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[파이썬/데이터분석] LendingClub 원금 상환 여부 예측하기(2) 시각화 소스코드 본문

파이썬/머신러닝

[파이썬/데이터분석] LendingClub 원금 상환 여부 예측하기(2) 시각화 소스코드

2020. 3. 12. 17:41

※ 주의 ※

본 게시글은 아래의 게시글에서 설명한 EDA와 시각화 과정에서 사용된 소스코드를 첨부한 게시글입니다. LendingClub EDA와 시각화에 대한 설명이나 상태를 확인하고 싶으시면 아래 링크를 통해서 이전 게시글을 확이 하시면 됩니다.

[파이썬/데이터 사이언스] - [파이썬 데이터 분석] LendingClub 원금 상환 여부 예측하기(1) : EDA와 데이터 시각화

[파이썬 데이터분석] LendingClub 원금 상환 여부 예측하기(1) : EDA와 데이터 시각화

이번 포스팅을 시작으로 Lending Club의 데이터를 활용해 파이썬 데이터 분석을 진행해볼 예정입니다. 파이썬이 데이터 분석하기에 좋고 강력한 library들도 많이 있습니다. 현재 4차 산업 양성과정의 빅데이터 AI..

chancoding.tistory.com

데이터셋은 Kaggle의 LendingClub Dataset입니다.

Intro

LendingClub 데이터셋을 사용해서 원금 상환 여부를 예측하는 프로젝트를 진행하고 있습니다. 게시글 첫 번째 과정에서 EDA와 데이터 시각화를 진행하였습니다. EDA와 시각화 과정에서 발생한 파이썬 코드를 이번 게시글을 통해서 공유하도록 하겠습니다.

사용된 라이브러리

전체 소스코드에서 시각화에 대한 부분이므로 전체 라이브러리가 나타나 있습니다.

데이터 프레임에서 바로 plot을 사용하여 데이터를 시간화한 부분도 있고 Seaborn 라이브러리 sns를 사용해서 그래프를 그린 케이스도 있습니다.

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline

# Plotly visualizations
import chart_studio.plotly as py
from plotly import tools
import plotly.figure_factory as ff
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)

# For oversampling Library (Dealing with Imbalanced Datasets)
from imblearn.over_sampling import SMOTE
from collections import Counter

# Other Libraries
import time

df = pd.read_csv("loan.csv", low_memory=False)

# Copy of the dataframe
df.head()

데이터 프레임의 head를 출력하여 전체적인 데이터 상태를 확인해보았습니다.

