新手参加比赛，不足之处敬请谅解
题目链接：链接：https://pan.baidu.com/s/1ll7Y1kTr1MiJTjErwnqiVA
提取码：zu9f
目录
题目：随着国家政策的逐步开放，越来越多的药品可以在网络上购买，医药电商平台蒸蒸日上，受新冠疫情的影响，线下药店购买困难，更让医药电商进入了更多消费者的视野，各大药企也纷纷加大力度布局医药电商领域。但电商模式与线下零售有所不同，如何更好的经营医药电商成为药企急需解决的问题。本题采集了天猫维生素类的药品，请针对维生素药品进行数据的清洗、分析与挖掘，并回答下列问题。
任务1 对店铺进行分析
对店铺进行分析，一共包含多少家店铺，各店铺的销售额占比如何？给出销售额占比最高的店铺，并分析该店铺的销售情况。
任务1.1 店铺种类统计
通过pandas的read_excel函数导入数据，使用value_counts函数对“shop_name”列计数统计，得出共包含26家店铺
import pandas as pd
data=pd.read_excel('F:\\2022全国大学生数据分析A题\\data\\data.xlsx')
data["shop_name"].value_counts()阿里健康大药房 16958 天猫国际进口超市 7760 康爱多大药房旗舰店 4051 天猫超市 3764 ChemistWarehouse海外旗舰店 3205 焦作国控大药房旗舰店 3027 阿里健康大药房海外店 2938 nyc美林健康海外专营店 2823 thejamy保健海外专营店 2345 百康保健品专营店 2224 康恩贝官方旗舰店 2196 hihealth海外专营店 2196 宜度海外专营店 2165 天猫国际妙颜社 1755 NRC营养优选海外专营店 1742 苏宁易购官方旗舰店 1681 nrfs湖畔海外专营店 1559 进口/国产保健品精品店 1525 康寿营养品店 1516 百秀大药房旗舰店 1505 LuckyVitamin海外旗舰店 1497 skyshop海外专营店 1435 搬运健康馆 1423 美加精品 1373 SASA美国直邮 1228 同堂保健品专营店 1219 Name: shop_name, dtype: int64
| 阿里健康大药房 |
天猫国际进口超市 |
康爱多大药房旗舰店 |
| 天猫超市 |
SASA美国直邮 |
焦作国控大药房旗舰店 |
| 阿里健康大药房海外店 |
同堂保健品专营店 |
skyshop海外专营店 |
| 百康保健品专营店 |
hihealth海外专营店 |
康恩贝官方旗舰店 |
| 宜度海外专营店 |
天猫国际妙颜社 |
搬运健康馆 |
| 苏宁易购官方旗舰店 |
nrfs湖畔海外专营店 |
进口/国产保健品精品店 |
| 康寿营养品店 |
百秀大药房旗舰店 |
美加精品 |
| nyc美林健康海外专营店 |
LuckyVitamin海外旗舰店 |
ChemistWarehouse海外旗舰店 |
| NRC营养优选海外专营店 |
thejamy保健海外专营店 |
任务1.2 店铺销售额占比
首先通过在Excel将“discount”列中的空值全部填充为“10折”，即无打折，接着根据公式计算出销售额，公式如下：
使用group_by函数计算各店铺销售总额并求其占比。
data["discount"].value_counts()9.5折 32338 9折 29929 8.5折 9959 10折 2884 Name: discount, dtype: int64
discount_num=[]
for i in range(len(data["shop_name"])):
discount_num.append(0)
for i in range(len(data["shop_name"])):
if data.loc[i,"discount"]=="10折":
discount_num[i]=1.0
if data.loc[i,"discount"]=="9.5折":
discount_num[i]=0.95
if data.loc[i,"discount"]=="9折":
discount_num[i]=0.90
if data.loc[i,"discount"]=="8.5折":
discount_num[i]=0.85
df_1=pd.DataFrame({'discount_num':discount_num})
df_1.to_excel('F:\\2022全国大学生数据分析A题\\data\\result_1.xlsx',sheet_name='sheet1',index=None)
total_price=[]
for i in range(len(data["shop_name"])):
total_price.append(0)
for i in range(len(data["shop_name"])):
price=data.loc[i,"price"]
sold=float(data.loc[i,"sold"])
discount=discount_num[i]
total_price[i]=float(price)*float(sold)*float(discount)
df_2=pd.DataFrame({'total_price':total_price})
df_2.to_excel('F:\\2022全国大学生数据分析A题\\data\\result_2.xlsx',sheet_name='sheet1',index=None)
data1=pd.read_excel('F:\\2022全国大学生数据分析A题\\data\\result_2.xlsx')
data1
-----------------------------------------------------------------------
shop_name total_price
0 宜度海外专营店 1041.390
1 天猫国际进口超市 31950.000
2 阿里健康大药房 7794.275
3 天猫国际进口超市 12029.850
4 阿里健康大药房 10704.000
... ... ...
