import numpy as np
import pandas as pd
from pandas import DataFrame,Series
import matplotlib.pyplot as plt
#CDNOW_master.txt
第一部分：数据类型处理
- 数据加载
- 字段含义：
- user_id:用户ID
- order_dt:购买日期
- order_product:购买产品的数量
- order_amount:购买金额
- 观察数据
- 查看数据的数据类型
- 数据中是否存储在缺失值
- 将order_dt转换成时间类型
- 查看数据的统计描述
- 计算所有用户购买商品的平均数量
- 计算所有用户购买商品的平均花费
- 在源数据中添加一列表示月份:astype('datetime64[M]')
#数据的加载
df = pd.read_csv('./data/CDNOW_master.txt',header=None,sep='s+',names=['user_id','order_dt','order_product','order_amount'])
df
|
user_id |
order_dt |
order_product |
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 |
| 5 |
3 |
19970402 |
2 |
19.54 |
| 6 |
3 |
19971115 |
5 |
57.45 |
| 7 |
3 |
19971125 |
4 |
20.96 |
| 8 |
3 |
19980528 |
1 |
16.99 |
| 9 |
4 |
19970101 |
2 |
29.33 |
| 10 |
4 |
19970118 |
2 |
29.73 |
| 11 |
4 |
19970802 |
1 |
14.96 |
| 12 |
4 |
19971212 |
2 |
26.48 |
| 13 |
5 |
19970101 |
2 |
29.33 |
| 14 |
5 |
19970114 |
1 |
13.97 |
| 15 |
5 |
19970204 |
3 |
38.90 |
| 16 |
5 |
19970411 |
3 |
45.55 |
| 17 |
5 |
19970531 |
3 |
38.71 |
| 18 |
5 |
19970616 |
2 |
26.14 |
| 19 |
5 |
19970722 |
2 |
28.14 |
| 20 |
5 |
19970915 |
3 |
40.47 |
| 21 |
5 |
19971208 |
4 |
46.46 |
| 22 |
5 |
19971212 |
3 |
40.47 |
| 23 |
5 |
19980103 |
3 |
37.47 |
| 24 |
6 |
19970101 |
1 |
20.99 |
| 25 |
7 |
19970101 |
2 |
28.74 |
| 26 |
7 |
19971011 |
7 |
97.43 |
| 27 |
7 |
19980322 |
9 |
138.50 |
| 28 |
8 |
19970101 |
1 |
9.77 |
| 29 |
8 |
19970213 |
1 |
13.97 |
| ... |
... |
... |
... |
... |
| 69629 |
23556 |
19970927 |
3 |
31.47 |
| 69630 |
23556 |
19980103 |
2 |
28.98 |
| 69631 |
23556 |
19980607 |
2 |
28.98 |
| 69632 |
23557 |
19970325 |
1 |
14.37 |
| 69633 |
23558 |
19970325 |
2 |
28.13 |
| 69634 |
23558 |
19970518 |
3 |
45.51 |
| 69635 |
23558 |
19970624 |
2 |
23.74 |
| 69636 |
23558 |
19980225 |
4 |
48.22 |
| 69637 |
23559 |
19970325 |
2 |
23.54 |
| 69638 |
23559 |
19970518 |
3 |
35.31 |
| 69639 |
23559 |
19970627 |
3 |
52.80 |
| 69640 |
23560 |
19970325 |
1 |
18.36 |
| 69641 |
23561 |
19970325 |
2 |
30.92 |
| 69642 |
23561 |
19980128 |
1 |
15.49 |
| 69643 |
23561 |
19980529 |
3 |
37.05 |
| 69644 |
23562 |
19970325 |
2 |
29.33 |
| 69645 |
23563 |
19970325 |
1 |
10.77 |