	id	member_id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	sub_grade	emp_title	emp_length	home_ownership	annual_inc	verification_status	issue_d	loan_status	pymnt_plan	url	desc	purpose	title	zip_code	addr_state	dti	earliest_cr_line	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	pub_rec	revol_bal	revol_util	total_acc	initial_list_status	out_prncp	out_prncp_inv	total_pymnt	total_pymnt_inv	...	pub_rec_bankruptcies	tot_hi_cred_lim	total_bal_ex_mort	total_bc_limit	total_il_high_credit_limit	revol_bal_joint	sec_app_earliest_cr_line	sec_app_inq_last_6mths	sec_app_mort_acc	sec_app_open_acc	sec_app_revol_util	sec_app_open_act_il	sec_app_num_rev_accts	sec_app_chargeoff_within_12_mths	sec_app_collections_12_mths_ex_med	sec_app_mths_since_last_major_derog	hardship_flag	hardship_type	hardship_reason	hardship_status	deferral_term	hardship_amount	hardship_start_date	hardship_end_date	payment_plan_start_date	hardship_length	hardship_dpd	hardship_loan_status	orig_projected_additional_accrued_interest	hardship_payoff_balance_amount	hardship_last_payment_amount	disbursement_method	debt_settlement_flag	debt_settlement_flag_date	settlement_status	settlement_date	settlement_amount	settlement_percentage	settlement_term
0	NaN	NaN	2500	2500	2500.0	36 months	13.56	84.92	C	C1	Chef	10+ years	RENT	55000.0	Not Verified	Dec-2018	Current	n	NaN	NaN	debt_consolidation	Debt consolidation	109xx	NY	18.24	Apr-2001	1.0	NaN	45.0	9.0	1.0	4341	10.3	34.0	w	2386.02	2386.02	167.02	167.02	...	1.0	60124.0	16901.0	36500.0	18124.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN
1	NaN	NaN	30000	30000	30000.0	60 months	18.94	777.23	D	D2	Postmaster	10+ years	MORTGAGE	90000.0	Source Verified	Dec-2018	Current	n	NaN	NaN	debt_consolidation	Debt consolidation	713xx	LA	26.52	Jun-1987	0.0	71.0	75.0	13.0	1.0	12315	24.2	44.0	w	29387.75	29387.75	1507.11	1507.11	...	1.0	372872.0	99468.0	15000.0	94072.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	5000	5000	5000.0	36 months	17.97	180.69	D	D1	Administrative	6 years	MORTGAGE	59280.0	Source Verified	Dec-2018	Current	n	NaN	NaN	debt_consolidation	Debt consolidation	490xx	MI	10.51	Apr-2011	0.0	NaN	NaN	8.0	0.0	4599	19.1	13.0	w	4787.21	4787.21	353.89	353.89	...	0.0	136927.0	11749.0	13800.0	10000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN
3	NaN	NaN	4000	4000	4000.0	36 months	18.94	146.51	D	D2	IT Supervisor	10+ years	MORTGAGE	92000.0	Source Verified	Dec-2018	Current	n	NaN	NaN	debt_consolidation	Debt consolidation	985xx	WA	16.74	Feb-2006	0.0	NaN	NaN	10.0	0.0	5468	78.1	13.0	w	3831.93	3831.93	286.71	286.71	...	0.0	385183.0	36151.0	5000.0	44984.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN
4	NaN	NaN	30000	30000	30000.0	60 months	16.14	731.78	C	C4	Mechanic	10+ years	MORTGAGE	57250.0	Not Verified	Dec-2018	Current	n	NaN	NaN	debt_consolidation	Debt consolidation	212xx	MD	26.35	Dec-2000	0.0	NaN	NaN	12.0	0.0	829	3.6	26.0	w	29339.02	29339.02	1423.21	1423.21	...	0.0	157548.0	29674.0	9300.0	32332.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 145 columns

# 데이터 상태 확인
df.info()
df.shape

(2260668, 145)

df.describe()

