实战项目-消费数据分析
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（预处理）
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数据加载
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字段含义：
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user_id:用户ID
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order_dt:购买日期
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order_product:购买产品的数量
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order_amount:购买金额
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观察数据
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查看数据的数据类型
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数据中是否存储在缺失值
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将order_dt转换成时间类型
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查看数据的统计描述
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计算所有用户购买商品的平均数量
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计算所有用户购买商品的平均花费
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在源数据中添加一列表示月份:astype('datetime64[M]')
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#数据的加载 并处理（分割 加 列索引）
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 columnsView Code
# 没有空值 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 MBView Code
#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 MBView Code
#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 1286.010000View Code
#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]
View Code
#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 1997-03-01View Code
第二部分：按月数据分析
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用户每月花费的总金额
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绘制曲线图展示
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所有用户每月的产品购买量
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所有用户每月的消费总次数
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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: float64View Code
# 绘图 # plt.plot(df.groupby(by='month')['order_amount'].sum()) df.groupby(by='month')['order_amount'].sum().plot()
#所有用户每月的产品购买量 df.groupby(by='month')['order_product'].sum().plot()
#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: int64View Code
#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: int64View Code
第三部分：用户个体消费数据分析
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用户消费总金额和消费总次数的统计描述
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用户消费金额和消费产品数量的散点图
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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)
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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
View Code
#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
View Code
#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)
#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()
#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()
第四部分：用户消费行为分析
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用户第一次消费的月份分布，和人数统计
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绘制线形图
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用户最后一次消费的时间分布，和人数统计
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绘制线形图
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新老客户的占比
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消费一次为新用户
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消费多次为老用户
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分析出每一个用户的第一个消费和最后一次消费的时间
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agg(['func1','func2']):对分组后的结果进行指定聚合
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分析出新老客户的消费比例
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用户分层
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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
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RFM模型设计
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R表示客户最近一次交易时间的间隔。
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/np.timedelta64(1,'D')：去除days
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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。
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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。
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将R，F，M作用到rfm表中
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根据价值分层，将用户分为：
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重要价值客户
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重要保持客户
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重要挽留客户
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重要发展客户
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一般价值客户
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一般保持客户
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一般挽留客户
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一般发展客户
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使用已有的分层模型即可rfm_func
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#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]
View Code
df.groupby(by='user_id')['month'].min().value_counts() #人数的统计 df.groupby(by='user_id')['month'].min().value_counts().plot()
#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()
#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: int64View Code
#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 columnsView Code
#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')
rfm.drop(labels='order_dt',axis=1,inplace=True)
rfm.columns = ['M','F','R'] 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 29 178.0View Code
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
View Code
第五部分：用户的生命周期
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将用户划分为活跃用户和其他用户
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统计每个用户每个月的消费次数
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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
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知识点：DataFrame的apply和applymap的区别
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applymap:返回df
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将函数做用于DataFrame中的所有元素(elements)
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apply:返回Series
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apply()将一个函数作用于DataFrame中的每个行或者列
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将用户按照每一个月份分成：
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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）
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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
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new:当前月就进行首次购买的用户在当前月为新用户
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active:连续月份购买的用户在这些月中为活跃用户
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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
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#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 0.0View Code
#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 columnsView Code
#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: objectView Code
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 unactiveView Code
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每月【不同活跃】用户的计数
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purchase_status_ct = df_purchase_new.apply(lambda x : pd.value_counts(x)).fillna(0)
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转置进行最终结果的查看
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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 0.0View Code
标签：数据分析,实战,00,消费,1997,unactive,0.0,01,03 From： https://www.cnblogs.com/erhuoyuan/p/16936837.html