在院的课程安排下在大一学完 Python 基础语法后，大二开始了 Python 数据分析的课程学习。
这次的练习题来源于 泰迪杯数据分析比赛2018年B题数睿思官网 : https://www.tipdm.org/bdrace/index.html 个人觉得挺有参考价值的。
数据素材：附件1
需要使用的库：
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| 1.pandas (pip install pandas)
2.datetime (pip install datetime)
3.numpy (pip install numpy)
4.matplotlib (pip install matplotlib)
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数据处理：
题 1 根据附件中的数据，提取每台售货机 A B C D E 对应的销售数据，分别保存为
“task-1A.csv”，”task-1B.csv”，”task-1C.csv”，”task-1D.csv”，”task-1E.csv”
实例代码：
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| import pandas as pd
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
def task1(data1, level):
data = pd.read_csv(r'附件1.csv', encoding='gb2312')
data.支付时间 = pd.to_datetime(data.支付时间, format='%Y/%m/%d')
data.drop(columns='设备ID', axis=1, inplace=True)
data = data.loc[data['地点'] == level, :]
data.drop(columns='地点', axis=1, inplace=True)
data.to_csv(r'D:/Study/Programming/Practice/Python/day50/' +
data1, encoding='gbk')
task1('task1-A.csv', 'A')
task1('task1-B.csv', 'B')
task1('task1-C.csv', 'C')
task1('task1-D.csv', 'D')
task1('task1-E.csv', 'E')
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题 2 根据每台售货机中的数据 2017 年 6月销量前 5 的商品进行分析并绘制柱状图
思路：先提取6月数据，随后对商品进行计算得出销售量，再通过排序对销售量前五的数据进行提取。然后绘制柱状图。
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| data = pd.read_csv(r'附件1.csv', encoding='gbk')
data.支付时间 = pd.to_datetime(data.支付时间, format='%Y/%m/%d')
data['月'] = data['支付时间'].dt.month
data_1 = data.loc[data['月'] == 6]
dalei = data_1['商品'].unique().tolist()
datasum = []
datasem = []
for i in dalei:
data_x = data_1[data_1['商品'] == i]['实际金额'].sum()
data_t = data_1[data_1['商品'] == i]['商品'].size
datasum.append(data_x)
datasem.append(data_t)
task1_2 = pd.DataFrame({'商品': dalei, '总实际金额': datasum,'销售量': datasem})
task1_2.sort_values(by='销售量', ascending=False,inplace=True)
taen = task1_2.head()
taen
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.style.use('ggplot')
plt.bar(x=range(taen.shape[0]),
tick_label=taen.商品,
height=taen.销售量,
color='blue')
plt.ylabel('销售量')
plt.title('6月份商品销售量前五排名')
for x, y in enumerate(taen.销售量):
plt.text(x, y+0.5, '%s' % round(y), ha='center')
plt.show()
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题 3 根据每台售货机每月交易额进行数据分析
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| def task3(data1):
data_2 = pd.read_csv(
r'D:/Study/Programming/Practice/Python/day50/'+data1, encoding='gbk')
data_2.支付时间 = pd.to_datetime(data_2.支付时间, format='%Y/%m/%d')
data_2['月'] = data_2['支付时间'].dt.month
date = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
a = 0
data_vm = []
data_vs = []
data_mone = []
data_sum = []
while a < 12:
a += 1
data_t = data_2.loc[data_2['月'] == a]
data_meny = data_t['实际金额'].sum()
data_xs = data_t['商品'].size
data_vmeny = int(data_meny)/int(data_xs)
data_vxs = int(data_xs)/int(date[a-1])
data_vm.append(data_vmeny)
data_vs.append(data_vxs)
data_mone.append(a)
data_sum.append(data_meny)
task = pd.DataFrame(
{'月份': data_mone, '每月的每单平均交易额': data_vm, '日均订单量': data_vs})
