This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879.
I used BigQuery to gain insight about American names as well as to study BigQuery.
Dataset: bigquery-public-data.usa_names.usa_1910_current
Schema:
Field name |
Type |
Mode |
Description |
state |
STRING |
NULLABLE |
2-digit state code |
gender |
STRING |
NULLABLE |
Sex (M=male or F=female) |
year |
INTEGER |
NULLABLE |
4-digit year of birth |
name |
STRING |
NULLABLE |
Given name of a person at birth |
number |
INTEGER |
NULLABLE |
Number of occurrences of the name |
Insight
- Average length of names over time
SELECT t1.year, male, female
FROM
(SELECT year, AVG(LENGTH(name)) as male
FROM `bigquery-public-data.usa_names.usa_1910_current`
WHERE gender = 'M'
GROUP BY year) t1
INNER JOIN
(SELECT year, AVG(LENGTH(name)) as female
FROM `bigquery-public-data.usa_names.usa_1910_current`
WHERE gender = 'F'
GROUP BY year) t2
on t1.year = t2.year

- Number of distinct name
select count(distinct(name))
from `bigquery-public-data.usa_names.usa_1910_current`
31595
- 10 most common name of all time
select name, sum(number) as count
from `bigquery-public-data.usa_names.usa_1910_current`
where gender = 'M'
group by name
order by count desc
limit 10;
name |
count |
James |
4997327 |
John |
4869607 |
Robert |
4734038 |
Michael |
4349307 |
William |
3890923 |
David |
3597725 |
Richard |
2539873 |
Joseph |
2522812 |
Charles |
2273068 |
Thomas |
2245124 |
name |
count |
Mary |
3741196 |
Patricia |
1569022 |
Elizabeth |
1537684 |
Jennifer |
1466161 |
Linda |
1447943 |
Barbara |
1424221 |
Margaret |
1130920 |
Susan |
1109309 |
Dorothy |
1053390 |
Jessica |
1043442 |
- Most common name of 2018
select name, sum(number) as count
from `bigquery-public-data.usa_names.usa_1910_current`
where year = 2018 and gender = "M"
group by name
order by count desc
limit 1;
Liam 19837
select name, sum(number) as count
from `bigquery-public-data.usa_names.usa_1910_current`
where year = 2018 and gender = "F"
group by name
order by count desc
limit 1;
Emma 18688
- Most common name in each decade
WITH name_decade AS
(SELECT
CASE WHEN CAST(year as STRING) like '188%' THEN '1880-1889'
WHEN CAST(year as STRING) like '189%' THEN '1890-1899'
WHEN CAST(year as STRING) like '190%' THEN '1900-1909'
WHEN CAST(year as STRING) like '191%' THEN '1910-1919'
WHEN CAST(year as STRING) like '192%' THEN '1920-1929'
WHEN CAST(year as STRING) like '193%' THEN '1930-1939'
WHEN CAST(year as STRING) like '194%' THEN '1940-1949'
WHEN CAST(year as STRING) like '195%' THEN '1950-1959'
WHEN CAST(year as STRING) like '196%' THEN '1960-1969'
WHEN CAST(year as STRING) like '197%' THEN '1970-1979'
WHEN CAST(year as STRING) like '198%' THEN '1980-1989'
WHEN CAST(year as STRING) like '199%' THEN '1990-1999'
