Changes

{{Project|Has project output=Data,Tool|Has sponsor=McNair ProjectsCenter

|Has owner=Adrian Smart, Jake Silberman, Meghana Gaur,|StatusHas keywords=ActiveData|Has project status=Subsume

==Arching Objective==This project page is largely redundant and needs to be cleaned up. The main project pages for this project are:To create a master venture capital data set on which all other projects requiring venture capital data will be built[[Retrieving US VC Data From SDC]], and [[VC Database Rebuild]].

==Project Objective=round level=======main round====*round date - Round Date *round amount - Round Amount, Disclosed ($ Thou) or Round Amount, Estimated ($ Thou)*coname (or primary key) - Company Name *round number - Round Number*stage - STAGE1, STAGE2, STAGE3 ====round To create a master venture capital data set on one line====*round on one line - COMBINED_ROUND_INFO**round date - Fund Portfolio Cos: Round Date**round amount - Fund Portfolio Cos: Round Amount, Disclosed ($ Thou) or Fund Portfolio Cos: Round Amount, Estimated (Thou)**name of investor - Fund Name *fund type - Fund Type *coname (or primary key) - Fund Portfolio Cos: Namewhich all other projects requiring venture capital data will be built.

===Fund Level?===Specifically, we will be building data to support the following:*fund name Lists and counts by year as well as state, MSA, and city, zip code, etc.*closing date Identify seed, early and later stage deals (first investment), dollars invested and rounds*last investmentIdentify transactional VC dollars invested and rounds*first investmentAcquisitions and IPOs of VC backed firms*msa Identify when VC backed firms are "alive"*msa code Geocode all portfolio companies*average investment Geocode VC headquarters and branch offices*number of Compute distances between investors and their portfolio companies invested*total known investment*addressIdentify lead investors for portfolio companies*fund sizeIdentify top 100 VCs

==Data sets to buildRetrieving data== #number of deals (first investments) by state, industry, msa, city, year, quarter#number of foundings (conditional on receiving vc later) by state, industry, msa, city, year, quarter#deal amount (first investment) by stage, state, industry, msa, city, year, quarter#invested amount (any round) by stage, state, industry, msa, city, year, quarter#number of deals by stage state, industry, msa, city, year, quarter#number of investments (any round) by stage, state, industry, msa, city, year, quarter#number of investors by stage, state, industry, msa, city, year, quarter

==Houston/Galveston/Brazoria Pull/Old notes are [[Old Venture Capital Data Work==*5 Pulls, all under MSA Houston/Galveston/Brazoria filter**Firm Level**Fund Level**Company Level**Round Level**Round on One Line]]

===Work Details===*{{:Retrieving US VC Data From SDC pulls at all 5 levels followed by normalization of data*Data can be found in E:\McNair\Projects\Houston\MarketNonMarket (more details about which are specific to this project will be posted later) and files for import to SQL can be found in Z:\VentureCapitalData*SQL tables are found in psql vcdata}}

==Project Idea==Examine health of entrepreneurship ecosystem within an MSA or CMSA and how it is affected by the proximity of venture-backed companies to one another Additional datafiles (measured by minimum circle area between firms within the MSAin E:\McNair\Projects\VC Database):*GeocodedVCData. The goal is to see whether MSA's txt 43,724 records, tab-delimited with closer or further proximity between companies see greater growth in successive yearscompanynames but with "none" for some geocoords.

