def analyze_sales_by_year():
_json = request.json
year = int(_json.get('year'))
if not year:
return jsonify(return_unsuccess('无法获取总销售额信息'))
types = DataAnalysisDao().query_type_from_goods()
dict_keys = []
for m in range(1, 13):
for typeOfGoods in types:
if m < 10:
month = "0" + str(m)
else:
month = str(m)
temp_key = month + str(typeOfGoods[0])
dict_keys.append(temp_key)
dict_of_year_month_type_and_total_sales = {key: 0 for key in dict_keys}
for i in range(1, 13):
if i < 10:
month = "0" + str(i)
else:
month = str(i)
info = DataAnalysisDao().query_sales_sum_proportions_by_year_and_type(
year, month)
for tu in info:
if i < 10:
month = "0" + str(i)
else:
month = str(i)
dict_of_year_month_type_and_total_sales[month +
str(tu[0])] = float(tu[1])
if not dict_of_year_month_type_and_total_sales:
return jsonify(return_unsuccess('无法获取总销售额信息'))
return jsonify(return_success(dict_of_year_month_type_and_total_sales))
def analyze_net_profit_rate():
net_profits = DataAnalysisDao().query_net_profit()
operating_incomes = DataAnalysisDao().query_operating_income()
dict_of_year_and_net_profit_rate = {
i + 2010: 100 * float(
float(net_profits[0][i + 1]) / float(operating_incomes[0][i + 1]))
for i in range(0, 10)
}
if not dict_of_year_and_net_profit_rate:
return jsonify(return_unsuccess('无法获取净利率信息'))
return jsonify(return_success(dict_of_year_and_net_profit_rate))
def analyze_cash_ratio():
total_diets = DataAnalysisDao().query_total_diets()
total_cash = DataAnalysisDao().query_cash()
dict_of_year_and_total_assets = {
i + 2010:
100 * float(total_cash[0][i + 1]) / float(total_diets[0][i + 1])
for i in range(0, 10)
}
if not dict_of_year_and_total_assets:
return jsonify(return_unsuccess('无法获取现金比率信息'))
return jsonify(return_success(dict_of_year_and_total_assets))
def analyze_turnover_rate():
operating_incomes = DataAnalysisDao().query_operating_income()
total_assets = DataAnalysisDao().query_total_assets()
dict_of_year_and_total_assets = {
i + 2010: 100 * float(
float(operating_incomes[0][i + 1]) / float(total_assets[0][i + 1]))
for i in range(0, 10)
}
if not dict_of_year_and_total_assets:
return jsonify(return_unsuccess('无法获取资产周转率信息'))
return jsonify(return_success(dict_of_year_and_total_assets))
def analyze_liquid_ratio_rate():
total_diets = DataAnalysisDao().query_total_diets()
total_assets = DataAnalysisDao().query_total_assets()
total_fixed_assets = DataAnalysisDao().query_fiexed_assets()
dict_of_year_and_total_assets = {
i + 2010: 100 * float(
float(total_assets[0][i + 1]) -
float(total_fixed_assets[0][i + 1])) / float(total_diets[0][i + 1])
for i in range(0, 9)
}
if not dict_of_year_and_total_assets:
return jsonify(return_unsuccess('无法获取流动比率信息'))
return jsonify(return_success(dict_of_year_and_total_assets))
def analyze_operating_expenditures_by_year():
_json = request.json
year = int(_json.get('year'))
types = ['食品类', '日用品类', '童装类', '营养品类', '玩具类', '其他类']
dict_keys = []
info = DataAnalysisDao().query_operating_expenditure_by_year_with_types(
str(year))
for m in range(1, 13):
for typeOfGoods in types:
if m < 10:
month = "0" + str(m)
else:
month = str(m)
temp_key = month + str(typeOfGoods)
dict_keys.append(temp_key)
dict_of_day_type_and_operating_expenditures = {key: 0 for key in dict_keys}
for tu in info:
if int(str(tu[0])) < 10:
dict_of_day_type_and_operating_expenditures["0" + str(tu[0]) +
str(tu[1])] = float(
tu[2])
else:
dict_of_day_type_and_operating_expenditures[str(tu[0]) +
str(tu[1])] = float(
tu[2])
if not dict_of_day_type_and_operating_expenditures:
return jsonify(return_unsuccess('无法获取当月营收信息'))
return jsonify(return_success(dict_of_day_type_and_operating_expenditures))
def analyze_operating_expenditures_by_year_and_month():
_json = request.json
year = int(_json.get('year'))
month = int(_json.get('month'))
info = DataAnalysisDao().query_operating_expenditure_by_year_and_month(