WHEN CAST(year as STRING) like '200%' THEN '2000-2009'
WHEN CAST(year as STRING) like '201%' THEN '2010-2019'
END AS decade,
Name, SUM(number) AS count
FROM `bigquery-public-data.usa_names.usa_1910_current`
WHERE Gender = 'F'
GROUP BY decade, Name)
SELECT t1.decade, t1.name, t1.count
FROM name_decade t1
INNER JOIN
(SELECT decade, MAX(count) as max_count from name_decade group by decade) t2
ON t1.decade = t2.decade AND t1.count = t2.max_count
decade |
name |
count |
1910-1919 |
Mary |
478639 |
1920-1929 |
Mary |
701754 |
1930-1939 |
Mary |
572956 |
1940-1949 |
Mary |
640031 |
1950-1959 |
Mary |
625568 |
1960-1969 |
Lisa |
496976 |
1970-1979 |
Jennifer |
581763 |
1980-1989 |
Jessica |
469487 |
1990-1999 |
Jessica |
303094 |
2000-2009 |
Emily |
223690 |
2010-2019 |
Emma |
177410 |
decade |
name |
count |
1910-1919 |
John |
376318 |
1920-1929 |
Robert |
576363 |
1930-1939 |
Robert |
590733 |
1940-1949 |
James |
795680 |
1950-1959 |
James |
843531 |
1960-1969 |
Michael |
833216 |
1970-1979 |
Michael |
707645 |
1980-1989 |
Michael |
663741 |
1990-1999 |
Michael |
462327 |
2000-2009 |
Jacob |
273844 |
2010-2019 |
Noah |
163657 |
- Most female common name in each state 2008 - 2018
WITH state_name AS
(SELECT state, name, sum(number) AS count
FROM `bigquery-public-data.usa_names.usa_1910_curren`
WHERE gender = 'F' AND year >= 2008
GROUP BY state, name)
SELECT t1.state, t1.name, t1.count
FROM state_name t1
INNER JOIN
(SELECT state, max(count) as max_count
FROM state_name
GROUP BY state) t2
ON t1.state = t2.state AND t1.count = t2.max_count
ORDER BY t1.state
Result
state |
name |
count |
AK |
Emma |
552 |
AL |
Emma |
3206 |
AR |
Emma |
2095 |
AZ |
Sophia |
5249 |
CA |
Sophia |
32754 |
CO |
Olivia |
3456 |
CT |
Olivia |
2504 |
DC |
Olivia |
455 |
DE |
Ava |
656 |
FL |
Isabella |
16462 |
GA |
Ava |
6204 |
HI |
Sophia |
624 |
IA |
Emma |
2203 |
ID |
Emma |
1249 |
IL |
Olivia |
8976 |
IN |
Emma |
4980 |
KS |
Emma |
2234 |
KY |
Emma |
3518 |
LA |
Ava |
3211 |
MA |
Olivia |
4838 |
MD |
Olivia |
3333 |
ME |
Emma |
941 |
MI |
Olivia |
6462 |
MN |
Olivia |
3940 |
MO |
Emma |
4465 |
MS |
Ava |
1898 |
MT |
Emma |
682 |
NC |
Emma |
6688 |
ND |
Emma |
717 |
NE |
Emma |
1412 |
NH |
Olivia |
955 |
NJ |
Isabella |
6505 |
NM |
Isabella |
1283 |
NV |
Sophia |
2023 |
NY |
Sophia |
13482 |
OH |
Emma |
8315 |
OK |
Emma |
2825 |
OR |
Emma |
2549 |
PA |
Emma |
8849 |
RI |
Sophia |
831 |
SC |
Emma |
2825 |
SD |
Emma |
661 |
TN |
Emma |
5079 |
TX |
Isabella |
21531 |
UT |
Olivia |
2947 |
VA |
Emma |
5256 |
VT |
Emma |
378 |
WA |
Olivia |
4789 |
WI |
Olivia |
3759 |
WV |
Emma |
1448 |
WY |
Emma |
369 |
- Top 10 gender neutral names
WITH name_gender AS
(