In order ==Processing the base tables== The SQL scripts need to do the following:*Extract company infomation from roundbase then process it**Add variable for growth (1), transactional (2), or non-VC (3) (see below)**Create clean 5-digit ZIP field*Extract firm information from firmbase, then:**Add indicator variable for US**Create clean 5-digit ZIP field*Build out BranchOffice (BO) table*Build lookups for:**State Code**raisestatus SQL file to process data is: Z:/VentureCapitalData/SDCVC/ProcessData.sql ==Lookup Tables== ===Stage of Investment=== We are generating 6 binary indicators as follows:  Growth VC (4) -------------- Seed (1) Early Stage (2) Later Stage (3)  Tranactional VC (5) -------------- Acq. for Expansion Acquisition Bridge Loan Expansion Pending Acq Recap or Turnaround  Exclude (non-VC) (6) -------------- LBO MBO Open Market Purchase PIPE Secondary Buyout VC Partnership Other ==Building companybase and round==Both are built from roundbase which contains portfolio companies and round details. The scripts are below. CREATE TABLE companybase AS SELECT DISTINCT coname,updateddate,foundingdate,datelastinv,datefirstinv,investedk,city,description,msa,msacode,nationcode, statecode,addr1,addr2,indclass,indsubgroup3,indminor,url,zip FROM roundbase ORDER BY coname;  CREATE TABLE round AS SELECT DISTINCT coname,statecode,datefirstinv,rounddate,stage1,stage3,rndamtdisck,rndamtestk,roundnum,numinvestors FROM roundbase ORDER BY coname; ==Building stageflags==Stageflags are built from the stage3 column of round. Using the tables above, I made 6 flags for seed, early, later, growth, transaction and exclude. The script is below. Verify that no row entries are missing flags in excel by copying the round table to a txt file. The last 3 columns should always sum to 1 for every row entry. There should be considered "livingno zero sums and no sums greater than 1. CREATE TABLE stageflags AS SELECT coname, stage3, CASE WHEN stage3 = 'Seed' THEN 1 ELSE 0 END AS seedflag, CASE WHEN stage3 = 'Early Stage' THEN 1 ELSE 0 END AS earlyflag, CASE WHEN stage3 = 'Later Stage' THEN 1 ELSE 0 END AS laterflag, CASE WHEN stage3 = 'Seed' OR stage3 = 'Later Stage' OR stage3 = 'Early Stage' THEN 1 ELSE 0 END AS growthflag," CASE WHEN stage3 = 'Acq. for Expansion' OR stage3 = 'Acquisition' OR stage3 = 'Bridge Loan' OR stage3 = 'Expansion' OR stage3 = 'Pending Acq' OR stage3 = 'Recap or Turnaround' OR stage3 = 'Mezzanine' THEN 1 ELSE 0 END AS transactionflag, CASE WHEN stage3 = 'LBO' OR stage3 = 'MBO' OR stage3 = 'Open Market Purchase' OR stage3 = 'PIPE' OR stage3 = 'Secondary Buyout' OR stage3 = 'Other' OR stage3 = 'VC Partnership' OR stage3 = 'Secondary Purchase' THEN 1 ELSE 0 END AS excludeflag FROM round; ==Sample output==The psql file was copied to an output text file using: \COPY stageflags TO 'flag.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV; A sum column was added in excel which is the sum of the last 3 columns. Notice how all entries are 1. This is good. ==Checking companybase table==The following psql code creates a new table called companybase1 that checks for Undisclosed companies and nonUS companies must have in the companybase table and also inserts a primary key column which is a concatenation of coname, statecode and datefirstinv. CREATE TABLE companybase1 AS SELECT *, concat(coname::text, statecode::text, datefirstinv::text) AS pkey, CASE WHEN nationcode = 'US' THEN 1::int ELSE 0::int END AS alwaysusflag, CASE WHEN coname = 'Undisclosed Company' THEN 1::int ELSE 0::int END AS undisclosedflag FROM companybase; \COPY companybase1 TO 'companybase1.