| 69646 |
23563 |
19971004 |
2 |
47.98 |
| 69647 |
23564 |
19970325 |
1 |
11.77 |
| 69648 |
23564 |
19970521 |
1 |
11.77 |
| 69649 |
23564 |
19971130 |
3 |
46.47 |
| 69650 |
23565 |
19970325 |
1 |
11.77 |
| 69651 |
23566 |
19970325 |
2 |
36.00 |
| 69652 |
23567 |
19970325 |
1 |
20.97 |
| 69653 |
23568 |
19970325 |
1 |
22.97 |
| 69654 |
23568 |
19970405 |
4 |
83.74 |
| 69655 |
23568 |
19970422 |
1 |
14.99 |
| 69656 |
23569 |
19970325 |
2 |
25.74 |
| 69657 |
23570 |
19970325 |
3 |
51.12 |
| 69658 |
23570 |
19970326 |
2 |
42.96 |
69659 rows × 4 columns
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 69659 entries, 0 to 69658
Data columns (total 4 columns):
user_id 69659 non-null int64
order_dt 69659 non-null int64
order_product 69659 non-null int64
order_amount 69659 non-null float64
dtypes: float64(1), int64(3)
memory usage: 2.1 MB
#将order_dt转换成时间类型
df['order_dt'] = pd.to_datetime(df['order_dt'],format='%Y%m%d')
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 69659 entries, 0 to 69658
Data columns (total 4 columns):
user_id 69659 non-null int64
order_dt 69659 non-null datetime64[ns]
order_product 69659 non-null int64
order_amount 69659 non-null float64
dtypes: datetime64[ns](1), float64(1), int64(2)
memory usage: 2.1 MB
#查看数据的统计描述
df.describe()
|
user_id |
order_product |
order_amount |
| count |
69659.000000 |
69659.000000 |
69659.000000 |
| mean |
11470.854592 |
2.410040 |
35.893648 |
| std |
6819.904848 |
2.333924 |
36.281942 |
| min |
1.000000 |
1.000000 |
0.000000 |
| 25% |
5506.000000 |
1.000000 |
14.490000 |
| 50% |
11410.000000 |
2.000000 |
25.980000 |
| 75% |
17273.000000 |
3.000000 |
43.700000 |
| max |
23570.000000 |
99.000000 |
1286.010000 |
#基于order_dt取出其中的月份
df['order_dt'].astype('datetime64[M]')
0 1997-01-01
1 1997-01-01
2 1997-01-01
3 1997-01-01
4 1997-03-01
5 1997-04-01
6 1997-11-01
7 1997-11-01
8 1998-05-01
9 1997-01-01
10 1997-01-01
11 1997-08-01
12 1997-12-01
13 1997-01-01
14 1997-01-01
15 1997-02-01
16 1997-04-01
17 1997-05-01
18 1997-06-01
19 1997-07-01
20 1997-09-01
21 1997-12-01
22 1997-12-01
23 1998-01-01
24 1997-01-01
25 1997-01-01
26 1997-10-01
27 1998-03-01
28 1997-01-01
29 1997-02-01
...