SELECT name, SUM(IF(gender='F',number,0)) female, SUM(IF(gender='M',number,0)) male
FROM `bigquery-public-data.usa_names.usa_1910_current`
GROUP BY name
)
SELECT name, ABS(male-female)/(male+female) balance, female, male
FROM name_gender
WHERE male * female>1000000
ORDER BY 2
LIMIT 10;
name |
balance |
female |
male |
Santana |
9.445843828715365E-4 |
3179 |
3173 |
Landry |
9.946949602122016E-4 |
3013 |
3019 |
Lennon |
0.0041753653444676405 |
3367 |
3339 |
Kris |
0.006668493651228647 |
11020 |
10874 |
Kerry |
0.007894221777613467 |
45620 |
46346 |
Justice |
0.012610626641164456 |
15229 |
15618 |
Arden |
0.015634580012262415 |
3211 |
3313 |
Quinn |
0.01792151435942118 |
29791 |
28742 |
Oakley |
0.025977733371395945 |
3412 |
3594 |
- Most common name of each first letter 2008-2018
WITH first_letter_name AS
(
SELECT SUBSTR(name, 0, 1) AS letter, name, SUM(number) AS count
FROM `bigquery-public-data.usa_names.usa_1910_current`
WHERE year >= 2008 and gender = 'F'
GROUP BY letter, name
ORDER BY letter, count DESC
)
SELECT letter, name, rank
FROM
(SELECT letter, name, count,
Rank() over (Partition BY letter
ORDER BY count DESC ) AS rank
FROM first_letter_name) t
WHERE rank <= 10
Result for male
letter |
name |
rank |
A |
Alexander |
1 |
A |
Aiden |
2 |
A |
Anthony |
3 |
A |
Andrew |
4 |
A |
Aaron |
5 |
A |
Angel |
6 |
A |
Adrian |
7 |
A |
Austin |
8 |
A |
Adam |
9 |
A |
Ayden |
10 |
B |
Benjamin |
1 |
B |
Brayden |
2 |
B |
Brandon |
3 |
B |
Blake |
4 |
B |
Brody |
5 |
B |
Bentley |
6 |
B |
Bryson |
7 |
B |
Bryan |
8 |
B |
Bryce |
8 |
B |
Brian |
10 |
C |
Christopher |
1 |
C |
Christian |
2 |
C |
Caleb |
3 |
C |
Carter |
4 |
C |
Connor |
5 |
C |
Charles |
6 |
C |
Cameron |
7 |
C |
Colton |
8 |
C |
Chase |
9 |
C |
Cooper |
10 |
D |
Daniel |
1 |
D |
David |
2 |
D |
Dylan |
3 |
D |
Dominic |
4 |
D |
Diego |
5 |
D |
Damian |
6 |
D |
Declan |
7 |
D |
Devin |
8 |
D |
Derek |
9 |
D |
Damien |
10 |
E |
Ethan |
1 |
E |
Elijah |
2 |
E |
Evan |
3 |
E |
Eli |
4 |
E |
Easton |
5 |
E |
Elias |
6 |
E |
Eric |
7 |
E |
Ezra |
8 |
E |
Edward |
9 |
E |
Ezekiel |
10 |
F |
Francisco |
1 |
F |
Fernando |
2 |
F |
Finn |
3 |
F |
Felix |
4 |
F |
Fabian |
5 |
F |
Frank |
6 |
F |
Finley |
7 |
F |
Finnegan |
8 |
F |
Franklin |
9 |
F |
Frederick |
10 |
G |
Gabriel |
1 |
G |
Gavin |
2 |
G |
Grayson |
3 |
G |
Giovanni |
4 |
G |
Greyson |
5 |
G |
George |
6 |
G |
Grant |
7 |
G |
Gage |
8 |
G |
Gael |
9 |
G |
Graham |
10 |
H |
Henry |
1 |
H |
Hunter |
2 |
H |
Hudson |
3 |
H |
Hayden |
4 |
H |
Harrison |
5 |
H |
Hector |
6 |
H |
Holden |
7 |
H |
Hugo |
8 |
H |
Hendrix |
9 |
H |
Hayes |
10 |
I |
Isaac |
1 |
I |
Isaiah |
2 |
I |
Ian |
3 |
I |
Ivan |
4 |
I |
Israel |
5 |
I |
Iker |
6 |
I |
Ismael |
7 |
I |
Izaiah |
8 |
I |
Ibrahim |
9 |
I |
Issac |
