Untitled

最新推荐文章于 2024-11-11 15:33:33 发布

吧啦吧啦123

最新推荐文章于 2024-11-11 15:33:33 发布

阅读量476

点赞数

本文链接：https://blog.youkuaiyun.com/weixin_51775815/article/details/115123168

版权

import pandas as pd
import numpy as np
t = pd.read_csv('C:/Users/user/Desktop/train.csv')
T = t.set_index('Loan_ID')
T.head(10)

	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area	Loan_Status
Loan_ID
LP001002	Male	No	0	Graduate	No	5849	0.0	NaN	360.0	1.0	Urban	Y
LP001003	Male	Yes	1	Graduate	No	4583	1508.0	128.0	360.0	1.0	Rural	N
LP001005	Male	Yes	0	Graduate	Yes	3000	0.0	66.0	360.0	1.0	Urban	Y
LP001006	Male	Yes	0	Not Graduate	No	2583	2358.0	120.0	360.0	1.0	Urban	Y
LP001008	Male	No	0	Graduate	No	6000	0.0	141.0	360.0	1.0	Urban	Y
LP001011	Male	Yes	2	Graduate	Yes	5417	4196.0	267.0	360.0	1.0	Urban	Y
LP001013	Male	Yes	0	Not Graduate	No	2333	1516.0	95.0	360.0	1.0	Urban	Y
LP001014	Male	Yes	3+	Graduate	No	3036	2504.0	158.0	360.0	0.0	Semiurban	N
LP001018	Male	Yes	2	Graduate	No	4006	1526.0	168.0	360.0	1.0	Urban	Y
LP001020	Male	Yes	1	Graduate	No	12841	10968.0	349.0	360.0	1.0	Semiurban	N

mask=(T['Education']=='Not Graduate')&(T['Loan_Status']=='Y')&(T['Gender']=='Female')
T.loc[mask,['Gender','Education','Loan_Status']]

	Gender	Education	Loan_Status
Loan_ID
LP001155	Female	Not Graduate	Y
LP001669	Female	Not Graduate	Y
LP001692	Female	Not Graduate	Y
LP001908	Female	Not Graduate	Y
LP002300	Female	Not Graduate	Y
LP002314	Female	Not Graduate	Y
LP002407	Female	Not Graduate	Y
LP002489	Female	Not Graduate	Y
LP002502	Female	Not Graduate	Y
LP002534	Female	Not Graduate	Y
LP002582	Female	Not Graduate	Y
LP002731	Female	Not Graduate	Y
LP002757	Female	Not Graduate	Y
LP002917	Female	Not Graduate	Y

def num_missing(x):
    return sum(x.isnull())

T.apply(num_missing,axis=0)

Gender               13
Married               3
Dependents           15
Education             0
Self_Employed        32
ApplicantIncome       0
CoapplicantIncome     0
LoanAmount           22
Loan_Amount_Term     14
Credit_History       50
Property_Area         0
Loan_Status           0
dtype: int64

T.apply(num_missing,axis=1)[:10]

Loan_ID
LP001002    1
LP001003    0
LP001005    0
LP001006    0
LP001008    0
LP001011    0
LP001013    0
LP001014    0
LP001018    0
LP001020    0
dtype: int64

T['Gender'].fillna(T['Gender'].mode().iloc[0],inplace=True)
T['Married'].fillna(T['Married'].mode().iloc[0],inplace=True)
T['Self_Employed'].fillna(T['Self_Employed'].mode().iloc[0],inplace=True)

pd.crosstab(T['Credit_History'],T['Loan_Status'],margins=True)

Loan_Status	N	Y	All
Credit_History
0.0	82	7	89
1.0	97	378	475
All	179	385	564

prop_rates = pd.DataFrame([1000, 5000, 12000], index=['Rural','Semiurban','Urban'],columns=['rates'])
prop_rates

	rates
Rural	1000
Semiurban	5000
Urban	12000

T1=T.merge(right=prop_rates,how='inner',left_on='Property_Area',right_index=True,sort=False)
T1