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV; ==Checking if pkey is valid ==The following psql code creates a new table called companybasepkeyvalid that counts the number of times the pkey created in companybase1 occurs in companybase1. In this case there are 3 pkeys that are not undergone IPOvalid for companies: Undisclosed Company, New York Digital Health LLC and Undisclosed Company. CREATE TABLE companybasepkeyvalid AS SELECT coname, pkey, COUNT(pkey) FROM companybase1 GROUP BY coname, pkey; --44771 \COPY companybasepkeyvalid TO 'companybasepkeyvalid.txt'sWITH DELIMITER AS E'\t' HEADER NULL AS '' CSV; The Undisclosed Companies will be updated manually using the description to extract the company name. To remove the duplicate records of the company New York Digital Health LLC from the roundbase table run the following command: DELETE FROM roundbase WHERE url = 'www.digitalhealthaccelerator.c'; --1After the Undisclosed company names are updated, you will need to recreate the companybase and roundbase tables.Copy all the undisclosed companies to a text file and pull out the company names from the description. DROP TABLE undisclosedcompanies CREATE TABLE undisclosedcompanies AS SELECT coname, liquidationsdescription, or acquisitions url FROM roundbase WHERE coname = 'Undisclosed Company'; --37 \COPY undisclosedcompanies TO 'undisclosedcompanies.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV; After manually pulling out the company names from the description from all 37 entries, I created the following commands to update the original roundbase table below. After the updates there are still 18 Undisclosed companies. Recheck to make sure there are no primary key issues again. UPDATE roundbase SET coname = 'Standard Bariatrics' WHERE coname = 'Undisclosed Company' AND description = 'Standard Bariatrics is taking bariatric procedure and must have received venture funding within creating instruments'; --1 UPDATE roundbase SET coname = 'Jersey Watch' WHERE coname = 'Undisclosed Company' AND description = 'Jersey Watch provides organizers to professional.'; --1 UPDATE roundbase SET coname = 'CareSkore' WHERE coname = 'Undisclosed Company' AND description = 'CareSkore provides an evidence-based predictive systems.'; --1 UPDATE roundbase SET coname = 'Navistone' WHERE coname = 'Undisclosed Company' AND description = 'Navistone provides digital marketers a way to target the last 5 yearsright web browsers'; --2 UPDATE roundbase SET coname = 'PolySync' WHERE coname = 'Undisclosed Company' AND description = 'Offers PolySync platform for driverless vehicle applications.'; --2 UPDATE roundbase SET coname = 'Platform9' WHERE coname = 'Undisclosed Company' AND description = 'Platform9 Systems Inc is a United States-based company.'; --2 UPDATE roundbase SET coname = 'Salamanca' WHERE coname = 'Undisclosed Company' AND description = 'Salamanca provides products and services.'; --1 UPDATE roundbase SET coname = 'Sourcegraph Inc.' WHERE coname = 'Undisclosed Company' AND description = 'Sourcegraph Inc. provides a code search engine for open source.'; --1 UPDATE roundbase SET coname = 'Sona' WHERE coname = 'Undisclosed Company' AND description = 'Sona provides products and services.'; --1 UPDATE roundbase SET coname = 'AlphaSource' WHERE coname = 'Undisclosed Company' AND description = 'AlphaSource in provides healthcare technology solutions.'; --2 UPDATE roundbase SET coname = 'Reflecktive' WHERE coname = 'Undisclosed Company' AND description = 'Reflecktive provides products and services.'; --1 UPDATE roundbase SET coname = 'SuperH' WHERE coname = 'Undisclosed Company' AND description = 'SuperH provides an SaaS platform for open source data'; --1 UPDATE roundbase SET coname = 'Islands Media Inc' WHERE coname = 'Undisclosed Company' AND description = 'Islands Media Inc is an IT company.'; --1 UPDATE roundbase SET coname = 'Envoy Commerce Corp.' WHERE coname = 'Undisclosed Company' AND description = 'Envoy Commerce Corp. provides online grocery delivery service. '; --1 UPDATE roundbase SET coname = 'Quilt' WHERE coname = 'Undisclosed Company' AND description = 'Quilt is a consumer-centric insurance company focused on renters insurances'; --1 ==Removing duplicates from ipos table==If you create the following two tables you'll notice that there are duplicate entries in the ipos table. DROP TABLE ipospkey; CREATE TABLE ipospkey AS SELECT *, concat(issuer::text, issuedate::text, statecode::text, principalamt::text) AS pkey FROM ipos; --10440  DROP TABLE iposcount; CREATE TABLE iposcount AS SELECT issuer, issuedate, statecode, COUNT(pkey) FROM ipospkey GROUP BY issuer, issuedate, statecode; --9491 To clean these requirements up I built the following tables and selected the max principalamt values if there were duplicates. There is probably a simpler way to do this but I'll leave that for the next person to discover. DROP TABLE iposcounthigh; CREATE TABLE iposcounthigh AS SELECT * FROM iposcount WHERE count > 1;   DROP TABLE duplicateipodata; CREATE TABLE duplicateipodata AS SELECT ipos.