	id	member_id	loan_amnt	funded_amnt	funded_amnt_inv	int_rate	installment	annual_inc	url	dti	delinq_2yrs	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	pub_rec	revol_bal	revol_util	total_acc	out_prncp	out_prncp_inv	total_pymnt	total_pymnt_inv	total_rec_prncp	total_rec_int	total_rec_late_fee	recoveries	collection_recovery_fee	last_pymnt_amnt	collections_12_mths_ex_med	mths_since_last_major_derog	policy_code	annual_inc_joint	dti_joint	acc_now_delinq	tot_coll_amt	tot_cur_bal	open_acc_6m	open_act_il	open_il_12m	...	num_actv_rev_tl	num_bc_sats	num_bc_tl	num_il_tl	num_op_rev_tl	num_rev_accts	num_rev_tl_bal_gt_0	num_sats	num_tl_120dpd_2m	num_tl_30dpd	num_tl_90g_dpd_24m	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	pub_rec_bankruptcies	tax_liens	tot_hi_cred_lim	total_bal_ex_mort	total_bc_limit	total_il_high_credit_limit	revol_bal_joint	sec_app_inq_last_6mths	sec_app_mort_acc	sec_app_open_acc	sec_app_revol_util	sec_app_open_act_il	sec_app_num_rev_accts	sec_app_chargeoff_within_12_mths	sec_app_collections_12_mths_ex_med	sec_app_mths_since_last_major_derog	deferral_term	hardship_amount	hardship_length	hardship_dpd	orig_projected_additional_accrued_interest	hardship_payoff_balance_amount	hardship_last_payment_amount	settlement_amount	settlement_percentage	settlement_term
count	0.0	0.0	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260664e+06	0.0	2.258957e+06	2.260639e+06	2.260638e+06	1.102166e+06	359156.000000	2.260639e+06	2.260639e+06	2.260668e+06	2.258866e+06	2.260639e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260668e+06	2.260523e+06	580775.000000	2260668.0	1.207100e+05	120706.000000	2.260639e+06	2.190392e+06	2.190392e+06	1.394538e+06	1.394539e+06	1.394539e+06	...	2.190392e+06	2.202078e+06	2.190392e+06	2.190392e+06	2.190392e+06	2.190391e+06	2.190392e+06	2.202078e+06	2.107011e+06	2.190392e+06	2.190392e+06	2.190392e+06	2.190237e+06	2.185289e+06	2.259303e+06	2.260563e+06	2.190392e+06	2.210638e+06	2.210638e+06	2.190392e+06	1.080200e+05	108021.000000	108021.000000	108021.000000	106184.000000	108021.000000	108021.000000	108021.000000	108021.000000	35942.000000	10613.0	10613.000000	10613.0	10613.000000	8426.000000	10613.000000	10613.000000	33056.000000	33056.000000	33056.000000
mean	NaN	NaN	1.504693e+04	1.504166e+04	1.502344e+04	1.309291e+01	4.458076e+02	7.799243e+04	NaN	1.882420e+01	3.068792e-01	5.768354e-01	3.454092e+01	72.312842	1.161240e+01	1.975278e-01	1.665846e+04	5.033770e+01	2.416255e+01	4.446293e+03	4.445295e+03	1.182403e+04	1.180594e+04	9.300142e+03	2.386352e+03	1.462469e+00	1.360740e+02	2.259328e+01	3.364015e+03	1.814580e-02	44.164220	1.0	1.236246e+05	19.251817	4.147942e-03	2.327317e+02	1.424922e+05	9.344199e-01	2.779407e+00	6.764314e-01	...	5.629468e+00	4.774183e+00	7.726402e+00	8.413439e+00	8.246523e+00	1.400463e+01	5.577951e+00	1.162813e+01	6.373958e-04	2.813652e-03	8.293767e-02	2.076755e+00	9.411458e+01	4.243513e+01	1.281935e-01	4.677109e-02	1.782428e+05	5.102294e+04	2.319377e+04	4.373201e+04	3.361728e+04	0.633256	1.538997	11.469455	58.169101	3.010554	12.533072	0.046352	0.077568	36.937928	3.0	155.006696	3.0	13.686422	454.840802	11628.036442	193.606331	5030.606922	47.775600	13.148596
std	NaN	NaN	9.190245e+03	9.188413e+03	9.192332e+03	4.832114e+00	2.671737e+02	1.126962e+05	NaN	1.418333e+01	8.672303e-01	8.859632e-01	2.190047e+01	26.464094	5.640861e+00	5.705150e-01	2.294831e+04	2.471307e+01	1.198753e+01	7.547612e+03	7.546657e+03	9.889599e+03	9.884835e+03	8.304886e+03	2.663086e+03	1.150210e+01	7.258317e+02	1.271114e+02	5.971757e+03	1.508131e-01	21.533121	0.0	7.416135e+04	7.822086	6.961656e-02	8.518462e+03	1.606926e+05	1.140700e+00	3.000784e+00	9.256354e-01	...	3.382874e+00	3.037921e+00	4.701430e+00	7.359114e+00	4.683928e+00	8.038868e+00	3.293434e+00	5.644027e+00	2.710643e-02	5.616522e-02	4.935732e-01	1.830711e+00	9.036140e+00	3.621616e+01	3.646130e-01	3.775338e-01	1.815748e+05	4.991124e+04	2.300656e+04	4.507298e+04	2.815387e+04	0.993401	1.760569	6.627271	25.548212	3.275893	8.150964	0.411496	0.407996	23.924584	0.0	129.113137	0.0	9.728138	375.830737	7615.161123	198.694368	3692.027842	7.336379	8.192319
