金融风控Task2-数据分析

目的

1. EDA（Exploratory Data Analysis）价值主要在于熟悉了解整个数据集的基本情况（缺失值，异常值），对数据集进行验证是否可以进行接下来的机器学习或者深度学习建模.
2. 了解变量间的相互关系、变量与预测值之间的存在关系。
3. 为特征工程做准备

内容介绍

• 数据总体了解：
  • 读取数据集并了解数据集大小，原始特征维度；
  • 通过info熟悉数据类型；
  • 粗略查看数据集中各特征基本统计量；
• 缺失值和唯一值：
  • 查看数据缺失值情况
  • 查看唯一值特征情况
• 深入数据-查看数据类型
  • 类别型数据
  • 数值型数据
    • 离散数值型数据
    • 连续数值型数据
• 数据间相关关系
  • 特征和特征之间关系
  • 特征和目标变量之间关系

代码

# 导入数据分析及可视化过程需要的库
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime

# 读取文件
data_train = pd.read_csv('./data/train.csv')
data_test_a = pd.read_csv('./data/testA.csv')

# 总体了解

# 查看数据及的样本个数和原始特征维度
data_test_a.shape

(200000, 46)

data_train.shape

(800000, 47)

data_train.columns

Index(['id', 'loanAmnt', 'term', 'interestRate', 'installment', 'grade',
       'subGrade', 'employmentTitle', 'employmentLength', 'homeOwnership',
       'annualIncome', 'verificationStatus', 'issueDate', 'isDefault',
       'purpose', 'postCode', 'regionCode', 'dti', 'delinquency_2years',
       'ficoRangeLow', 'ficoRangeHigh', 'openAcc', 'pubRec',
       'pubRecBankruptcies', 'revolBal', 'revolUtil', 'totalAcc',
       'initialListStatus', 'applicationType', 'earliesCreditLine', 'title',
       'policyCode', 'n0', 'n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8',
       'n9', 'n10', 'n11', 'n12', 'n13', 'n14'],
      dtype='object')

data_test_a.columns

Index(['id', 'loanAmnt', 'term', 'interestRate', 'installment', 'grade',
       'subGrade', 'employmentTitle', 'employmentLength', 'homeOwnership',
       'annualIncome', 'verificationStatus', 'issueDate', 'purpose',
       'postCode', 'regionCode', 'dti', 'delinquency_2years', 'ficoRangeLow',
       'ficoRangeHigh', 'openAcc', 'pubRec', 'pubRecBankruptcies', 'revolBal',
       'revolUtil', 'totalAcc', 'initialListStatus', 'applicationType',
       'earliesCreditLine', 'title', 'policyCode', 'n0', 'n1', 'n2', 'n3',
       'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12', 'n13', 'n14'],
      dtype='object')

data_train.info()

RangeIndex: 800000 entries, 0 to 799999
Data columns (total 47 columns):
id                    800000 non-null int64
loanAmnt              800000 non-null float64
term                  800000 non-null int64
interestRate          800000 non-null float64
installment           800000 non-null float64
grade                 800000 non-null object
subGrade              800000 non-null object
employmentTitle       799999 non-null float64
employmentLength      753201 non-null object
homeOwnership         800000 non-null int64
annualIncome          800000 non-null float64
verificationStatus    800000 non-null int64
issueDate             800000 non-null object
isDefault             800000 non-null int64
purpose               800000 non-null int64
postCode              799999 non-null float64
regionCode            800000 non-null int64
dti                   799761 non-null float64
delinquency_2years    800000 non-null float64
ficoRangeLow          800000 non-null float64
ficoRangeHigh         800000 non-null float64
openAcc               800000 non-null float64
pubRec                800000 non-null float64
pubRecBankruptcies    799595 non-null float64
revolBal              800000 non-null float64
revolUtil             799469 non-null float64
totalAcc              800000 non-null float64
initialListStatus     800000 non-null int64
applicationType       800000 non-null int64
earliesCreditLine     800000 non-null object
title                 799999 non-null float64
policyCode            800000 non-null float64
n0                    759730 non-null float64
n1                    759730 non-null float64
n2                    759730 non-null float64
n3                    759730 non-null float64
n4                    766761 non-null float64
n5                    759730 non-null float64
n6                    759730 non-null float64
n7                    759730 non-null float64
n8                    759729 non-null float64
n9                    759730 non-null float64
n10                   766761 non-null float64
n11                   730248 non-null float64
n12                   759730 non-null float64
n13                   759730 non-null float64
n14                   759730 non-null float64
dtypes: float64(33), int64(9), object(5)
memory usage: 286.9+ MB

