According to results on November 6, 2024, former President Donald Trump had received *** Electoral College votes in the race to become the next President of the United States, securing him the presidency. With all states counted, Trump received a total of *** electoral votes. Candidates need *** votes to become the next President of the United States.

The Cumulative Report includes complete official election results for the 2020 Presidential General Election as of November 29, 2020. Results are released in three separate reports: The Vote By Mail 1 report contains complete results for ballots received by the Board of Elections on or before October 21, 2020, that could be accepted and opened before Election Day. The Vote By Mail 2 Canvass report contains complete results for all remaining Vote By Mail ballots that were received in a drop box or in person at the Board of Elections by 8:00pm on November 3, or were postmarked by November 3 and received timely by the Board of Elections by 10:00am on Friday, November 13. The Vote By Mail 2 Canvass begins on Thursday, November 5. The Provisional Canvass contains complete results for all provisional ballots issued to voters at Early Voting or on Election Day. For more information on this process, please visit the 2020 Presidential General Election Ballot Canvass webpage at https://www.montgomerycountymd.gov/Elections/2020GeneralElection/general-ballot-canvass.html. For turnout information, please visit the Maryland State Board of Elections Press Room webpage at https://elections.maryland.gov/press_room/index.html.

This graph shows the results of the 2016 presidential elections in the United States. Donald Trump has won the election with 306 votes in the electoral college. In the contested state of Florida he captured 49 percent of the vote.

This dataset contains county-level returns for presidential elections from 2000 to 2024.

This contains data relevant for the 2016 US Presidential Election, including up-to-date primary results.

Exploration Ideas

• What candidates within the Republican party have results that are the most anti-correlated?
• Which Republican candidate is Hillary Clinton most correlated with based on county voting patterns? What about Bernie Sanders?
• What insights can you discover by mapping this data?

Do you have answers or other exploration ideas? Add your ideas to this forum post and share your insights through Kaggle Scripts!

Do you think that we should augment this dataset with more data sources? Let us know here!

Data Description

The 2016 US Election dataset contains several main files and folders at the moment. You may download the entire archive via the "Download Data" link at the top of the page, or interact with the data in Kaggle Scripts through the ../input directory.

• PrimaryResults.csv: main primary results file
  • State: state where the primary or caucus was held
  • StateAbbreviation: two letter state abbreviation
  • County: county where the results come from
  • Party: Democrat or Republican
  • Candidate: name of the candidate
  • Votes: number of votes the candidate received in the corresponding state and county (may be missing)
  • FractionVotes: fraction of votes the president received in the corresponding state, county, and primary
• database.sqlite: SQLite database containing the PrimaryResults table with identical data and schema
• county_shapefiles: directory containing county shapefiles at three different resolutions for mapping

Original Data Sources

• Iowa County-Level Results
• New Hampshire County-Level Results
• County Shapefiles

County-level files include results from every state except Alaska, which does not have county equivalents. The District of Columbia is included and is treated as a single "county" for the purposes of this file. For Connecticut, New Hampshire, Maine, Massachusetts, Rhode Island and Vermont, the results are at the town level instead of the county level. All results are from official certified results files published by the states and the District of Columbia. Wisconsin's 2016 results are from the state's post-election recount. These files are collections of multiple states and as such they are not uniform in what they contain. Some states provide vote totals for each county and statewide, while others provide under votes and over votes. Some states provide write-in votes as a single bloc while others break them out by recipient. In addition, candidate and party names can differ across states for the same person or party, particularly for minor-party candidates. If you're going to do some sort of analysis or mapping of this data, you'll need to standardize the contents of the files first. These data have been collected and converted into a CSV file by the OpenElections project: http://www.openelections.net/

Abstract

The L2 Voter and Demographic Dataset includes demographic and voter history tables for all 50 states and the District of Columbia. The dataset is built from publicly available government records about voter registration and election participation. These records indicate whether a person voted in an election or not, but they do not record whom that person voted for. Voter registration and election participation data are augmented by demographic information from outside data sources.

