datamagus
Packages and API interface for basic data analysis processing, graphing and modeling, and practical use.
Installation
pip install datamagus
Model
The datamagus model module contains several analysis models, including RFM, Pareto, etc.
RFM Example
Example 1: In most cases the data we use to study customer value is based on order sales, consisting of user id,transaction time and transaction amount.
# https://www.kaggle.com/datasets/regivm/retailtransactiondata?select=Retail_Data_Transactions.csv
>>> from datamagus.model import RFMModel
>>> rfm=RFMModel()
>>> df=pd.read_csv('https://raw.githubusercontent.com/Alazia/datamagus/main/src/test/Retail_Data_Transactions.csv')
customer_id trans_date tran_amount
0 CS5295 11-Feb-13 35
1 CS4768 15-Mar-15 39
>>> rfm.load_data(df,its=['customer_id','trans_date','tran_amount'],t="2022-06-27")
>>> rfm.rfm
id R F M
0 CS1112 2721 15 1012.0
1 CS1113 2695 20 1490.0
2 CS1114 2692 19 1432.0
>>> rfm.unleash()
>>> rfm.rfm_score
R F M R_score F_score M_score RFM
id
CS1112 2721 15 1012.0 2 1 1 一般发展客户
CS1113 2695 20 1490.0 2 2 2 重要价值客户
CS1114 2692 19 1432.0 2 2 2 重要价值客户Example 2: When we have data in RFM format.
>>> df = pd.DataFrame({
'id': np.arange(1, 10001),
'R': np.random.randint(1, 10, 10000),
'F': np.random.randint(1, 100, 10000),
'M': np.random.randint(1000, 10000, 10000),
})
>>> rfm=RFMModel()
>>> rfm.load_data(df)
>>> rfm.unleash()
>>> rfm.rfm_score
R F M R_score F_score M_score RFM
id
1 1 93 3841 2 2 1 一般价值客户
2 6 86 1833 1 2 1 一般保持客户
3 7 31 4474 1 1 1 一般挽留客户
>>> rfm.visualize()RFM Visualization
Pareto
>>> np.random.seed(1)
>>> a = np.random.randint(110000,120000,2)
>>> b = np.random.randint(20000, 80000, 3)
>>> c = np.random.randint(2000, 8000, 5)
>>> df = pd.DataFrame({
'id': np.arange(1, 11),
'M':np.append(np.append(a,b),c)
})
>>> pm=ParetoModel()
>>> pm.load_data(df)
>>> pm.unleash()
>>> pm.res
id M Accumulate Percent Class
0 2 115192.0 115192.0 26.37% A
1 1 110235.0 225427.0 51.60% A
2 4 70057.0 295484.0 67.64% A
3 5 63723.0 359207.0 82.23% B
4 3 52511.0 411718.0 94.25% C
5 7 7056.0 418774.0 95.87% C
6 9 6225.0 424999.0 97.29% C
7 6 4895.0 429894.0 98.41% C
8 10 4797.0 434691.0 99.51% C
9 8 2144.0 436835.0 100.00% C
>>> pm.results[1]
ID Count_Percent Money_Percent
Class
A '2', '1', '4' 30.00% 67.64%
B '5' 10.00% 14.59%
C '3', '7', '9', '6', '10', '8' 60.00% 17.77%
>>> pm.visualize()Pareto Visualization