data_train.describe()

	id	loanAmnt	term	interestRate	installment	employmentTitle	homeOwnership	annualIncome	verificationStatus	isDefault	...	n5	n6	n7	n8	n9	n10	n11	n12	n13	n14
count	800000.000000	800000.000000	800000.000000	800000.000000	800000.000000	799999.000000	800000.000000	8.000000e+05	800000.000000	800000.000000	...	759730.000000	759730.000000	759730.000000	759729.000000	759730.000000	766761.000000	730248.000000	759730.000000	759730.000000	759730.000000
mean	399999.500000	14416.818875	3.482745	13.238391	437.947723	72005.351714	0.614213	7.613391e+04	1.009683	0.199513	...	8.107937	8.575994	8.282953	14.622488	5.592345	11.643896	0.000815	0.003384	0.089366	2.178606
std	230940.252015	8716.086178	0.855832	4.765757	261.460393	106585.640204	0.675749	6.894751e+04	0.782716	0.399634	...	4.799210	7.400536	4.561689	8.124610	3.216184	5.484104	0.030075	0.062041	0.509069	1.844377
min	0.000000	500.000000	3.000000	5.310000	15.690000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	...	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	199999.750000	8000.000000	3.000000	9.750000	248.450000	427.000000	0.000000	4.560000e+04	0.000000	0.000000	...	5.000000	4.000000	5.000000	9.000000	3.000000	8.000000	0.000000	0.000000	0.000000	1.000000
50%	399999.500000	12000.000000	3.000000	12.740000	375.135000	7755.000000	1.000000	6.500000e+04	1.000000	0.000000	...	7.000000	7.000000	7.000000	13.000000	5.000000	11.000000	0.000000	0.000000	0.000000	2.000000
75%	599999.250000	20000.000000	3.000000	15.990000	580.710000	117663.500000	1.000000	9.000000e+04	2.000000	0.000000	...	11.000000	11.000000	10.000000	19.000000	7.000000	14.000000	0.000000	0.000000	0.000000	3.000000
max	799999.000000	40000.000000	5.000000	30.990000	1715.420000	378351.000000	5.000000	1.099920e+07	2.000000	1.000000	...	70.000000	132.000000	79.000000	128.000000	45.000000	82.000000	4.000000	4.000000	39.000000	30.000000

8 rows × 42 columns

data_train.head(3).append(data_train.tail(3))

	id	loanAmnt	term	interestRate	installment	grade	subGrade	employmentTitle	employmentLength	homeOwnership	...	n5	n6	n7	n8	n9	n10	n11	n12	n13	n14
0	0	35000.0	5	19.52	917.97	E	E2	320.0	2 years	2	...	9.0	8.0	4.0	12.0	2.0	7.0	0.0	0.0	0.0	2.0
1	1	18000.0	5	18.49	461.90	D	D2	219843.0	5 years	0	...	NaN	NaN	NaN	NaN	NaN	13.0	NaN	NaN	NaN	NaN
2	2	12000.0	5	16.99	298.17	D	D3	31698.0	8 years	0	...	0.0	21.0	4.0	5.0	3.0	11.0	0.0	0.0	0.0	4.0
799997	799997	6000.0	3	13.33	203.12	C	C3	2582.0	10+ years	1	...	4.0	26.0	4.0	10.0	4.0	5.0	0.0	0.0	1.0	4.0
799998	799998	19200.0	3	6.92	592.14	A	A4	151.0	10+ years	0	...	10.0	6.0	12.0	22.0	8.0	16.0	0.0	0.0	0.0	5.0
799999	799999	9000.0	3	11.06	294.91	B	B3	13.0	5 years	0	...	3.0	4.0	4.0	8.0	3.0	7.0	0.0	0.0	0.0	2.0

6 rows × 47 columns

# 查看数据集种特征缺失值，唯一值等

# 有多少列特征值有缺失值
data_train.isnull().any().sum()

22

data_train.isnull().sum()

id                        0
loanAmnt                  0
term                      0
interestRate              0
installment               0
grade                     0
subGrade                  0
employmentTitle           1
employmentLength      46799
homeOwnership             0
annualIncome              0
verificationStatus        0
issueDate                 0
isDefault                 0
purpose                   0
postCode                  1
regionCode                0
dti                     239
delinquency_2years        0
ficoRangeLow              0
ficoRangeHigh             0
openAcc                   0
pubRec                    0
pubRecBankruptcies      405
revolBal                  0
revolUtil               531
totalAcc                  0
initialListStatus         0
applicationType           0
earliesCreditLine         0
title                     1
policyCode                0
n0                    40270
n1                    40270
n2                    40270
n3                    40270
n4                    33239
n5                    40270
n6                    40270
n7                    40270
n8                    40271
n9                    40270
n10                   33239
n11                   69752
n12                   40270
n13                   40270
n14                   40270
dtype: int64