min	NaN	NaN	5.000000e+02	5.000000e+02	0.000000e+00	5.310000e+00	4.930000e+00	0.000000e+00	NaN	-1.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	-9.500000e-09	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	1.0	5.693510e+03	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	0.640000	3.0	0.000000	1.920000	55.730000	0.010000	44.210000	0.200000	0.000000
25%	NaN	NaN	8.000000e+03	8.000000e+03	8.000000e+03	9.490000e+00	2.516500e+02	4.600000e+04	NaN	1.189000e+01	0.000000e+00	0.000000e+00	1.600000e+01	55.000000	8.000000e+00	0.000000e+00	5.950000e+03	3.150000e+01	1.500000e+01	0.000000e+00	0.000000e+00	4.272580e+03	4.257730e+03	2.846180e+03	6.936100e+02	0.000000e+00	0.000000e+00	0.000000e+00	3.086400e+02	0.000000e+00	27.000000	1.0	8.340000e+04	13.530000	0.000000e+00	0.000000e+00	2.909200e+04	0.000000e+00	1.000000e+00	0.000000e+00	...	3.000000e+00	3.000000e+00	4.000000e+00	3.000000e+00	5.000000e+00	8.000000e+00	3.000000e+00	8.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	9.130000e+01	0.000000e+00	0.000000e+00	0.000000e+00	5.073100e+04	2.089200e+04	8.300000e+03	1.500000e+04	1.510675e+04	0.000000	0.000000	7.000000	39.800000	1.000000	7.000000	0.000000	0.000000	16.000000	3.0	59.370000	3.0	5.000000	174.967500	5628.730000	43.780000	2227.000000	45.000000	6.000000
50%	NaN	NaN	1.290000e+04	1.287500e+04	1.280000e+04	1.262000e+01	3.779900e+02	6.500000e+04	NaN	1.784000e+01	0.000000e+00	0.000000e+00	3.100000e+01	74.000000	1.100000e+01	0.000000e+00	1.132400e+04	5.030000e+01	2.200000e+01	0.000000e+00	0.000000e+00	9.060870e+03	9.043080e+03	6.823385e+03	1.485280e+03	0.000000e+00	0.000000e+00	0.000000e+00	5.884700e+02	0.000000e+00	44.000000	1.0	1.100000e+05	18.840000	0.000000e+00	0.000000e+00	7.924000e+04	1.000000e+00	2.000000e+00	0.000000e+00	...	5.000000e+00	4.000000e+00	7.000000e+00	6.000000e+00	7.000000e+00	1.200000e+01	5.000000e+00	1.100000e+01	0.000000e+00	0.000000e+00	0.000000e+00	2.000000e+00	1.000000e+02	3.750000e+01	0.000000e+00	0.000000e+00	1.142985e+05	3.786400e+04	1.630000e+04	3.269600e+04	2.651650e+04	0.000000	1.000000	10.000000	60.200000	2.000000	11.000000	0.000000	0.000000	36.000000	3.0	119.040000	3.0	15.000000	352.605000	10044.220000	132.890000	4172.855000	45.000000	14.000000
75%	NaN	NaN	2.000000e+04	2.000000e+04	2.000000e+04	1.599000e+01	5.933200e+02	9.300000e+04	NaN	2.449000e+01	0.000000e+00	1.000000e+00	5.000000e+01	92.000000	1.400000e+01	0.000000e+00	2.024600e+04	6.940000e+01	3.100000e+01	6.712632e+03	6.710320e+03	1.670797e+04	1.668257e+04	1.339750e+04	3.052220e+03	0.000000e+00	0.000000e+00	0.000000e+00	3.534965e+03	0.000000e+00	62.000000	1.0	1.479950e+05	24.620000	0.000000e+00	0.000000e+00	2.132040e+05	1.000000e+00	3.000000e+00	1.000000e+00	...	7.000000e+00	6.000000e+00	1.000000e+01	1.100000e+01	1.000000e+01	1.800000e+01	7.000000e+00	1.400000e+01	0.000000e+00	0.000000e+00	0.000000e+00	3.000000e+00	1.000000e+02	7.140000e+01	0.000000e+00	0.000000e+00	2.577550e+05	6.435000e+04	3.030000e+04	5.880425e+04	4.376900e+04	1.000000	2.000000	15.000000	78.600000	4.000000	17.000000	0.000000	0.000000	56.000000	3.0	213.260000	3.0	22.000000	622.792500	16114.940000	284.180000	6870.782500	50.000000	18.000000
max	NaN	NaN	4.000000e+04	4.000000e+04	4.000000e+04	3.099000e+01	1.719830e+03	1.100000e+08	NaN	9.990000e+02	5.800000e+01	3.300000e+01	2.260000e+02	129.000000	1.010000e+02	8.600000e+01	2.904836e+06	8.923000e+02	1.760000e+02	4.000000e+04	4.000000e+04	6.329688e+04	6.329688e+04	4.000000e+04	2.819250e+04	1.427250e+03	3.985955e+04	7.174719e+03	4.219205e+04	2.000000e+01	226.000000	1.0	7.874821e+06	69.490000	1.400000e+01	9.152545e+06	9.971659e+06	1.800000e+01	5.700000e+01	2.500000e+01	...	7.200000e+01	7.100000e+01	8.600000e+01	1.590000e+02	9.100000e+01	1.510000e+02	6.500000e+01	1.010000e+02	7.000000e+00	4.000000e+00	5.800000e+01	3.200000e+01	1.000000e+02	1.000000e+02	1.200000e+01	8.500000e+01	9.999999e+06	3.408095e+06	1.569000e+06	2.118996e+06	1.110019e+06	6.000000	27.000000	82.000000	434.300000	43.000000	106.000000	21.000000	23.000000	185.000000	3.0	943.940000	3.0	37.000000	2680.890000	40306.410000	1407.860000	33601.000000	521.350000	181.000000