75105 阿里健康大药房 182044.800
75106 阿里健康大药房海外店 34938.000
75107 hihealth海外专营店 7416.900
75108 阿里健康大药房 7871.850
75109 阿里健康大药房 19806.840
75110 rows × 2 columnstotal_shop = data1.groupby("shop_name")["total_price"].sum().reset_index()
total_shop=total_shop.sort_values("total_price",ascending=True)
total_shop
----------------------------------------------------------------------------
shop_name total_price
9 同堂保健品专营店 6.539738e+04
3 SASA美国直邮 3.258235e+05
1 LuckyVitamin海外旗舰店 3.697916e+05
14 康寿营养品店 6.198878e+05
21 美加精品 6.466264e+05
23 进口/国产保健品精品店 7.276764e+05
17 搬运健康馆 7.948006e+05
5 nrfs湖畔海外专营店 8.727912e+05
2 NRC营养优选海外专营店 1.408813e+06
18 焦作国控大药房旗舰店 1.546531e+06
20 百秀大药房旗舰店 2.843618e+06
4 hihealth海外专营店 3.382795e+06
6 nyc美林健康海外专营店 4.343254e+06
16 康爱多大药房旗舰店 4.986436e+06
19 百康保健品专营店 5.288855e+06
13 宜度海外专营店 6.001669e+06
7 skyshop海外专营店 6.506159e+06
8 thejamy保健海外专营店 9.798825e+06
22 苏宁易购官方旗舰店 1.065959e+07
10 天猫国际妙颜社 3.569804e+07
0 ChemistWarehouse海外旗舰店 5.539680e+07
15 康恩贝官方旗舰店 9.559872e+07
25 阿里健康大药房海外店 1.065089e+08
12 天猫超市 1.148217e+08
11 天猫国际进口超市 3.561012e+08
24 阿里健康大药房 6.751027e+08total_shop = data1.groupby("shop_name")["total_price"].sum().reset_index()
total_shop
---------------------------------------------------------------------------
shop_name total_price
0 ChemistWarehouse海外旗舰店 5.539680e+07
1 LuckyVitamin海外旗舰店 3.697916e+05
2 NRC营养优选海外专营店 1.408813e+06
3 SASA美国直邮 3.258235e+05
4 hihealth海外专营店 3.382795e+06
5 nrfs湖畔海外专营店 8.727912e+05
6 nyc美林健康海外专营店 4.343254e+06
7 skyshop海外专营店 6.506159e+06
8 thejamy保健海外专营店 9.798825e+06
9 同堂保健品专营店 6.539738e+04
10 天猫国际妙颜社 3.569804e+07
11 天猫国际进口超市 3.561012e+08
12 天猫超市 1.148217e+08
13 宜度海外专营店 6.001669e+06
14 康寿营养品店 6.198878e+05
15 康恩贝官方旗舰店 9.559872e+07
16 康爱多大药房旗舰店 4.986436e+06
17 搬运健康馆 7.948006e+05
18 焦作国控大药房旗舰店 1.546531e+06
19 百康保健品专营店 5.288855e+06
20 百秀大药房旗舰店 2.843618e+06
21 美加精品 6.466264e+05
22 苏宁易购官方旗舰店 1.065959e+07
23 进口/国产保健品精品店 7.276764e+05
24 阿里健康大药房 6.751027e+08
25 阿里健康大药房海外店 1.065089e+08total_shop_new = total_shop
for i in range(len(total_shop["total_price"])):
total_shop_new["total_price"][i]=(total_shop_new["total_price"][i])/total
total_shop_new
------------------------------------------------------------------------------
shop_name total_price
0 ChemistWarehouse海外旗舰店 0.036921
1 LuckyVitamin海外旗舰店 0.000246
2 NRC营养优选海外专营店 0.000939
3 SASA美国直邮 0.000217
4 hihealth海外专营店 0.002255
5 nrfs湖畔海外专营店 0.000582
6 nyc美林健康海外专营店 0.002895
7 skyshop海外专营店 0.004336
8 thejamy保健海外专营店 0.006531
9 同堂保健品专营店 0.000044
10 天猫国际妙颜社 0.023792
11 天猫国际进口超市 0.237335
12 天猫超市 0.076527
13 宜度海外专营店 0.004000
14 康寿营养品店 0.000413
15 康恩贝官方旗舰店 0.063715
16 康爱多大药房旗舰店 0.003323
17 搬运健康馆 0.000530
18 焦作国控大药房旗舰店 0.001031
19 百康保健品专营店 0.003525
20 百秀大药房旗舰店 0.001895
21 美加精品 0.000431
22 苏宁易购官方旗舰店 0.007104
23 进口/国产保健品精品店 0.000485
24 阿里健康大药房 0.449943
25 阿里健康大药房海外店 0.070986total_shop=total_shop.sort_values("total_price",ascending=True)
total_shop
---------------------------------------------------------------------
shop_name total_price
9 同堂保健品专营店 0.000044
3 SASA美国直邮 0.000217
1 LuckyVitamin海外旗舰店 0.000246
14 康寿营养品店 0.000413
21 美加精品 0.000431
23 进口/国产保健品精品店 0.000485
17 搬运健康馆 0.000530
5 nrfs湖畔海外专营店 0.000582
2 NRC营养优选海外专营店 0.000939
18 焦作国控大药房旗舰店 0.001031
20 百秀大药房旗舰店 0.001895
4 hihealth海外专营店 0.002255
6 nyc美林健康海外专营店 0.002895
16 康爱多大药房旗舰店 0.003323
19 百康保健品专营店 0.003525
13 宜度海外专营店 0.004000
7 skyshop海外专营店 0.004336
8 thejamy保健海外专营店 0.006531
22 苏宁易购官方旗舰店 0.007104
10 天猫国际妙颜社 0.023792
0 ChemistWarehouse海外旗舰店 0.036921
15 康恩贝官方旗舰店 0.063715
25 阿里健康大药房海外店 0.070986
12 天猫超市 0.076527
11 天猫国际进口超市 0.237335
24 阿里健康大药房 0.449943total_shop=total_shop.sort_values("total_price",ascending=True)
total_shop
-------------------------------------------------------------------
shop_name total_price
9 同堂保健品专营店 0.000044
3 SASA美国直邮 0.000217
1 LuckyVitamin海外旗舰店 0.000246
14 康寿营养品店 0.000413
21 美加精品 0.000431
23 进口/国产保健品精品店 0.000485
17 搬运健康馆 0.000530
5 nrfs湖畔海外专营店 0.000582
2 NRC营养优选海外专营店 0.000939
18 焦作国控大药房旗舰店 0.001031
20 百秀大药房旗舰店 0.001895
4 hihealth海外专营店 0.002255
6 nyc美林健康海外专营店 0.002895
16 康爱多大药房旗舰店 0.003323
19 百康保健品专营店 0.003525
13 宜度海外专营店 0.004000
7 skyshop海外专营店 0.004336
8 thejamy保健海外专营店 0.006531
22 苏宁易购官方旗舰店 0.007104
10 天猫国际妙颜社 0.023792
0 ChemistWarehouse海外旗舰店 0.036921
15 康恩贝官方旗舰店 0.063715
25 阿里健康大药房海外店 0.070986
12 天猫超市 0.076527
11 天猫国际进口超市 0.237335
24 阿里健康大药房 0.449943import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
#调节图形大小，宽，高
plt.figure(figsize=(9,6))
#定义饼状图的标签，标签是列表
labels = [u"阿里健康大药房",u"天猫国际进口超市",u"其它"]
#每个标签占多大，会自动去算百分比
# sizes = [0.458,0.236,0.071,0.069,0.064,0.037,0.022,0.043]
# colors = ['red','yellowgreen','lightskyblue','cyan','lightseagreen','lime','darkorange','pink']
sizes = [0.450,0.237,0.313]
colors = ['red','yellowgreen','lightskyblue']
#将某部分爆炸出来， 使用括号，将第一块分割出来，数值的大小是分割出来的与其他两块的间隙
explode = (0.05,0,0)
patches,l_text,p_text = plt.pie(sizes,explode=explode,labels=labels,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
#labeldistance，文本的位置离远点有多远，1.1指1.1倍半径的位置
#autopct，圆里面的文本格式，%3.1f%%表示小数有三位，整数有一位的浮点数
#shadow，饼是否有阴影
#startangle，起始角度，0，表示从0开始逆时针转，为第一块。一般选择从90度开始比较好看
#pctdistance，百分比的text离圆心的距离
#patches, l_texts, p_texts，为了得到饼图的返回值，p_texts饼图内部文本的，l_texts饼图外label的文本
#改变文本的大小
#方法是把每一个text遍历。调用set_size方法设置它的属性
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(20)
# 设置x，y轴刻度一致，这样饼图才能是圆的
plt.axis('equal')
plt.title('药店销售额占比图', size=20)
plt.legend()
plt.show()
plt.figure(figsize=(15,6),dpi=200)
x=total_shop["shop_name"]
plt.bar(x, total_shop["total_price"], color='#87CEFA')
# plt.xlabel('时间')
plt.xticks(total_shop["shop_name"],rotation=60)
plt.ylabel('销售额占比')
plt.title('各药店销售额占比')
plt.show()
任务1.3 阿里健康大药房销售情况
data2=data[data["shop_name"]=="阿里健康大药房"]
data2["discount"].value_counts()9.5折 10768 9折 4953 10折 663 8.5折 574 Name: discount, dtype: int64
plt.figure(figsize=(9,6))
labels = [u"9.5折",u"9折",u"8.5折",u"无打折"]
sizes = [10768,4953,574,663]
colors = ['orange','yellowgreen','lightskyblue','pink']
explode = (0.05,0,0,0)
patches,l_text,p_text = plt.pie(sizes,explode=explode,labels=labels,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(12)
for t in p_text:
t.set_size(12)
plt.axis('equal')
plt.title('阿里健康大药房打折情况', size=15)
plt.legend()
plt.show()
df3=data2.sort_values(by="sold" , ascending=False)
df3
--------------------------------------------------------------
date_time id shop_name title sku_name price sold discount brand parameter
58239 2021-12-01 4418809521757 阿里健康大药房 3盒】星鲨维生素D滴剂(胶囊型)24粒补钙维生素D3佝偻病官方婴儿钙 3盒装 | 拍2优惠 | 维D促进钙吸收 106.666667 103940 9折 NaN 药品名称:维生素D滴剂（胶囊型）||生产企业:国药控股星鲨制药（厦门）有限公司||产品名称:...
