sum = date.groupby('month')[['order_dt']].count()

month_user_sum = month_user_sum.cumsum()

user_class = date.groupby('user_id')

user_min = user_class.month.agg('min').reset_index()
user_min = user_min.groupby('month').count()

user_min
user_id
month
1997-01-01 7846
1997-02-01 8476
1997-03-01 7248 
new_user = month_user_sum.join(user_min).fillna(0)

new_user
order_dt user_id
month
1997-01-01 8928 7846.0
1997-02-01 20200 8476.0
1997-03-01 31798 7248.0
1997-04-01 35579 0.0
1997-05-01 38474 0.0
1997-06-01 41528 0.0
1997-07-01 44470 0.0
1997-08-01 46790 0.0
1997-09-01 49086 0.0
1997-10-01 51648 0.0
1997-11-01 54398 0.0
1997-12-01 56902 0.0
1998-01-01 58934 0.0
1998-02-01 60960 0.0
1998-03-01 63753 0.0
1998-04-01 65631 0.0
1998-05-01 67616 0.0
1998-06-01 69659 0.0 
def func_handel(x):return x.user_id / x.order_dt

new_user['proportion'] = new_user.apply(func_handel, axis=1).values

new_user
order_dt user_id proportion
month
1997-01-01 8928 7846.0 0.878808
1997-02-01 20200 8476.0 0.419604
1997-03-01 31798 7248.0 0.227939
1997-04-01 35579 0.0 0.000000
1997-05-01 38474 0.0 0.000000
1997-06-01 41528 0.0 0.000000
1997-07-01 44470 0.0 0.000000
1997-08-01 46790 0.0 0.000000
1997-09-01 49086 0.0 0.000000
1997-10-01 51648 0.0 0.000000
1997-11-01 54398 0.0 0.000000
1997-12-01 56902 0.0 0.000000
1998-01-01 58934 0.0 0.000000
1998-02-01 60960 0.0 0.000000
1998-03-01 63753 0.0 0.000000
1998-04-01 65631 0.0 0.000000
1998-05-01 67616 0.0 0.000000
1998-06-01 69659 0.0 0.000000

将每月新客占比进行图的绘制

plt.plot(new_user.index, new_user.proportion)
plt.xticks(rotation=45)
plt.show()

从图中可以看出新客用户都集中在前三个月，并且每月新客占比逐月减少，到第三月后再无新增客户

rfm = date.pivot_table(index='user_id', values=['order_products','order_amount','order_dt'],aggfunc={'order_dt':'max','order_products':'sum','order_amount':'sum'})

rfm.head()
order_amount order_dt order_products
user_id
1 11.77 1997-01-01 1
2 89.00 1997-01-12 6
3 156.46 1998-05-28 16
4 100.50 1997-12-12 7
5 385.61 1998-01-03 29 
# -(rfm.order_dt - rfm.order_dt.max()) / np.timedelta64(1, 'D')    # 将其转化为数字，而不是带着 days 的单位

rfm['R'] = -(rfm.order_dt - rfm.order_dt.max()) / np.timedelta64(1, 'D')
rfm.rename(columns={'order_products':'F','order_amount':'M'}, inplace=True)

rfm.head()
M order_dt F R
user_id
1 11.77 1997-01-01 1 545.0
2 89.00 1997-01-12 6 534.0
3 156.46 1998-05-28 16 33.0
4 100.50 1997-12-12 7 200.0
5 385.61 1998-01-03 29 178.0 
def rfm_func(x):level = x.apply(lambda x:'1' if x>=0 else '0')label = level.R + level.F + level.Md = {'111':'重要客户','011':'重要保持客户','101':'重要挽留客户','001':'重要发展客户','110':'一般客户','010':'一般保持客户','100':'一般挽留客户','000':'一般发展客户'}result = d[label]return result

rfm['label'] = rfm[['R','F','M']].apply(lambda x:x-x.mean()).apply(rfm_func, axis=1)

