2019年华中杯数学建模B题数据处理代码

编程语言 2019-05-03 17:38:19 阅读次数: 0

在5月一号晚上，我参加了华中杯数学建模。对于数据处理，我用到了下面代码，希望能帮助到大家。

myDivision.py文件，用于对附件一的数据进行划分：

import pandas as pd #分析excel表格
import matplotlib.pyplot as plt # matplotlib中有很多可用的模块，我们使用pyplot模块
from numpy import *

#读取excel表格并返回日期
def readExcel():
    df = pd.read_excel('附件1.xlsx',sheet_name='销量数据')
    return set(df['货号'])    #按货号分
    #return set(df['日期'])  # 按日期分

#按货号进行分开
def changeGoods(x):
    df2 = pd.read_excel('附件1.xlsx',sheet_name='销量数据')
    #按货号分
    for i in range(len(x)):
        k = df2[df2['货号'] == x[i]]
        k.to_csv('huowu//{name}.csv'.format(name = x[i]), sep=',', header=True, index=True)

#按日期进行分开
def changeDay(x):
    df2 = pd.read_excel('附件1.xlsx',sheet_name='销量数据')
    #按日期分
    for i in range(len(x)):
        k = df2[df2['日期'] == x[i]]
        # my_data = x[i][0:4] + x[i][5:7] + x[i][8:10]
        k.to_csv('data//{name}.csv'.format(name = x[i]), sep=',', header=True, index=True)


#把每个日期和货物总量算出来
def summaryAll(x):
    sum = 0
    df = pd.read_csv(x)
    for m in range(len(df['销售件数'])):
        sum += df['销售件数'][m]
    return sum

#创建表格
def createExcel(day,data):
    my_day = []
    for i in range(len(day)):
        # my_day.append(day[i][0:4] + '/' + day[i][4:6] + '/' +day[i][6:8])
        my_day.append(day[i][0:4] + day[i][4:6] + day[i][6:8])

    df1 = pd.DataFrame({'日期': my_day ,'总销量': data})
    df1.to_excel('每个日期的总销量二.xlsx', sheet_name='每天的总销量', startcol=0, index=False)

def main():
    this = readExcel()  #读取excel表格并返回日期
    my_list = list(this)
    #changeDay(my_list) #按日期进行分开
    changeGoods(my_list)  # 按货号进行分开
    my_data = []
    for i in range(len(my_list)) :
        my_data.append(str(my_list[i][0:4]+my_list[i][5:7]+my_list[i][8:10]))
    my_data.sort()

    my_data_sum = []
    for j in range(len(my_data)):
        a = summaryAll('day/{my_day}.csv'.format(my_day = my_data[j]))
        my_data_sum.append(a)

    print(my_data)
    print(my_data_sum)

    data = []
    for k in range(len(my_data)):
        data.append(k)

    my_data_sum2 = []
    for n in range(len(my_data_sum)):
        my_data_sum2.append(math.log(my_data_sum[n]))
    print(my_data_sum2)
    createExcel(my_data, my_data_sum)

if __name__ == '__main__':
    main()

myDivision2.py用于对每月的货号总销量进行汇总：

import pandas as pd #分析excel表格

#读取excel表格并返回产品
def readExcelProdect():
    # df = pd.read_excel('附件一.xlsx',sheet_name='销量数据')
    df = pd.read_excel('附件1.xlsx', sheet_name='销量数据')
    return set(df['货号'])    #按货物分


#读取excel表格并返回日期
def readExcelDay():
    # df = pd.read_excel('附件一.xlsx',sheet_name='销量数据')
    df = pd.read_excel('附件1.xlsx', sheet_name='销量数据')
    return set(df['日期'])  # 按日期分


#生成汇总表
def createExcel(x,y):
    dict = {}
    dict[x[0]] = y
    for i in range(1,len(x)):
        w = []
        for j in range(0,len(y)):
            w.append('')
        dict[x[i]] = w
    df = pd.DataFrame(dict)
    df.to_excel('汇总.xlsx')


#处理每一种货物的表格
def createData(x,day,goods):
    every_month = []
    for i in range(0,len(x['月份'])):
        every_month.append(str(x['月份'][i]))
    month = list(set(every_month))
    month.sort()

    all_num = []
    for i in range(len(month)):
        sum = 0
        my = x[x['月份'] == int(month[i])]
        n = list(my['销售件数'])
        for j in range(len(n)):
            sum += n[j]
        all_num.append(sum)
    # print(month)
    # print(all_num)
    #进行补零
    all_month = day
    all_xiao_shou = []
    #print(day)
    n1 = 0
    for m in range(len(day)):
        if day[m] == month[0]:
            n1 = m       #每个货物的月份开始日期
    n2 = n1 + len(month) - 1     #每个货物的二月份结束日期
    for n in range(len(day)):
        if n < n1  or n > n2 :
            all_xiao_shou.append(0)
        else:
            all_xiao_shou.append(all_num[n-n1])
    # print(day)
    print(all_xiao_shou)