26620 2021-06-01 4543217834215 阿里健康大药房 3盒】星鲨维生素D滴剂(胶囊型)24粒补钙维生素D佝偻病官方婴儿钙 3盒装 | 拍2优惠| 促进钙吸收 109.000000 73061 9.5折 NaN 药品名称:维生素D滴剂（胶囊型）||生产企业:国药控股星鲨制药（厦门）有限公司||产品名称:...
31883 2021-08-01 4418809521757 阿里健康大药房 3盒】星鲨维生素D滴剂(胶囊型)24粒补钙维生素D佝偻病官方婴儿钙 3盒装 | 拍2更优惠 | D促进钙吸收 109.000000 70625 9.5折 NaN 药品名称:维生素D滴剂（胶囊型）||生产企业:国药控股星鲨制药（厦门）有限公司||产品名称:...
74446 2021-02-01 3435079967513 阿里健康大药房 恒健维生素C片VC 维C100片补充维生素C妊娠呕吐发少发黄vc片药用 标准装1盒 3.400000 45895 9折 NaN 药品名称:维生素C片||生产企业:广东恒健制药有限公司||规格:100mg*100片/片||...
47489 2021-02-01 571172601110 阿里健康大药房 CONBA/康恩贝 维生素C咀嚼片（香橙味） 1.2g/片*100片补充维C VC NaN 18.900000 44511 8.5折 NaN 厂名:华润圣海健康科技有限公司||产品名称:CONBA/康恩贝 维生素C咀嚼片（香橙味） 1...
... ... ... ... ... ... ... ... ... ... ...
70770 2021-09-01 559409418908 阿里健康大药房 礼盒装汤臣倍健维生素C片vc片维C咀嚼片E官网天然维生素e软胶囊ve NaN 109.000000 0 9折 NaN 品牌:汤臣倍健||品牌:BY－HEALTH/汤臣倍健||系列:维生素C片（橘子味）+维生素E...
44963 2021-01-01 3621111355030 阿里健康大药房 恒健维生素C片VC 维C100mg*100片/瓶补充维生素C妊娠呕吐发少发黄 1盒本品+川奇3+1牌铁锌钙氨基酸口服液 10ml/支*10支/盒 13.000000 0 8.5折 恒健 药品名称:维生素C片||生产企业:广东恒健制药有限公司||规格:100mg*100片/片||...
11440 2021-05-01 4475310558004 阿里健康大药房 海迪威维生素C泡腾片15片过敏维生素缺乏坏血病vc片泡腾 维c泡腾 套餐二：5盒，家庭装 93.000000 0 9.5折 NaN 产品名称:海迪威 维生素C泡腾片 0.5g*15片/盒||品牌:海迪威||药品通用名:维生素...
14259 2021-03-01 4475654714725 阿里健康大药房 亚宝维和维生素C泡腾片15片增强免疫力 过敏 预防传染性疾病 流感 套餐一：3盒常备装 115.000000 0 9.5折 NaN 药品通用名:维生素C泡腾片||规格:1g*15片/盒||药品名称:维生素C泡腾片||生产企业...
69475 2021-04-01 3619151867229 阿里健康大药房 呀吖维生素AD滴剂(胶囊型)20粒儿童补充维生素AD抽搐夜盲症佝偻病 1盒本品+贝亲婴儿润肤乳70G 53.300000 0 9折 NaN 品牌:呀吖||产品名称:呀吖 维生素AD滴剂(胶囊型) 20粒/盒||药品通用名:维生素AD...
16958 rows × 10 columnsdata3=pd.read_excel('F:\\2022全国大学生数据分析A题\\data\\new_data.xlsx')
data4=data3[data3["shop_name"]=="阿里健康大药房"]
total_month= data4.groupby("date_time")["total_price"].sum().reset_index()
total_month
----------------------------------------------------------------------------
date_time total_price
0 2020-01-01 1.092576e+07
1 2020-02-01 9.885606e+06
2 2020-03-01 1.656051e+07
3 2020-04-01 1.579910e+07
4 2020-05-01 1.383416e+07
5 2020-06-01 2.813641e+07
6 2020-07-01 1.408467e+07
7 2020-08-01 1.767333e+07
8 2020-09-01 1.865158e+07
9 2020-10-01 1.443941e+07
10 2020-11-01 1.935696e+07
11 2020-12-01 3.204151e+07
12 2021-01-01 2.240306e+07
13 2021-02-01 2.787846e+07
14 2021-03-01 3.541297e+07
15 2021-04-01 2.932532e+07
16 2021-05-01 3.180636e+07
17 2021-06-01 6.001385e+07
18 2021-07-01 3.384237e+07
19 2021-08-01 3.590081e+07
20 2021-09-01 3.550590e+07
21 2021-10-01 4.110051e+07
22 2021-11-01 4.706524e+07
23 2021-12-01 6.345886e+07plt.figure(figsize=(15,6),dpi=200)
x=["2020-01","2020-02","2020-03","2020-04","2020-05","2020-06","2020-07","2020-08","2020-09","2020-10","2020-11","2020-12",
"2021-01","2021-02","2021-03","2021-04","2021-05","2021-06","2021-07","2021-08","2021-09","2021-10","2021-11","2021-12"]
y=total_month["total_price"]
plt.plot(x, y, color='orange')
plt.xlabel('时间')
plt.xticks(x,rotation=70)
plt.ylabel('当月销售额')
plt.title('阿里健康大药房2020-2021年各月份销售额')
plt.show()
data5=data4.groupby("title")["sold"].sum().reset_index()
data5.sort_values(by="sold" , ascending=False)
---------------------------------------------------------------------------
title sold
363 丽点维A酸乳膏20g去闭口粉刺暗疮软膏维生素a维a乳酸膏乳软膏祛痘 404107
105 3盒】星鲨维生素D滴剂(胶囊型)24粒补钙维生素D佝偻病官方婴儿钙 267120
1190 浙江医药维生素E软胶囊30粒心脑血管疾病 习惯性流产不孕ve胶囊 252963
9 21金维他多维元素片100片复合维生素B b6 b1 b2维生素c钙铁锌21 232891
1026 星鲨维生素D滴剂(胶囊型)30粒儿童补钙补充维生素D发育迟缓佝偻病 227536
... ... ...