rfm.head()
M order_dt F R label
user_id
1 11.77 1997-01-01 1 545.0 一般挽留客户
2 89.00 1997-01-12 6 534.0 一般挽留客户
3 156.46 1998-05-28 16 33.0 重要保持客户
4 100.50 1997-12-12 7 200.0 一般发展客户
5 385.61 1998-01-03 29 178.0 重要保持客户 
rfm.groupby('label').sum()
M F R
label
一般保持客户 19937.45 1712 29448.0
一般发展客户 196971.23 13977 591108.0
一般客户 7181.28 650 36295.0
一般挽留客户 438291.81 29346 6951815.0
重要保持客户 1592039.62 107789 517267.0
重要发展客户 45785.01 2023 56636.0
重要客户 167080.83 11121 358363.0
重要挽留客户 33028.40 1263 114482.0 
# 为相关的分组添加上标签
rfm.loc[rfm.label == '重要客户', 'color'] = 'g'
rfm.loc[~(rfm.label == '重要客户'), 'color'] = 'r'
rfm.plot.scatter('F','R',c=rfm.color)

<matplotlib.axes._subplots.AxesSubplot at 0xca0f908>

由RFM分层可得，大部分银行为重要保持客户，但这是由于极值的影响，所以RFM的划分标准应该以业务为准

  • 尽量用小部分的用户涵盖大部分的额度
  • 不要为了数据好看划分等级
# 新、活跃、回流、流失/不活跃
pivot_counts = date.pivot_table(index='user_id', columns='month', values='order_dt', aggfunc='count').fillna(0)

pivot_counts.head()
month 1997-01-01 00:00:00 1997-02-01 00:00:00 1997-03-01 00:00:00 1997-04-01 00:00:00 1997-05-01 00:00:00 1997-06-01 00:00:00 1997-07-01 00:00:00 1997-08-01 00:00:00 1997-09-01 00:00:00 1997-10-01 00:00:00 1997-11-01 00:00:00 1997-12-01 00:00:00 1998-01-01 00:00:00 1998-02-01 00:00:00 1998-03-01 00:00:00 1998-04-01 00:00:00 1998-05-01 00:00:00 1998-06-01 00:00:00
user_id
1 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
4 2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
5 2.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 
purchase_user = pivot_counts.applymap(lambda x:1 if x>0 else 0)
purchase_user.tail()
month 1997-01-01 00:00:00 1997-02-01 00:00:00 1997-03-01 00:00:00 1997-04-01 00:00:00 1997-05-01 00:00:00 1997-06-01 00:00:00 1997-07-01 00:00:00 1997-08-01 00:00:00 1997-09-01 00:00:00 1997-10-01 00:00:00 1997-11-01 00:00:00 1997-12-01 00:00:00 1998-01-01 00:00:00 1998-02-01 00:00:00 1998-03-01 00:00:00 1998-04-01 00:00:00 1998-05-01 00:00:00 1998-06-01 00:00:00
user_id
23566 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23567 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23568 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23569 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23570 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 
def active_status(data):status = []for i in range(18):# 本月没有消费if data[i] == 0:if len(status) == 0:status.append('unregister')else:if status[i-1] == 'unregister':status.append('unregister')else:status.append('unactive')# 本月消费else:if len(status) == 0:status.append('new')else:if status[i-1] == 'unregister':status.append('new')elif status[i-1] == 'unactive':status.append('return')else:status.append('active')return status

