🐾 Name My Pet

Most popular Shiba Inu names

The names real owners gave 4,252 Shiba Inus in public license and shelter records.

Top 100 names

  1. Shiba121
  2. Mochi43
  3. Max39
  4. Yoshi38
  5. Kuma35
  6. Yuki35
  7. Luna35
  8. Bella23
  9. Kobe21
  10. Hiro21
  11. Not21
  12. Rocky20
  13. Hachi20
  14. Charlie20
  15. Foxy19
  16. Momo19
  17. Milo18
  18. Hunter17
  19. Miso17
  20. Mia16
  21. Kitsune16
  22. Leo16
  23. Nala16
  24. Kenji16
  25. Simba15
  26. Ruby15
  27. Toby14
  28. Mika14
  29. Loki14
  30. Lucky14
  31. Oliver13
  32. Cooper13
  33. Niko13
  34. Kiki13
  35. Haru13
  36. Zoe13
  37. Lola12
  38. Lucy12
  39. Coco12
  40. Akira12
  41. Kai12
  42. Keiko11
  43. Bear10
  44. Daisy10
  45. Teddy10
  46. Koda10
  47. Miko10
  48. Ginger10
  49. Suki10
  50. Riley10
  51. Uni10
  52. Kira10
  53. Sheba10
  54. Sammy9
  55. Tofu9
  56. Aiko9
  57. Stella9
  58. Lily9
  59. Ollie9
  60. Aki9
  61. Koji9
  62. Prince9
  63. Yumi9
  64. Sushi9
  65. Bruce9
  66. Bentley8
  67. Kona8
  68. Oscar8
  69. Bailey8
  70. Jack8
  71. Nova8
  72. Cookie8
  73. Penny8
  74. Jasper8
  75. Kiba8
  76. Zeus8
  77. Toshi7
  78. Princess7
  79. Chloe7
  80. Nikko7
  81. Yuna7
  82. Soba7
  83. Shiro7
  84. Taro7
  85. Rex7
  86. Sake7
  87. Athena7
  88. Kenzo7
  89. Benji7
  90. Buddy7
  91. Ace7
  92. Bodhi6
  93. Logan6
  94. Pepper6
  95. Lulu6
  96. Lilo6
  97. Tokyo6
  98. Bambi6
  99. Maya6
  100. Shelby6
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Counts come from public open data: NYC Dog Licensing Dataset (NYC Open Data); Seattle Pet Licenses (public domain); Dogs of Cambridge (ODC PDDL); Licensed Dog and Cat Names, City of Toronto (Open Government Licence – Toronto); Hundenamen, Stadt Zürich (Open Data Zürich); Dog Registrations, City of Adelaide (CC BY 3.0 AU); Burnside Dog Registrations (CC BY 3.0 AU); City of Prospect Dog Registrations (CC BY 3.0 AU); Pet Licenses, City of Richmond; Animal Shelter Intake and Outcome, County of Sonoma; Austin Animal Center Outcomes (public domain). License records count registered pets; shelter records count animals that passed through a shelter (some names are given by staff). Pets that appear in several years of one dataset are counted once. Names are counted as written, so spelling variants are separate.