1614 限量买1送2 汤臣倍健R葡萄籽维生素C加E片 410mg/片*90片VCVE成人 0
1372 维生素B2片5mg*100片/瓶脂溢性皮炎口角炎唇干裂结膜炎阴囊炎 0
1108 汤臣倍健 维生素C片（橘子味）+维生素E60粒礼盒天然维生素e维C 0
1133 汤臣倍健R葡萄籽维生素C加E片 410mg/片*90片VCVE成人 0
1268 礼盒装汤臣倍健维生素C片vc片维C咀嚼片E官网天然维生素e软胶囊ve 0
1663 rows × 2 columnsdata5=data4.groupby("title")["total_price"].sum().reset_index()
data5.sort_values(by="total_price" , ascending=False)
-------------------------------------------------------------------
title total_price
1637 领券减】爱乐维复合维生素100片孕妇叶酸补充备孕早期多维元素B 3.569218e+07
105 3盒】星鲨维生素D滴剂(胶囊型)24粒补钙维生素D佝偻病官方婴儿钙 2.836576e+07
116 3盒包邮】达因伊可新维生素AD滴剂30粒1岁以上AD预防佝偻病ad滴剂 1.622559e+07
104 3盒】星鲨维生素D滴剂(胶囊型)24粒补钙维生素D3佝偻病官方婴儿钙 1.612723e+07
9 21金维他多维元素片100片复合维生素B b6 b1 b2维生素c钙铁锌21 1.508954e+07
... ... ...
1133 汤臣倍健R葡萄籽维生素C加E片 410mg/片*90片VCVE成人 0.000000e+00
171 BY－HEALTH/汤臣倍健 维生素C片（橘子味）+维生素E60粒礼盒 0.000000e+00
1614 限量买1送2 汤臣倍健R葡萄籽维生素C加E片 410mg/片*90片VCVE成人 0.000000e+00
1372 维生素B2片5mg*100片/瓶脂溢性皮炎口角炎唇干裂结膜炎阴囊炎 0.000000e+00
1108 汤臣倍健 维生素C片（橘子味）+维生素E60粒礼盒天然维生素e维C 0.000000e+00
1663 rows × 2 columns任务2 对所有药品进行分析
对所有药品进行分析，一共包含多少个药品，各药品的销售额占比如何？给出销售额占比最高的 10 个药品，并绘制这 10 个药品每月销售额曲线图。
任务2.1 药品种类统计
import pandas as pd
data=pd.read_excel('F:\\2022全国大学生数据分析A题\\data\\new_data.xlsx')
data["id"].value_counts()
------------------------------------------------------------------------
3566974752915 24
564979974262 24
3601587058427 24
3232160081409 24
544066576806 24
..
4391874217388 1
4629962755717 1
544106976534 1
4631336339308 1
590797340368 1
Name: id, Length: 9697, dtype: int64任务2.2 药品销售额占比
total_id=data.groupby("id")["total_price"].sum().reset_index()
total_id=total_id.sort_values("total_price",ascending=False)
total_id
------------------------------------------------------------------
id total_price
6594 4169804230645 3.975427e+07
8951 4521420762240 2.521464e+07
5311 3415285443577 2.503826e+07
8093 4418809521757 2.403036e+07
1058 542510921766 1.787574e+07
... ... ...
6270 4012427304925 0.000000e+00
6271 4012427304926 0.000000e+00
6272 4012427304927 0.000000e+00
6273 4012427304928 0.000000e+00
6499 4135649044700 0.000000e+00
9697 rows × 2 columnsid_ten=total_id.head(10)
id_ten
----------------------------------------------------
id total_price
6594 4169804230645 3.975427e+07
8951 4521420762240 2.521464e+07
5311 3415285443577 2.503826e+07
8093 4418809521757 2.403036e+07
1058 542510921766 1.787574e+07
1747 551699740249 1.625078e+07
5306 3414540274727 1.316437e+07
9055 4543217834215 1.287603e+07
5400 3452991932319 1.238747e+07
7207 4296851710480 1.211168e+07total=total_id["total_price"].sum()
total
-------------------------------------------------
1500417429.02092id_sold_percent=[]
for i in range(len(id_ten["id"])):
id_sold_percent.append(0)
for i in range(len(id_ten["id"])):
id_sold_percent[i]=list(id_ten["total_price"])[i]/total
id_sold_percent
--------------------------------------------------------------
[0.02649547009406077,
0.01680508355154942,
0.016687532759692366,
0.01601578263171197,
0.011913841790457338,
0.010830836469024661,
0.00877380237351032,
0.008581630718860754,
0.008256018576608164,
0.008072208383971744]任务2.3 药品月销售额曲线图
data_yp1=data[data["id"]==4169804230645]
#data_yp1
total_month_yp1= data_yp1.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp1data_yp2=data[data["id"]==4521420762240]
# data_yp2
total_month_yp2= data_yp2.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp2data_yp3=data[data["id"]==3415285443577]
# data_yp3
total_month_yp3= data_yp3.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp3data_yp4=data[data["id"]==4418809521757]
# data_yp4
total_month_yp4= data_yp4.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp4data_yp5=data[data["id"]==542510921766]
# data_yp5
total_month_yp5= data_yp5.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp5data_yp6=data[data["id"]==551699740249]
# data_yp6
total_month_yp6= data_yp6.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp6data_yp7=data[data["id"]==3414540274727]
# data_yp7
total_month_yp7= data_yp7.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp7data_yp8=data[data["id"]==4543217834215]
# data_yp8
total_month_yp8= data_yp8.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp8data_yp9=data[data["id"]==3452991932319]
data_yp9
total_month_yp9= data_yp9.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp9data_yp10=data[data["id"]==4296851710480]
data_yp10
total_month_yp10= data_yp10.groupby("date_time")["total_price"].sum().reset_index()
total_month_yp10import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.figure(figsize=(20,30),dpi=200)
ax=plt.subplot(5,2,1)
x_1=["2020-7","2020-8","2020-9","2020-10","2020-11","2020-12","2021-01","2021-02","2021-03","2021-04","2021-05","2021-06",
"2021-07","2021-08","2021-09","2021-10","2021-11","2021-12"]
y_1=total_month_yp1["total_price"]
plt.plot(x_1, y_1, color='orange')
