八大直播作业一

一、

# -*- coding: utf-8 -*-

"""

Created on Thu May 31 19:20:38 2018

@author: gxg

"""

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import matplotlib

import seaborn as sns

import os

os.chdir(r'D:\work\card\HW4')

snd = pd.read_csv("auto_ins.csv",encoding = 'gbk')

snd.head()

#首先对loss重新编码为1/0，有数值为1，命名为loss_flag

snd["Loss_flag"] = snd.Loss.map(lambda x: 1 if x >0 else 0)

#对loss_flag分布情况进行描述分析

print(snd.Loss_flag.value_counts())

"""

0    3028

1    1205

Name: Loss_flag, dtype: int64

"""

snd.Loss_flag.value_counts().plot(kind="bar")

print(snd.Loss_flag.agg(['mean','median','sum','std','skew']))

"""

mean         0.284668

median       0.000000

sum       1205.000000

std          0.451310

skew         0.954705

Name: Loss_flag, dtype: float64

"""

#分析是否出险和年龄、驾龄、性别、婚姻状态等变量之间的关系(提示:使用分类盒须图,堆叠柱形图)

from stack2dim import *

#堆叠柱形图

stack2dim(snd, i="Loss_flag", j="Age")

stack2dim(snd, i="Loss_flag", j="vAge")

stack2dim(snd, i="Loss_flag", j="Gender")

stack2dim(snd, i="Loss_flag", j="Marital")

#分类盒须图

sns.boxplot(x = 'Loss_flag', y = 'Age', data = snd)

sns.boxplot(x = 'Loss_flag', y = 'vAge', data = snd)

sns.boxplot(x = 'Loss_flag', y = 'Gender', data = snd)

sns.boxplot(x = 'Loss_flag', y = 'Marital', data = snd)

、

二、

# -*- coding: utf-8 -*-

"""

Created on Fri Jun  1 19:22:49 2018

@author: gxg

"""

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import matplotlib

import seaborn as sns

import os

import datetime as dt

##########################################################################################################################################################################################

# # 1 数据整理

# ## 1.1导入数据

# In[2]:

os.chdir("D:\work\card\HW5")

loanfile = os.listdir()

createVar = locals()

for i in loanfile:

    if i.endswith("csv"):        

        createVar[i.split('.')[0]] = pd.read_csv(i, encoding = 'gbk')

        print(i.split('.')[0])

data2 = pd.merge(loans, disp, on = 'account_id', how = 'left')

data2 = pd.merge(data2, clients, on = 'client_id', how = 'left')

data2=data2[data2.type=='所有者']

data2 = pd.merge(data2, card, on = 'disp_id', how = 'inner')

data2.head()

from stack2dim import *

#不同类型卡的持卡人的性别对比，

stack2dim(data2, i="type_y", j="sex")

#不同类型卡的持卡人在办卡时的平均年龄对比

data2["agetmp"]=pd.to_datetime(data2["birth_date"])

now_year=dt.datetime.now().year

data2["age"]=now_year-data2.agetmp.dt.year

data2.head()

#data_avgtmp = data2.groupby('account_id')['age'].agg([('avg_balance','mean')])

#data7 = pd.merge(data2, data_avgtmp, left_on='account_id', right_index= True, how = 'left')

stack2dim(data2, i="type_y", j="age")

#不同类型卡的持卡人在办卡前一年内的平均帐户余额对比

data_4temp1 = pd.merge(loans[['account_id', 'date']],

                       trans[['account_id','type','amount','balance','date']],

                       on = 'account_id')

data_4temp1.columns = ['account_id', 'date', 'type', 'amount', 'balance', 't_date']

data_4temp1 = data_4temp1.sort_values(by = ['account_id','t_date'])

data_4temp1['date']=pd.to_datetime(data_4temp1['date'])

data_4temp1['t_date']=pd.to_datetime(data_4temp1['t_date'])

data_4temp1.tail()

data_4temp1['balance2'] = data_4temp1['balance'].map(

    lambda x: int(''.join(x[1:].split(','))))

data_4temp1['amount2'] = data_4temp1['amount'].map(

    lambda x: int(''.join(x[1:].split(','))))

data_4temp1.head()

import datetime

data_4temp2 = data_4temp1[data_4temp1.date>data_4temp1.t_date][

    data_4temp1.date<data_4temp1.t_date+datetime.timedelta(days=365)]

data_4temp2.tail()

data_4temp3 = data_4temp2.groupby('account_id')['balance2'].agg([('avg_balance','mean'), ('stdev_balance','std')])

data_4temp3['cv_balance'] = data_4temp3[['avg_balance','stdev_balance']].apply(lambda x: x[1]/x[0],axis = 1)

data_4temp3.head()

data4 = pd.merge(data2, data_4temp3, left_on='account_id', right_index= True, how = 'left')

data4.head()

#stack2dim(data4, i="type_y", j="avg_balance")

#不同类型卡的持卡人在办卡前一年内的平均收入对比

type_dict = {'借':'out','贷':'income'}

data_4temp2['type1'] = data_4temp2.type.map(type_dict)

data_4temp4 = data_4temp2.groupby(['account_id','type1'])[['amount2']].sum()

data_4temp4.head()

data_4temp5 = pd.pivot_table(

    data_4temp4, values = 'amount2', 

    index = 'account_id', columns = 'type1')

data_4temp5.fillna(0, inplace = True)

data_4temp5['r_out_in'] = data_4temp5[

    ['out','income']].apply(lambda x: x[0]/x[1], axis = 1)

data_4temp5.head()

data5 = pd.merge(data2, data_4temp5, left_on='account_id', right_index= True, how = 'left')

data5.head()

#stack2dim(data5, i="type_y", j="income")