# 缺失特征中缺失率大于50%的特征
have_null_fea_dict = (data_train.isnull().sum() / len(data_train)).to_dict()
fea_null_moreThanHalf = {}
for key, value in have_null_fea_dict.items():
    if value > 0.5:
        fea_null_moreThanHalf[key] = value

fea_null_moreThanHalf

{}

# 具体的查看缺失特征及缺失率
# nan 可视化
missing = data_train.isnull().sum() / len(data_train)
missing = missing[missing > 0]
missing.sort_values(inplace=True)
missing.plot.bar()

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# 查看训练集测试集种特征属性只有一值的特征

one_value_fea = [col for col in data_train.columns if data_train[col].nunique() <= 1]
one_value_fea_test = [col for col in data_test_a.columns if data_test_a[col].nunique() <= 1]

one_value_fea

['policyCode']

one_value_fea_test

['policyCode']

# 查看特征的数值类型有哪些，对象类型有哪些
# 特征一般由类别型特征和数值型特征组成，二数值型特征又分为连续性和离散型

numerical_fea = list(data_train.select_dtypes(exclude=['object']).columns)
category_fea = list(filter(lambda x: x not in numerical_fea, list(data_train.columns)))

numerical_fea

['id',
 'loanAmnt',
 'term',
 'interestRate',
 'installment',
 'employmentTitle',
 'homeOwnership',
 'annualIncome',
 'verificationStatus',
 'isDefault',
 'purpose',
 'postCode',
 'regionCode',
 'dti',
 'delinquency_2years',
 'ficoRangeLow',
 'ficoRangeHigh',
 'openAcc',
 'pubRec',
 'pubRecBankruptcies',
 'revolBal',
 'revolUtil',
 'totalAcc',
 'initialListStatus',
 'applicationType',
 'title',
 'policyCode',
 'n0',
 'n1',
 'n2',
 'n3',
 'n4',
 'n5',
 'n6',
 'n7',
 'n8',
 'n9',
 'n10',
 'n11',
 'n12',
 'n13',
 'n14']

category_fea

['grade', 'subGrade', 'employmentLength', 'issueDate', 'earliesCreditLine']

# 数值型包括连续性变量和离散型变量

# 过滤数值型类别特征
def get_numerical_serial_fea(data, feas):
    numerical_serial_fea = []
    numerical_noserial_fea = []
    for fea in feas:
        temp = data[fea].nunique()
        if temp <= 10:
            numerical_noserial_fea.append(fea)
            continue
        numerical_serial_fea.append(fea)
    return numerical_serial_fea, numerical_noserial_fea
numerical_serial_fea, numerical_noserial_fea = get_numerical_serial_fea(data_train, numerical_fea)

numerical_serial_fea

['id',
 'loanAmnt',
 'interestRate',
 'installment',
 'employmentTitle',
 'annualIncome',
 'purpose',
 'postCode',
 'regionCode',
 'dti',
 'delinquency_2years',
 'ficoRangeLow',
 'ficoRangeHigh',
 'openAcc',
 'pubRec',
 'pubRecBankruptcies',
 'revolBal',
 'revolUtil',
 'totalAcc',
 'title',
 'n0',
 'n1',
 'n2',
 'n3',
 'n4',
 'n5',
 'n6',
 'n7',
 'n8',
 'n9',
 'n10',
 'n13',
 'n14']

numerical_noserial_fea

['term',
 'homeOwnership',
 'verificationStatus',
 'isDefault',
 'initialListStatus',
 'applicationType',
 'policyCode',
 'n11',
 'n12']

# 数值类别型变量分析

data_train['term'].value_counts()

3    606902
5    193098
Name: term, dtype: int64

data_train['homeOwnership'].value_counts()

0    395732
1    317660
2     86309
3       185
5        81
4        33
Name: homeOwnership, dtype: int64

data_train['verificationStatus'].value_counts()

1    309810
2    248968
0    241222
Name: verificationStatus, dtype: int64

data_train['initialListStatus'].value_counts()

0    466438
1    333562
Name: initialListStatus, dtype: int64

data_train['applicationType'].value_counts()

0    784586
1     15414
Name: applicationType, dtype: int64

data_train['policyCode'].value_counts()

1.0    800000
Name: policyCode, dtype: int64

data_train['n11'].value_counts()