plt.xticks(x_1,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:4169804230645月份销售额')
ax=plt.subplot(5,2,2)
x_2=["2021-04","2021-05","2021-06","2021-07","2021-08","2021-10","2021-12",]
y_2=total_month_yp2["total_price"]
plt.plot(x_2, y_2, color='orange')
plt.xticks(x_2,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:4521420762240月份销售额')
ax=plt.subplot(5,2,3)
x_3=["2020-01","2020-02","2020-03","2020-04","2020-05","2020-06","2020-08","2020-09","2020-10","2020-12","2021-01","2021-02","2021-03"]
y_3=total_month_yp3["total_price"]
plt.plot(x_3, y_3, color='orange')
plt.xticks(x_3,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:3415285443577月份销售额')
ax=plt.subplot(5,2,4)
x_4=["2021-07","2021-08","2021-10","2021-11","2021-12"]
y_4=total_month_yp4["total_price"]
plt.plot(x_4, y_4, color='orange')
plt.xticks(x_4,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:4418809521757月份销售额')
ax=plt.subplot(5,2,5)
x_5=["2020-03","2020-04","2020-05","2020-06","2020-07","2020-08","2020-09","2020-10","2020-11","2020-12",
"2021-01","2021-02","2021-03","2021-04","2021-05","2021-06","2021-07","2021-08","2021-09","2021-10","2021-11","2021-12"]
y_5=total_month_yp5["total_price"]
plt.plot(x_5, y_5, color='orange')
plt.xticks(x_5,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:542510921766月份销售额')
ax=plt.subplot(5,2,6)
x_6=["2020-01","2020-02","2020-03","2020-04","2020-05","2020-06","2020-07","2020-08","2020-09","2020-10","2020-11","2020-12",
"2021-01","2021-02"]
y_6=total_month_yp6["total_price"]
plt.plot(x_6, y_6, color='orange')
plt.xticks(x_6,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:551699740249月份销售额')
ax=plt.subplot(5,2,7)
x_7=["2020-01","2020-02",
"2020-03","2020-04","2021-03","2021-04","2021-07"]
y_7=total_month_yp7["total_price"]
plt.plot(x_7, y_7, color='orange')
plt.xticks(x_7,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:3414540274727月份销售额')
ax=plt.subplot(5,2,8)
x_8=["2021-05","2021-06","2021-07"]
y_8=total_month_yp8["total_price"]
plt.plot(x_8, y_8, color='orange')
plt.xticks(x_8,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:4543217834215月份销售额')
ax=plt.subplot(5,2,9)
x_9=["2020-01","2020-02","2020-03","2020-04","2020-05","2020-06","2020-07","2020-08","2020-09","2020-10","2020-11","2020-12",
"2021-01","2021-02","2021-03","2021-04","2021-05","2021-06","2021-07","2021-08","2021-10","2021-11","2021-12"]
y_9=total_month_yp9["total_price"]
plt.plot(x_9, y_9, color='orange')
plt.xticks(x_9,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:3452991932319月份销售额')
ax=plt.subplot(5,2,10)
x_10=["2020-01","2020-05","2020-06","2020-07","2020-09","2020-10","2020-11","2020-12",
"2021-01","2021-02"]
y_10=total_month_yp10["total_price"]
plt.plot(x_10, y_10, color='orange')
plt.xticks(x_10,rotation=70)
plt.ylabel('当月销售额')
plt.title('id:4296851710480月份销售额')
plt.show()
任务3 对所有药品品牌进行分析
对所有药品品牌进行分析，一共包含多少个品牌，各品牌的销售额占比如何？给出销售额占比最高的 10 个品牌，并分析这 10 个品牌销售较好的原因？
任务3.1 药品品牌种类统计
import pandas as pd
data=pd.read_excel('F:\\2022全国大学生数据分析A题\\data\\new_data.xlsx')
import numpy as np
data.dropna(axis=0,subset = ['parameter'],inplace=True)
data = data.reset_index(drop=True)brand=[]
for i in range(len(data["parameter"])):
brand.append(0)
for i in range(len(data["parameter"])):
str1=data.loc[i,"parameter"].replace("||",",")
#print(i)
parameter_dict = {}
content=str1.splitlines()
str2=content[0].split(',')
for j in str2:
k, v = j.split(':')
parameter_dict[k] = v
brand_parameter=parameter_dict["品牌"]
brand[i]=brand_parameter
data["brand"]=brand
data
-----------------------------------------------------------------
date_time id shop_name title sku_name price sold discount total_price brand parameter
0 2020-11-01 3985971101463 宜度海外专营店 特价处理！超低价19.9元起！含维生素ABCDE B族B12 维生素锌硒片 维生素e 300粒--买3送1 60.900000 19 9折 1041.390 Puritan‘s Pride/普丽普莱 厂名:Puritan's Pride||品牌:Puritan‘s Pride/普丽普莱||生...
1 2020-11-01 4224064440294 天猫国际进口超市 FANCL男40岁综合营养包30包/袋*2复合多种维生素日本芳珂fancl (【直营】日本FANCL/芳珂 40岁男性综合维生素营养 30袋（30天量）)*2 500.000000 71 9折 31950.000 FANCL 品牌:FANCL||系列:综合营养包||生产企业:FANCL株式会社
2 2021-12-01 3437759489038 阿里健康大药房 民生21金维他多维元素片30片儿童男女成人补充维生素矿物质缺乏 国民大牌| 补充21种维矿 22.416667 366 9.5折 7794.275 21金维他 产品名称:21金维他 多维元素片（21） 30片*1瓶/盒||药品通用名:多维元素片（21）...
3 2021-12-01 576002931540 天猫国际进口超市 【直营】美国进口安利简装倍立健多种复合维生素矿物质营养186片 NaN 452.250000 28 9.5折 12029.850 AMWAY/安利 厂名:Amway Dominican Republic LLC||生产企业:Amway||系...
4 2021-03-01 4363644898037 阿里健康大药房 买2送礼】elevit/爱乐维复合维生素片140粒维生素缺乏症贫血官方 爱乐维玛咖锌淫羊藿胶囊140粒+爱乐维 复合维生素片30片/盒 371.666667 32 9折 10704.000 elevit/爱乐维 品牌:elevit/爱乐维||药品名称:复合维生素片||药品通用名:复合维生素片||生产企业...
... ... ... ... ... ... ... ... ... ... ... ...
72553 2021-03-01 3435079967513 阿里健康大药房 恒健维生素C片VC 维C100片补充维生素C妊娠呕吐发少发黄vc片药用 标准装1盒 6.000000 33712 9折 182044.800 恒健 药品名称:维生素C片||生产企业:广东恒健制药有限公司||规格:100mg*100片/片||...