69629 1997-09-01
69630 1998-01-01
69631 1998-06-01
69632 1997-03-01
69633 1997-03-01
69634 1997-05-01
69635 1997-06-01
69636 1998-02-01
69637 1997-03-01
69638 1997-05-01
69639 1997-06-01
69640 1997-03-01
69641 1997-03-01
69642 1998-01-01
69643 1998-05-01
69644 1997-03-01
69645 1997-03-01
69646 1997-10-01
69647 1997-03-01
69648 1997-05-01
69649 1997-11-01
69650 1997-03-01
69651 1997-03-01
69652 1997-03-01
69653 1997-03-01
69654 1997-04-01
69655 1997-04-01
69656 1997-03-01
69657 1997-03-01
69658 1997-03-01
Name: order_dt, Length: 69659, dtype: datetime64[ns]
#在源数据中添加一列表示月份:astype('datetime64[M]')
df['month'] = df['order_dt'].astype('datetime64[M]')
df.head()
|
user_id |
order_dt |
order_product |
order_amount |
month |
| 0 |
1 |
1997-01-01 |
1 |
11.77 |
1997-01-01 |
| 1 |
2 |
1997-01-12 |
1 |
12.00 |
1997-01-01 |
| 2 |
2 |
1997-01-12 |
5 |
77.00 |
1997-01-01 |
| 3 |
3 |
1997-01-02 |
2 |
20.76 |
1997-01-01 |
| 4 |
3 |
1997-03-30 |
2 |
20.76 |
1997-03-01 |
第二部分：按月数据分析
- 用户每月花费的总金额
- 所有用户每月的产品购买量
- 所有用户每月的消费总次数
- 统计每月的消费人数
#用户每月花费的总金额
df.groupby(by='month')['order_amount'].sum()
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
1997-06-01 108395.87
1997-07-01 122078.88
1997-08-01 88367.69
1997-09-01 81948.80
1997-10-01 89780.77
1997-11-01 115448.64
1997-12-01 95577.35
1998-01-01 76756.78
1998-02-01 77096.96
1998-03-01 108970.15
1998-04-01 66231.52
1998-05-01 70989.66
1998-06-01 76109.30
Name: order_amount, dtype: float64
# plt.plot(df.groupby(by='month')['order_amount'].sum())
df.groupby(by='month')['order_amount'].sum().plot()
<matplotlib.axes._subplots.AxesSubplot at 0x111536c50>
#所有用户每月的产品购买量
df.groupby(by='month')['order_product'].sum().plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1115d2978>
#所有用户每月的消费总次数(原始数据中的一行数据表示一次消费记录)
df.groupby(by='month')['user_id'].count()
month
1997-01-01 8928
1997-02-01 11272
1997-03-01 11598
1997-04-01 3781
1997-05-01 2895
1997-06-01 3054
1997-07-01 2942
1997-08-01 2320
1997-09-01 2296
1997-10-01 2562
1997-11-01 2750
1997-12-01 2504
1998-01-01 2032
1998-02-01 2026
1998-03-01 2793
1998-04-01 1878
1998-05-01 1985
1998-06-01 2043
Name: user_id, dtype: int64
#统计每月的消费人数(可能同一天一个用户会消费多次) nunique表示统计去重后的个数
df.groupby(by='month')['user_id'].nunique()
month
1997-01-01 7846
1997-02-01 9633
1997-03-01 9524
1997-04-01 2822
1997-05-01 2214
1997-06-01 2339
1997-07-01 2180
1997-08-01 1772
1997-09-01 1739
1997-10-01 1839
1997-11-01 2028
1997-12-01 1864
1998-01-01 1537
1998-02-01 1551
1998-03-01 2060
1998-04-01 1437
1998-05-01 1488
1998-06-01 1506
Name: user_id, dtype: int64
第三部分：用户个体消费数据分析
- 用户消费总金额和消费总次数的统计描述
- 用户消费金额和消费产品数量的散点图
- 各个用户消费总金额的直方分布图(消费金额在1000之内的分布)
- 各个用户消费的总数量的直方分布图(消费商品的数量在100次之内的分布)
#用户消费总金额和消费总次数的统计描述
df.groupby(by='user_id')['order_amount'].sum() #每一个用户消费的总金额
user_id
1 11.77
2 89.00
3 156.46
4 100.50
5 385.61
6 20.99
7 264.67
8 197.66
9 95.85
10 39.31
11 58.55
12 57.06
13 72.94
14 29.92
15 52.87
16 79.87
17 73.22
18 14.96
19 175.12
20 653.01
21 75.11
22 14.37
23 24.74
24 57.77
25 137.53
26 102.69
27 135.87
28 90.99
29 435.81
30 28.34
...