0.0    729682
1.0       540
2.0        24
4.0         1
3.0         1
Name: n11, dtype: int64

data_train['n12'].value_counts()

0.0    757315
1.0      2281
2.0       115
3.0        16
4.0         3
Name: n12, dtype: int64

data_train['isDefault'].value_counts()

0    640390
1    159610
Name: isDefault, dtype: int64

# 数值连续型变量分析

# 每个数字特征得分分布可视化
f = pd.melt(data_train, value_vars=numerical_serial_fea)
g = sns.FacetGrid(f, col="variable", col_wrap=3, sharex=False, sharey=False)
g = g.map(sns.distplot, "value")

D:\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

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# 查看某一个数值型变量的分布，查看变量是否符合正态分布，如果不符合正太分布的变量可以log化后再观察下是否符合正态分布
#Ploting Transaction Amount Values Distribution
plt.figure(figsize=(16,12))
plt.suptitle('Transaction Values Distribution', fontsize=22)
plt.subplot(221)
sub_plot_1 = sns.distplot(data_train['loanAmnt'])
sub_plot_1.set_title("loanAmnt Distribuition", fontsize=18)
sub_plot_1.set_xlabel("")
sub_plot_1.set_ylabel("Probability", fontsize=15)

plt.subplot(222)
sub_plot_2 = sns.distplot(np.log(data_train['loanAmnt']))
sub_plot_2.set_title("loanAmnt (Log) Distribuition", fontsize=18)
sub_plot_2.set_xlabel("")
sub_plot_2.set_ylabel("Probability", fontsize=15)

Text(0, 0.5, 'Probability')

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# 非数值类别型变量分析

category_fea

['grade', 'subGrade', 'employmentLength', 'issueDate', 'earliesCreditLine']

data_train['grade'].value_counts()

B    233690
C    227118
A    139661
D    119453
E     55661
F     19053
G      5364
Name: grade, dtype: int64

data_train['subGrade'].value_counts()

C1    50763
B4    49516
B5    48965
B3    48600
C2    47068
C3    44751
C4    44272
B2    44227
B1    42382
C5    40264
A5    38045
A4    30928
D1    30538
D2    26528
A1    25909
D3    23410
A3    22655
A2    22124
D4    21139
D5    17838
E1    14064
E2    12746
E3    10925
E4     9273
E5     8653
F1     5925
F2     4340
F3     3577
F4     2859
F5     2352
G1     1759
G2     1231
G3      978
G4      751
G5      645
Name: subGrade, dtype: int64

data_train['employmentLength'].value_counts()

10+ years    262753
2 years       72358
< 1 year      64237
3 years       64152
1 year        52489
5 years       50102
4 years       47985
6 years       37254
8 years       36192
7 years       35407
9 years       30272
Name: employmentLength, dtype: int64

data_train['issueDate'].value_counts()

2016-03-01    29066
2015-10-01    25525
2015-07-01    24496
2015-12-01    23245
2014-10-01    21461
2016-02-01    20571
2015-11-01    19453
2015-01-01    19254
2015-04-01    18929
2015-08-01    18750
2015-05-01    17119
2016-01-01    16792
2014-07-01    16355
2015-06-01    15236
2015-09-01    14950
2016-04-01    14248
2014-11-01    13793
2015-03-01    13549
2016-08-01    13301
2015-02-01    12881
2016-07-01    12835
2016-06-01    12270
2016-12-01    11562
2016-10-01    11245
2016-11-01    11172
2014-05-01    10886
2014-04-01    10830
2016-05-01    10680
2014-08-01    10648
2016-09-01    10165
              ...  
2010-01-01      355
2009-10-01      305
2009-09-01      270
2009-08-01      231
2009-07-01      223
2009-06-01      191
2009-05-01      190
2009-04-01      166
2009-03-01      162
2009-02-01      160
2009-01-01      145
2008-12-01      134
2008-03-01      130
2008-11-01      113
2008-02-01      105
2008-04-01       92
2008-01-01       91
2008-10-01       62
2007-12-01       55
2008-07-01       52
2008-05-01       38
2008-08-01       38
2008-06-01       33
2007-10-01       26
2007-11-01       24
2007-08-01       23
2007-07-01       21
2008-09-01       19
2007-09-01        7
2007-06-01        1
Name: issueDate, Length: 139, dtype: int64

data_train['earliesCreditLine'].value_counts()

