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Mar 18th, 2019
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  1. Date Value
  2. 0 2017-07-18 07:40:00 1
  3. 1 2017-07-18 07:45:00 1
  4. 2 2017-07-18 07:50:00 1
  5. 3 2017-07-18 07:55:00 2414
  6. 4 2017-07-18 08:00:00 2
  7. 5 2017-07-18 08:05:00 2
  8. 6 2017-07-18 08:10:00 4416
  9. 7 2017-07-18 08:15:00 4416
  10. 8 2017-07-18 08:20:00 3
  11. 9 2017-07-18 08:25:00 3
  12. 10 2017-07-18 08:30:00 3
  13. 11 2017-07-18 08:35:00 6998
  14.  
  15. df['Value'] = df['Value'].loc[df['Value'].shift() != df['Value']]
  16.  
  17. Date Value
  18. 0 2017-07-18 07:40:00 1.0
  19. 1 2017-07-18 07:45:00 NaN
  20. 2 2017-07-18 07:50:00 NaN
  21. 3 2017-07-18 07:55:00 2414.0
  22. 4 2017-07-18 08:00:00 2.0
  23. 5 2017-07-18 08:05:00 NaN
  24. 6 2017-07-18 08:10:00 4416.0
  25. 7 2017-07-18 08:15:00 NaN
  26. 8 2017-07-18 08:20:00 3.0
  27. 9 2017-07-18 08:25:00 NaN
  28. 10 2017-07-18 08:30:00 NaN
  29. 11 2017-07-18 08:35:00 6998.0
  30.  
  31. Date Value
  32. 0 2017-07-18 07:40:00 NaN
  33. 1 2017-07-18 07:45:00 NaN
  34. 2 2017-07-18 07:50:00 NaN
  35. 3 2017-07-18 07:55:00 2414.0
  36. 4 2017-07-18 08:00:00 2.0
  37. 5 2017-07-18 08:05:00 2.0
  38. 6 2017-07-18 08:10:00 4416.0
  39. 7 2017-07-18 08:15:00 4416.0
  40. 8 2017-07-18 08:20:00 NaN
  41. 9 2017-07-18 08:25:00 NaN
  42. 10 2017-07-18 08:30:00 NaN
  43. 11 2017-07-18 08:35:00 6998.0
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