23541 57.34
23542 77.43
23543 50.76
23544 134.63
23545 24.99
23546 13.97
23547 23.54
23548 23.54
23549 27.13
23550 25.28
23551 264.63
23552 49.38
23553 98.58
23554 36.37
23555 189.18
23556 203.00
23557 14.37
23558 145.60
23559 111.65
23560 18.36
23561 83.46
23562 29.33
23563 58.75
23564 70.01
23565 11.77
23566 36.00
23567 20.97
23568 121.70
23569 25.74
23570 94.08
Name: order_amount, Length: 23570, dtype: float64
#每一个用户消费的总次数
df.groupby(by='user_id').count()['order_dt']
user_id
1 1
2 2
3 6
4 4
5 11
6 1
7 3
8 8
9 3
10 1
11 4
12 1
13 1
14 1
15 1
16 4
17 1
18 1
19 2
20 2
21 2
22 1
23 1
24 2
25 8
26 2
27 2
28 3
29 12
30 2
..
23541 2
23542 1
23543 1
23544 3
23545 1
23546 1
23547 2
23548 1
23549 1
23550 1
23551 6
23552 2
23553 2
23554 2
23555 5
23556 7
23557 1
23558 4
23559 3
23560 1
23561 3
23562 1
23563 2
23564 3
23565 1
23566 1
23567 1
23568 3
23569 1
23570 2
Name: order_dt, Length: 23570, dtype: int64
#用户消费金额和消费产品数量的散点图
user_amount_sum = df.groupby(by='user_id')['order_amount'].sum()
user_product_sum = df.groupby(by='user_id')['order_product'].sum()
plt.scatter(user_product_sum,user_amount_sum)
<matplotlib.collections.PathCollection at 0x112253588>
#各个用户消费总金额的直方分布图(消费金额在1000之内的分布)
df.groupby(by='user_id').sum().query('order_amount <= 1000')['order_amount']
df.groupby(by='user_id').sum().query('order_amount <= 1000')['order_amount'].hist()
<matplotlib.axes._subplots.AxesSubplot at 0x1122f1d30>
#各个用户消费的总数量的直方分布图(消费商品的数量在100次之内的分布)
df.groupby(by='user_id').sum().query('order_product <= 100')['order_product'].hist()
<matplotlib.axes._subplots.AxesSubplot at 0x11491f828>
第四部分：用户消费行为分析
- 用户第一次消费的月份分布，和人数统计
- 用户最后一次消费的时间分布，和人数统计
- 新老客户的占比
- 消费一次为新用户
- 消费多次为老用户
- 分析出每一个用户的第一个消费和最后一次消费的时间
- agg(['func1','func2']):对分组后的结果进行指定聚合
- 分析出新老客户的消费比例
- 用户分层
- 分析得出每个用户的总购买量和总消费金额and最近一次消费的时间的表格rfm
- RFM模型设计
- R表示客户最近一次交易时间的间隔。
- /np.timedelta64(1,'D')：去除days
- F表示客户购买商品的总数量,F值越大，表示客户交易越频繁，反之则表示客户交易不够活跃。
- M表示客户交易的金额。M值越大，表示客户价值越高，反之则表示客户价值越低。
- 将R，F，M作用到rfm表中
- 根据价值分层，将用户分为：
- 重要价值客户
- 重要保持客户
- 重要挽留客户
- 重要发展客户
- 一般价值客户
- 一般保持客户
- 一般挽留客户
- 一般发展客户
#用户第一次消费的月份分布，和人数统计
#第一次消费的月份：每一个用户消费月份的最小值就是该用户第一次消费的月份
df.groupby(by='user_id')['month'].min()
user_id
1 1997-01-01
2 1997-01-01
3 1997-01-01
4 1997-01-01
5 1997-01-01
6 1997-01-01
7 1997-01-01
8 1997-01-01
9 1997-01-01
10 1997-01-01
11 1997-01-01
12 1997-01-01
13 1997-01-01
14 1997-01-01
15 1997-01-01
16 1997-01-01
17 1997-01-01
18 1997-01-01
19 1997-01-01
20 1997-01-01
21 1997-01-01
22 1997-01-01
23 1997-01-01
24 1997-01-01
25 1997-01-01
26 1997-01-01
27 1997-01-01
28 1997-01-01
29 1997-01-01
30 1997-01-01
...