10 |
J |
Jacob |
1 |
J |
James |
2 |
J |
Jayden |
3 |
J |
Joseph |
4 |
J |
Joshua |
5 |
J |
Jackson |
6 |
J |
John |
7 |
J |
Jonathan |
8 |
J |
Jack |
9 |
J |
Julian |
10 |
K |
Kevin |
1 |
K |
Kayden |
2 |
K |
Kaleb |
3 |
K |
Kyle |
4 |
K |
Kaden |
5 |
K |
Kaiden |
6 |
K |
Kai |
7 |
K |
Kingston |
8 |
K |
Kenneth |
9 |
K |
King |
10 |
L |
Liam |
1 |
L |
Logan |
2 |
L |
Lucas |
3 |
L |
Luke |
4 |
L |
Landon |
5 |
L |
Levi |
6 |
L |
Luis |
7 |
L |
Lincoln |
8 |
L |
Leo |
9 |
L |
Leonardo |
10 |
M |
Michael |
1 |
M |
Mason |
2 |
M |
Matthew |
3 |
M |
Mateo |
4 |
M |
Micah |
5 |
M |
Max |
6 |
M |
Miles |
7 |
M |
Maxwell |
8 |
M |
Miguel |
9 |
M |
Marcus |
10 |
N |
Noah |
1 |
N |
Nathan |
2 |
N |
Nicholas |
3 |
N |
Nolan |
4 |
N |
Nathaniel |
5 |
N |
Nicolas |
6 |
N |
Nehemiah |
7 |
N |
Nash |
8 |
N |
Noel |
9 |
N |
Nasir |
10 |
O |
Owen |
1 |
O |
Oliver |
2 |
O |
Oscar |
3 |
O |
Omar |
4 |
O |
Orion |
5 |
O |
Orlando |
6 |
O |
Odin |
7 |
O |
Omari |
8 |
O |
Otto |
9 |
O |
Oakley |
10 |
P |
Parker |
1 |
P |
Preston |
2 |
P |
Patrick |
3 |
P |
Paul |
4 |
P |
Peter |
5 |
P |
Peyton |
6 |
P |
Paxton |
7 |
P |
Pedro |
8 |
P |
Phoenix |
9 |
P |
Phillip |
10 |
Q |
Quinn |
1 |
Q |
Quentin |
2 |
Q |
Quinton |
3 |
Q |
Quincy |
4 |
Q |
Quintin |
5 |
Q |
Quinten |
6 |
Q |
Quadir |
7 |
Q |
Quinlan |
8 |
Q |
Quran |
9 |
Q |
Qasim |
10 |
R |
Ryan |
1 |
R |
Robert |
2 |
R |
Ryder |
3 |
R |
Roman |
4 |
R |
Richard |
5 |
R |
Riley |
6 |
R |
Ryker |
7 |
R |
Rylan |
8 |
R |
Ricardo |
9 |
R |
Rowan |
10 |
S |
Samuel |
1 |
S |
Sebastian |
2 |
S |
Santiago |
3 |
S |
Sawyer |
4 |
S |
Steven |
5 |
S |
Sean |
6 |
S |
Silas |
7 |
S |
Seth |
8 |
S |
Stephen |
9 |
S |
Spencer |
10 |
T |
Tyler |
1 |
T |
Thomas |
2 |
T |
Tristan |
3 |
T |
Theodore |
4 |
T |
Timothy |
5 |
T |
Tucker |
6 |
T |
Tanner |
7 |
T |
Trevor |
8 |
T |
Travis |
9 |
T |
Trenton |
10 |
U |
Uriel |
1 |
U |
Uriah |
2 |
U |
Ulises |
3 |
U |
Urijah |
4 |
U |
Ulysses |
5 |
U |
Unknown |
6 |
U |
Umar |
7 |
U |
Uziel |
8 |
U |
Usher |
9 |
U |
Ulisses |
10 |
V |
Vincent |
1 |
V |
Victor |
2 |
V |
Vicente |
3 |
V |
Valentino |
4 |
V |
Vihaan |
5 |
V |
Vincenzo |
6 |
V |
Vance |
7 |
V |
Van |
8 |
V |
Valentin |
9 |
V |
Vaughn |
10 |
W |
William |
1 |
W |
Wyatt |
2 |
W |
Wesley |
3 |
W |
Weston |
4 |
W |
Waylon |
5 |
W |
Walter |
6 |
W |
Walker |
7 |
W |
Warren |
8 |
W |
Wade |
9 |
W |
Winston |
10 |
X |
Xavier |
1 |
X |
Xander |
2 |
X |
Xzavier |
3 |
X |
Xavi |
4 |
X |
Xavion |
5 |
X |
Xavian |
6 |
X |
Xaiden |
7 |
X |
Xavior |
8 |
X |
Xzavion |
9 |
X |
Xayden |
9 |
Y |
Yahir |
1 |
Y |
Yusuf |
2 |
Y |
Yosef |
3 |
Y |
Yousef |
4 |
Y |
Yehuda |
5 |
Y |
Yandel |
6 |
Y |
Yisroel |
7 |
Y |
Yadiel |
8 |
Y |
Yael |
9 |
Y |
Yaakov |
10 |
Z |
Zachary |
1 |
