Most Popular Baby Names In District Of Columbia
The 100 most-used girls' and boys' names among recorded births from District of Columbia, 2016–2025 — 62,785 applications in that window. Below them: the names District of Columbia uses far more than the rest of the country.
Girls’ names
- 1Charlotte4371.6%
- 2Olivia4251.5%
- 3Ava4211.5%
- 4Emma3671.3%
- 5Elizabeth3421.2%
- 6Eleanor3351.2%
- 7Sophia3351.2%
- 8Zoe3251.2%
- 9Maya2951.1%
- 10Sofia2911.0%
- 11Isabella2831.0%
- 12Mia2741.0%
- 13Nora2580.9%
- 14Amelia2500.9%
- 15Naomi2480.9%
- 16Grace2450.9%
- 17Evelyn2410.9%
- 18Abigail2400.9%
- 19Chloe2380.8%
- 20Caroline2210.8%
- 21Genesis2170.8%
- 22Margaret2140.8%
- 23Sophie2040.7%
- 24Josephine2010.7%
- 25Penelope1970.7%
- 26Alice1960.7%
- 27Ella1900.7%
- 28Emilia1900.7%
- 29Lucy1880.7%
- 30Aria1810.6%
- 31Riley1800.6%
- 32Eliana1780.6%
- 33Madison1780.6%
- 34Clara1750.6%
- 35Camila1700.6%
- 36Eva1680.6%
- 37Hannah1670.6%
- 38Lily1670.6%
- 39Skylar1630.6%
- 40Mila1620.6%
- 41Layla1580.6%
- 42Alexandra1570.6%
- 43Claire1570.6%
- 44Victoria1560.6%
- 45Anna1530.5%
- 46Emily1530.5%
- 47Rose1480.5%
- 48Zoey1460.5%
- 49Lillian1440.5%
- 50Madeline1440.5%
- 51Harper1430.5%
- 52Quinn1420.5%
- 53Violet1400.5%
- 54Avery1370.5%
- 55Margot1370.5%
- 56Julia1340.5%
- 57Leah1310.5%
- 58Isla1300.5%
- 59Valentina1290.5%
- 60Eloise1280.5%
- 61Luna1280.5%
- 62Elena1270.5%
- 63Hazel1260.4%
- 64Cora1250.4%
- 65Maeve1240.4%
- 66Bella1230.4%
- 67Aurora1200.4%
- 68Katherine1200.4%
- 69Cecilia1160.4%
- 70Isabel1160.4%
- 71Sloane1140.4%
- 72Zuri1140.4%
- 73Lucia1130.4%
- 74Amira1100.4%
- 75Stella1090.4%
- 76Mary1070.4%
- 77Ruby1060.4%
- 78Serenity1050.4%
- 79Nova1040.4%
- 80Autumn1030.4%
- 81Sydney1020.4%
- 82Ashley1000.4%
- 83Nina990.4%
- 84Scarlett980.3%
- 85Ariana970.3%
- 86Catherine950.3%
- 87Isabelle930.3%
- 88Aaliyah920.3%
- 89Audrey920.3%
- 90Maria920.3%
- 91Sadie920.3%
- 92Ayla910.3%
- 93Summer910.3%
- 94Vivian910.3%
- 95June900.3%
- 96Jane890.3%
- 97Demi870.3%
- 98Genevieve870.3%
- 99Madeleine870.3%
- 100Samantha870.3%
Boys’ names
- 1William6511.9%
- 2Henry6321.8%
- 3James6221.8%
- 4Noah5771.7%
- 5Theodore5081.5%
- 6Liam4881.4%
- 7Benjamin4681.3%
- 8John4461.3%
- 9Alexander4151.2%
- 10Samuel3871.1%
- 11Charles3861.1%
- 12Jack3721.1%
- 13Oliver3711.1%
- 14Leo3701.1%
- 15Ethan3581.0%
- 16Michael3421.0%
- 17Thomas3351.0%
- 18Daniel3280.9%
- 19Dylan3230.9%
- 20Lucas3230.9%
- 21Jacob3220.9%
- 22Julian3010.9%
- 23Sebastian3000.9%
- 24Elijah2860.8%
- 25George2830.8%
- 26Mateo2830.8%
- 27Gabriel2780.8%
- 28David2750.8%
- 29Joseph2720.8%
- 30Aiden2700.8%
- 31Logan2670.8%
- 32Miles2580.7%
- 33Luca2520.7%
- 34Isaac2460.7%
- 35Amir2440.7%
- 36Owen2390.7%
- 37Christopher2270.7%
- 38Mason2230.6%
- 39Matthew2230.6%
- 40Nathan2130.6%
- 41Joshua2100.6%
- 42Anthony2090.6%
- 43Luke2060.6%
- 44Robert2050.6%
- 45Josiah2030.6%
- 46Max2020.6%