23541 1997-03-01
23542 1997-03-01
23543 1997-03-01
23544 1997-03-01
23545 1997-03-01
23546 1997-03-01
23547 1997-03-01
23548 1997-03-01
23549 1997-03-01
23550 1997-03-01
23551 1997-03-01
23552 1997-03-01
23553 1997-03-01
23554 1997-03-01
23555 1997-03-01
23556 1997-03-01
23557 1997-03-01
23558 1997-03-01
23559 1997-03-01
23560 1997-03-01
23561 1997-03-01
23562 1997-03-01
23563 1997-03-01
23564 1997-03-01
23565 1997-03-01
23566 1997-03-01
23567 1997-03-01
23568 1997-03-01
23569 1997-03-01
23570 1997-03-01
Name: month, Length: 23570, dtype: datetime64[ns]
df.groupby(by='user_id')['month'].min().value_counts() #人数的统计
df.groupby(by='user_id')['month'].min().value_counts().plot()
<matplotlib.axes._subplots.AxesSubplot at 0x11dddba90>
#用户最后一次消费的时间分布，和人数统计
#用户消费月份的最大值就是用户最后一次消费的月份
df.groupby(by='user_id')['month'].max().value_counts().plot()
<matplotlib.axes._subplots.AxesSubplot at 0x11e35ba58>
#新老客户的占比
#消费一次为新用户，消费多次为老用户
#如何获知用户是否为第一次消费？可以根据用户的消费时间进行判定？
#如果用户的第一次消费时间和最后一次消费时间一样，则该用户只消费了一次为新用户，否则为老用户
new_old_user_df = df.groupby(by='user_id')['order_dt'].agg(['min','max'])#agg对分组后的结果进行多种指定聚合
new_old_user_df['min'] == new_old_user_df['max'] #True新用户，False老用户
#统计True和False的个数
(new_old_user_df['min'] == new_old_user_df['max']).value_counts()
True 12054
False 11516
dtype: int64
#分析得出每个用户的总购买量和总消费金额and最近一次消费的时间的表格rfm
rfm = df.pivot_table(index='user_id',aggfunc={'order_product':'sum','order_amount':'sum','order_dt':"max"})
rfm
|
order_amount |
order_dt |
order_product |
| 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 |
| 6 |
20.99 |
1997-01-01 |
1 |
| 7 |
264.67 |
1998-03-22 |
18 |
| 8 |
197.66 |
1998-03-29 |
18 |
| 9 |
95.85 |
1998-06-08 |
6 |
| 10 |
39.31 |
1997-01-21 |
3 |
| 11 |
58.55 |
1998-02-20 |
4 |
| 12 |
57.06 |
1997-01-01 |
4 |
| 13 |
72.94 |
1997-01-01 |
4 |
| 14 |
29.92 |
1997-01-01 |
2 |
| 15 |
52.87 |
1997-01-01 |
4 |
| 16 |
79.87 |
1997-09-10 |
8 |
| 17 |
73.22 |
1997-01-01 |
5 |
| 18 |
14.96 |
1997-01-04 |
1 |
| 19 |
175.12 |
1997-06-10 |
11 |
| 20 |
653.01 |
1997-01-18 |
46 |
| 21 |
75.11 |
1997-01-13 |
4 |
| 22 |
14.37 |
1997-01-01 |
1 |
| 23 |
24.74 |
1997-01-01 |
2 |
| 24 |
57.77 |
1998-01-20 |
4 |
| 25 |
137.53 |
1998-06-08 |
12 |
| 26 |
102.69 |
1997-01-26 |
6 |
| 27 |
135.87 |
1997-01-12 |
10 |
| 28 |
90.99 |
1997-03-08 |
7 |
| 29 |
435.81 |
1998-04-26 |
28 |
| 30 |
28.34 |
1997-02-14 |
2 |
| ... |
... |
... |
... |
| 23541 |
57.34 |
1997-04-02 |
2 |
| 23542 |
77.43 |
1997-03-25 |
5 |
| 23543 |
50.76 |
1997-03-25 |
2 |
| 23544 |
134.63 |
1998-01-24 |
12 |
| 23545 |
24.99 |
1997-03-25 |
1 |
| 23546 |
13.97 |
1997-03-25 |
1 |
| 23547 |
23.54 |
1997-04-07 |
2 |
| 23548 |
23.54 |
1997-03-25 |
2 |
| 23549 |
27.13 |
1997-03-25 |
2 |
| 23550 |
25.28 |
1997-03-25 |
2 |
| 23551 |
264.63 |
1997-09-11 |
12 |
| 23552 |
49.38 |
1997-04-03 |
4 |
| 23553 |
98.58 |
1997-03-28 |
8 |
| 23554 |
36.37 |
1998-02-01 |
3 |
| 23555 |
189.18 |
1998-06-10 |
14 |
| 23556 |
203.00 |
1998-06-07 |
15 |
| 23557 |
14.37 |
1997-03-25 |
1 |
| 23558 |
145.60 |
1998-02-25 |
