Wednesday, June 14, 2017

Running SQL Queries Against Baby Names from Social Security Card Applications

I decided to look around to see if I can find some interesting data sets
I came across the Baby Names from Social Security Card Applications-National Level Data data set and decided it would be a fun exercise to import this data into SQL Server and run some queries

You can download the zip file from the link above, you will see 137 files with a txt extension in the zip file

Here is what the description is for these files

National Data on the relative frequency of given names in the population of U.S. births where the individual has a Social Security Number 

(Tabulated based on Social Security records as of March 5, 2017)

For each year of birth YYYY after 1879, we created a comma-delimited file called yobYYYY.txt. Each record in the individual annual files has the format "name,sex,number," where name is 2 to 15 characters, sex is M (male) or F (female) and "number" is the number of occurrences of the name. Each file is sorted first on sex and then on number of occurrences in descending order. When there is a tie on the number of occurrences, names are listed in alphabetical order. This sorting makes it easy to determine a name's rank. The first record for each sex has rank 1, the second record for each sex has rank 2, and so forth.
To safeguard privacy, we restrict our list of names to those with at least 5 occurrences.

The format of the files looks like this


So these files are simple comma separated values files

Download the zip file from this url:

Unzip the files in a folder on your hard drive, in my case I unzipped it to C:\Names

Let's get started with the fun part

First I created a new DB


USE Scratch

Now that we have our database, let's create this very simple table, it will store all the data from the file, I also added a year column to this table

CREATE TABLE Names (FirstName varchar(500), Gender char(1), YearInFile date)

Because the file doesn't have a year column, I created this view so that I can bulk insert into the view instead of having to mess around with SSIS or format files

AS SELECT FirstName,Gender,NameCount
FROM Names

I extracted all the files into a folder with the name Names, I placed this on the C drive

Now that you have the table and view created, try running the following

BULK INSERT myView FROM 'c:\Names\yob1932.txt' WITH (FIELDTERMINATOR =',')

It should insert 9382 rows

If that is working, it is time to import all 137 files

I am presenting you a solution here so that you can run this all in SSMS, of course you could have as easy used PowerShell, Python or any other language to import these files. Nothing prevents you from doing that :-)

We will be using xp_cmdshell

It is not a best practice to have xp_cmdshell enabled. As a matter of fact beginning with SQL Server 2005, the product ships with xp_cmdshell disabled. If you try to run xp_cmdshell you will get the following message if it is not enabled
Server: Msg 15281, Level 16, State 1, Procedure xp_cmdshell, Line 1

SQL Server blocked access to procedure ‘sys.xp_cmdshell’ of component ‘xp_cmdshell’ because this component is turned off as part of the security configuration for this server. A system administrator can enable the use of ‘xp_cmdshell’ by using sp_configure. For more information about enabling ‘xp_cmdshell’, see “Surface Area Configuration” in SQL Server Books Online.

To enable xp_cmdshell execute the following code

EXECUTE SP_CONFIGURE 'show advanced options', 1
EXECUTE SP_CONFIGURE 'xp_cmdshell', '1'
EXECUTE SP_CONFIGURE 'show advanced options', 0

Now it is time to import all the files. First we want to store all the files that we want to import in a table

We are creating a temp table, we then call a DIR command with the B switch. we delete everything that doesn't end in txt. Finally we add an identity column, we will use this to loop over this table

IF OBJECT_ID('tempdb..#tempList') IS NOT NULL
DROP TABLE #tempList
CREATE TABLE #tempList ([FileName] VARCHAR(500))
--plain vanilla dos dir command with /B switch (bare format)
--delete the null values
DELETE #tempList WHERE [FileName] IS NULL
-- Delete all the files that don't have txt extension
DELETE #tempList WHERE [FileName] NOT LIKE '%.txt'
--this will be used to loop over the table

Let's see what we have in the table

SELECT * FROM #tempList

You will see something like this

FileName id
yob1880.txt 1
yob1881.txt 2
yob1882.txt 3
yob1883.txt 4
yob1884.txt 5
yob1885.txt 6

Now it is time to import all the files, here is the code that will import all 137 files, I put comments in the code so you know what it does

DECLARE @LoopID int, @MaxID int
SELECT @LoopID = min(id),@MaxID = max(ID)
FROM #tempList


-- if you want to run this multiple time, wipe out the data

-- Change this to your directory
DECLARE @Directory varchar(50) = 'c:\Names\'


DECLARE @FileName varchar(500),@FullFileName varchar(500)
Declare @year varchar(10)

-- loop over all the file
WHILE @LoopID <= @MaxID BEGIN SELECT @FileNAme = filename FROM #tempList WHERE id = @LoopID -- concatenate the directory and filename SELECT @FullFileName = @Directory + @FileName BEGIN SELECT @SQL = N'BULK INSERT myView FROM ''' + @FullFileName +''' WITH (FIELDTERMINATOR ='','')' -- you can remove this..this basically prints what will be executed PRINT @SQL
    -- run the command
  -- this grabs the year part from the file name
 SELECT @year = right(parsename(@filename,2),4)

  -- This will update the rows we just inserted to have the year value
  UPDATE Names 
  SET YearInFile = @year
  WHERE YearInFile is null
    --Get the next id, instead of +1 we grab the next value in case of skipped id values
    SELECT @LoopID = min(id)
    FROM #tempList
    WHERE id > @LoopID

And now that we have the data we can run some queries

Let's see if we have all the data


That should return 1,891,894 rows

let's see..did the baby boomers parents really get it on after WW II?