purchase_user = purchase_user.apply(active_status, axis=1)
purchase_user.tail()
month 1997-01-01 00:00:00 1997-02-01 00:00:00 1997-03-01 00:00:00 1997-04-01 00:00:00 1997-05-01 00:00:00 1997-06-01 00:00:00 1997-07-01 00:00:00 1997-08-01 00:00:00 1997-09-01 00:00:00 1997-10-01 00:00:00 1997-11-01 00:00:00 1997-12-01 00:00:00 1998-01-01 00:00:00 1998-02-01 00:00:00 1998-03-01 00:00:00 1998-04-01 00:00:00 1998-05-01 00:00:00 1998-06-01 00:00:00
user_id
23566 unregister unregister new unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive
23567 unregister unregister new unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive
23568 unregister unregister new active unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive
23569 unregister unregister new unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive
23570 unregister unregister new unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive unactive 
purchase_user_status = purchase_user.replace('unregister', np.NaN).apply(lambda x:pd.value_counts(x))
purchase_user_status
month 1997-01-01 00:00:00 1997-02-01 00:00:00 1997-03-01 00:00:00 1997-04-01 00:00:00 1997-05-01 00:00:00 1997-06-01 00:00:00 1997-07-01 00:00:00 1997-08-01 00:00:00 1997-09-01 00:00:00 1997-10-01 00:00:00 1997-11-01 00:00:00 1997-12-01 00:00:00 1998-01-01 00:00:00 1998-02-01 00:00:00 1998-03-01 00:00:00 1998-04-01 00:00:00 1998-05-01 00:00:00 1998-06-01 00:00:00
active NaN 1157.0 1681 1773.0 852.0 747.0 746.0 604.0 528.0 532.0 624.0 632.0 512.0 472.0 571.0 518.0 459.0 446.0
new 7846.0 8476.0 7248 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
return NaN NaN 595 1049.0 1362.0 1592.0 1434.0 1168.0 1211.0 1307.0 1404.0 1232.0 1025.0 1079.0 1489.0 919.0 1029.0 1060.0
unactive NaN 6689.0 14046 20748.0 21356.0 21231.0 21390.0 21798.0 21831.0 21731.0 21542.0 21706.0 22033.0 22019.0 21510.0 22133.0 22082.0 22064.0 
purchase_user_status.fillna(0).T
active new return unactive
month
1997-01-01 0.0 7846.0 0.0 0.0
1997-02-01 1157.0 8476.0 0.0 6689.0
1997-03-01 1681.0 7248.0 595.0 14046.0
1997-04-01 1773.0 0.0 1049.0 20748.0
1997-05-01 852.0 0.0 1362.0 21356.0
1997-06-01 747.0 0.0 1592.0 21231.0
1997-07-01 746.0 0.0 1434.0 21390.0
1997-08-01 604.0 0.0 1168.0 21798.0
1997-09-01 528.0 0.0 1211.0 21831.0
1997-10-01 532.0 0.0 1307.0 21731.0
1997-11-01 624.0 0.0 1404.0 21542.0
1997-12-01 632.0 0.0 1232.0 21706.0
1998-01-01 512.0 0.0 1025.0 22033.0
1998-02-01 472.0 0.0 1079.0 22019.0
1998-03-01 571.0 0.0 1489.0 21510.0
1998-04-01 518.0 0.0 919.0 22133.0
1998-05-01 459.0 0.0 1029.0 22082.0
1998-06-01 446.0 0.0 1060.0 22064.0 
purchase_user_status.fillna(0).T.plot.area()

<matplotlib.axes._subplots.AxesSubplot at 0xc9fc0b8>


purchase_user_status.fillna(0).T.apply(lambda x:x/x.sum(), axis=1)
active new return unactive
month
1997-01-01 0.000000 1.000000 0.000000 0.000000
1997-02-01 0.070886 0.519299 0.000000 0.409815
1997-03-01 0.071319 0.307510 0.025244 0.595927
1997-04-01 0.075223 0.000000 0.044506 0.880272
1997-05-01 0.036148 0.000000 0.057785 0.906067
1997-06-01 0.031693 0.000000 0.067543 0.900764
1997-07-01 0.031650 0.000000 0.060840 0.907510
1997-08-01 0.025626 0.000000 0.049555 0.924820
1997-09-01 0.022401 0.000000 0.051379 0.926220
1997-10-01 0.022571 0.000000 0.055452 0.921977
1997-11-01 0.026474 0.000000 0.059567 0.913958
1997-12-01 0.026814 0.000000 0.052270 0.920916
1998-01-01 0.021723 0.000000 0.043487 0.934790
1998-02-01 0.020025 0.000000 0.045779 0.934196
1998-03-01 0.024226 0.000000 0.063174 0.912601
1998-04-01 0.021977 0.000000 0.038990 0.939033
1998-05-01 0.019474 0.000000 0.043657 0.936869
1998-06-01 0.018922 0.000000 0.044972 0.936105

