This project employs PostgreSQL to analyze a dataset about American baby names seeking the following information:
- Names with high usage for over 100 years.
- Trends on different time periods.
- The top 10 male/female names.
- The most popular male/female name starting with a certain letter since some year.
- The most popular male/female names by year.
- The most popular male/female name for the largest number of years.
The idea came from a Datacamp's article about good projects to practice PostgreSQL.
The dataset was obtained from Social Security's website on March 23, 2024.
The downloaded pack consisted of one text file for every year between 1880 and 2022, presenting a CSV-like layout with 3 columns and no headers:
Mary,F,7065
Anna,F,2604
Emma,F,2003
...
We run some preparing Python procedures before getting into SQL. They are all available in the helper.py script.
As previously said, the dataset gives us one text file for every year in the 1822-2022 period. We put everything into a single CSV file by running the assemble() function.
Since we're investigating the most frequently used American names, the least used can be discarded without harming our goals. This undersampling might be obtained by two distinct functions described in helper.py.
Only the data used in the following tests were uploaded into the data folder. If you download the complete data and want to run helper.py, put all the content into data/raw before doing it.
Once the data for the study has been defined, we start the database by running createdb, a PostgreSQL command:
> createdb american_names
After accessing the database from root directory, we get into the sql folder and run the available scripts in order to create the the database objects:
> american_names=# \cd sql
> american_names=# \i creation.sql
CREATE TYPE
CREATE TABLE
COPY 104189
> american_names=# \i analysis.sql
CREATE FUNCTION
CREATE FUNCTION
CREATE FUNCTION
CREATE FUNCTION
CREATE FUNCTION
CREATE FUNCTION
The 104,189 copied instances are from data/rel_common_names.csv, generated by helper.py.
The functions in sql/analysis.sql offer answers to our previous questions about the data.
We can see that 45 names had at least 1000 occurences in every year between 1922 and 2022:
> american_names=# select count(*) from query_recurrent_names(1000, 1922::smallint, 2022::smallint);
count
-------
45
(1 row)
Five of these names are:
> american_names=# select * from query_recurrent_names(1000, 1922::smallint, 2022::smallint) limit 5;
recurrent_name
----------------
Andrew
Anna
Anthony
Benjamin
Calvin
(5 rows)
We can query the names with highest linear growth in a time period. So, how about the 21st century?
> american_names=# select * from query_trends(2001::smallint, 2022::smallint) limit 5;
trendy_name | growth
-------------+---------
Liam | 1049.96
Oliver | 797.49
Charlotte | 711.26
Harper | 665.46
Amelia | 596.71
(5 rows)
Let's see the 2011-2020 decade:
> american_names=# select * from query_trends(2011::smallint, 2020::smallint) limit 5;
trendy_name | growth
-------------+---------
Oliver | 1088.75
Harper | 975.11
Mateo | 854.72
Luna | 852.52
Theodore | 832.52
(5 rows)
This function considers only the names that appear every year in the appointed time period.
Top 10 male names:
> american_names=# select top_names, total_occurences from query_top_names('M') limit 10;
top_names | total_occurences
-----------+------------------
James | 5214844
John | 5158428
Robert | 4838129
Michael | 4393742
William | 4167487
David | 3654723
Joseph | 2647283
Richard | 2572740
Charles | 2417569
Thomas | 2338310
(10 rows)
The female part can be found by running this same function, but passing 'F' as argument instead of 'M'.
The number of male names starting with "Y" in the 21st century.
> american_names=# select * from special_start_count('M', 'Y', 2001::smallint);
special_start_count
---------------------
52
(1 row)
The most popular female names in the 1980's:
> american_names=# select year_of_exercise, commonest_name, total_occurences from query_annual_leaders('F') where year_of_exercise >= 1980 limit 10;
year_of_exercise | commonest_name | total_occurences
------------------+----------------+------------------
1980 | Jennifer | 58381
1981 | Jennifer | 57048
1982 | Jennifer | 57119
1983 | Jennifer | 54350
1984 | Jennifer | 50561
1985 | Jessica | 48346
1986 | Jessica | 52682
1987 | Jessica | 55996
1988 | Jessica | 51552
1989 | Jessica | 47889
(10 rows)
Amongst the male names, Michael dominated the entire decade.
John and Michael are the male names that most oftenly appeared as annual leaders:
> american_names=# select * from prevalent_annual_leader('M');
name | leaderships
---------+-------------
John | 44
Michael | 44
Robert | 17
Jacob | 14
James | 13
Liam | 6
Noah | 4
David | 1
(8 rows)
And Mary is by far the female champion of this metric:
> american_names=# select * from prevalent_annual_leader('F');
name | leaderships
----------+-------------
Mary | 76
Jennifer | 15
Emily | 12
Jessica | 9
Lisa | 8
Linda | 6
Emma | 6
Olivia | 4
Sophia | 3
Ashley | 2
Isabella | 2
(11 rows)