	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area	Loan_Status	rates
Loan_ID
LP001002	Male	No	0	Graduate	No	5849	0.0	NaN	360.0	1.0	Urban	Y	12000
LP001005	Male	Yes	0	Graduate	Yes	3000	0.0	66.0	360.0	1.0	Urban	Y	12000
LP001006	Male	Yes	0	Not Graduate	No	2583	2358.0	120.0	360.0	1.0	Urban	Y	12000
LP001008	Male	No	0	Graduate	No	6000	0.0	141.0	360.0	1.0	Urban	Y	12000
LP001011	Male	Yes	2	Graduate	Yes	5417	4196.0	267.0	360.0	1.0	Urban	Y	12000
LP001013	Male	Yes	0	Not Graduate	No	2333	1516.0	95.0	360.0	1.0	Urban	Y	12000
LP001018	Male	Yes	2	Graduate	No	4006	1526.0	168.0	360.0	1.0	Urban	Y	12000
LP001024	Male	Yes	2	Graduate	No	3200	700.0	70.0	360.0	1.0	Urban	Y	12000
LP001027	Male	Yes	2	Graduate	No	2500	1840.0	109.0	360.0	1.0	Urban	Y	12000
LP001028	Male	Yes	2	Graduate	No	3073	8106.0	200.0	360.0	1.0	Urban	Y	12000
LP001030	Male	Yes	2	Graduate	No	1299	1086.0	17.0	120.0	1.0	Urban	Y	12000
LP001032	Male	No	0	Graduate	No	4950	0.0	125.0	360.0	1.0	Urban	Y	12000
LP001034	Male	No	1	Not Graduate	No	3596	0.0	100.0	240.0	NaN	Urban	Y	12000
LP001036	Female	No	0	Graduate	No	3510	0.0	76.0	360.0	0.0	Urban	N	12000
LP001041	Male	Yes	0	Graduate	No	2600	3500.0	115.0	NaN	1.0	Urban	Y	12000
LP001043	Male	Yes	0	Not Graduate	No	7660	0.0	104.0	360.0	0.0	Urban	N	12000
LP001046	Male	Yes	1	Graduate	No	5955	5625.0	315.0	360.0	1.0	Urban	Y	12000
LP001073	Male	Yes	2	Not Graduate	No	4226	1040.0	110.0	360.0	1.0	Urban	Y	12000
LP001086	Male	No	0	Not Graduate	No	1442	0.0	35.0	360.0	1.0	Urban	N	12000
LP001091	Male	Yes	1	Graduate	No	4166	3369.0	201.0	360.0	NaN	Urban	N	12000
LP001095	Male	No	0	Graduate	No	3167	0.0	74.0	360.0	1.0	Urban	N	12000
LP001106	Male	Yes	0	Graduate	No	2275	2067.0	NaN	360.0	1.0	Urban	Y	12000
LP001109	Male	Yes	0	Graduate	No	1828	1330.0	100.0	NaN	0.0	Urban	N	12000
LP001114	Male	No	0	Graduate	No	4166	7210.0	184.0	360.0	1.0	Urban	Y	12000
LP001119	Male	No	0	Graduate	No	3600	0.0	80.0	360.0	1.0	Urban	N	12000
LP001120	Male	No	0	Graduate	No	1800	1213.0	47.0	360.0	1.0	Urban	Y	12000
LP001123	Male	Yes	0	Graduate	No	2400	0.0	75.0	360.0	NaN	Urban	Y	12000
LP001136	Male	Yes	0	Not Graduate	Yes	4695	0.0	96.0	NaN	1.0	Urban	Y	12000
LP001137	Female	No	0	Graduate	No	3410	0.0	88.0	NaN	1.0	Urban	Y	12000
LP001138	Male	Yes	1	Graduate	No	5649	0.0	44.0	360.0	1.0	Urban	Y	12000
...	...	...	...	...	...	...	...	...	...	...	...	...	...
LP002729	Male	No	1	Graduate	No	11250	0.0	196.0	360.0	NaN	Semiurban	N	5000
LP002738	Male	No	2	Graduate	No	3617	0.0	107.0	360.0	1.0	Semiurban	Y	5000
LP002741	Female	Yes	1	Graduate	No	4608	2845.0	140.0	180.0	1.0	Semiurban	Y	5000
LP002743	Female	No	0	Graduate	No	2138	0.0	99.0	360.0	0.0	Semiurban	N	5000
LP002753	Female	No	1	Graduate	No	3652	0.0	95.0	360.0	1.0	Semiurban	Y	5000
