金融风控 Task2-数据探索性分析

1 数据总体了解：

#导包
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import warnings
warnings.filterwarnings('ignore')

1.1 读取数据集并了解数据集大小，原始特征维度

train = pd.read_csv('./data/train.csv')
testA = pd.read_csv('./data/testA.csv')
print(train.shape)
print(testA.shape)

train.head(3).append(train.tail(3))

testA.head(3).append(testA.tail(3))

train.columns

1.2 通过info熟悉数据类型

train.info()

1.3粗略查看数据集中各特征基本统计量

train.describe()

2 缺失值和唯一值

2.1 查看数据缺失值情况

#存在缺失值的列数
train.isnull().any().sum()

#查看是否存在一半以上缺失值的列
have_null_fea_dict = ((train.isnull().sum())/len(train)).to_dict()
fea_null = {
    
    }
for k,v in have_null_fea_dict.items():
    if v > 0.5:
        fea_null[k] = v
print(fea_null)

2.2 查看缺失特征及缺失率

missing = (train.isnull().sum())/len(train)
miss = missing[missing>0]
# miss.sort_values(ascending = True)
miss = miss.sort_values(ascending=True)
miss.plot.bar()

2.3 查看训练集测试集中特征属性只有一值的特征

numerical_fea = list(train.select_dtypes(exclude=['object']).columns)#数据类型
category_fea = list(filter(lambda x: x not in numerical_fea,list(train.columns)))#对象类型
print(numerical_fea)
print(category_fea)

2.5 数值型变量分析，数值型包括连续型变量和离散型变量

2.5.1划分数值型变量中的连续变量和离散型变量

def get_numerical_serial_fea(data,feas):
    numerical_serial_fea = []
    numerical_noserial_fea = []
    for fea in feas:
        temp = data[fea].nunique()
        if temp <= 20:
            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(train,numerical_fea)
print(numerical_serial_fea)
print(numerical_noserial_fea)

2.5.2离散型变量逐一查看

train['term'].value_counts()
train['homeOwnership'].value_counts()
train['verificationStatus'].value_counts()
train['purpose'].value_counts()
train['pubRecBankruptcies'].value_counts()#数据量相差悬殊
train['initialListStatus'].value_counts()
train['applicationType'].value_counts()#数据量相差悬殊
train['policyCode'].value_counts()#单一值，无关变量
train['n11'].value_counts()#数据量相差悬殊
train['n12'].value_counts()#数据量相差悬殊

2.5.3数值连续型变量分析 -每个数字特征的分布可视化

f = pd.melt(train,value_vars=numerical_serial_fea)
g = sns.FacetGrid(f,col='variable',col_wrap=4,sharex=False,sharey=False)
g = g.map(sns.distplot,'value')

2.6 非数值类别变量分析

train['grade'].value_counts()
train['subGrade'].value_counts()
train['employmentLength'].value_counts()
train['issueDate'].value_counts()
train['earliesCreditLine'].value_counts()

3 变量分布可视化

3.1 单一变量分布可视化

plt.figure(figsize=(8,8))
sns.barplot(train['employmentLength'].value_counts(dropna=False)[:20],
           train['employmentLength'].value_counts(dropna=False).keys()[:20])
plt.show()

3.2 时间格式数据处理及查看

# train转换成时间格式
train['issueDate']=pd.to_datetime(train['issueDate'])
startdate = datetime.datetime.strptime('2007-06-01','%Y-%m-%d')
train['issueDateDT'] = train['issueDate'].apply(lambda x: x-startdate).dt.days

# testA转换成时间格式
testA['issueDate']=pd.to_datetime(testA['issueDate'])
startdate = datetime.datetime.strptime('2007-06-01','%Y-%m-%d')
testA['issueDateDT'] = testA['issueDate'].apply(lambda x: x-startdate).dt.days

# 画图
plt.hist(train['issueDateDT'],label='train')
plt.hist(testA['issueDateDT'],label='testA')
plt.legend()

3.3 利用透视图了解数据

#透视图 索引可以有多个，“columns（列）”是可选的，聚合函数aggfunc最后是被应用到了变量“values”中你所列举的项目上。
pd.pivot_table(train, index=['grade'], columns=['issueDateDT'], values=
['loanAmnt'], aggfunc=np.sum)

3.4 用pandas_profiling生成数据报告

import pandas_profiling
pfr = pandas_profiling.ProfileReport(train)
pfr.to_file("./example.html")