8 rows × 109 columns

결측치 확인

nulls = df.isnull().sum(axis=0)
nulls.sort_values().plot(kind='bar',)

<matplotlib.axes._subplots.AxesSubplot at 0x7faa9f5af828>

# 데이터 결측치 시각화 2
sns.barplot(data=nulls, x=nulls.index, y=nulls.values)
plt.title('The number of nulls', fontsize=20)
plt.figsize = (19,16)

Loan Status

# Loan Status 범주 값들의 수 확인
df['loan_status'].value_counts().plot(kind='bar')
plt.title('Loan State value counts')
plt.show()

# 파이플롯으로 비율 시각화
fig = plt.figure()
df['loan_status'].value_counts().plot.pie(autopct='%1.2f%%',labels=None)
plt.title('Loan State value counts')
plt.show()

df['loan_status'].value_counts()

Fully Paid                                             1041952
Current                                                 919695
Charged Off                                             261655
Late (31-120 days)                                       21897
In Grace Period                                           8952
Late (16-30 days)                                         3737
Does not meet the credit policy. Status:Fully Paid        1988
Does not meet the credit policy. Status:Charged Off        761
Default                                                     31
Name: loan_status, dtype: int64

Loan Condition

# 현재 진행중인 대출과 정책위반 대출 제거
loan_status = ['Fully Paid', 'Late (31-120 days)', 'In Grace Period',
       'Charged Off', 'Late (16-30 days)', 'Default']

def needed(status):
  if status in loan_status:
    return True
  else:
    return False
df['status'] = df['loan_status'].apply(needed)
df.drop(df[df['status']==False].index, inplace=True, errors='ignore')

# 40%의 데이터가 제거되어 행 개수가 많이 줄어든 것을 확인할 수 있음
len(df)

# 대출 상태 분류
def loan_conditions(status):
  if status == 'Fully Paid':
    return 'Good'
  else:
    return 'Bad'
df['loan_condition'] = df['loan_status'].apply(loan_conditions)

