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Most popular dog names

The top 1,000 dog names across the pet license records we use.

Names 301–400

Share = about how many in every 1,000 licensed dogs

  1. Booabout 1 in 1,000
  2. Avaabout 1 in 1,000
  3. Jasmineabout 1 in 1,000
  4. Clementineabout 1 in 1,000
  5. Felixabout 1 in 1,000
  6. Brandyabout 1 in 1,000
  7. Misoabout 1 in 1,000
  8. Lincolnabout 1 in 1,000
  9. Gretaabout 1 in 1,000
  10. Delilahabout 1 in 1,000
  11. Aliceabout 1 in 1,000
  12. Jessieabout 1 in 1,000
  13. Zekeabout 1 in 1,000
  14. Russellabout 1 in 1,000
  15. Woodyabout 1 in 1,000
  16. Cricketabout 1 in 1,000
  17. Brutusabout 1 in 1,000
  18. Kirbyabout 1 in 1,000
  19. Aryaabout 1 in 1,000
  20. Juneabout 1 in 1,000
  21. Graceabout 1 in 1,000
  22. Chewieabout 1 in 1,000
  23. Pumpkinabout 1 in 1,000
  24. Pebblesabout 1 in 1,000
  25. Milesabout 1 in 1,000
  26. Cassieabout 1 in 1,000
  27. Snickersabout 1 in 1,000
  28. Elsaabout 1 in 1,000
  29. Gemmaunder 1 in 1,000
  30. Tysonunder 1 in 1,000
  31. Bernieunder 1 in 1,000
  32. Banjounder 1 in 1,000
  33. Percyunder 1 in 1,000
  34. Blazeunder 1 in 1,000
  35. Ryderunder 1 in 1,000
  36. Reeseunder 1 in 1,000
  37. Atlasunder 1 in 1,000
  38. Billieunder 1 in 1,000
  39. Summerunder 1 in 1,000
  40. Sullyunder 1 in 1,000
  41. Sonnyunder 1 in 1,000
  42. Noraunder 1 in 1,000
  43. Sawyerunder 1 in 1,000
  44. Starunder 1 in 1,000
  45. Tacounder 1 in 1,000
  46. Emmyunder 1 in 1,000
  47. Ghostunder 1 in 1,000
  48. Kumaunder 1 in 1,000
  49. Dottieunder 1 in 1,000
  50. Whiskeyunder 1 in 1,000
  51. Katieunder 1 in 1,000
  52. Cinnamonunder 1 in 1,000
  53. Sydneyunder 1 in 1,000
  54. Rooneyunder 1 in 1,000
  55. Clydeunder 1 in 1,000
  56. Muffinunder 1 in 1,000
  57. Tonyunder 1 in 1,000
  58. Maxwellunder 1 in 1,000
  59. Dashunder 1 in 1,000
  60. Ernieunder 1 in 1,000
  61. Tillieunder 1 in 1,000
  62. Bluunder 1 in 1,000
  63. Roseunder 1 in 1,000
  64. Bubbaunder 1 in 1,000
  65. Heidiunder 1 in 1,000
  66. Herculesunder 1 in 1,000
  67. Benunder 1 in 1,000
  68. Hopeunder 1 in 1,000
  69. Peteyunder 1 in 1,000
  70. Darwinunder 1 in 1,000
  71. Happyunder 1 in 1,000
  72. Kayaunder 1 in 1,000
  73. Barleyunder 1 in 1,000
  74. Spikeunder 1 in 1,000
  75. Caseyunder 1 in 1,000
  76. Harveyunder 1 in 1,000
  77. Bradyunder 1 in 1,000
  78. Samsonunder 1 in 1,000
  79. Titounder 1 in 1,000
  80. Libbyunder 1 in 1,000
  81. Noodleunder 1 in 1,000
  82. Sassyunder 1 in 1,000
  83. Pixieunder 1 in 1,000
  84. Archerunder 1 in 1,000
  85. Ricounder 1 in 1,000
  86. Theodoreunder 1 in 1,000
  87. Captainunder 1 in 1,000
  88. Colbyunder 1 in 1,000
  89. Willyunder 1 in 1,000
  90. Osounder 1 in 1,000
  91. Sophiaunder 1 in 1,000
  92. Buttercupunder 1 in 1,000
  93. Artemisunder 1 in 1,000
  94. Madisonunder 1 in 1,000
  95. Shilohunder 1 in 1,000
  96. Ralphunder 1 in 1,000
  97. Scoobyunder 1 in 1,000
  98. Oliviaunder 1 in 1,000
  99. Pippiunder 1 in 1,000
  100. Barkleyunder 1 in 1,000

About the data

We combine pet license records from 4 US cities and counties into one ranking. Each place counts equally, so one big city can't drown out the rest. From each record we keep only the name, species and breed. We tidy spelling and capitals, drop entries that are not names, and count.

Sources and licenses
PlacePublisherLicenseRecordsLast updatedRetrieved
New York, NYNYC Department of Health and Mental Hygiene, via NYC Open DataNYC Open Data, no restrictions on use92,949 dogs2026-07-152026-09-30
Seattle, WACity of Seattle, via data.seattle.govPublic domain26,211 dogs, 12,921 cats2026-07-072026-09-30
Cambridge, MACity of Cambridge Animal Commission, via the Cambridge Open Data PortalPublic domain (PDDL)27,436 dogs2026-09-282026-09-30
Allegheny County, PAAllegheny County, via the Western Pennsylvania Regional Data Center (WPRDC)Public domain (CC0)62,547 dogs2026-09-082026-09-30
209,143 dogs, 12,921 cats

Baby names: Social Security Administration, national baby name data, 2005 to 2025 (public domain). It lists every name given to at least 5 babies of a sex in a year; shares are of those babies.

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