- 47Andrew1980.6%
- 48Caleb1930.6%
- 49Ryan1910.5%
- 50Ezra1870.5%
- 51Jackson1860.5%
- 52Adrian1800.5%
- 53Carter1780.5%
- 54Jayden1770.5%
- 55Kai1750.5%
- 56Adam1720.5%
- 57Cameron1720.5%
- 58August1710.5%
- 59Asher1670.5%
- 60Maxwell1670.5%
- 61Christian1650.5%
- 62Arthur1600.5%
- 63Zion1520.4%
- 64Jonathan1510.4%
- 65Micah1500.4%
- 66Isaiah1490.4%
- 67Felix1470.4%
- 68Wesley1470.4%
- 69Eli1460.4%
- 70Aaron1420.4%
- 71Jeremiah1400.4%
- 72Amari1390.4%
- 73Nicholas1380.4%
- 74Simon1380.4%
- 75Chase1370.4%
- 76King1370.4%
- 77Ian1350.4%
- 78Xavier1350.4%
- 79Nathaniel1330.4%
- 80Santiago1330.4%
- 81Connor1320.4%
- 82Messiah1320.4%
- 83Levi1310.4%
- 84Nasir1290.4%
- 85Patrick1250.4%
- 86Nolan1240.4%
- 87Axel1230.4%
- 88Diego1230.4%
- 89Angel1210.3%
- 90Elias1210.3%
- 91Graham1170.3%
- 92Legend1160.3%
- 93Edward1150.3%
- 94Peter1150.3%
- 95Kevin1120.3%
- 96Evan1090.3%
- 97Everett1090.3%
- 98Kayden1080.3%
- 99Oscar1080.3%
- 100Nicolas1070.3%
District of Columbia's signature names
Names used at least 2× as often in District of Columbia as across all states combined, 2016–2025. The multiple compares District of Columbia's share of listed births with the same share pooled across every state — the closest thing to "how District of Columbia does it differently" the data can support.
Girls’ names
- Zora10.6× · 52 recorded
- Dior8.5× · 59 recorded
- Louisa8× · 56 recorded
- Milan6.4× · 50 recorded
- Demi5.7× · 87 recorded
- Madeleine5.5× · 87 recorded
- Beatrice5.3× · 56 recorded
- Nina5× · 99 recorded
- Noa4.9× · 79 recorded
- Mira4.9× · 67 recorded
- Zuri4.7× · 114 recorded
- Margaret4.6× · 214 recorded
- Margot4.6× · 137 recorded
- Frances4.6× · 71 recorded
- Dream4.6× · 61 recorded
- Eve4.6× · 56 recorded
- Virginia4.5× · 50 recorded
- Helen4.4× · 64 recorded
- Wynter4.4× · 61 recorded
- Logan4.2× · 79 recorded
- Alexandra3.9× · 157 recorded
- Catherine3.9× · 95 recorded
- Daphne3.8× · 82 recorded
- Amira3.7× · 110 recorded
- Jane3.7× · 89 recorded
Boys’ names
- Sincere8× · 78 recorded
- Nasir7.3× · 129 recorded
- Samir6.8× · 57 recorded
- Ari4.9× · 93 recorded
- Hugo4.7× · 76 recorded
- Simon4.3× · 138 recorded
- George4.2× · 283 recorded
- Remy4.2× · 72 recorded
- Zyaire4× · 61 recorded
- Kieran3.9× · 51 recorded
- Kairo3.8× · 88 recorded
- Francis3.8× · 54 recorded
- Felix3.6× · 147 recorded
- Amir3.5× · 244 recorded
- Max3.4× · 202 recorded
- Amari3.4× · 139 recorded
- Cairo3.3× · 59 recorded
- Major3.3× · 56 recorded
- King3.1× · 137 recorded
- Khalil3.1× · 65 recorded
- Quinn3.1× · 51 recorded
- Messiah3× · 132 recorded
- Maxwell2.9× · 167 recorded
- Arthur2.9× · 160 recorded
- Patrick2.9× · 125 recorded
All-time favorites, 1910–2025
The most-used names across everything District of Columbia has recorded since 1910 — a very different list, dominated by the high-birth, low-variety decades of the mid-century.
