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2019年华中杯数学建模B题数据处理代码

作者:互联网

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。

标签:建模,len,range,2019,数据处理,my,day,append,延期
来源: https://blog.csdn.net/ITxiaoangzai/article/details/89789496