LP002757	Female	Yes	0	Not Graduate	No	3017	663.0	102.0	360.0	NaN	Semiurban	Y	5000
LP002768	Male	No	0	Not Graduate	No	3358	0.0	80.0	36.0	1.0	Semiurban	N	5000
LP002776	Female	No	0	Graduate	No	5000	0.0	103.0	360.0	0.0	Semiurban	N	5000
LP002792	Male	Yes	1	Graduate	No	5468	1032.0	26.0	360.0	1.0	Semiurban	Y	5000
LP002795	Male	Yes	3+	Graduate	Yes	10139	0.0	260.0	360.0	1.0	Semiurban	Y	5000
LP002798	Male	Yes	0	Graduate	No	3887	2669.0	162.0	360.0	1.0	Semiurban	Y	5000
LP002804	Female	Yes	0	Graduate	No	4180	2306.0	182.0	360.0	1.0	Semiurban	Y	5000
LP002807	Male	Yes	2	Not Graduate	No	3675	242.0	108.0	360.0	1.0	Semiurban	Y	5000
LP002813	Female	Yes	1	Graduate	Yes	19484	0.0	600.0	360.0	1.0	Semiurban	Y	5000
LP002821	Male	No	0	Not Graduate	Yes	5800	0.0	132.0	360.0	1.0	Semiurban	Y	5000
LP002862	Male	Yes	2	Not Graduate	No	6125	1625.0	187.0	480.0	1.0	Semiurban	N	5000
LP002863	Male	Yes	3+	Graduate	No	6406	0.0	150.0	360.0	1.0	Semiurban	N	5000
LP002872	Male	Yes	0	Graduate	No	3087	2210.0	136.0	360.0	0.0	Semiurban	N	5000
LP002892	Male	Yes	2	Graduate	No	6540	0.0	205.0	360.0	1.0	Semiurban	Y	5000
LP002894	Female	Yes	0	Graduate	No	3166	0.0	36.0	360.0	1.0	Semiurban	Y	5000
LP002917	Female	No	0	Not Graduate	No	2165	0.0	70.0	360.0	1.0	Semiurban	Y	5000
LP002925	Male	No	0	Graduate	No	4750	0.0	94.0	360.0	1.0	Semiurban	Y	5000
LP002926	Male	Yes	2	Graduate	Yes	2726	0.0	106.0	360.0	0.0	Semiurban	N	5000
LP002928	Male	Yes	0	Graduate	No	3000	3416.0	56.0	180.0	1.0	Semiurban	Y	5000
LP002931	Male	Yes	2	Graduate	Yes	6000	0.0	205.0	240.0	1.0	Semiurban	N	5000
LP002933	Male	No	3+	Graduate	Yes	9357	0.0	292.0	360.0	1.0	Semiurban	Y	5000
LP002943	Male	No	NaN	Graduate	No	2987	0.0	88.0	360.0	0.0	Semiurban	N	5000
LP002959	Female	Yes	1	Graduate	No	12000	0.0	496.0	360.0	1.0	Semiurban	Y	5000
LP002961	Male	Yes	1	Graduate	No	3400	2500.0	173.0	360.0	1.0	Semiurban	Y	5000
LP002990	Female	No	0	Graduate	Yes	4583	0.0	133.0	360.0	0.0	Semiurban	N	5000

614 rows × 13 columns

impute_grps = T1.pivot_table(values=["Credit_History"],index=["Property_Area","rates"],aggfunc=np.mean)

ind = tuple([row['Property_Area'],row['rates']])
T1.loc["Credit_History"] = impute_grps.loc[ind].values[0]

grps = T1.groupby(["Property_Area","rates"])

grps["Credit_History"].mean()

Property_Area       rates       
0.8617511520737328  0.861751        0.861751
Rural               1000.000000     0.830303
Semiurban           5000.000000     0.861751
Urban               12000.000000    0.829670
Name: Credit_History, dtype: float64

T2=T1.groupby(["Property_Area","rates"])["Credit_History"].count().reset_index()
T2.set_index("Property_Area")

	rates	Credit_History
Property_Area
Rural	1000	165
Semiurban	5000	217
Urban	12000	182