* FROM ipos INNER JOIN iposcounthigh ON (ipos.issuer = iposcounthigh.issuer AND ipos.issuedate = iposcounthigh.issuedate AND ipos.statecode = iposcounthigh.statecode) ORDER BY principalamt DESC;  DROP TABLE ipoduplicatemin; CREATE TABLE ipoduplicatemin AS SELECT issuedate, issuer, statecode, MIN(principalamt) AS principalamt FROM duplicateipodata GROUP BY issuedate, issuer, statecode; --939 \COPY ipoduplicatemin TO 'ipoduplicatemin.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV  DROP TABLE ipoduplicatekeys; CREATE TABLE ipoduplicatekeys AS SELECT *, concat(issuer::text, issuedate::text, statecode::text, principalamt::text) AS pkey FROM ipoduplicatemin;  DROP TABLE iponoduplicates; CREATE TABLE iponoduplicates AS SELECT ipospkey.* FROM ipospkey WHERE NOT EXISTS(SELECT * FROM ipoduplicatekeys WHERE (ipospkey.pkey = ipoduplicatekeys.pkey)); --9490 To verify that there are not metno more duplicates create another table. DROP TABLE iposcountdupes1; CREATE TABLE iposcountdupes1 AS SELECT issuer, issuedate, statecode, COUNT(pkey) FROM iponoduplicates GROUP BY issuer, issuedate, they statecode; \COPY iposcountdupes1 TO 'iposcountdupes1.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV You will notice that there are still 9 duplicate records that slipped thru somehow. Remove these manually. DELETE FROM iponoduplicates WHERE pkey = 'PacTel Corp1993-12-02CA$299.00'; --1 DELETE FROM iponoduplicates WHERE pkey = 'Templeton Dragon Fund Inc1994-09-21FL$169.50'; --1 DELETE FROM iponoduplicates WHERE pkey = 'Sterling Commerce1996-03-08TX$240.00' AND proceedsamt = '288'; --1 DELETE FROM iponoduplicates WHERE pkey = 'Sothebys Holdings Inc1988-05-13NY$27.00'; --1 DELETE FROM iponoduplicates WHERE pkey = 'TD Waterhouse Group Inc1999-06-23NY$655.20'; --1 DELETE FROM iponoduplicates WHERE pkey = 'Berlitz International Inc1989-12-13NJ$34.65'; --1 DELETE FROM iponoduplicates WHERE pkey = 'Spain Fund Inc1988-06-21NY$39.00'; --1 DELETE FROM iponoduplicates WHERE pkey = 'Ultramar Corp1992-06-26CT$85.39'; --1 DELETE FROM iponoduplicates WHERE pkey = 'Goldman Sachs Group Inc1999-05-03NY$424.00'; --1 iponoduplicates is now the master ipo table that contains unique keys which are dropped concatenated from issuer, issuedate and prinicpalamt. It should contain 9481 lines. ==Cleaning mas table==There were duplicate records in this file because the original text file from SDC contained addresses that were on two rows for some companies. Therefore the easiest way to get rid of these records is to remove lines in textpad and reimport into the mas table. Remove lines in textpad using regex: ^#.*\n and replacing with nothing.After reimporting there will still be duplicate rows. Slap an id number on them and take the min as shown below. CREATE TABLE mas1 AS SELECT *, concat(targetname::text, targetstate::text, announceddate::text) AS pkey FROM mas; ALTER TABLE mas1 ADD COLUMN id SERIAL PRIMARY KEY; --114890  DROP TABLE masminid; CREATE TABLE masminid AS SELECT targetname, targetstate, announceddate, MIN(id) FROM mas1 GROUP BY targetname, targetstate, announceddate; --114825  DROP TABLE masnodupes; CREATE TABLE masnodupes AS SELECT mas1.* FROM mas1 JOIN masminid ON mas1.id = masminid.min; \COPY masnodupes TO 'masnodupes.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV masnodupes should have no duplicates based on targetname, targetstate, announceddate. After running it thru the matcher you will get about 800 matching errors. We decided to exclude any rows that had equivalent targetname, targetstate where the announced dates fell within the same week. In this case we took the minimum date value and excluded the other row. Below are the queries to make those tables.  DROP TABLE masdistinctkeys; CREATE TABLE masdistinctkeys AS SELECT DISTINCT targetname, targetstate, announceddate FROM masnodupes; --114825 \COPY masdistinctkeys TO 'masdistinctkeys.