72554 2021-10-01 568442923286 阿里健康大药房海外店 日本参天维生素B12+E眼药水*12ml 滴眼液 缓解眼疲劳 眼疾预防 NaN 60.000000 647 9折 34938.000 参天 品牌:Santen/参天||药品通用名:滴眼液||生产企业:参天製薬株式会社||药品规格:1...
72555 2020-03-01 534742130942 hihealth海外专营店 【3倍购买】美国GNC综合营养包30天套装 含钙片/DHA/维生素 NaN 201.000000 41 9折 7416.900 GNC/健安喜 系列:维生素矿物质精华(孕妇)||厂名:General Nutrition Corporat...
72556 2020-04-01 3733029515023 阿里健康大药房 星鲨维生素E软胶囊100mg*60粒/盒心脑血管疾病 流产 不孕症 本品5盒 优惠装 节省2.5元 73.500000 119 9折 7871.850 星鲨 规格:100mg*60粒/盒||生产企业:国药控股星鲨制药（厦门）有限公司||产品名称:星鲨...
72557 2020-08-01 4068429729290 阿里健康大药房 力生 复合维生素B片100片/盒 维生素缺乏症脚气矿物质缺乏症厌食 标准装 7.400000 2974 9折 19806.840 力生 产品名称:力生 复合维生素B片 100片/盒||生产企业:天津力生制药股份有限公司||药品通...
72558 rows × 11 columnsdata["brand"].value_counts().head(10)
---------------------------------------------------
BY－HEALTH/汤臣倍健 5675
CONBA/康恩贝 4196
GNC/健安喜 3967
CENTRUM/善存 3394
swisse 3205
汤臣倍健 3186
养生堂 2939
Puritan‘s Pride/普丽普莱 1907
Jamieson/健美生 1841
NOW/诺奥 1546
Name: brand, dtype: int64total_brand= data.groupby("brand")["total_price"].sum().reset_index()
total_brand.sort_values("total_price",inplace=True, ascending=False)
total_brand
-----------------------------------------------------------------------
brand total_price
317 swisse 2.821440e+08
55 CONBA/康恩贝 1.226368e+08
51 CENTRUM/善存 1.128456e+08
284 elevit/爱乐维 1.008569e+08
423 星鲨 9.825920e+07
... ... ...
45 Botanic Choice 8.550000e+01
370 吉医堂 8.000000e+01
186 OUT OF AFRICA 5.015000e+01
435 永昇金维 4.940000e+01
521 难老泉 4.200000e+01
530 rows × 2 columns任务3.2 药品品牌销售额占比
total=total_brand["total_price"].sum()
ten_total_brand=total_brand.head(10)
ten_total_brand
------------------------------------------------
brand total_price
317 swisse 2.821440e+08
55 CONBA/康恩贝 1.226368e+08
51 CENTRUM/善存 1.128456e+08
284 elevit/爱乐维 1.008569e+08
423 星鲨 9.825920e+07
339 伊可新 5.991355e+07
355 养生堂 5.900897e+07
33 BY－HEALTH/汤臣倍健 4.588620e+07
85 FANCL 3.761334e+07
425 朗迪 3.281707e+07brand_sold_percent=[]
for i in range(len(ten_total_brand["brand"])):
brand_sold_percent.append(0)
for i in range(len(ten_total_brand["brand"])):
brand_sold_percent[i]=list(total_brand["total_price"])[i]/total
brand_sold_percent
-----------------------------------------------------------------------
[0.18973373776485433,
0.08246969958533347,
0.07588545618742933,
0.0678233886888395,
0.06607649132273377,
0.04029014581431487,
0.03968184231814414,
0.030857155568868446,
0.02529389012975293,
0.022068541829650644]
plt.figure(figsize=(8,6),dpi=100)
x_10=["swisse","CONBA/康恩贝","CENTRUM/善存","elevit/爱乐维","星鲨","伊可新","养生堂","BY－HEALTH/汤臣倍健","FANCL","朗迪"]
y_10=[18.97,8.25,7.59,6.78,6.61,4.03,3.97,3.09,2.53,2.21]
plt.barh(x_10, y_10, color='cyan')
plt.xlabel('品牌销售额占比 单位：%')
plt.ylabel('品牌')
plt.title('销售额占比最高的十大品牌销售额占比')
plt.show()
任务3.3 药品品牌销售分析
data_pp1=data[data["brand"]=="swisse"]
data_pp1["discount"].value_counts()
----------------------------------------------
data_pp1["price"].mean() #238.76599858001455
----------------------------------------------
top1_sold=data_pp1.sort_values("sold" ,ascending=False)
top1_sold.head(10)
----------------------------------------------
top1_total_price=data_pp1.sort_values("total_price" ,ascending=False)
top1_total_price.head(10)data_pp2=data[data["brand"]=="CONBA/康恩贝"]
data_pp2["discount"].value_counts()
------------------------------------------------
data_pp2["price"].mean() #57.49846914842359
-----------------------------------------------------
top2_sold=data_pp2.sort_values("sold" ,ascending=False)
top2_sold.head(10)
-----------------------------------------------------
top2_total_price=data_pp2.sort_values("total_price" ,ascending=False)
top2_total_price.head(10)data_pp3=data[data["brand"]=="CENTRUM/善存"]
data_pp3["discount"].value_counts()
--------------------------------------------------
data_pp3["price"].mean()
--------------------------------------------------
top3_sold=data_pp3.sort_values("sold" ,ascending=False)
top3_sold.head(10)
--------------------------------------------------
top3_total_price=data_pp3.sort_values("total_price" ,ascending=False)
top3_total_price.head(10)data_pp4=data[data["brand"]=="elevit/爱乐维"]
data_pp4["discount"].value_counts()
----------------------------------------------------
data_pp4["price"].mean()
----------------------------------------------------
top4_sold=data_pp4.sort_values("sold" ,ascending=False)
top4_sold.head(10)
----------------------------------------------------
top4_total_price=data_pp4.sort_values("total_price" ,ascending=False)
top4_total_price.head(10)
data_pp5=data[data["brand"]=="星鲨"]
data_pp5["discount"].value_counts()
-------------------------------------------------
data_pp5["price"].mean()
-------------------------------------------------
top5_sold=data_pp5.sort_values("sold" ,ascending=False)
top5_sold.head(10)
-------------------------------------------------
top5_total_price=data_pp5.sort_values("total_price" ,ascending=False)
top5_total_price.head(10)data_pp6=data[data["brand"]=="伊可新"]
data_pp6["discount"].value_counts()
---------------------------------------
data_pp6["price"].mean()
---------------------------------------
top6_sold=data_pp6.sort_values("sold" ,ascending=False)
top6_sold.head(10)
---------------------------------------
top6_total_price=data_pp6.sort_values("total_price" ,ascending=False)
top6_total_price.head(10)data_pp7=data[data["brand"]=="养生堂"]
data_pp7["discount"].value_counts()
------------------------------------------
data_pp7["price"].mean()
----------------------------------------
top7_sold=data_pp7.sort_values("sold" ,ascending=False)
top7_sold.head(10)
----------------------------------------
top7_total_price=data_pp7.sort_values("total_price" ,ascending=False)
top7_total_price.head(10)data_pp8=data[data["brand"]=="BY－HEALTH/汤臣倍健"]