def main():
    this1 = readExcelProdect()  # 读取excel表格并返回货号
    my_goods = list(this1)
    my_goods_new = []
    for i in range(len(my_goods)):
        my_goods_new.append(my_goods[i][2:7])

    my_goods_new.sort()
    this_goods = []
    for j in range(len(my_goods_new)):
        this_goods.append('SS'+my_goods_new[j])
    #print(this_goods)
    #print(len(this_goods))

    this2 = readExcelDay()  # 读取excel表格并返回日期
    my_day = list(this2)

    my_day_new = []
    for i in range(len(my_day)):
        this_day = str(my_day[i])
        my_day_new.append(this_day[0:6])

    my_day_new = list(set(my_day_new))
    my_day_new.sort()

    # print(my_day_new)
    # createExcel(my_day_new, this_goods)

    df = pd.read_csv('huowu/SS61146.csv')
    createData(df, my_day_new, this_goods)
    for j in range(len(this_goods)):
        df = pd.read_csv('huowu/{n}.csv'.format(n = this_goods[j]))
        # print(this_goods[j])
        createData(df,my_day_new,this_goods)


if __name__ == '__main__':
    main()

处理延期比数据并生成频率直方图，并把每个区间赋值为1-10以内数据，自动生成txt文件，方便matlib处理：
yanQiBi1.py 
import pandas as pd
# 柱形图-折线图
from pyecharts import Bar, Line, Overlap
import numpy as np
import matplotlib.pyplot as plt


#读取按季度分的表格并统计每个区间的个数
def readExcel(x):
    yy = []
    a1 = [];a11 = [];a12 = [];a13 = []
    a2 = [];a3 = [];a4 = [];a5 = [];a6 = []
    for i in range(len(x['延期比'])):
        # if x['延期比'][i] >= 0 and x['延期比'][i] < 0.01:
        #     a1.append(x['延期比'][i])

        #0-0.01
        # if x['延期比'][i] == 0:
        #     # a11.append(x['延期比'][i])
        #     a11.append(1)
        if x['延期比'][i] > 0 and x['延期比'][i] < 0.001:
            # a12.append(x['延期比'][i])
            a12.append(1)
        elif x['延期比'][i] >= 0.001 and x['延期比'][i] < 0.01:
            # a13.append(x['延期比'][i])
            a13.append(2)
        elif x['延期比'][i] >= 0.01 and x['延期比'][i] < 0.02:
            # a2.append(x['延期比'][i])
            a2.append(3)
        elif x['延期比'][i] >= 0.02 and x['延期比'][i] < 0.1:
            # a3.append(x['延期比'][i])
            a3.append(4)
        elif x['延期比'][i] >= 0.1 and x['延期比'][i] < 0.2:
            # a4.append(x['延期比'][i])
            a4.append(5)
        elif x['延期比'][i] >= 0.2 and x['延期比'][i] < 0.35:
            # a5.append(x['延期比'][i])
            a5.append(6)
        elif x['延期比'][i] >= 0.35 and x['延期比'][i] <= 0.5:
            # a6.append(x['延期比'][i])
            a6.append(7)

    # yy.append(len(a11))
    yy.append(len(a12))
    yy.append(len(a13))
    yy.append(len(a2))
    yy.append(len(a3))
    yy.append(len(a4))
    yy.append(len(a5))
    yy.append(len(a6))
    return yy


#生成matlab需要数据
def createMatl(myData):
    print(myData)
    my_list = []
    for i in range(myData[0]):
        my_list.append(1)
    for i in range(myData[1]):
        my_list.append(2)
    for i in range(myData[2]):
        my_list.append(3)
    for i in range(myData[3]):
        my_list.append(4)
    for i in range(myData[4]):
        my_list.append(5)
    for i in range(myData[5]):
        my_list.append(6)
    for i in range(myData[6]):
        my_list.append(7)
    print(my_list)
    return my_list
    # with open('all_data.txt','w') as f:
    #     f.write(str(my_list))


def createChart(my_list):
    xx = [1,2,3,4,5,6,7]

    bar = Bar("柱形图-折线图")
    bar.add('bar', xx, my_list)
    line = Line()
    line.add('line', xx, my_list)

    overlap = Overlap()
    overlap.add(bar)
    overlap.add(line)
    overlap.show_config()
    overlap.render(path='第二题延期比.html')





def main():
    df = pd.read_excel('附件二.xlsx',sheet_name='汇总')
    list = readExcel(df)
    #createChart(list)
    #createMatl(list)
    list = createMatl(list)
    # 求均值
    arr_mean = np.mean(list)
    # 求方差
    arr_var = np.var(list)
    # 求标准差
    arr_std = np.std(list)
    print(arr_mean)
    print(arr_var)
    print(arr_std)
    # plt.style.use('seaborn-white')
    # # 最基本的频次直方图命令
    # plt.hist(list)
    # plt.show()

if __name__ == '__main__':
    main()

yanQiBi2.py 
import pandas as pd
# 柱形图-折线图
from pyecharts import Bar, Line, Overlap

