#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 3 18:20:08 2019
@author: lg
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu May 30 09:44:52 2019
@author: lg
"""
from src.utils.db_processor import postgre_processor,mysql_processor
from collections import Counter
import pandas as pd
from collections import Counter
import numpy as np
#select * from dw.dw_platform_quotation_recycler_quote_record where product_combine_sku_name like '苹果 iPhone X%' limit 1000
#buying price
sql="""
SELECT
aa.platform_reference_price,
aa.recycler_start_price,
aa.recycler_reference_price,
aa.quotation_amount_num,
aa.is_confirm_flag_id,
aa.product_combine_sku_id,
aa.product_combine_level_id,
aa.quotation_document_no,
bb.is_quote_flag_id,
bb.inventory_amount_num,
bb.start_amount_num,
bb.recycler_quote_amount_num,
bb.last_start_amount_num,
bb.last_settle_amount_num,
bb.number_shots_thirty
--aa.*,
--bb.*
FROM dw.dw_platform_quotation_recycler_quote_record aa
INNER JOIN dw.dw_platform_document_product bb
on aa.product_combine_sku_id =bb.product_combine_sku_id and aa.product_combine_level_id=bb.product_combine_level_id and aa.quotation_document_no=bb.quotation_document_no
where bb.quotation_document_begin_date > '2019-05-01' and aa.quote_date>'2019-05-01'
limit 10000
"""
data = postgre_processor.load_sql(sql)
#data2=pd.read_excel('data2.xlsx')
#ll=list(data2.columns)
ll=['platform_reference_price',
'recycler_start_price',
'recycler_reference_price',
'quotation_amount_num',
'product_combine_sku_id',
'product_combine_level_id',
'is_quote_flag_id',
'start_amount_num',
'number_shots_thirty']
data3=data[ll]