由上表可知，每月用户消费状态变化

  • 回流用户：之前没消费，本月才消费（唤回运营）
  • 活跃用户：持续消费（消费运营的质量）
  • 不活跃用户：流失用户
# 用户购买周期（按订单）    shift()函数是使得数据矩阵偏移一个单位
order_diff = group_user.apply(lambda x:(x.order_dt - x.order_dt.shift()) / np.timedelta64(1, 'D'))
order_diff.hist(bins=20)

<matplotlib.axes._subplots.AxesSubplot at 0x1299db00>


# 用户生命周期（按第一次和最后一次消费时间）
user_period = group_user.order_dt.agg(['max', 'min'])
user_period = user_period.apply(lambda x:(x['max'] - x['min']) / np.timedelta64(1, 'D'), axis=1)

user_period.describe()

count    23570.000000
mean       134.871956
std        180.574109
min          0.000000
25%          0.000000
50%          0.000000
75%        294.000000
max        544.000000
dtype: float64

user_period = pd.DataFrame(user_period, columns=['diff'])
user_period.head()
diff
user_id
1 0.0
2 0.0
3 511.0
4 345.0
5 367.0 
user_period[user_period['diff']!=0].hist(bins=40)   # 将生命周期为0的全部剔除掉

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000B3A2DD8>]], dtype=object)

复购率和回购率分析

  • 复购率

    • 自然月内，购买多次的用户占比
  • 回购率
    • 曾经购买过的用户在某一段时间内的再次购买的占比
pivot_counts.head(10)
month 1997-01-01 00:00:00 1997-02-01 00:00:00 1997-03-01 00:00:00 1997-04-01 00:00:00 1997-05-01 00:00:00 1997-06-01 00:00:00 1997-07-01 00:00:00 1997-08-01 00:00:00 1997-09-01 00:00:00 1997-10-01 00:00:00 1997-11-01 00:00:00 1997-12-01 00:00:00 1998-01-01 00:00:00 1998-02-01 00:00:00 1998-03-01 00:00:00 1998-04-01 00:00:00 1998-05-01 00:00:00 1998-06-01 00:00:00
user_id
1 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
4 2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
5 2.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0
6 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
7 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
8 1.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 2.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0
9 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
10 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
re_purchase = pivot_counts.applymap(lambda x:1 if x>1 else np.NaN if x==0 else 0)
re_purchase.head(10)
month 1997-01-01 00:00:00 1997-02-01 00:00:00 1997-03-01 00:00:00 1997-04-01 00:00:00 1997-05-01 00:00:00 1997-06-01 00:00:00 1997-07-01 00:00:00 1997-08-01 00:00:00 1997-09-01 00:00:00 1997-10-01 00:00:00 1997-11-01 00:00:00 1997-12-01 00:00:00 1998-01-01 00:00:00 1998-02-01 00:00:00 1998-03-01 00:00:00 1998-04-01 00:00:00 1998-05-01 00:00:00 1998-06-01 00:00:00
user_id
1 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 0.0 NaN 0.0 0.0 NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN 0.0 NaN
4 1.0 NaN NaN NaN NaN NaN NaN 0.0 NaN NaN NaN 0.0 NaN NaN NaN NaN NaN NaN
5 1.0 0.0 NaN 0.0 0.0 0.0 0.0 NaN 0.0 NaN NaN 1.0 0.0 NaN NaN NaN NaN NaN
6 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 0.0 NaN NaN NaN NaN NaN NaN NaN NaN 0.0 NaN NaN NaN NaN 0.0 NaN NaN NaN
8 0.0 0.0 NaN NaN NaN 0.0 0.0 NaN NaN NaN 1.0 0.0 NaN NaN 0.0 NaN NaN NaN
9 0.0 NaN NaN NaN 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0
10 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 
re_purchase.apply(lambda x:x.sum()/x.count()).plot(figsize=(10, 4))