Z |
Zane |
2 |
Z |
Zion |
3 |
Z |
Zayden |
4 |
Z |
Zander |
5 |
Z |
Zachariah |
6 |
Z |
Zaiden |
7 |
Z |
Zayne |
8 |
Z |
Zackary |
9 |
Z |
Zayn |
10 |
Result for female
letter |
name |
rank |
A |
Ava |
1 |
A |
Abigail |
2 |
A |
Amelia |
3 |
A |
Addison |
4 |
A |
Avery |
5 |
A |
Aubrey |
6 |
A |
Anna |
7 |
A |
Alexis |
8 |
A |
Allison |
9 |
A |
Audrey |
10 |
B |
Brooklyn |
1 |
B |
Brianna |
2 |
B |
Bella |
3 |
B |
Bailey |
4 |
B |
Brooke |
5 |
B |
Brielle |
6 |
B |
Brooklynn |
7 |
B |
Brynn |
8 |
B |
Briana |
9 |
B |
Bianca |
10 |
C |
Chloe |
1 |
C |
Charlotte |
2 |
C |
Camila |
3 |
C |
Claire |
4 |
C |
Caroline |
5 |
C |
Clara |
6 |
C |
Cora |
7 |
C |
Catherine |
8 |
C |
Cecilia |
9 |
C |
Callie |
10 |
D |
Destiny |
1 |
D |
Delilah |
2 |
D |
Daisy |
3 |
D |
Daniela |
4 |
D |
Diana |
5 |
D |
Danielle |
6 |
D |
Delaney |
7 |
D |
Dakota |
8 |
D |
Daniella |
9 |
D |
Daphne |
10 |
E |
Emma |
1 |
E |
Emily |
2 |
E |
Elizabeth |
3 |
E |
Ella |
4 |
E |
Evelyn |
5 |
E |
Ellie |
6 |
E |
Eva |
7 |
E |
Eleanor |
8 |
E |
Elena |
9 |
E |
Eliana |
10 |
F |
Faith |
1 |
F |
Fiona |
2 |
F |
Finley |
3 |
F |
Fatima |
4 |
F |
Fernanda |
5 |
F |
Francesca |
6 |
F |
Felicity |
7 |
F |
Freya |
8 |
F |
Frances |
9 |
F |
Farrah |
10 |
G |
Grace |
1 |
G |
Gabriella |
2 |
G |
Genesis |
3 |
G |
Gianna |
4 |
G |
Gabrielle |
5 |
G |
Gracie |
6 |
G |
Giselle |
7 |
G |
Gabriela |
8 |
G |
Genevieve |
9 |
G |
Georgia |
10 |
H |
Harper |
1 |
H |
Hannah |
2 |
H |
Hailey |
3 |
H |
Hazel |
4 |
H |
Hadley |
5 |
H |
Hayden |
6 |
H |
Haley |
7 |
H |
Harmony |
8 |
H |
Hope |
9 |
H |
Heaven |
10 |
I |
Isabella |
1 |
I |
Isabelle |
2 |
I |
Isabel |
3 |
I |
Ivy |
4 |
I |
Isla |
5 |
I |
Izabella |
6 |
I |
Iris |
7 |
I |
Itzel |
8 |
I |
Imani |
9 |
I |
Irene |
10 |
J |
Julia |
1 |
J |
Jasmine |
2 |
J |
Jocelyn |
3 |
J |
Jade |
4 |
J |
Jordyn |
5 |
J |
Jessica |
6 |
J |
Josephine |
7 |
J |
Juliana |
8 |
J |
Jennifer |
9 |
J |
Jayla |
10 |
K |
Kaylee |
1 |
K |
Kylie |
2 |
K |
Katherine |
3 |
K |
Kayla |
4 |
K |
Kennedy |
5 |
K |
Khloe |
6 |
K |
Kimberly |
7 |
K |
Kaitlyn |
8 |
K |
Kinsley |
9 |
K |
Kendall |
10 |
L |
Lily |
1 |
L |
Lillian |
2 |
L |
Layla |
3 |
L |
Leah |
4 |
L |
Lucy |
5 |
L |
Lauren |
6 |
L |
Lydia |
7 |
L |
London |
8 |
L |
Liliana |
9 |
L |
Luna |
10 |
M |
Mia |
1 |
M |
Madison |
2 |
M |
Madelyn |
3 |
M |
Maya |
4 |
M |
Mackenzie |
5 |
M |
Madeline |
6 |
M |
Mila |
7 |
M |
Makayla |
8 |
M |
Melanie |
9 |
M |
Morgan |
10 |
N |
Natalie |
1 |
N |
Nevaeh |
2 |
N |
Nora |
3 |
N |
Naomi |
4 |
N |
Natalia |
5 |
N |
Nicole |
6 |
N |
Norah |
7 |
N |
Nova |
8 |
N |
Nadia |
9 |
N |
Noelle |
10 |
O |
Olivia |
1 |
O |
Olive |
2 |
O |
Oakley |
3 |
O |
Ophelia |
4 |
O |
Octavia |
5 |
O |
Opal |
6 |
O |
Oaklyn |