11 |
| 23559 |
111.65 |
1997-06-27 |
8 |
| 23560 |
18.36 |
1997-03-25 |
1 |
| 23561 |
83.46 |
1998-05-29 |
6 |
| 23562 |
29.33 |
1997-03-25 |
2 |
| 23563 |
58.75 |
1997-10-04 |
3 |
| 23564 |
70.01 |
1997-11-30 |
5 |
| 23565 |
11.77 |
1997-03-25 |
1 |
| 23566 |
36.00 |
1997-03-25 |
2 |
| 23567 |
20.97 |
1997-03-25 |
1 |
| 23568 |
121.70 |
1997-04-22 |
6 |
| 23569 |
25.74 |
1997-03-25 |
2 |
| 23570 |
94.08 |
1997-03-26 |
5 |
23570 rows × 3 columns
#R表示客户最近一次交易时间的间隔
max_dt = df['order_dt'].max() #今天的日期
#每一个用户最后一次交易的时间
-(df.groupby(by='user_id')['order_dt'].max() - max_dt)
rfm['R'] = -(df.groupby(by='user_id')['order_dt'].max() - max_dt)/np.timedelta64(1,'D') # 将R列的days后缀去掉 /np.timedelta64(1,'D')
rfm.drop(labels='order_dt',axis=1,inplace=True)
rfm.columns = ['M','F','R'] # 修改列标签名
rfm.head()
|
M |
F |
R |
| user_id |
|
|
|
| 1 |
11.77 |
1 |
545.0 |
| 2 |
89.00 |
6 |
534.0 |
| 3 |
156.46 |
16 |
33.0 |
| 4 |
100.50 |
7 |
200.0 |
| 5 |
385.61 |
29 |
178.0 |
def rfm_func(x):
#存储存储的是三个字符串形式的0或者1
level = x.map(lambda x :'1' if x >= 0 else '0')
label = level.R + level.F + level.M
d = {
'111':'重要价值客户',
'011':'重要保持客户',
'101':'重要挽留客户',
'001':'重要发展客户',
'110':'一般价值客户',
'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
#df.apply(func):可以对df中的行或者列进行某种（func）形式的运算
rfm['label'] = rfm.apply(lambda x : x - x.mean()).apply(rfm_func,axis = 1)
rfm.head()
|
M |
F |
R |
label |
| user_id |
|
|
|
|
| 1 |
11.77 |
1 |
545.0 |
一般挽留客户 |
| 2 |
89.00 |
6 |
534.0 |
一般挽留客户 |
| 3 |
156.46 |
16 |
33.0 |
重要保持客户 |
| 4 |
100.50 |
7 |
200.0 |
一般发展客户 |
| 5 |
385.61 |
29 |
178.0 |
重要保持客户 |
第五部分：用户的生命周期
- 将用户划分为活跃用户和其他用户
- 统计每个用户每个月的消费次数
- 统计每个用户每个月是否消费，消费记录为1否则记录为0
- 知识点：DataFrame的apply和applymap的区别
- applymap:传入每个单个元素返回df
- 将函数做用于DataFrame中的所有元素(elements)
- apply:返回Series
- apply()将一个函数作用于DataFrame中的每个行或者列
- 将用户按照每一个月份分成：
- unreg:观望用户（前两月没买，第三个月才第一次买,则用户前两个月为观望用户）
- unactive:首月购买后，后序月份没有购买则在没有购买的月份中该用户的为非活跃用户
- new:当前月就进行首次购买的用户在当前月为新用户
- active:连续月份购买的用户在这些月中为活跃用户
- return:购买之后间隔n月再次购买的第一个月份为该月份的回头客
#统计每个用户每个月的消费次数
user_month_count_df = df.pivot_table(index='user_id',values='order_dt',aggfunc='count',columns='month').fillna(0)
user_month_count_df.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 |
#统计每个用户每个月是否消费，消费记录为1否则记录为0
df_purchase = user_month_count_df.applymap(lambda x:1 if x >= 1 else 0)
| 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 |
| 11 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
| 12 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 13 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 14 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 15 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 16 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 17 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 18 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 19 |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 20 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 21 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 22 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 24 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