<matplotlib.axes._subplots.AxesSubplot at 0x28a1390>

复购率在前三月增长的较快，可能是新增用户基数较大，但大部分都是只购买了一次，导致复购率较低，随后一直稳定在20%左右

# 回购率是指本月消费了的用户中，在下一月继续消费的用户有多少
repurchase = pivot_counts.applymap(lambda x:1 if x>0 else  0)
repurchase.head(10)
month 1997-01-01 00:00:00 1997-02-01 00:00:00 1997-03-01 00:00:00 1997-04-01 00:00:00 1997-05-01 00:00:00 1997-06-01 00:00:00 1997-07-01 00:00:00 1997-08-01 00:00:00 1997-09-01 00:00:00 1997-10-01 00:00:00 1997-11-01 00:00:00 1997-12-01 00:00:00 1998-01-01 00:00:00 1998-02-01 00:00:00 1998-03-01 00:00:00 1998-04-01 00:00:00 1998-05-01 00:00:00 1998-06-01 00:00:00
user_id
1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0
4 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0
5 1 1 0 1 1 1 1 0 1 0 0 1 1 0 0 0 0 0
6 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0
8 1 1 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0 0
9 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1
10 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 
def Repurchase(data):is_purchase = []for i in range(0, 17):# 本月消费了if data[i] > 0:# 下月消费了if data[i+1] > 0:is_purchase.append(1)# 下月没有消费else:is_purchase.append(0)# 本月没有消费else:# 不管下月消费与否is_purchase.append(np.NaN)is_purchase.append(np.NaN)return is_purchase

repurchase = repurchase.apply(Repurchase, axis=1)
repurchase.head(5)
month 1997-01-01 00:00:00 1997-02-01 00:00:00 1997-03-01 00:00:00 1997-04-01 00:00:00 1997-05-01 00:00:00 1997-06-01 00:00:00 1997-07-01 00:00:00 1997-08-01 00:00:00 1997-09-01 00:00:00 1997-10-01 00:00:00 1997-11-01 00:00:00 1997-12-01 00:00:00 1998-01-01 00:00:00 1998-02-01 00:00:00 1998-03-01 00:00:00 1998-04-01 00:00:00 1998-05-01 00:00:00 1998-06-01 00:00:00
user_id
1 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 0.0 NaN 1.0 0.0 NaN NaN NaN NaN NaN NaN 0.0 NaN NaN NaN NaN NaN 0.0 NaN
4 0.0 NaN NaN NaN NaN NaN NaN 0.0 NaN NaN NaN 0.0 NaN NaN NaN NaN NaN NaN
5 1.0 0.0 NaN 1.0 1.0 1.0 0.0 NaN 0.0 NaN NaN 1.0 0.0 NaN NaN NaN NaN NaN 
repurchase.apply(lambda x:x.sum()/x.count()).plot(figsize=(10,4))

D:\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide"""Entry point for launching an IPython kernel.<matplotlib.axes._subplots.AxesSubplot at 0xaad40b8>

用户购买CD行为数据分析相关推荐

  1. 用户购买CD消费行为分析

    用户购买CD消费行为分析 1.进行用户消费趋势的分析(按月) 1.1每月的消费总金额 1.2每月的消费次数 1.3每月的产品购买量 1.4每月的消费人数 2.用户个体消费分析 2.1用户消费金额.消费 ...

  2. 用pandas进行数据分析：结合JData ”用户购买时间预测“数据分析实例（三）

    表3:用户行为表(jdata_user_action) 1. 读取数据,并获取数据基本信息 2. 获取部分特征的频率统计和最大/最小值 3. 获取行为次数的统计

  3. 天猫用户重复购买预测之数据分析

    赛题理解 赛题链接 赛题背景: 商家有时会在特定日期,例如Boxing-day,黑色星期五或是双十一(11月11日)开展大型促销活动或者发放优惠券以吸引消费者,然而很多被吸引来的买家都是一次性消费者, ...