data_pp8["discount"].value_counts()
------------------------------------------------
data_pp8["price"].mean()
------------------------------------------------
top8_sold=data_pp8.sort_values("sold" ,ascending=False)
top8_sold.head(10)
------------------------------------------------
top8_total_price=data_pp8.sort_values("total_price" ,ascending=False)
top8_total_price.head(10)data_pp9=data[data["brand"]=="FANCL"]
data_pp9["discount"].value_counts()
---------------------------------------------
data_pp9["price"].mean()
---------------------------------------------
top9_sold=data_pp9.sort_values("sold" ,ascending=False)
top9_sold.head(10)
---------------------------------------------
top9_total_price=data_pp9.sort_values("total_price" ,ascending=False)
top9_total_price.head(10)data_pp10=data[data["brand"]=="朗迪"]
data_pp10["discount"].value_counts()
---------------------------------------------
data_pp10["price"].mean()
---------------------------------------------
top10_sold=data_pp10.sort_values("sold" ,ascending=False)
top10_sold.head(10)
---------------------------------------------
top10_total_price=data_pp10.sort_values("total_price" ,ascending=False)
top10_total_price.head(10)plt.figure(figsize=(20,18))
ax=plt.subplot(3,4,1)
labels_1 = [u"9折",u"9.5折",u"8.5折",u"无打折"]
#每个标签占多大，会自动去算百分比
sizes_1 = [2037,805,336,27]
colors = ['orange','yellowgreen','lightskyblue','yellow']
#将某部分爆炸出来， 使用括号，将第一块分割出来，数值的大小是分割出来的与其他两块的间隙
explode = (0.05,0,0,0)
patches,l_text,p_text = plt.pie(sizes_1,explode=explode,labels=labels_1,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
# 设置x，y轴刻度一致，这样饼图才能是圆的
plt.axis('equal')
plt.title('swisse', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,2)
labels_2 = [u"9.5折",u"9折",u"8.5折",u"无打折"]
sizes_2 = [2366,1384,311,135]
colors = ['orange','yellowgreen','lightskyblue','yellow']
explode = (0.05,0,0,0)
patches,l_text,p_text = plt.pie(sizes_2,explode=explode,labels=labels_2,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('CONBA/康恩贝', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,3)
labels_3 = [u"9.5折",u"9折",u"8.5折",u"无打折"]
sizes_3 = [1699,1403,137,155]
colors = ['orange','yellowgreen','lightskyblue','yellow']
explode = (0.05,0,0,0)
patches,l_text,p_text = plt.pie(sizes_3,explode=explode,labels=labels_3,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('CENTRUM/善存', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,4)
labels_4 = [u"9.5折",u"9折",u"无打折"]
sizes_4 = [266,127,10]
colors = ['orange','yellowgreen','lightskyblue','yellow']
explode = (0.05,0,0)
patches,l_text,p_text = plt.pie(sizes_4,explode=explode,labels=labels_4,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('elevit/爱乐维', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,5)
labels_5 = [u"9.5折",u"9折",u"无打折"]
sizes_5= [812,216,7]
colors = ['orange','yellowgreen','lightskyblue','yellow']
explode = (0.05,0,0)
patches,l_text,p_text = plt.pie(sizes_5,explode=explode,labels=labels_5,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('星鲨', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,6)
labels_6 = [u"9.5折",u"9折",u"无打折"]
sizes_6= [250,219,1]
colors = ['orange','yellowgreen','blue']
explode = (0.05,0,0)
patches,l_text,p_text = plt.pie(sizes_6,explode=explode,labels=labels_6,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('伊可新', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,7)
labels_7 = [u"9折",u"9.5折",u"8.5折",u"无打折"]
sizes_7= [1434,1155,254,96]
colors = ['orange','yellowgreen','lightskyblue','yellow']
explode = (0.05,0,0,0)
patches,l_text,p_text = plt.pie(sizes_7,explode=explode,labels=labels_7,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('养生堂', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,8)
labels_8 = [u"9.5折",u"9折",u"8.5折",u"无打折"]
sizes_8= [2403,2129,967,176]
colors = ['orange','yellowgreen','lightskyblue','yellow']
explode = (0.05,0,0,0)
patches,l_text,p_text = plt.pie(sizes_8,explode=explode,labels=labels_8,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('BY－HEALTH/汤臣倍健', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,9)
labels_9 = [u"9折",u"9.5折",u"8.5折",u"无打折"]
sizes_9= [657,219,170,2]
colors = ['orange','yellowgreen','lightskyblue','blue']
explode = (0.05,0,0,0)
patches,l_text,p_text = plt.pie(sizes_9,explode=explode,labels=labels_9,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('FANCL', size=15)
plt.legend(loc=2)
ax=plt.subplot(3,4,10)
labels_10 = [u"9.5折",u"9折",u"无打折"]
sizes_10= [59,32,1]
colors = ['orange','yellowgreen','lightskyblue','yellow']
explode = (0.05,0,0)
patches,l_text,p_text = plt.pie(sizes_10,explode=explode,labels=labels_10,colors=colors,
labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6)
for t in l_text:
t.set_size(15)
for t in p_text:
t.set_size(15)
plt.axis('equal')
plt.title('朗迪', size=15)
plt.legend(loc=2)
plt.show()
任务4
预测天猫维生素类药品未来三个月的销售总额并绘制拟合曲线，评估模型性能和误差。
import pandas as pd
data=pd.read_excel('F:\\比赛\\2022全国大学生数据分析大赛\\2022全国大学生数据分析A题\\data\\new_data.xlsx')
total_month= data.groupby("date_time")["total_price"].sum().reset_index()
total_month
----------------------------------------------
date_time total_price
0 2020-01-01 2.907732e+07
1 2020-02-01 2.402555e+07
2 2020-03-01 3.883273e+07
3 2020-04-01 3.378211e+07
4 2020-05-01 3.248638e+07
5 2020-06-01 7.414630e+07
6 2020-07-01 3.305252e+07
7 2020-08-01 4.263468e+07
8 2020-09-01 4.516212e+07
9 2020-10-01 3.485498e+07
10 2020-11-01 4.926924e+07
11 2020-12-01 9.077518e+07
12 2021-01-01 5.548417e+07
13 2021-02-01 5.734087e+07
14 2021-03-01 7.462681e+07
15 2021-04-01 6.681211e+07
16 2021-05-01 6.643281e+07
17 2021-06-01 1.210244e+08
18 2021-07-01 6.617255e+07
19 2021-08-01 7.787211e+07
20 2021-09-01 8.814363e+07
21 2021-10-01 8.217563e+07