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV  DROP TABLE masdistinctkeysmindates; CREATE TABLE masdistinctkeysmindates AS SELECT targetname, targetstate, MIN(announceddate) AS announceddate FROM masdistinctkeys GROUP BY targetname, targetstate; --113236  DROP TABLE masdistinctkeysdatewindow; CREATE TABLE masdistinctkeysdatewindow AS SELECT masdistinctkeys.*, masdistinctkeysmindates.announceddate as minanndate, CASE WHEN masdistinctkeys.announceddate - INTERVAL '7 day' > masdistinctkeysmindates.announceddate THEN 1::int ELSE 0::int END AS dateflag FROM masdistinctkeys JOIN masdistinctkeysmindates ON (masdistinctkeys.targetname = masdistinctkeysmindates.targetname AND masdistinctkeys.targetstate = masdistinctkeysmindates.targetstate); --114825 \COPY masdistinctkeysdatewindow TO 'masdistinctkeysdatewindow.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV   CREATE TABLE masdistinctkeys1 AS SELECT targetname, targetstate, announceddate FROM masdistinctkeysdatewindow WHERE dateflag = 0; --113267 \COPY masdistinctkeys1 TO 'masdistinctkeys1.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV Now when you re-run the matcher on portcokeys and masdistinctkeys1 you will get 428 errors and excluding Undisclosed fields gets you down to 356. In Excel create a column to exclude any entries where the announced date occurs before the firstinvdate. Afterwards, manually flag the other duplicate mismatches picking the earlier announced date.  ==Import Flagged Matcher Data Into PSQL==After everything in the matcher output files is manually flagged create two new tables in Excel for iposmatcherouput and masmatcheroutput and copy those files into the tables. CREATE TABLE masmatcheroutputwithexcludeflag( file1coname varchar(255), file2targetname varchar(255), warningmsg varchar(255), file1coname1 varchar(255), file1statecode varchar(2), file1datefirstinv date, --mm-dd-yyyy file2targetname2 varchar(255), file2statecode varchar(2), file2announceddate date, exclude boolean, excludemaster boolean, excludemanual boolean, excludemanualmaster boolean, excludefinal boolean ); \COPY masmatcheroutputwithexcludeflag FROM 'masmatcheroutput-nohead.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV --9400  CREATE TABLE ipomatcheroutputwithexcludeflag( file1coname varchar(255), file2issuer varchar(255), warningmsg varchar(255), file1coname1 varchar(255), file1statecode varchar(2), file1datefirstinv date, --mm-dd-yyyy file2issuer1 varchar(255), file2issuedate date, file2statecode varchar(2), excludedate boolean, excludemaster boolean, excludemanual boolean, excludemanualmaster boolean, excludefinal boolean ); \COPY ipomatcheroutputwithexcludeflag FROM 'iposmatcheroutput-nohead.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV --2591 ==Joining portco with ipo and mas==In order to match the company with the ipo or acquisition date we need to join the three tables together. Do this in a two step process. First join with ipo and then take the resulting table and join with mas. This will give you companies with an ipo column and acquisition column. There will be a lot of blanks because some companies didn't have ipos or acquisitions. DROP TABLE portcoipojoin; CREATE TABLE portcoipojoin AS SELECT coname, statecode, datefirstinv, matcher.file2issuer, matcher.file2issuedate FROM companybase LEFT JOIN ipomatcheroutputwithexcludeflag AS matcher ON companybase.coname = matcher.file1coname AND companybase.statecode = matcher.file1statecode AND companybase.datefirstinv = matcher.file1datefirstinv AND matcher.excludefinal = FALSE; --44773 \COPY portcoipojoin TO 'portcoipojoin.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV Now join with the iposnoduplicates table to grab proceedamt and principalamt, etc. DROP TABLE portcoipojoinamt; CREATE TABLE portcoipojoinamt AS SELECT portcoipojoin.*, ipo.principalamt, ipo.proceedsamt, ipo.naiccode, ipo.zipcode, ipo.status, ipo.foundeddate, ipo.pkey FROM portcoipojoin LEFT JOIN iponoduplicates AS ipo ON portcoipojoin.file2issuer = ipo.issuer AND portcoipojoin.file2issuedate = ipo.issuedate AND portcoipojoin.statecode = ipo.statecode;  DROP TABLE portcoipojoinmajoin; CREATE TABLE portcoipojoinmajoin AS SELECT portco.