SELECT  SUM(nameCount) as SumCount,YearInFile FROM names
group by YearInFile

SumCount YearInFile
4200097 1957
4156576 1959
4154809 1960
4140160 1961
4131802 1958
4121130 1956
4035499 1962
4012933 1955
3993206 2007
3980407 1954

Looks like it... but what happened in 2007, top of the housing bubble was 2006/2007 so people started buying houses and needed to populate those rooms with humans  :-)

What are the top 10 most popular names over time?

SELECT TOP 10 FirstName, gender,SUM(Namecount) as Alltime
FROM names
GROUP BY FirstName,gender

Here are the results, looks like males don't have as many unique names

FirstName gender Alltime
James M 5136026
John M 5105834
Robert M 4809176
Michael M 4338144
Mary F 4120692
William M 4087525
David M 3601032
Joseph M 2592856
Richard M 2560771
Charles M 2378806

What were the most popular names in 1880?

SELECT TOP 10  FirstName, gender,SUM(Namecount)  as [1880]
FROM names
where YearInFile = 1880
GROUP BY FirstName,gender

Here are those results

FirstName gender 1880
John M 9655
William M 9532
Mary F 7065
James M 5927
Charles M 5348
George M 5126
Frank M 3242
Joseph M 2632
Anna F 2604
Thomas M 2534

For 2016

SELECT TOP 10 FirstName, gender,SUM(Namecount)  as [2016]
FROM names
where YearInFile = 2016
GROUP BY FirstName,gender

Now we have some names which I recognize since my kids friends are named like hte ones on this list

FirstName gender 2016
Emma F 19414
Olivia F 19246
Noah M 19015
Liam M 18138
Ava F 16237
Sophia F 16070
William M 15668
Mason M 15192
James M 14776
Isabella F 14722

Anybody named after months?

I guess nobody likes February

SELECT FirstName , count(*)
FROM names
WHERE FirstName in('January','February','March','April','May','June',
GROUP BY FirstName

FirstName Count
April 135
August 206
December 56
January 59
July 73
June 273
March 41
May 213
November 37
October 34
September 62

I wonder how many of these names are the result of someone putting the month in the first name field  :-)

How many names are both male and female?

;WITH cte as(
SELECT FirstName 
FROM names
GROUP BY FirstName

SELECT FirstName, gender, count(*) FROM Names n
WHERE EXISTS (SELECT * FROM cte c where c.FirstName = n.FirstName)
GROUP BY FirstName, Gender

As you can see, there are a lot

Here are some where number of males and females doesn't differ that much

FirstName gender Count
Courtney F 123
Courtney M 126
Cruz F 121
Cruz M 114
Tom F 62
Tom M 137

One more query and that is it for this post..

How many people are named after weekdays?

SELECT FirstName , count(*)
FROM names
WHERE FirstName in('Monday','Tuesday','Wednesday','Thursday','Friday','Saturday',
GROUP BY FirstName

Here are the results

FirstName Count
-------------       -----------------
Friday          4
Monday          5
Sunday        89
Tuesday        59
Wednesday     51

Last Monday I saw Tuesday who told me that she was going to see Friday on Sunday....

Yep, crystal clear...

That is it for this post.... feel free to run your own queries and if you find some interesting things, feel free to leave me a comment with the results or the query

One more query for you, I added this at the bottom since there are 52 rows in the result

What is the most popular letter a name starts with?

Here is that query, there are a couple of counts here:, the total count, the occurance and the distinct count of a name starting with the letter across all years

SELECT  LEFT(FirstName,1) AS Initial, gender,
SUM(Namecount) AS SumCount, 
COUNT(NameCount) AS TotalCount ,
COUNT(DISTINCT firstname) AS DistinctCount
FROM names
GROUP BY left(FirstName,1),gender

Here are the results

Initial gender SumCount TotalCount DistinctCount
J M 29,396,710 63,543 3,995
M F 19,855,157 105,326 5,583
A F 16,744,520 117,270 7,501
R M 16,329,781 46,772 2,135
J F 14,887,120 79,019 4,622
D M 14,375,824 62,369 3,380
S F 13,587,753 90,382 6,059
M M 12,717,485 51,078 2,643
C M 12,652,140 51,820 2,255
C F 12,640,899 80,401 4,159
L F 12,099,095 86,872 4,473
A M 11,611,302 66,139 3,594
K F 11,065,934 74,797 4,967
D F 9,723,142 66,592 3,916
E F 9,644,797 49,929 2,477
B F 8,485,706 39,199 2,251
T M 8,331,252 42,525 2,592
B M 8,038,739 37,772 1,701
S M 7,586,177 42,524 2,524
R F 7,216,686 51,892 2,837
E M 7,122,968 36,781 1,542
W M 7,073,357 17,052 577
L M 6,594,743 42,492 1,799
G M 6,587,004 26,930 1,095
T F 5,913,655 61,915 4,277
K M 5,744,651 44,110 3,096
P F 5,024,379 19,759 1,090
N F 4,315,987 36,667 2,507
H F 4,203,255 19,717 1,159
P M 4,181,853 15,150 666
G F 4,083,933 28,566 1,398
H M 3,962,710 26,661 1,128
V F 3,546,832 23,367 1,083
N M 3,354,035 20,738 1,180
F M 3,048,688 15,174 592
F F 2,084,083 17,670 842
I F 1,966,942 17,719 1,066
I M 1,350,255 11,261 573
W F 1,235,983 9,462 569
V M 1,183,415 11,609 559
O M 1,112,184 15,521 724
O F 951,873 12,914 718
Z M 926,015 9,284 833
Y F 656,093 11,343 1,072
Z F 525,515 13,263 1,139
Q M 183,280 3,684 296
X M 172,430 1,175 127
Y M 163,545 7,031 579
U M 93,783 2,875 152
Q F 75,612 2,663 274
X F 54,170 1,466 150
U F 50,450 1,654 169

Not that many names that start with a letter at the end of the alphabet

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