#!/usr/bin/python

# coding=utf-8

'''

@author: lenovo

@software: 3.6 PyCharm

@file: 8W信贷数据处理.py

@time: 20170531

@function:Credit data processing and preliminary analysis

         信贷数据处理与初步分析

@edition :1.0

'''

#导入模块

from __future__ import division, print_function

import os

import pandas as pd

import zipfile

#设置文件位置文件夹名（本文件为zip）

dataset_path = 'C:\UserslenovoDesktop...dataset'

#设置文件名注意后缀

zip_file_name = 'loan.zip'

#此位置CSV

csv_file_name = './loan.csv'

def run_main():

'''

   主函数

   '''

zip_file_path=os.path.join(dataset_path,zip_file_name)

csv_file_path=os.path.join(dataset_path,csv_file_name)

#如果不存在CSV文件，解压zip文件

if not os.path.exists(csv_file_path):

with zipfile.ZipFile(zip_file_path) as zf:

zf.extractall(dataset_path)

# 读取数据

raw_data = pd.read_csv(csv_file_path,engine='python')

#查看数据集

print('n数据预览：',raw_data.head())

print(' n 数据描述: ')

print(raw_data.describe())

print('n数据集基本信息: ')

print(raw_data.info())

#选择列

used_cols = ['loan_amnt', 'term', 'int_rate', 'grade', 'issue_d', 'addr_state']

used_data = raw_data[used_cols]

print('n数据预览',used_data.head())

# #Q:按月份统计借贷金额

print('n时间序列转换ING')

#原来为dec_12 改为 datetime

used_data['issue_d2']=pd.to_datetime(used_data['issue_d'])

print('n数据预览')

print(used_data.head())

print('数据基本信息',used_data.info)

#分组求和

data_group_by_date=used_data.groupby(['issue_d2']).sum()

#给新列命名

data_group_by_date.reset_index(inplace=True)

#apply(直接跟函数M)

data_group_by_date['issue_month']=data_group_by_date['issue_d2'].apply(lambda x:x.to_period('M'))

load_amout_group_by_month=data_group_by_date.groupby('issue_month')['loan_amnt'].sum()

#结果转换为dataframe

load_amout_group_by_month_df=pd.DataFrame(load_amout_group_by_month).reset_index()

print('n按月统计借贷总额预览：',load_amout_group_by_month_df.head())

#保存结果，输出结果为load_amout_group_by_month无df

load_amout_group_by_month_df.to_csv('C:/Users/lenovo/Desktop/.../output/load_amouta_by_month.csv',index=False)

#Q:按州统计借贷金额`

data_group_by_state=used_data.groupby(['addr_state'])['loan_amnt'].sum()

#结果转DATAFRAME

load_amout_group_by_state_df=pd.DataFrame(data_group_by_state).reset_index()

print('/n按州统计预览',data_group_by_state.head())

load_amout_group_by_state_df.to_csv('C:/Users/lenovo/Desktop/...output/load_amout_by_state.csv',index=False)

#Q:借贷评级、期限和利率关系

#根据grade,term分组，int_rate求平均

data_group_by_grade_term=used_data.groupby(['grade','term'])['int_rate'].mean()

data_group_by_grade_term_df=pd.DataFrame(data_group_by_grade_term).reset_index()

print('/n借贷评级、期限和利率关系预览：',data_group_by_grade_term_df.head())

data_group_by_grade_term_df.to_csv('C:/Users/lenovo/Desktop/...output/intrate_by_grade_term.scv',index=False)

#if exists CSV文件删除，释放空间

if os.path.exists(csv_file_path):

os.remove(csv_file_path)

'''

   if  used_data['']=  :

       return

   if  used_data['']=    :

       return

   else:

       return

   print(raw_data.head())

'''

if __name__ =='__main__':

run_main()