#读取按季度分的表格并统计每个区间的个数
def readExcel(x):
    yy = []
    a1 = [];a2 = [];a3 = [];a4 = [];a5 = []
    a6 = [];a7 = [];a8 = [];a9 = [];a10 = []
    for i in range(len(x['延期比'])):
        if x['延期比'][i] >= 0 and x['延期比'][i] < 0.1:
            # a1.append(x['延期比'][i])
            a1.append(1)
        elif x['延期比'][i] >= 0.1 and x['延期比'][i] < 0.2:
            # a2.append(x['延期比'][i])
            a2.append(2)
        elif x['延期比'][i] >= 0.2 and x['延期比'][i] < 0.3:
            #a3.append(x['延期比'][i])
            a3.append(3)
        elif x['延期比'][i] >= 0.3 and x['延期比'][i] < 0.4:
            # a4.append(x['延期比'][i])
            a4.append(4)
        elif x['延期比'][i] >= 0.4 and x['延期比'][i] < 0.5:
            # a5.append(x['延期比'][i])
            a5.append(5)
        elif x['延期比'][i] >= 0.5 and x['延期比'][i] < 0.6:
            # a6.append(x['延期比'][i])
            a6.append(6)
        elif x['延期比'][i] >= 0.6 and x['延期比'][i] < 0.7:
            # a7.append(x['延期比'][i])
        elif x['延期比'][i] >= 0.7 and x['延期比'][i] < 0.8:
            a8.append(x['延期比'][i])
        elif x['延期比'][i] >= 0.8 and x['延期比'][i] < 0.9:
            a9.append(x['延期比'][i])
        elif x['延期比'][i] >= 0.9 and x['延期比'][i] <= 1:
            a10.append(x['延期比'][i])

    yy.append(len(a1))
    yy.append(len(a2))
    yy.append(len(a3))
    yy.append(len(a4))
    yy.append(len(a5))
    yy.append(len(a6))
    yy.append(len(a7))
    yy.append(len(a8))
    yy.append(len(a9))
    yy.append(len(a10))
    print(yy)
    return yy


def createChart(my_list):
    xx = ['0-0.01','0.1-0.2','0.2-0.3','0.3-0.4','0.4-0.5',
          '0.5-0.6','0.6-0.7','0.7-0.8','0.8-0.9','0.9-1']

    bar = Bar("柱形图-折线图")
    bar.add('bar', xx, my_list)
    line = Line()
    line.add('line', xx, my_list)

    overlap = Overlap()
    overlap.add(bar)
    overlap.add(line)
    overlap.show_config()
    overlap.render(path='第三季度.html')





def main():
    df = pd.read_excel('按季度分.xlsx',sheet_name='第三季度')
    list = readExcel(df)
    createChart(list)


if __name__ == '__main__':
    main()

还有上新量的值类似：

import pandas as pd
# 柱形图-折线图
from pyecharts import Bar, Line, Overlap
import numpy as np
import matplotlib.pyplot as plt


#统计每个区间的个数
def readExcel(x):
 shang_xin = []
 for i in range(len(x['上新日销量'])):
     shang_xin.append(x['上新日销量'][i])
 shang_xin.sort()
 jian_ge = (max(shang_xin) - min(shang_xin))/10

 yy = []
 a1 = [];a2 = [];a3 = [];a4 = []
 a5 = [];a6 = [];a7 = [];a8 = []
 for i in range(len(x['上新日销量'])):
     if x['上新日销量'][i] >= shang_xin[0] and x['上新日销量'][i] < shang_xin[0] + jian_ge/5:
         a1.append(x['上新日销量'][i])

     elif x['上新日销量'][i] >= shang_xin[0] + jian_ge/5 and x['上新日销量'][i] < shang_xin[0] + jian_ge/5*2:
         a2.append(x['上新日销量'][i])

     elif x['上新日销量'][i] >= shang_xin[0] + jian_ge/5*2 and x['上新日销量'][i] < shang_xin[0] + jian_ge/5*3:
         a3.append(x['上新日销量'][i])

     elif x['上新日销量'][i] >= shang_xin[0] + jian_ge/5*3 and x['上新日销量'][i] < shang_xin[0] + jian_ge:
         a4.append(x['上新日销量'][i])

     elif x['上新日销量'][i] >= shang_xin[0] + jian_ge and x['上新日销量'][i] < shang_xin[0] + jian_ge*2/3*2:
         a5.append(x['上新日销量'][i])