# 간단하게 Good Loan과 Bad Loan을 파이 플롯으로 확인
df['loan_condition'].value_counts().plot.pie(autopct="%1.2f%%")

<matplotlib.axes._subplots.AxesSubplot at 0x7faa9d2ae668>

연도별 분류

# 연도 구분 열 추가
dt_series = pd.to_datetime(df['issue_d'])
df['year'] = dt_series.dt.year

# 대출 상태에 따른 시각화
f = plt.figure(figsize=(16,12))
ax1 = f.add_subplot(221)
ax2 = f.add_subplot(222)
ax3 = f.add_subplot(212)

colors = ["#3791D7", "#D72626"]
labels =["Good Loans", "Bad Loans"]

# 전체 plot에 대한 타이틀
plt.suptitle('Information on Loan Conditions', fontsize=20)

# Pie 플롯을 사용해 비율 확인
df["loan_condition"].value_counts().plot.pie(
    explode=[0,0.25], autopct='%1.2f%%', ax=ax1, 
    shadow=True, colors=colors,labels=labels, 
    fontsize=12, startangle=0)
# explode : 살짝 때어지는 것
# autopct : 파이 그래프 내의 비율 수치 

# ax1의 타이틀과 라벨
ax1.set_title('State of Loan', fontsize=16)
ax1.set_xlabel('% of Condition of Loans', fontsize=14)

# countpot을 사용해 상태별 개수 비교
sns.countplot('loan_condition', data=df, ax=ax2, palette=colors)
ax2.set_title('Condition of Loans', fontsize=20)
ax2.set_xticklabels(labels=labels, rotation='horizontal')
ax2.set(ylabel=('count'))

# 연도별로 대출 개수 비교
sns.barplot(x="year", y="loan_amnt", hue="loan_condition", data=df, palette=colors, 
            estimator=lambda y: len(y) / len(df) * 100)
# estimator 그래프에 적용할 함수 
# y 값으로 표현할 값에 대한 함수 적용 가능
# mean, median 또는 자신이 원하는 것 등
ax3.set(ylabel="(%)")
ax3.set_title('Proportion of Loan Condition by Year', fontsize=20)

plt.show()

대출 금액 비교

fig = plt.figure(figsize=(12,7))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
#대출 세부 상태에 따른 금액
df.groupby(['year','loan_status'])['loan_amnt'].sum().unstack().plot(kind='bar', figsize=(12,6), ax = ax1)
#대출 세부 상태에 따른 금액 로그스케일
df.groupby(['year','loan_status'])['loan_amnt'].sum().unstack().plot(kind='bar', figsize=(12,6), logy=True, ax = ax2)

<matplotlib.axes._subplots.AxesSubplot at 0x7faa9cb52ef0>

# 대출 세부 상태에 따른 금액
# sns를 사용해서
fig = plt.figure(figsize=(10,6))
ax1 = fig.subplots()

sns.barplot(x='year',y='loan_amnt',hue='loan_status',data=df,
            estimator = lambda y : len(y) / len(df) * 100,
            ax = ax1)
ax1.set_title('The Proprotion of Loan by Year and Status', fontsize=20)
plt.ylabel('(%)', fontsize=20)
plt.xlabel('year', fontsize=20)
ax1.tick_params(axis='x', labelsize=14)
ax1.tick_params(axis='y', labelsize=14)

fig = plt.figure(figsize=(10,6))
ax1 = fig.subplots()

# estimator의 default는 mean
sns.barplot(x='year',y='loan_amnt', data=df,  ax = ax1)
ax1.set_title('Average Amount of Loan by Year', fontsize=20)
plt.ylabel('($)', fontsize=20)
plt.xlabel('year', fontsize=20)
ax1.tick_params(axis='x', labelsize=14)
ax1.tick_params(axis='y', labelsize=14)

fig = plt.figure(figsize=(10,6))
ax1 = fig.subplots()

# lambda로 estimator 정의 가능
# len()으로 건수로 확인할 수 있음
# 연도벌 대출 건수를 시각화해서 확인
sns.barplot(x='year',y='loan_amnt', data=df,  ax = ax1, estimator=lambda x : len(x))
ax1.set_title('Average Amount of Loan by Year', fontsize=20)
plt.ylabel('count', fontsize=20)
plt.xlabel('year', fontsize=20)
ax1.tick_params(axis='x', labelsize=14)
ax1.tick_params(axis='y', labelsize=14)