print(task)
return data_sum
sum_A = task3('task1-A.csv')
sum_B = task3('task1-B.csv')
sum_C = task3('task1-C.csv')
sum_D = task3('task1-D.csv')
sum_E = task3('task1-E.csv')
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售货机 A
| 编号 |
月份 |
每月的每单平均交易额 |
日均订单量 |
| 0 |
1 |
4.504478 |
10.806452 |
| 1 |
2 |
3.859649 |
4.071429 |
| 2 |
3 |
3.584314 |
8.225806 |
| 3 |
4 |
4.035794 |
14.900000 |
| 4 |
5 |
4.477513 |
24.387097 |
| 5 |
6 |
4.047334 |
55.633333 |
| 6 |
7 |
4.096639 |
15.354839 |
| 7 |
8 |
3.357357 |
21.483871 |
| 8 |
9 |
4.306731 |
34.666667 |
| 9 |
10 |
4.020447 |
50.483871 |
| 10 |
11 |
4.471552 |
38.666667 |
| 11 |
12 |
3.787818 |
64.612903 |
售货机 B
| 编号 |
月份 |
每月的每单平均交易额 |
日均订单量 |
| 0 |
1 |
3.751366 |
11.806452 |
| 1 |
2 |
3.254054 |
6.607143 |
| 2 |
3 |
3.611321 |
8.548387 |
| 3 |
4 |
4.074627 |
20.100000 |
| 4 |
5 |
4.235903 |
28.032258 |
| 5 |
6 |
4.067888 |
61.866667 |
| 6 |
7 |
4.400000 |
11.129032 |
| 7 |
8 |
3.584098 |
31.645161 |
| 8 |
9 |
4.130086 |
58.166667 |
| 9 |
10 |
4.112043 |
65.354839 |
| 10 |
11 |
4.268341 |
67.700000 |
| 11 |
12 |
3.666968 |
71.290323 |
售货机 C
| 编号 |
月份 |
每月的每单平均交易额 |
日均订单量 |
| 0 |
1 |
4.327177 |
12.225806 |
| 1 |
2 |
3.826087 |
7.392857 |
| 2 |
3 |
3.768061 |
8.483871 |
| 3 |
4 |
4.403270 |
24.466667 |
| 4 |
5 |
4.726236 |
25.451613 |
| 5 |
6 |
4.501594 |
62.733333 |
| 6 |
7 |
3.988220 |
24.645161 |
| 7 |
8 |
3.913423 |
40.612903 |
| 8 |
9 |
4.427294 |
55.933333 |
| 9 |
10 |
4.273014 |
71.483871 |
| 10 |
11 |
4.352033 |
64.766667 |
| 11 |
12 |
3.942833 |
76.741935 |
售货机 D
| 编号 |
月份 |
每月的每单平均交易额 |
日均订单量 |
| 0 |
1 |
3.691120 |
8.354839 |
| 1 |
2 |
3.085106 |
5.035714 |
| 2 |
3 |
4.302083 |
6.193548 |
| 3 |
4 |
3.790068 |
14.766667 |
| 4 |
5 |
4.241135 |
18.193548 |
| 5 |
6 |
4.025962 |
34.666667 |
| 6 |
7 |
4.227129 |
10.225806 |
| 7 |
8 |
3.316084 |
23.064516 |
| 8 |
9 |
3.899288 |
32.766667 |
| 9 |
10 |
3.883642 |
38.258065 |
| 10 |
11 |
3.861983 |
40.333333 |
| 11 |
12 |
3.572459 |
53.645161 |
售货机 E
| 编号 |
月份 |
每月的每单平均交易额 |
日均订单量 |
| 0 |
1 |
4.677966 |
11.419355 |
| 1 |
2 |
3.635659 |
9.214286 |
| 2 |
3 |
4.305714 |
11.290323 |
| 3 |
4 |
4.159777 |
29.833333 |
| 4 |
5 |
4.410991 |
41.677419 |
| 5 |
6 |
3.817586 |
86.433333 |
| 6 |
7 |
3.918819 |
26.225806 |
| 7 |
8 |
3.804188 |
57.000000 |
| 8 |
9 |
4.125302 |
137.800000 |
| 9 |
10 |
3.675909 |
89.580645 |
| 10 |
11 |
4.283068 |
167.333333 |
| 11 |
12 |
4.168819 |
104.903226 |
题 4 根据上面的数据绘制折线图
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| mone = []
a = 0
while a < 12:
a += 1
mone.append(a)
plt.plot( sum_A, linestyle='-', linewidth=2, label='A')
plt.plot( sum_B, linestyle='-', linewidth=2, label='B')
plt.plot( sum_C, linestyle='-', linewidth=2, label='C')
plt.plot( sum_D, linestyle='-', linewidth=2, label='D')
plt.plot( sum_E, linestyle='-', linewidth=2, label='E')
plt.ylabel('交易额')
plt.ylabel('月份')
plt.title('每月交易额折线图')
plt.rcParams['font.sans-serif']='SimHei'
plt.legend()
plt.show()
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