Aug-2001    5567
Aug-2002    5403
Sep-2003    5403
Oct-2001    5258
Aug-2000    5246
Sep-2004    5219
Sep-2002    5170
Aug-2003    5116
Oct-2000    5034
Oct-2002    5034
Oct-2003    4969
Aug-2004    4904
Nov-2000    4798
Sep-2001    4787
Sep-2000    4780
Nov-1999    4773
Oct-1999    4678
Oct-2004    4647
Sep-2005    4608
Jul-2003    4586
Nov-2001    4514
Aug-2005    4494
Jul-2001    4480
Aug-1999    4446
Sep-1999    4441
Dec-2001    4379
Jul-2002    4342
Aug-2006    4283
Mar-2001    4268
May-2001    4223
            ... 
Feb-1962       2
Jul-1958       2
Apr-1960       2
Dec-1956       2
Jun-1952       2
Sep-1961       2
Oct-1958       2
Nov-1954       1
Sep-1953       1
Oct-1957       1
Dec-1960       1
Jun-1958       1
Mar-1958       1
Sep-1957       1
May-1957       1
Mar-1957       1
Mar-1962       1
Jul-1955       1
Nov-1953       1
Aug-1955       1
Feb-1960       1
Aug-1958       1
Jan-1944       1
Oct-1954       1
Apr-1958       1
May-1960       1
Oct-2015       1
Dec-1951       1
Jan-1946       1
Aug-1946       1
Name: earliesCreditLine, Length: 720, dtype: int64

# 变量分布可视化

# 单一变量分布可视化
plt.figure(figsize=(8, 8))
sns.barplot(data_train['employmentLength'].value_counts(dropna=False)[:20],
            data_train['employmentLength'].value_counts(dropna=False).keys()[:20])
plt.show()

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# 根绝y值不同可视化x某个特征的分布

train_loan_fr = data_train.loc[data_train['isDefault'] == 1]
train_loan_nofr = data_train.loc[data_train['isDefault'] == 0]

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 8))
train_loan_fr.groupby('grade')['grade'].count().plot(kind='barh', ax=ax1, title='Count of grade fraud')
train_loan_nofr.groupby('grade')['grade'].count().plot(kind='barh', ax=ax2, title='Count of grade non-fraud')
train_loan_fr.groupby('employmentLength')['employmentLength'].count().plot(kind='barh', ax=ax3, title='Count of employmentLength fraud')
train_loan_nofr.groupby('employmentLength')['employmentLength'].count().plot(kind='barh', ax=ax4, title='Count of employmentLength non-fraud')
plt.show()

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fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=(15, 6))
data_train.loc[data_train['isDefault'] == 1] \
    ['loanAmnt'].apply(np.log) \
    .plot(kind='hist',
          bins=100,
          title='Log Loan Amt - Fraud',
          color='r',
          xlim=(-3, 10),
         ax= ax1)
data_train.loc[data_train['isDefault'] == 0] \
    ['loanAmnt'].apply(np.log) \
    .plot(kind='hist',
          bins=100,
          title='Log Loan Amt - Not Fraud',
          color='b',
          xlim=(-3, 10),
         ax=ax2)

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total = len(data_train)
total_amt = data_train.groupby(['isDefault'])['loanAmnt'].sum().sum()
plt.figure(figsize=(12,5))
plt.subplot(121)##1代表行，2代表列，所以一共有2个图，1代表此时绘制第一个图。
plot_tr = sns.countplot(x='isDefault',data=data_train)#data_train‘isDefault’这个特征每种类别的数量**
plot_tr.set_title("Fraud Loan Distribution \n 0: good user | 1: bad user", fontsize=14)
plot_tr.set_xlabel("Is fraud by count", fontsize=16)
plot_tr.set_ylabel('Count', fontsize=16)
for p in plot_tr.patches:
    height = p.get_height()
    plot_tr.text(p.get_x()+p.get_width()/2.,
            height + 3,
            '{:1.2f}%'.format(height/total*100),
            ha="center", fontsize=15) 
    
percent_amt = (data_train.groupby(['isDefault'])['loanAmnt'].sum())
percent_amt = percent_amt.reset_index()
plt.subplot(122)
plot_tr_2 = sns.barplot(x='isDefault', y='loanAmnt',  dodge=True, data=percent_amt)
plot_tr_2.set_title("Total Amount in loanAmnt  \n 0: good user | 1: bad user", fontsize=14)
plot_tr_2.set_xlabel("Is fraud by percent", fontsize=16)
plot_tr_2.set_ylabel('Total Loan Amount Scalar', fontsize=16)
for p in plot_tr_2.patches:
    height = p.get_height()
    plot_tr_2.text(p.get_x()+p.get_width()/2.,
            height + 3,
            '{:1.2f}%'.format(height/total_amt * 100),
            ha="center", fontsize=15)     

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