| 25 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
| 26 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 27 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 28 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 29 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| 30 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 23541 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23542 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23543 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23544 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
| 23545 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23546 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23547 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23548 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23549 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23550 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23551 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23552 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23553 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23554 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
| 23555 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
1 |
| 23556 |
0 |
0 |
1 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
| 23557 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23558 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
| 23559 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23560 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23561 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
| 23562 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23563 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23564 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 23565 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 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 |
23570 rows × 18 columns
df_purchase.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 |
| 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 |
#将df_purchase中的原始数据0和1修改为new，unactive......,返回新的df叫做df_purchase_new
#固定算法
def active_status(data):
status = []#某个用户每一个月的活跃度
for i in range(18):
#若本月没有消费
if data[i] == 0:
if len(status) > 0:
if status[i-1] == 'unreg':
status.append('unreg')
else:
status.append('unactive')
else:
status.append('unreg')
#若本月消费
else:
if len(status) == 0:
status.append('new')
else:
if status[i-1] == 'unactive':
status.append('return')
elif status[i-1] == 'unreg':
status.append('new')
else:
status.append('active')
return status
pivoted_status = df_purchase.apply(active_status,axis = 1)
pivoted_status.head()
user_id
1 [new, unactive, unactive, unactive, unactive, ...
2 [new, unactive, unactive, unactive, unactive, ...
3 [new, unactive, return, active, unactive, unac...
4 [new, unactive, unactive, unactive, unactive, ...
5 [new, active, unactive, return, active, active...