22 2021-11-01 8.188439e+07
23 2021-12-01 1.343489e+08import matplotlib.pyplot as plt
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.figure(figsize=(15,6))
x=["2020-01","2020-02","2020-03","2020-04","2020-05","2020-06","2020-07","2020-08","2020-09","2020-10","2020-11","2020-12",
"2021-01","2021-02","2021-03","2021-04","2021-05","2021-06","2021-07","2021-08","2021-09","2021-10","2021-11","2021-12"]
y=total_month["total_price"]
plt.plot(x, y, color='blue')
plt.scatter(x, y, marker='o',label='真实值')
plt.xlabel('时间')
plt.xticks(x,rotation=70)
plt.ylabel('当月销售总额')
plt.title('天猫维生素类药品2020-2021月销售总额')
plt.show()
x=range(1,25)
y=total_month["total_price"]
data_1 = pd.DataFrame({'month':x,
'total':y})
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
import pandas as pd
x=data_1['month']
y=data_1['total']
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.8,random_state=420)
x_train=np.array(x_train)
x_train=x_train.reshape(-1,1)
x_test=np.array(x_test)
x_test=x_test.reshape(-1,1)# 逻辑回归
from sklearn.linear_model import LogisticRegression
# k近邻回归
from sklearn.neighbors import KNeighborsRegressor
# 梯度提升回归，随机森林回归
from sklearn.ensemble import GradientBoostingRegressor,RandomForestRegressor
import numpy as np
from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error
def predict(ml_model):
print("Model is: ", ml_model)
model = ml_model.fit(x_train, y_train)
print("Training score: ", model.score(x_train,y_train))
print("Testing score: ", model.score(x_test,y_test))
predictions = model.predict(x_train)
new_predictions1=model.predict(x_test)
print(new_predictions1)
print(x_test1[0],model.predict([x_test1[0]]))
print(x_test1[1],model.predict([x_test1[1]]))
print(x_test1[2],model.predict([x_test1[2]]))
print("Predictions: ", predictions)
print("未来三个月: ", new_predictions1)
print('-----------------')
r2score = r2_score(y_train, predictions)
print("r2 score is: ", r2score)
print('MAE:{}', mean_absolute_error(y_train,predictions))
print('MSE:{}', mean_squared_error(y_train,predictions))
print('RMSE:{}', np.sqrt(mean_squared_error(y_train,predictions)))predict(RandomForestRegressor(n_estimators=1))Model is: RandomForestRegressor(n_estimators=1)
Training score: 0.5991487172457902
Testing score: 0.42010461927846376
[33052515.35550007 33052515.35550007 45162116.98549996 45162116.98549996
45162116.98549996 45162116.98549996 33052515.35550007 77872105.11344971
33052515.35550007 77872105.11344971 33052515.35550007 77872105.11344971
77872105.11344971 33052515.35550007 33052515.35550007 77872105.11344971
77872105.11344971 77872105.11344971 45162116.98549996 77872105.11344971]
[25] [77872105.11344971]
[26] [77872105.11344971]
[27] [77872105.11344971]
Predictions: [77872105.11344971 33052515.35550007 45162116.98549996 77872105.11344971]
未来三个月: [33052515.35550007 33052515.35550007 45162116.98549996 45162116.98549996
45162116.98549996 45162116.98549996 33052515.35550007 77872105.11344971
33052515.35550007 77872105.11344971 33052515.35550007 77872105.11344971
77872105.11344971 33052515.35550007 33052515.35550007 77872105.11344971
77872105.11344971 77872105.11344971 45162116.98549996 77872105.11344971]
-----------------
r2 score is: 0.5991487172457902
MAE:{} 10788062.82364067
MSE:{} 465529197947271.7
RMSE:{} 21576125.64728134
predict(KNeighborsRegressor()) Model is: KNeighborsRegressor()
Training score: 0.6370239169965028
Testing score: 0.3394163469624
[40508925.39704183 40508925.39704183 75633066.65863588 60460618.77413338
60460618.7741334 46823311.32433338 46823311.32433337 84057002.94282563]
[25] [92884930.5029359]
[26] [92884930.5029359]
[27] [92884930.5029359]
Predictions: [40508925.39704183 81353638.70452581 43725883.86203337 81353638.70452581
92884930.5029359 40508925.39704183 40508925.39704183 92884930.5029359
92884930.5029359 92884930.5029359 53784559.06508334 75633066.65863587
87129707.09845188 43725883.86203337 46823311.32433338 84057002.94282563]
未来三个月: [40508925.39704183 40508925.39704183 75633066.65863588 60460618.77413338
60460618.7741334 46823311.32433338 46823311.32433337 84057002.94282563]
-----------------
r2 score is: 0.6370239169965028
MAE:{} 13628745.447811129
MSE:{} 322974512565174.06
RMSE:{} 17971491.662218083
predict(GradientBoostingRegressor())Model is: GradientBoostingRegressor()
Training score: 0.9999325937942306
Testing score: -0.7114452499656005
[3.35533345e+07 2.92082264e+07 7.41115578e+07 4.92738241e+07
4.92738241e+07 4.52227237e+07 3.34846423e+07 1.20764272e+08]
[25] [1.34212591e+08]
[26] [1.34212591e+08]
[27] [1.34212591e+08]
Predictions: [2.92082264e+07 6.68768505e+07 7.38043656e+07 6.66349711e+07
1.34212591e+08 3.35533345e+07 3.27093976e+07 8.22873770e+07
8.79706007e+07 8.20561062e+07 4.92738241e+07 7.41115578e+07
7.81258889e+07 3.34846423e+07 4.52227237e+07 1.20764272e+08]
未来三个月: [3.35533345e+07 2.92082264e+07 7.41115578e+07 4.92738241e+07
4.92738241e+07 4.52227237e+07 3.34846423e+07 1.20764272e+08]
-----------------
r2 score is: 0.9999325937942306
MAE:{} 206922.37756619183
MSE:{} 59977743635.64004
RMSE:{} 244903.53945102557
x_1=range(1,28)
y_1=list(y_train)
y_1.append(1.33865662e+08)
y_1.append(1.33865662e+08)
y_1.append(1.33865662e+08)
x_2=[25,26,27]
y_2=[1.33865662e+08,1.33865662e+08,1.33865662e+08]
plt.figure(figsize=[12,6])
plot1 = plt.plot(x, y, '*',c="blue",label='原始数据')
plt.plot(x_2,y_2,'o',c='red',label='未来三个月')
plot2 = plt.plot(x_1, y_1, 'black',label='拟合曲线')
plt.xlabel("月份")
plt.xticks(x_1)
plt.ylabel('y')
plt.legend(loc=4,prop = {'size':18}) #指定legend的位置右下角
plt.title('多项式拟合')
plt.show()
任务5
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总结
1）本次比赛的代码过于冗杂，很多地方都可以进行优化
2）论文整体质量比上一次参加稍有进步
最后插个小广告hhh（适合数据分析初学者&入门者）
（出售本次比赛的论文、源程序、支撑材料，仅供学习使用，仅供学习使用，仅供学习使用）