     elif x['上新日销量'][i] >= shang_xin[0] + jian_ge*2/3*2 and x['上新日销量'][i] < shang_xin[0] + jian_ge*2:
         a6.append(x['上新日销量'][i])

     elif x['上新日销量'][i] >= shang_xin[0] + jian_ge*2 and x['上新日销量'][i] < shang_xin[0] + jian_ge*3:
         a7.append(x['上新日销量'][i])

     elif x['上新日销量'][i] >= shang_xin[0] + jian_ge*3 and x['上新日销量'][i] <= shang_xin[0] + jian_ge*10:
         a8.append(x['上新日销量'][i])


 yy.append(len(a1))
 yy.append(len(a2))
 yy.append(len(a3))
 yy.append(len(a4))
 yy.append(len(a5))
 yy.append(len(a6))
 yy.append(len(a7))
 yy.append(len(a8))
 return yy


#生成matlab需要数据
def createMatl(myData):
 print(myData)
 my_list = []
 for i in range(myData[0]):
     my_list.append(1)
 for i in range(myData[1]):
     my_list.append(2)
 for i in range(myData[2]):
     my_list.append(3)
 for i in range(myData[3]):
     my_list.append(4)
 for i in range(myData[4]):
     my_list.append(5)
 for i in range(myData[5]):
     my_list.append(6)
 for i in range(myData[6]):
     my_list.append(7)
 for i in range(myData[7]):
     my_list.append(8)
 #print(my_list)
 with open('第二题上新量.txt','w') as f:
     f.write(str(my_list))
 return my_list


#生成图表
def createChart(my_list):
 xx = ['294-806','806-1319','1319-1832','1832-2857',
       '2857-3712','3712-5421','5421-7984','7984-25930']

 bar = Bar("柱形图-折线图")
 bar.add('bar', xx, my_list)
 line = Line()
 line.add('line', xx, my_list)

 overlap = Overlap()
 overlap.add(bar)
 overlap.add(line)
 overlap.show_config()
 overlap.render(path='第二题上新量未赋值区间.html')



def main():
 df = pd.read_excel('附件二.xlsx',sheet_name='汇总')
 list = readExcel(df)    #统计每个区间的个数
 #createChart(list)  #生成图表
 #createMatl(list)
 list = createMatl(list)
 # 求均值
 arr_mean = np.mean(list)
 # 求方差
 arr_var = np.var(list)
 # 求标准差
 arr_std = np.std(list)
 print(arr_mean)
 print(arr_var)
 print(arr_std)
 # plt.style.use('seaborn-white')
 # # 最基本的频次直方图命令
 # plt.hist(list)
 # plt.show()

if __name__ == '__main__':
 main()

其他的数据类似，祝福大家有参加建模的同学能够马到成功，如果有需求的话，请联系qq:1657264184。题目的话，在建模完后会上传到资源下载区，有想研究的可以下载。

猜你喜欢

转载自blog.csdn.net/ITxiaoangzai/article/details/89789496
今日推荐
开源日报 | Chrome内置Gemini的意义不在于Gemini；中国AI追随之路的五大误区；ECharts创始人“下海”养鱼；谷歌I/O开发者大会什么都有，只是没有惊喜
微软回应中国区AI团队“打包赴美”传闻
基于大语言模型的开源知识库问答系统 MaxKB GitHub Star 数量突破 5,000 个！
美国拟限制 AI 大模型出口中国和俄罗斯
苹果将与 OpenAI 达成协议，将 ChatGPT 应用于 iPhone
openKylin 社区生态委员会第六次会议圆满召开
阿里云正式发布通义千问 2.5
Python 3.13 发布首个 Beta：实验性自由线程模式和 JIT、改进交互式解释器
Stack Overflow 拿我的代码去训练 AI 大模型，还封了我的账号
Pop!_OS 的 COSMIC 桌面完成 App Store 上架工作
《2024 年一季度互联网投融资运行情况》研究报告
报告：Django 仍然是 74% 开发者的首选
周排行
laravle中orm简单的增删改查
文本分类 特征选取之CHI开方检验
Spark核心编程-WordCount
大数据开发实战系列之电信客服(1)
读书笔记 - 把时间当作朋友 by 李笑来
python 笔记--if else
SpringBoot/Mybatis/Druid, 多数据源MultiDataSource配置思路
排序三个整数
redis集群搭建【2】-Windows中Redis集群搭建
STM32F030驱动TM1650点亮4联数码管
每日归档
更多
2024-05-16(6)
2024-05-15(24)
2024-05-14(0)
2024-05-13(18)
2024-05-12(0)
2024-05-11(38)
2024-05-10(38)
2024-05-09(35)
2024-05-08(42)
2024-05-07(14)

代码天地 © 2018 codetd.com

境外服务器   最新电视剧2022