year, month)
dict_of_day_type_and_operating_expenditure = {}
days = list(range(1, 32))
for i in range(len(days)):
if days[i] < 10:
days[i] = '0' + str(days[i])
else:
days[i] = str(days[i])
for d in days:
for t in ['食品类', '日用品类', '童装类', '营养品类', '玩具类', '其他类']:
dict_of_day_type_and_operating_expenditure[d + t] = 0
for tu in info:
if int(tu[0]) < 10:
dict_of_day_type_and_operating_expenditure['0' + str(tu[0]) +
str(tu[1])] = float(
tu[2])
else:
dict_of_day_type_and_operating_expenditure[str(tu[0]) +
str(tu[1])] = float(
tu[2])
if not dict_of_day_type_and_operating_expenditure:
return jsonify(return_unsuccess('无法获取当月营收信息'))
return jsonify(return_success(dict_of_day_type_and_operating_expenditure))
def analyze_net_profit():
info = DataAnalysisDao().query_net_profit()
dict_of_year_and_net_profit = {
i + 2010: info[0][i + 1]
for i in range(0, 10)
}
if not dict_of_year_and_net_profit:
return jsonify(return_unsuccess('无法获取净利润信息'))
return jsonify(return_success(dict_of_year_and_net_profit))
def analyze_total_profits():
info = DataAnalysisDao().query_total_profits()
dict_of_year_and_operating_profits = {
i + 2010: info[0][i + 1]
for i in range(0, 10)
}
if not dict_of_year_and_operating_profits:
return jsonify(return_unsuccess('无法获取利润总额信息'))
return jsonify(return_success(dict_of_year_and_operating_profits))
def get_earnings_per_share():
info = DataAnalysisDao().query_main_indicators()
for i in range(len(info)):
if info[i][0] == '每股收益（元）':
dict_of_years_and_rates = {
x + 2008: info[i][x]
for x in range(1, 11)
}
return jsonify(return_success(dict_of_years_and_rates))
return jsonify(return_unsuccess('无法获取相关每股收益信息'))
def get_asset_growth_rate_per_year():
info = DataAnalysisDao().query_main_indicators()
for i in range(len(info)):
if info[i][0] == '净利润增长率（%）':
dict_of_years_and_rates = {
x + 2008: info[i][x]
for x in range(1, 11)
}
return jsonify(return_success(dict_of_years_and_rates))
return jsonify(return_unsuccess('无法获取相关净利润增长率信息'))
def analyze_goods_ratio():
info = DataAnalysisDao().query_goods_in_warehouse()
dict_of_type_and_ratio = {
t: float(0)
for t in ['食品类', '日用品类', '童装类', '营养品类', '玩具类', '其他类']
}
for tu in info:
dict_of_type_and_ratio[str(tu[0])] = float(tu[1])
if not dict_of_type_and_ratio:
return jsonify(return_unsuccess('无法获取仓库信息'))
return jsonify(return_success(dict_of_type_and_ratio))
def analyze_total_operating_income():
info = DataAnalysisDao().query_total_operating_income()
dict_of_type_and_total_operating_income = {
t: float(0)
for t in ['食品类', '日用品类', '童装类', '营养品类', '玩具类', '其他类']
}
for tu in info:
dict_of_type_and_total_operating_income[str(tu[0])] = float(tu[1])
if not dict_of_type_and_total_operating_income:
return jsonify(return_unsuccess('无法获取总营收信息'))
return jsonify(return_success(dict_of_type_and_total_operating_income))
def analyze_purchase_detail_by_category():
_json = request.json
category = str(_json.get('category'))
info = DataAnalysisDao().query_purchase_info_by_category(category)
detail = []
for tu in info:
print(detail)
detail.append((str(tu[0]), str(tu[1]), str(tu[2]), str(tu[3]),
str(tu[4]), str(tu[5]), str(tu[6]), str(tu[7])))
dict_of_index_and_detail = {i: detail[i] for i in range(len(detail))}
if not dict_of_index_and_detail:
return jsonify(return_unsuccess('无法获取采购信息'))
return jsonify(return_success(dict_of_index_and_detail))
def analyze_sales_detail_by_category():
_json = request.json
category = str(_json.get('category'))
info = DataAnalysisDao().query_sales_info_by_category(category)
print(info)
detail = []
for tu in info:
detail.append((str(tu[6]), str(tu[5]), str(round(tu[6] / tu[3], 2)),
str(tu[3]), str(tu[0]), str(tu[1])))
print(detail)
dict_of_index_and_detail = {i: detail[i] for i in range(len(detail))}
if not dict_of_index_and_detail:
return jsonify(return_unsuccess('无法获取销售信息'))