지역 단위 구분

df['addr_state'].unique()

# Make a list with each of the regions by state.

west = ['CA', 'OR', 'UT','WA', 'CO', 'NV', 'AK', 'MT', 'HI', 'WY', 'ID']
south_west = ['AZ', 'TX', 'NM', 'OK']
south_east = ['GA', 'NC', 'VA', 'FL', 'KY', 'SC', 'LA', 'AL', 'WV', 'DC', 'AR', 'DE', 'MS', 'TN' ]
mid_west = ['IL', 'MO', 'MN', 'OH', 'WI', 'KS', 'MI', 'SD', 'IA', 'NE', 'IN', 'ND']
north_east = ['CT', 'NY', 'PA', 'NJ', 'RI','MA', 'MD', 'VT', 'NH', 'ME']



df['region'] = np.nan

def finding_regions(state):
    if state in west:
        return 'West'
    elif state in south_west:
        return 'SouthWest'
    elif state in south_east:
        return 'SouthEast'
    elif state in mid_west:
        return 'MidWest'
    elif state in north_east:
        return 'NorthEast'



df['region'] = df['addr_state'].apply(finding_regions)

f, ax = plt.subplots()
cmap = plt.cm.inferno

by_interest_rate = df.groupby(['year', 'region']).annual_inc.mean()
by_interest_rate.unstack().plot(kind='bar', colormap=cmap, grid=False, 
                                legend=False, ax=ax, figsize=(10,6))
ax1.set_title('Average Interest Rate by Region', fontsize=14)

Text(0.5, 1, 'Average Interest Rate by Region')

신용 등급

# Let's visualize how many loans were issued by creditscore
f, ((ax1, ax2)) = plt.subplots(1, 2)
cmap = plt.cm.coolwarm

by_credit_score = df.groupby(['year', 'grade']).loan_amnt.mean()
by_credit_score.unstack().plot(legend=False, ax=ax1, figsize=(14, 4), colormap=cmap)
ax1.set_title('Loans issued by Credit Score', fontsize=14)


by_inc = df.groupby(['year', 'grade']).int_rate.mean()
by_inc.unstack().plot(ax=ax2, figsize=(14, 4), colormap=cmap)
ax2.set_title('Interest Rates by Credit Score', fontsize=14)

ax2.legend(bbox_to_anchor=(-1.0, -0.3, 1.7, 0.1), loc=5, prop={'size':12},
           ncol=7, mode="expand", borderaxespad=0.)

<matplotlib.legend.Legend at 0x7faa9b337ac8>

# 세부 신용 등급에 따른 대출 상태 비교
test = df.groupby(['sub_grade','loan_condition']).loan_amnt.size()
test.unstack().plot(colormap=cmap, kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x7faa9b2fb668>

# 세부 신용 등급별 대출 상태의 비율

## 신용 등급 총합과 비율 데이터 프레임 생성
test2 = test.unstack()
test2.loc['Total',:] = test2.sum(axis=0)
test2['Total'] = test2.sum(axis=1)
test2['Good Ratio'] = test2['Good'] * 100 / test2['Total']
test2['Bad Ratio'] = test2['Bad'] * 100 / test2['Total']

## plot
test2.ix[:-1,['Good Ratio','Bad Ratio']].plot(kind='bar', colormap=cmap, figsize=(15,6))
plt.xlabel('Grade', fontsize=15)
plt.ylabel('(%)', fontsize=15)
plt.title('Loan Condition by Credit Score', fontsize=20)
plt.xticks(rotation=0)
plt.show()
test2