*, matcher.file2targetname, matcher.file2announceddate FROM portcoipojoin AS portco LEFT JOIN masmatcheroutputwithexcludeflag AS matcher ON portco.coname = matcher.file1coname AND portco.statecode = matcher.file1statecode AND portco.datefirstinv = matcher.file1datefirstinv AND matcher.excludefinal = FALSE; --44774 \COPY portcoipojoinmajoin TO 'portcoipojoinmajoin.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV For mas you will need to join to the masnodupes table because you will want acquiror name and transaction amount. The matcher file only contains the key without the extra information. DROP TABLE portcomajoinmasnodupes; CREATE TABLE portcomajoinmasnodupes AS SELECT portcomajoin.*, masnodupes.* FROM portcomajoin LEFT JOIN masnodupes ON masnodupes.targetname = portcomajoin.file2targetname AND masnodupes.targetstatecode = portcomajoin.statecode AND masnodupes.announceddate = portcomajoin.file2announceddate; --44755 Then join with the portco table again and extract out the city and address. DROP TABLE portcoipomalocation; CREATE TABLE portcoipomalocation AS SELECT portcojoin.*, city, addr1, addr2, indclass, indsubgroup3, indminor FROM portcoipojoinmajoin AS portcojoin LEFT JOIN companybase ON portcojoin.coname = companybase.coname AND portcojoin.statecode = companybase.statecode; --44878 \COPY portcoipomalocation TO 'portcoipomalocation.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV --now join the two ma and ipo tables together on coname, statecode, datefirstinvDROP TABLE portcoipoma;CREATE TABLE portcoipoma ASSELECT ma.*, ipo.file2issuer, ipo.file2issuedate, ipo.principalamt, ipo.proceedsamt, ipo.naiccode, ipo.zipcode, ipo.status, ipo.foundeddate FROM portcomajoinmasnodupes AS ma JOIN portcoipojoinamt AS ipo ON ma.coname = ipo.coname AND ma.statecode = ipo.statecode AND ma.datefirstinv = ipo.datefirstinv;--44755\COPY portcoipoma TO 'portcoipoma.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV DROP TABLE portcoipomanodupes;CREATE TABLE portcoipomanodupes ASSELECT *FROM portcoipoma WHERE transactionamt IS NULL OR proceedsamt IS NULL;--44706\COPY portcoipomanodupes TO 'portcoipomanodupes.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV ==Joining geo with portcoipomas==Check the portcoipomas table first. Undisclosed Companies may have slipped through so you might have to rerun the joins against the companybase1 table selecting the appropriate flags. You will match portcoipomas with the geo datausing coname, city, year so you will need to strip the year from the datefirstinv.Use the EXTRACT(YEAR FROM datefirstinv) command. I broke these into separate tables: CREATE TABLE portcoipomalocationexclude AS SELECT *, EXTRACT(YEAR FROM datefirstinv) AS datefirstyear FROM portcoipomalocation AS portco WHERE NOT EXISTS(SELECT * FROM excludeipomadupes as exclude WHERE (portco.coname = exclude.coname AND portco.statecode = exclude.statecode AND portco.datefirstinv = exclude.datefirstinv AND exclude.excludeflag = 1));  CREATE TABLE companybasegeo AS SELECT portco.*, geo.lattitude, geo.longitude, geo.noaddress FROM portcoipomalocationexclude as portco LEFT JOIN geo ON (portco.coname = geo.coname AND portco.city = geo.city AND portco.datefirstyear = geo.startyear); You will notice that the output has blank entries for the noaddress flag. This indicates that the tables aren't joining correctly. Indeed you will see that the company Capella Systems, Inc. in the geo table is named Capella Systems Inc. in the portcoipomalocationexclude table. Therefore you will need to use the Matcher tool to match the keys in the two tables.  CREATE TABLE geomatcher ( geoconame varchar(255), geocity varchar(100), geostartyear varchar(4), file2coname varchar(255), file2city varchar(100), file2datefirstyear varchar(4) ); \COPY geomatcher FROM 'matcheroutputgeo.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV --43770 Geolookupbatch1 contains the first 2299 records. Geolookupbatch2 contains 6597 records. Geolookupbatch3 contains 2527 records. This sums to 11423 which matches the number of initial lookups?