  4. php汽车购买意向调查,2012年中国汽车用户购买意向调查报告(已购汽车篇)

    近年来,随着国民生活质量的日渐提高,汽车已经逐渐成为普通家庭日常生活的必需品,越来越多的消费者开始考虑购买属于自己的汽车.为了更好了解中国消费者对于汽车的消费需求,互联网消费调研中心ZDC进行了本次调 ...

  5. JData数据处理及高潜用户购买意向预测

    竞赛概述: 本次大赛以京东商城真实的用户.商品和行为数据(脱敏后)为基础,参赛队伍需要通过数据挖掘的技术和机器学习的算法,构建用户购买商品的预测模型,输出高潜用户和目标商品的匹配结果,为精准营销提供高 ...

  6. 32销售是合理的引导用户购买

    销售一定是合理的去引导用户,如果不进行合理的引导,那么在用户的面前,这件事很可能觉得莫名其妙. 对于一个产品任何一个用户都是陌生的,无论是否有这种需要,他都是陌生的,这个陌生来自于对使用的陌生.对该产 ...

  7. oracle:用户购买平台案例分析与优化

    用户购买平台案例,涉及时间型数据.个人第一眼感觉特别简单,但是当深入处理是难成狗了.虽然在测试样例中的结果中通过,但是在最终提交过程中,却显示超时.唉,还得优化呀!本文就是关于这个问题的分析和总结. ...

  8. 操作系统的不确定性是指程序执行结果的不确定性_用不确定性促销策略提高用户购买意愿...

    本文作者整理了关于不确定性促销策略的相关问题,结合案例对其一一展开了分析探究. 一.前言 促销玩法是电商运营的重要组成部分,我们整理了目前有关不确定性促销策略的前沿学术研究成果,希望以此解答以下问题, ...

  9. Java初学者作业——编写Java程序， 实现根据用户购买商品总金额， 计算实际支付的金额及所获得的购物券金额。

    返回本章节 返回作业目录 需求说明: 编写Java程序, 实现根据用户购买商品总金额, 计算实际支付的金额及所获得的购物券金额. 购买总金额达到或超过 1000元,按 8折优惠,送 200元的购物券: ...

最新文章

  1. 命令行避免输入错误文件名_GitHub 60000+ Star 登顶，命令行的艺术
  2. Exchange 2007迁移Exchange 2010应该注意的13件事
  3. 同网段DHCP配置实验
  4. 学习共享--产品思维
  5. mysql 导入导出 备份_MySQL - 数据备份与还原(导出导入)
  6. [2018.10.11 T2] 整除
  7. SQLServer常用SQL语句
  8. 网络安全实验四 防火墙技术的具体应用
  9. 经济应用文写作【10】
  10. mui如何对接java后台_MUI框架-09-MUI 与后台数据交互
  11. C++优先级队列priority_queue详解及其模拟实现
  12. 抗洪救灾，共克时艰，城联优品捐赠10万元爱心物资驰援英德
  13. CentOS cowsay “会说话的小动物”
  14. 从C到B，20岁的腾讯正在经历一场“生死”腾挪
  15. 世界杯期间怎么做营销活动？
  16. 乳腺癌诊断和药物技术行业调研报告 - 市场现状分析与发展前景预测
  17. 【000】欢迎来到嵌入式开发教程
  18. 口嫌体正直，“苹果”们纷纷下场造车
  19. 三坐标最小二乘法原理_全最小二乘法在三坐标测量中的应用
  20. 骚操作！用Python自动下载抖音小姐姐

热门文章

  1. html文字段落分割,HTML设置水平分割线_html/css_WEB-ITnose
  2. Subversion常见问题及解决方法
  3. Linux进程同步机制-Futex
  4. 魅蓝metal android固件,魅族：魅蓝Metal备货百万台以迎战双11！
  5. rnn循环神经网络进行自动生成诗歌等
  6. Hadoop的NameNode
  7. python 量化交易靠谱吗_个人做量化交易靠谱吗？
  8. 广州美食--特色小吃桂林米粉
  9. 花了20W才怠结出的短视频运营真话
  10. React封装文件预览组件