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  1. date   phone        sensor pallet
  2. 126  2019-04-15  940203  C0382C391A4D     47
  3. 127  2019-04-15  940203  C0382D392A4D     47
  4. 133  2019-04-16  940203  C0382C391A4D     47
  5. 134  2019-04-16  940203  C0382D392A4D     47
  6. 138  2019-04-17  940203  C0382C391A4D     47
  7. 139  2019-04-17  940203  C0382D392A4D     47
  8. 144  2019-04-18  940203  C0382C391A4D     47
  9. 145  2019-04-18  940203  C0382D392A4D     47
  10. 156  2019-04-19  940203  C0382D392A4D     47
  11. 157  2019-04-19  940203  C0382C391A4D     47
  12. 277  2019-04-15  941557  C0392D362735     32
  13. 279  2019-04-15  941557  C03633364D50     32
  14. 286  2019-04-16  941557  C03633364D50     32
  15. 287  2019-04-16  941557  C0392D362735     32
  16. 296  2019-04-17  941557  C03633364D50     32
  17. 297  2019-04-17  941557  C0392D362735     32
  18. 305  2019-04-18  941557  C0392D362735     32
  19. 306  2019-04-18  941557  C03633364D50     32
  20. 317  2019-04-19  941557  C03633364D50     32
  21. 318  2019-04-19  941557  C0392D362735     32
  22. 561  2019-04-15  942316  C0384639224D     45
  23. 562  2019-04-15  942316  C03632364950     45
  24. 563  2019-04-15  942316  C03920363835     45
  25. 564  2019-04-15  942316  C0382939384D     45
  26. 573  2019-04-16  942316  C0382939384D     45
  27. 574  2019-04-16  942316  C0384639224D     45
  28. 575  2019-04-16  942316  C03632364950     45
  29.      
  30. grouped = pallets_arrived.groupby('pallet')
  31.  
  32.         nrows = 2
  33.         ncols = 2
  34.         fig, axs = plt.subplots(nrows, ncols)
  35.  
  36.         targets = zip(grouped.groups.keys(), axs.flatten())
  37.         for i, (key, ax) in enumerate(targets):
  38.             ax.plot_date(grouped.get_group(key)['date'], grouped.get_group(key)['sensor'], 'o')
  39.         plt.show()
  40.         return pallets_arrived
  41.      
  42. grouped = pallets_arrived.groupby('pallet')
  43.         nrows = 2
  44.         ncols = 2
  45.         fig, axs = plt.subplots(nrows, ncols)
  46.  
  47.         targets = zip(grouped.groups.keys(), axs.flatten())
  48.         for i, (key, ax) in enumerate(targets):
  49.             grouped.get_group(key).plot(x='date', y='sensor', ax=ax)
  50.         ax.legend()
  51.         plt.show()
  52.      
  53. grouped = pallets_arrived.set_index('date').groupby('pallet')
  54.     nrows = 2
  55.     ncols = 2
  56.     fig, axs = plt.subplots(nrows, ncols)
  57.  
  58.     targets = zip(grouped.groups.keys(), axs.flatten())
  59.     for i, (key, ax) in enumerate(targets):
  60.         grouped.get_group(key).plot(grouped.get_group(key).index, y='sensor', ax=ax)
  61.     ax.legend()
  62.     plt.show()
  63.      
  64. grouped = pallets_arrived.groupby('pallet')
  65.         nrows = 2
  66.         ncols = 2
  67.         fig, axs = plt.subplots(nrows, ncols)
  68.  
  69.         targets = zip(grouped.groups.keys(), axs.flatten())
  70.         for i, (key, ax) in enumerate(targets):
  71.             plt.sca(ax)
  72.             plt.plot(grouped.get_group(key)['date'], grouped.get_group(key)['sensor'])
  73.         ax.legend()
  74.         plt.show()
  75.      
  76. fig, ax = plt.subplots()
  77. ax.scatter(df['date'], df['sensor'])
  78. plt.show()
  79.      
  80. fig, ax = plt.subplots()
  81. for _,g in df.groupby('pallet'):
  82.     ax.scatter(g['date'], g['sensor'])
  83. plt.show()
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