如何平滑pandas/matplotlib线图？

Question

我有以下df，每周在多家商店消费：

             shop1    shop2    shop3    shop4    shop5    shop6    shop7  \
date_week                                                                  
2          4328.85  5058.17  3028.68  2513.28  4204.10  1898.26  2209.75   
3          5472.00  5085.59  3874.51  1951.60  2984.71  1416.40  1199.42   
4          4665.53  4264.05  2781.70  2958.25  4593.46  2365.88  2079.73   
5          5769.36  3460.79  3072.47  1866.19  3803.12  2166.84  1716.71   
6          6267.00  4033.58  4053.70  2215.04  3991.31  2382.02  1974.92   
7          5436.83  4402.83  3225.98  1761.87  4202.22  2430.71  3091.33   
8          4850.43  4900.68  3176.00  3280.95  3483.53  4115.09  2594.01   
9          6782.88  3800.03  3865.65  2221.43  4116.28  2638.28  2321.55   
10         6248.18  4096.60  5186.52  3224.96  3614.24  2541.00  2708.36   
11         4505.18  2889.33  2937.74  2418.34  5565.57  1570.55  1371.54   
12         3115.26  1216.82  1759.49  2559.81  1403.61  1550.77   478.34   
13         4561.82   827.16  4661.51  3197.90  1515.63  1688.57   247.25   

             shop8    shop9  
date_week                    
2          3578.81  3134.39  
3          4625.10  2676.20  
4          3417.16  3870.00  
5          3980.78  3439.60  
6          3899.42  4192.41  
7          4190.60  3989.00  
8          4786.40  3484.51  
9          6433.02  3474.66  
10         4414.19  3809.20  
11         3590.10  3414.50  
12         4297.57  2094.00  
13         3963.27   871.25  

如果我把这些画成直线图或“意大利面图”，效果很好

我们的目标是观察过去三个月9家商店每周销售额的趋势

但是看起来有点乱：

newgraph.plot()

我看了类似的问题，比如使用df.interpolate()的this one，但看起来我需要先在其中包含缺失的值this answer似乎需要一个时间序列

有没有其他方法来平滑线条

不管这些值是否不再精确，一些插值是可以的。我感兴趣的是过去几周的趋势。我还尝试了logy=True中的plot()方法来稍微平静一些，但没有帮助

我的df，对于pd.DataFrame.fromt_dict()：

{'shop1': {2: 4328.849999999999,
  3: 5472.0,
  4: 4665.530000000001,
  5: 5769.36,
  6: 6267.0,
  7: 5436.83,
  8: 4850.43,
  9: 6782.879999999999,
  10: 6248.18,
  11: 4505.18,
  12: 3115.26,
  13: 4561.82},
 'shop2': {2: 5058.169999999993,
  3: 5085.589999999996,
  4: 4264.049999999997,
  5: 3460.7899999999977,
  6: 4033.579999999998,
  7: 4402.829999999999,
  8: 4900.679999999997,
  9: 3800.0299999999997,
  10: 4096.5999999999985,
  11: 2889.3300000000004,
  12: 1216.8200000000002,
  13: 827.16},
 'shop3': {2: 3028.679999999997,
  3: 3874.5099999999984,
  4: 2781.6999999999994,
  5: 3072.4699999999984,
  6: 4053.6999999999966,
  7: 3225.9799999999987,
  8: 3175.9999999999973,
  9: 3865.6499999999974,
  10: 5186.519999999996,
  11: 2937.74,
  12: 1759.49,
  13: 4661.509999999998},
 'shop4': {2: 2513.2799999999997,
  3: 1951.6000000000001,
  4: 2958.25,
  5: 1866.1900000000003,
  6: 2215.04,
  7: 1761.8700000000001,
  8: 3280.9499999999994,
  9: 2221.43,
  10: 3224.9600000000005,
  11: 2418.3399999999997,
  12: 2559.8099999999995,
  13: 3197.9},
 'shop5': {2: 4204.0999999999985,
  3: 2984.71,
  4: 4593.459999999999,
  5: 3803.12,
  6: 3991.31,
  7: 4202.219999999999,
  8: 3483.529999999999,
  9: 4116.279999999999,
  10: 3614.24,
  11: 5565.569999999997,
  12: 1403.6100000000001,
  13: 1515.63},
 'shop6': {2: 1898.260000000001,
  3: 1416.4000000000005,
  4: 2365.8799999999997,
  5: 2166.84,
  6: 2382.019999999999,
  7: 2430.71,
  8: 4115.0899999999965,
  9: 2638.2800000000007,
  10: 2541.0,
  11: 1570.5500000000004,
  12: 1550.7700000000002,
  13: 1688.5700000000004},
 'shop7': {2: 2209.75,
  3: 1199.42,
  4: 2079.7300000000005,
  5: 1716.7100000000005,
  6: 1974.9200000000005,
  7: 3091.329999999999,
  8: 2594.0099999999993,
  9: 2321.5499999999997,
  10: 2708.3599999999983,
  11: 1371.5400000000004,
  12: 478.34,
  13: 247.25000000000003},
 'shop8': {2: 3578.8100000000004,
  3: 4625.1,
  4: 3417.1599999999994,
  5: 3980.7799999999997,
  6: 3899.4200000000005,
  7: 4190.600000000001,
  8: 4786.4,
  9: 6433.019999999998,
  10: 4414.1900000000005,
  11: 3590.1,
  12: 4297.57,
  13: 3963.27},
 'shop9': {2: 3134.3900000000003,
  3: 2676.2,
  4: 3870.0,
  5: 3439.6,
  6: 4192.41,
  7: 3989.0,
  8: 3484.51,
  9: 3474.66,
  10: 3809.2,
  11: 3414.5,
  12: 2094.0,
  13: 871.25}}

Answer 1

您可以通过绘制过去几周的回归线来显示趋势，可能与实际数据分开，因为该图已经非常拥挤。我会使用seaborn，因为它具有方便的regplot()函数：

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

df.plot(figsize=[12, 10], style=' ')
plt.xlim(2, 18)

last4 = df[len(df)-4:]

plt.gca().set_prop_cycle(None)
for shop in df.columns:
    sns.regplot(last4.index + 4, shop, data=last4, ci=None, scatter=False)
plt.ylabel(None)
plt.xticks(list(df.index)+[14, 17], labels=list(df.index)+[10, 13]);