return jsonify(return_success(dict_of_index_and_detail))
def analyze_sales_detail_by_month():
_json = request.json
year = int(_json.get('year'))
month = int(_json.get('month'))
info = DataAnalysisDao().query_sales_info_by_year_and_month(year, month)
detail = []
for tu in info:
detail.append(
str(tu[2]) + ' ' + str(tu[0]) + '向' + str(tu[1]) + '购买了' +
str(tu[3]) + str(tu[4]) + str(tu[5]) + '，共计' + str(tu[6]) + '元。')
dict_of_index_and_detail = {i: detail[i] for i in range(len(detail))}
if not dict_of_index_and_detail:
return jsonify(return_unsuccess('无法获取销售信息'))
return jsonify(return_success(dict_of_index_and_detail))
def analyze_BackorderGoods():
_method = request.method
if _method == 'GET':
_data = request.args
else:
_data = request.json
page = int(_data.get('page'))
offset = num_one_page * page
rows = data_analysis_dao.query_backorder_goods(limit=num_one_page,
offset=offset)
if rows:
return json.dumps(return_success(
DataAnalysisDao.goods_store_desc_to_dict(rows)),
cls=DecimalEncoder)
else:
return jsonify(return_unsuccess('无更多低库存商品信息'))
def analyze_sales_by_year_and_month(year, month):
info = DataAnalysisDao().query_sales_sum_proportions_by_year_and_month(
year, month)
total_num = 0
try:
for i in range(len(info)):
total_num += info[i][1]
except ():
Exception('sql failed!')
info = tuple(
map(lambda tu: tuple([tu[0],
round(int(tu[1]) / total_num, 4), tu[2]]), info))
sum_proportions = 0
for i in range(len(info)):
sum_proportions += info[i][1]
if sum_proportions != 1:
info = tuple(
map(
lambda tu: tuple(
[tu[0], tu[1] + (1 - sum_proportions) / len(info), tu[2]]),
info))
return info
def analyze_sales():
info = DataAnalysisDao().query_sales_proportions()
total_num = 0
for i in range(len(info)):
total_num += info[i][1]
info = tuple(
map(
lambda tu: tuple(
[tu[0], round(int(tu[1]) / total_num, 4),
float(tu[2])]), info))
sum_proportions = 0
for i in range(len(info)):
sum_proportions += info[i][1]
if sum_proportions != 1:
info = tuple(
map(
lambda tu: tuple(
[tu[0], tu[1] + (1 - sum_proportions) / len(info), tu[2]]),
info))
sales_dict = {t[0]: t[2] for t in info}
if not sales_dict:
return jsonify(return_unsuccess('无法获取相关销售比例信息'))
return jsonify(return_success(sales_dict))
def analyze_SellWellGoods():
_method = request.method
if _method == 'GET':
_data = request.args
else:
_data = request.json
# 获取参数中的页数和近期多少天参数，并检查参数格式
try:
page = int(_data.get('page'))
days = int(_data.get('days'))
except Exception as e:
print(e)
return jsonify(return_unsuccess('参数格式有误'))
if page < 0 or days <= 0:
return jsonify(return_unsuccess('参数值有误'))
# 当前日期作为时间区间上界
high = datetime.datetime.now()
if not days:
# 默认查看一周以内的畅销商品
days = 7
# 根据近期天数计算时间区间
delta = datetime.timedelta(days=int(days))
# 上界减去长度后得到下界
low = high - delta
# 查询数据
offset = page * num_one_page
rows = data_analysis_dao.query_sell_well_goods(low=low,
high=high,
limit=num_one_page,
offset=offset)
if rows:
return json.dumps(return_success(
DataAnalysisDao.goods_store_desc_to_dict(rows)),
cls=DecimalEncoder)
else:
return jsonify(return_unsuccess("无更多畅销商品信息"))
from flask import Blueprint, render_template, request
from app.dao.DataAnalysisDao import DataAnalysisDao
from app.utils.auth import check_token
from app.utils.json_util import *
from flask import Blueprint, render_template, request, session, jsonify
from app.utils.json_util import *
from app.utils.crawler import *
import uuid
import urllib
import re
# 都是对于利润表/负债表/主要指标表/现金流量表的分析操作
# 因为我们的数据，所以时间均是09-18 / 10-19
analysis_results = Blueprint('analysis_results', __name__)
data_analysis_dao = DataAnalysisDao()
@analysis_results.before_request
@check_token
def res():
pass
# 0 时间（年份tuple)
@analysis_results.route("/data/time0918", methods=["GET", "POST"])
def get09_18_tuple():
res = {x: x + 2009 for x in range(0, 10)}
return jsonify(return_success(res))