Girls’ names
- 1Mary22,4613.3%
- 2Patricia12,8071.9%
- 3Barbara11,3691.7%
- 4Elizabeth10,2471.5%
- 5Linda9,1371.3%
- 6Margaret9,1191.3%
- 7Karen7,7031.1%
- 8Susan7,6011.1%
- 9Deborah6,9131.0%
- 10Jennifer6,5030.9%
- 11Nancy6,0690.9%
- 12Sharon6,0380.9%
- 13Lisa5,9370.9%
- 14Catherine5,4330.8%
- 15Dorothy5,4200.8%
- 16Michelle5,1900.8%
- 17Donna5,0950.7%
- 18Sandra5,0940.7%
- 19Kathleen5,0810.7%
- 20Carol4,9670.7%
- 21Helen4,7260.7%
- 22Katherine4,7230.7%
- 23Cynthia4,6430.7%
- 24Kimberly4,6380.7%
- 25Carolyn4,5200.7%
- 26Pamela4,4990.7%
- 27Angela4,3400.6%
- 28Sarah4,3160.6%
- 29Diane4,1560.6%
- 30Shirley4,1130.6%
- 31Jacqueline4,0730.6%
- 32Betty3,9990.6%
- 33Joan3,9870.6%
- 34Laura3,9820.6%
- 35Stephanie3,9410.6%
- 36Brenda3,8110.6%
- 37Joyce3,6890.5%
- 38Jean3,5970.5%
- 39Ruth3,4990.5%
- 40Christine3,4980.5%
- 41Ann3,4370.5%
- 42Anne3,3920.5%
- 43Frances3,3660.5%
- 44Janet3,2400.5%
- 45Jessica3,2300.5%
- 46Denise3,2010.5%
- 47Cheryl3,0670.4%
- 48Gloria3,0340.4%
- 49Anna3,0230.4%
- 50Judith2,9700.4%
Boys’ names
- 1John34,6754.3%
- 2James33,2874.1%
- 3Robert29,7063.7%
- 4Michael29,6703.7%
- 5William29,3163.6%
- 6David21,7772.7%
- 7Charles17,3442.1%
- 8Thomas15,3491.9%
- 9Joseph15,0591.9%
- 10Richard14,9421.8%
- 11Christopher11,7181.4%
- 12Anthony10,3331.3%
- 13George9,9301.2%
- 14Kevin9,5541.2%
- 15Daniel9,2561.1%
- 16Paul8,7511.1%
- 17Edward8,2851.0%
- 18Mark8,1981.0%
- 19Kenneth7,7921.0%
- 20Ronald7,7421.0%
- 21Donald7,5920.9%
- 22Stephen7,1230.9%
- 23Brian6,8120.8%
- 24Steven6,8020.8%
- 25Andrew6,6150.8%
- 26Matthew6,5720.8%
- 27Gregory6,5430.8%
- 28Eric6,4430.8%
- 29Timothy5,8240.7%
- 30Jeffrey5,3160.7%
- 31Samuel4,7440.6%
- 32Gary4,6730.6%
- 33Patrick4,6500.6%
- 34Jonathan4,5560.6%
- 35Raymond4,5290.6%
- 36Lawrence4,2890.5%
- 37Peter4,2070.5%
- 38Benjamin4,1850.5%
- 39Alexander4,1560.5%
- 40Henry4,1480.5%
- 41Frank3,8910.5%
- 42Larry3,8420.5%
- 43Jason3,7200.5%
- 44Walter3,5570.4%
- 45Joshua3,5360.4%
- 46Keith3,5160.4%
- 47Nicholas3,2570.4%
- 48Douglas3,2200.4%
- 49Sean3,1950.4%
- 50Aaron3,0040.4%
State data starts in 1910 (national data goes back to 1880) and is censored per state and year: a name needs 5 uses in District of Columbia in a year to appear at all. That censoring means state counts never sum to the national total, and it bites hardest on uncommon names in small states.
Counts are based on U.S. births and are tracked separately for girls and boys. Names used fewer than 5 times in a year aren’t published for that year, so totals here are a floor — the shortfall is largest in the sparse early years.