loan_condition	Bad	Good	Total	Good Ratio	Bad Ratio
sub_grade
---	---	---	---	---	---
A1	1608.0	39898.0	41506.0	96.125861	3.874139
A2	1986.0	34121.0	36107.0	94.499682	5.500318
A3	2443.0	34574.0	37017.0	93.400330	6.599670
A4	4043.0	47043.0	51086.0	92.085894	7.914106
A5	5937.0	56841.0	62778.0	90.542865	9.457135
B1	8373.0	61658.0	70031.0	88.043866	11.956134
B2	9389.0	63533.0	72922.0	87.124599	12.875401
B3	11717.0	69071.0	80788.0	85.496608	14.503392
B4	13729.0	68606.0	82335.0	83.325439	16.674561
B5	15465.0	66207.0	81672.0	81.064502	18.935498
C1	18015.0	67050.0	85065.0	78.822077	21.177923
C2	18215.0	60870.0	79085.0	76.967819	23.032181
C3	18796.0	56283.0	75079.0	74.965037	25.034963
C4	20701.0	54100.0	74801.0	72.325236	27.674764
C5	19705.0	48363.0	68068.0	71.051008	28.948992
D1	15682.0	35999.0	51681.0	69.656160	30.343840
D2	14575.0	30585.0	45160.0	67.725864	32.274136
D3	13292.0	26517.0	39809.0	66.610565	33.389435
D4	12625.0	23339.0	35964.0	64.895451	35.104549
D5	11054.0	19399.0	30453.0	63.701442	36.298558
E1	9107.0	14802.0	23909.0	61.909741	38.090259
E2	8539.0	13059.0	21598.0	60.463932	39.536068
E3	7682.0	10972.0	18654.0	58.818484	41.181516
E4	6759.0	9189.0	15948.0	57.618510	42.381490
E5	6636.0	8184.0	14820.0	55.222672	44.777328
F1	4469.0	5633.0	10102.0	55.761235	44.238765
F2	3426.0	3851.0	7277.0	52.920159	47.079841
F3	2916.0	3284.0	6200.0	52.967742	47.032258
F4	2453.0	2489.0	4942.0	50.364225	49.635775
F5	2063.0	1963.0	4026.0	48.758073	51.241927
G1	1536.0	1514.0	3050.0	49.639344	50.360656
G2	1106.0	1067.0	2173.0	49.102623	50.897377
G3	871.0	773.0	1644.0	47.019465	52.980535
G4	707.0	613.0	1320.0	46.439394	53.560606
G5	652.0	502.0	1154.0	43.500867	56.499133
Total	296272.0	1041952.0	1338224.0	77.860807	22.139193

fig = plt.figure(figsize=(16,12))

ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(212)

cmap = plt.cm.coolwarm_r

# 등급별 대출양
loans_by_region = df.groupby(['grade', 'loan_condition']).size()
loans_by_region.unstack().plot(kind='bar', stacked=True, colormap=cmap, ax=ax1, grid=False)
ax1.set_title('Type of Loans by Grade', fontsize=14)

# 세부 등급별
loans_by_grade = df.groupby(['sub_grade', 'loan_condition']).size()
loans_by_grade.unstack().plot(kind='bar', stacked=True, colormap=cmap, ax=ax2, grid=False)
ax2.set_title('Type of Loans by Sub-Grade', fontsize=14)

# 연도별 평균 이자율
by_interest = df.groupby(['year', 'loan_condition']).int_rate.mean()
by_interest.unstack().plot(ax=ax3, colormap=cmap)
ax3.set_title('Average Interest rate by Loan Condition', fontsize=14)
ax3.set_ylabel('Interest Rate (%)', fontsize=12)
plt.show()

마무리

EDA 진행 과정에서 발생된 기본적인 데이터 시각화에 관련해서 소스코드를 모아서 작성하였습니다.

다음 글에서는 데이터 전처리를 진행하고 전처리에 해당되는 소스코드를 첨부하겠습니다.

이전글 / 참고자료 / 소스코드첨부

lendingclub_EDA_(1).ipynb

/ 0.57MB

[파이썬 데이터분석] LendingClub 원금 상환 여부 예측하기(1) : EDA와 데이터 시각화

chancoding.tistory.com

Lending Club || Risk Analysis and Metrics

Explore and run machine learning code with Kaggle Notebooks | Using data from Lending Club Loan Data

www.kaggle.com

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차밍이

차밍이