dtype: object
df_purchase_new = DataFrame(data=pivoted_status.values.tolist(),index=df_purchase.index,columns=df_purchase.columns)
df_purchase_new
| 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 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 2 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 3 |
new |
unactive |
return |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
| 4 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 5 |
new |
active |
unactive |
return |
active |
active |
active |
unactive |
return |
unactive |
unactive |
return |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
| 6 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 7 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
| 8 |
new |
active |
unactive |
unactive |
unactive |
return |
active |
unactive |
unactive |
unactive |
return |
active |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
| 9 |
new |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
| 10 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 11 |
new |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
| 12 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 13 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 14 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 15 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 16 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 17 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 18 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 19 |
new |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 20 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 21 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 22 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 24 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
| 25 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
active |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
active |
active |
| 26 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 27 |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 28 |
new |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 29 |
new |
active |
active |
active |
active |
unactive |
return |
unactive |
return |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
| 30 |
new |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 23541 |
unreg |
unreg |
new |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23542 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23543 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23544 |
unreg |
unreg |
new |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23545 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23546 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23547 |
unreg |
unreg |
new |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23548 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23549 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23550 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23551 |
unreg |
unreg |
new |
unactive |
unactive |
return |
unactive |
return |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23552 |
unreg |
unreg |
new |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23553 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23554 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
| 23555 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
return |
active |
| 23556 |
unreg |
unreg |
new |
unactive |
unactive |
return |
active |
unactive |
return |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
return |
| 23557 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23558 |
unreg |
unreg |
new |
unactive |
return |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
| 23559 |
unreg |
unreg |
new |
unactive |
return |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23560 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23561 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
return |
unactive |
| 23562 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23563 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23564 |
unreg |
unreg |
new |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
return |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23565 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23566 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23567 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23568 |
unreg |
unreg |
new |
active |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23569 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
| 23570 |
unreg |
unreg |
new |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
unactive |
23570 rows × 18 columns
- 每月【不同活跃】用户的计数
- purchase_status_ct = df_purchase_new.apply(lambda x : pd.value_counts(x)).fillna(0)
- 转置进行最终结果的查看
purchase_status_ct = df_purchase_new.apply(lambda x : pd.value_counts(x)).fillna(0)
purchase_status_ct
| 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 |
0.0 |
1157.0 |
1681.0 |
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 |
7248.0 |
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 |
| return |
0.0 |
0.0 |
595.0 |
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 |
0.0 |
6689.0 |
14046.0 |
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 |
| unreg |
15724.0 |
7248.0 |
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 |
purchase_status_ct.T
|
active |
new |
return |
unactive |
unreg |
| month |
|
|
|
|
|
| 1997-01-01 |
0.0 |
7846.0 |
0.0 |
0.0 |
15724.0 |
| 1997-02-01 |
1157.0 |
8476.0 |
0.0 |
6689.0 |
7248.0 |
| 1997-03-01 |
1681.0 |
7248.0 |
595.0 |
14046.0 |
0.0 |
| 1997-04-01 |
1773.0 |
0.0 |
1049.0 |
20748.0 |
0.0 |
| 1997-05-01 |
852.0 |
0.0 |
1362.0 |
21356.0 |
0.0 |
| 1997-06-01 |
747.0 |
0.0 |
1592.0 |
21231.0 |
0.0 |
| 1997-07-01 |
746.0 |
0.0 |
1434.0 |
21390.0 |
0.0 |
| 1997-08-01 |
604.0 |
0.0 |
1168.0 |
21798.0 |
0.0 |
| 1997-09-01 |
528.0 |
0.0 |
1211.0 |
21831.0 |
0.0 |
| 1997-10-01 |
532.0 |
0.0 |
1307.0 |
21731.0 |
0.0 |
| 1997-11-01 |
624.0 |
0.0 |
1404.0 |
21542.0 |
0.0 |
| 1997-12-01 |
632.0 |
0.0 |
1232.0 |
21706.0 |
0.0 |
| 1998-01-01 |
512.0 |
0.0 |
1025.0 |
22033.0 |
0.0 |
| 1998-02-01 |
472.0 |
0.0 |
1079.0 |
22019.0 |
0.0 |
| 1998-03-01 |
571.0 |
0.0 |
1489.0 |
21510.0 |
0.0 |
| 1998-04-01 |
518.0 |
0.0 |
919.0 |
22133.0 |
0.0 |
| 1998-05-01 |
459.0 |
0.0 |
1029.0 |
22082.0 |
0.0 |
| 1998-06-01 |
446.0 |
0.0 |
1060.0 |
22064.0 |
0.0 |