任务13：绘制折线趋势图、叠加区域图
!!!!本课下载下来的excel附件名字是Order.xlsx. 编辑代码时注意修改！！！
本课代码：
import pandas as pd
import matplotlib.pyplot as plt
weeks = pd.read_excel('C:/Temp/Orders.xlsx', index_col='Week')
print(weeks)
print(weeks.columns)
#weeks.plot.area(y=['Accessories', 'Bikes', 'Clothing', 'Components', 'Grand Total'])
weeks.plot.bar(y=['Accessories', 'Bikes', 'Clothing', 'Components', 'Grand Total'], stacked=True)
plt.title('Sales Weekly Trend', fontsize=16, fontweight='bold')
plt.ylabel('Total', fontsize=12, fontweight='bold')
plt.xticks(weeks.index, fontsize=8)
plt.show()
打印结果：
Accessories Bikes ... Components Grand Total
Week ...
1 9939.465500 2.258337e+06 ... 7.872110e+04 2.356639e+06
2 12626.660000 6.005350e+05 ... 0.000000e+00 6.204234e+05
3 14414.950000 5.547708e+05 ... 0.000000e+00 5.759616e+05
4 12924.580000 5.892557e+05 ... 0.000000e+00 6.083717e+05
5 40443.498516 5.749222e+06 ... 4.709014e+05 6.360041e+06
6 13735.460000 5.539423e+05 ... 0.000000e+00 5.753385e+05
7 13588.800000 6.053847e+05 ... 0.000000e+00 6.247596e+05
8 13997.810000 5.320056e+05 ... 0.000000e+00 5.526938e+05
9 52392.263204 4.701389e+06 ... 6.852023e+05 5.567191e+06
10 14276.640000 5.815496e+05 ... 0.000000e+00 6.037474e+05
11 13584.320000 6.169319e+05 ... 0.000000e+00 6.372021e+05
12 14128.770000 5.985606e+05 ... 0.000000e+00 6.204505e+05
13 34372.148628 5.154563e+06 ... 6.173474e+05 5.899177e+06
14 58097.712659 3.361458e+06 ... 5.296900e+05 4.041540e+06
15 16287.020000 6.655744e+05 ... 0.000000e+00 6.894546e+05
16 15990.960000 6.749113e+05 ... 0.000000e+00 6.991723e+05
17 15120.710000 6.581411e+05 ... 0.000000e+00 6.795171e+05
18 67753.596201 5.739679e+06 ... 1.091392e+06 7.059584e+06
19 15416.040000 7.537814e+05 ... 0.000000e+00 7.770648e+05
20 16113.010000 7.323520e+05 ... 0.000000e+00 7.571492e+05
21 15903.150000 7.380932e+05 ... 0.000000e+00 7.620668e+05
22 57090.139277 4.388471e+06 ... 1.137457e+06 5.746683e+06
23 11900.146000 8.310402e+05 ... 3.152596e+04 8.824418e+05
24 11336.820000 4.095025e+05 ... 0.000000e+00 4.251703e+05
25 10573.210000 4.065446e+05 ... 0.000000e+00 4.232483e+05
26 29376.532664 3.101888e+06 ... 7.994845e+05 4.039316e+06
27 72003.211677 4.932875e+06 ... 1.043760e+06 6.191153e+06
28 11621.900000 4.086479e+05 ... 0.000000e+00 4.258483e+05
29 11640.460000 4.056193e+05 ... 0.000000e+00 4.225788e+05
30 12359.920000 4.156482e+05 ... 0.000000e+00 4.325679e+05
31 81276.364307 5.475372e+06 ... 1.593068e+06 7.363333e+06
32 15208.996500 1.494933e+06 ... 1.025128e+05 1.623856e+06
33 13187.100000 3.938290e+05 ... 0.000000e+00 4.135150e+05
34 13046.980000 4.383911e+05 ... 0.000000e+00 4.571066e+05
35 50187.449516 3.732927e+06 ... 5.842441e+05 4.495606e+06
36 15063.164000 1.173016e+06 ... 6.180437e+04 1.260751e+06
37 11505.920000 4.814773e+05 ... 0.000000e+00 4.985188e+05
38 13170.670000 4.883039e+05 ... 0.000000e+00 5.066807e+05
39 11278.600000 3.675468e+05 ... 0.000000e+00 3.845985e+05
40 70170.520320 7.878476e+06 ... 1.177747e+06 9.303972e+06
41 12441.460000 4.658615e+05 ... 0.000000e+00 4.834701e+05
42 12924.950000 4.715758e+05 ... 0.000000e+00 4.907546e+05
43 13314.310000 4.931541e+05 ... 0.000000e+00 5.127108e+05
44 62745.172581 5.156642e+06 ... 9.100149e+05 6.286165e+06
45 19630.003108 2.199453e+06 ... 1.478740e+05 2.381990e+06
46 14822.180000 7.092621e+05 ... 0.000000e+00 7.304781e+05
47 13728.300000 6.623922e+05 ... 0.000000e+00 6.830832e+05
48 36075.469500 3.240381e+06 ... 2.592590e+05 3.616344e+06
49 13642.418500 1.227307e+06 ... 2.369806e+04 1.273325e+06
50 12388.980000 5.217178e+05 ... 0.000000e+00 5.419306e+05
51 12852.400000 5.264518e+05 ... 0.000000e+00 5.455141e+05
52 13085.680000 5.412245e+05 ... 0.000000e+00 5.604452e+05
53 31315.891268 4.790804e+06 ... 4.568886e+05 5.375680e+06
[53 rows x 5 columns]
Index(['Accessories', 'Bikes', 'Clothing', 'Components', 'Grand Total'], dtype='object')