7 |
O |
Oaklee |
8 |
O |
Oaklynn |
9 |
O |
Oakleigh |
10 |
P |
Peyton |
1 |
P |
Penelope |
2 |
P |
Paisley |
3 |
P |
Piper |
4 |
P |
Payton |
5 |
P |
Paige |
6 |
P |
Presley |
7 |
P |
Parker |
8 |
P |
Phoebe |
9 |
P |
Paris |
10 |
Q |
Quinn |
1 |
Q |
Queen |
2 |
Q |
Quincy |
3 |
Q |
Queenie |
4 |
Q |
Quetzalli |
5 |
Q |
Queena |
6 |
Q |
Quetzaly |
7 |
Q |
Quetzali |
8 |
Q |
Quincey |
9 |
Q |
Quincee |
10 |
R |
Riley |
1 |
R |
Ruby |
2 |
R |
Rylee |
3 |
R |
Reagan |
4 |
R |
Rachel |
5 |
R |
Reese |
6 |
R |
Rebecca |
7 |
R |
Ryleigh |
8 |
R |
Rose |
9 |
R |
Raelynn |
10 |
S |
Sophia |
1 |
S |
Sofia |
2 |
S |
Samantha |
3 |
S |
Sarah |
4 |
S |
Savannah |
5 |
S |
Scarlett |
6 |
S |
Stella |
7 |
S |
Serenity |
8 |
S |
Sophie |
9 |
S |
Sadie |
10 |
T |
Taylor |
1 |
T |
Trinity |
2 |
T |
Tessa |
3 |
T |
Teagan |
4 |
T |
Talia |
5 |
T |
Tatum |
6 |
T |
Tiffany |
7 |
T |
Tatiana |
8 |
T |
Tiana |
9 |
T |
Thea |
10 |
U |
Unique |
1 |
U |
Unknown |
2 |
U |
Uma |
3 |
U |
Una |
4 |
U |
Ursula |
5 |
U |
Udy |
6 |
U |
Uriah |
7 |
U |
Unity |
8 |
U |
Ulyana |
9 |
U |
Umme |
10 |
U |
Umaiza |
10 |
V |
Victoria |
1 |
V |
Violet |
2 |
V |
Vivian |
3 |
V |
Valeria |
4 |
V |
Valentina |
5 |
V |
Vanessa |
6 |
V |
Valerie |
7 |
V |
Vivienne |
8 |
V |
Veronica |
9 |
V |
Vera |
10 |
W |
Willow |
1 |
W |
Willa |
2 |
W |
Whitney |
3 |
W |
Winter |
4 |
W |
Wendy |
5 |
W |
Wren |
6 |
W |
Wynter |
7 |
W |
Whitley |
8 |
W |
Winnie |
9 |
W |
Waverly |
10 |
X |
Ximena |
1 |
X |
Xiomara |
2 |
X |
Xochitl |
3 |
X |
Xitlali |
4 |
X |
Xitlaly |
5 |
X |
Xena |
6 |
X |
Xenia |
7 |
X |
Xyla |
8 |
X |
Xochilt |
9 |
X |
Ximenna |
10 |
Y |
Yaretzi |
1 |
Y |
Yareli |
2 |
Y |
Yasmin |
3 |
Y |
Yamileth |
4 |
Y |
Yaritza |
5 |
Y |
Yesenia |
6 |
Y |
Yazmin |
7 |
Y |
Yoselin |
8 |
Y |
Yuliana |
9 |
Y |
Yara |
10 |
Z |
Zoey |
1 |
Z |
Zoe |
2 |
Z |
Zara |
3 |
Z |
Zariah |
4 |
Z |
Zuri |
5 |
Z |
Zoie |
6 |
Z |
Zaniyah |
7 |
Z |
Zaria |
8 |
Z |
Zion |
9 |
Z |
Zariyah |
10 |
Some interesting insights:
- American have around 30000 ways to name their children.
- American female's names tend to be longer than male's names.
- Most common American name: James for male and Mary for female.
- Most common American name in 2018: Liam for male and Emma for female.
- In 5 continuous decades, 1910s -> 1950s, the most common female name is Mary.
Mary is both a traditional American name and a symbol religious Christianity.
But in recent year, Mary is no longer a favourite name. According to sociologist Stanley Lieberson, the reason is "As the role of the extended family, religious rules, and other institutional pressures declines," he wrote, "choices are increasingly free to be matters of taste." - The most gender-neutral name is: Santana.