Methodology

To create this file, L2 processes registered voter data on an ongoing basis for all 50 states and the District of Columbia, with refreshes of the underlying state voter data typically at least every six months and refreshes of telephone numbers and National Change of Address processing approximately every 30 to 60 days. These data are standardized and enhanced with propriety commercial data and modeling codes and consist of approximately 185,000,000 records nationwide.

Usage

For each state, there are two available tables: demographic and voter history. The demographic and voter tables can be joined on the LALVOTERIDvariable. One can also use the LALVOTERIDvariable to link the L2 Voter and Demographic Dataset with the L2 Consumer Dataset.

In addition, the LALVOTERIDvariable can be used to validate the state. For example, let's look at the LALVOTERID = LALCA3169443. The characters in the fourth and fifth positions of this identifier are 'CA' (California). The second way to validate the state is by using the RESIDENCE_ADDRESSES_STATEvariable, which should have a value of 'CA' (California).

The date appended to each table name represents when the data was last updated. These dates will differ state by state because states update their voter files at different cadences.

The demographic files use 698 consistent variables. For more information about these variables, see 2025-01-10-VM2-File-Layout.xlsx.

The voter history files have different variables depending on the state. The ***2025-07-09-L2-Voter-Dictionaries.tar.gz file contains .csv data dictionaries for each state's demographic and voter files. While the demographic file data dictionaries should mirror the 2025-01-10-VM2-File-Layout.xlsx*** file, the voter file data dictionaries will be unique to each state.

***2025-04-24-National-File-Notes.pdf ***contains L2 Voter and Demographic Dataset ("National File") release notes from 2018 to 2025.

***2025-07-09-L2-Voter-Fill-Rate.tar.gz ***contains .tab files tracking the percent of non-null values for any given field.

Bulk Data Access

Data access is required to view this section.

DataMapping Tool

Data access is required to view this section.

This table contains data on the percent of adults (18 years or older) who are registered voters and the percent of adults who voted in general elections, for California, its regions, counties, cities/towns, and census tracts. Data is from the Statewide Database, University of California Berkeley Law, and the California Secretary of State, Elections Division. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Political participation can be associated with the health of a community through two possible mechanisms: through the implementation of social policies or as an indirect measure of social capital. Disparities in political participation across socioeconomic groups can influence political outcomes and the resulting policies could have an impact on the opportunities available to the poor to live a healthy life. Lower representation of poorer voters could result in reductions of social programs aimed toward supporting disadvantaged groups. Although there is no direct evidentiary connection between voter registration or participation and health, there is evidence that populations with higher levels of political participation also have greater social capital. Social capital is defined as resources accessed by individuals or groups through social networks that provide a mutual benefit. Several studies have shown a positive association between social capital and lower mortality rates, and higher self- assessed health ratings. There is also evidence of a cycle where lower levels of political participation are associated with poor self-reported health, and poor self-reported health hinders political participation. More information about the data table and a data dictionary can be found in the About/Attachments section.

Analysis of ‘🗳 Primary Candidates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/primary-candidatese on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This folder contains the data behind the stories:

• We Researched Hundreds Of Races. Here’s Who Democrats Are Nominating.
• How’s The Progressive Wing Doing In Democratic Primaries So Far?
• We Looked At Hundreds Of Endorsements. Here’s Who Republicans Are Listening To.

This project looks at patterns in open Democratic and Republican primary elections for the U.S. Senate, U.S. House and governor in 2018.

dem_candidates.csv contains information about the 811 candidates who have appeared on the ballot this year in Democratic primaries for Senate, House and governor, not counting races featuring a Democratic incumbent, as of August 7, 2018.

rep_candidates.csv contains information about the 774 candidates who have appeared on the ballot this year in Republican primaries for Senate, House and governor, not counting races featuring a Republican incumbent, through September 13, 2018.

Here is a description and source for each column in the accompanying datasets.

dem_candidates.csv and rep_candidates.csv include:

Column	Description
Candidate	All candidates who received votes in 2018’s Democratic primary elections for U.S. Senate, U.S. House and governor in which no incumbent ran. Supplied by Ballotpedia.
State	The state in which the candidate ran. Supplied by Ballotpedia.
District	The office and, if applicable, congressional district number for which the candidate ran. Supplied by Ballotpedia.
Office Type	The office for which the candidate ran. Supplied by Ballotpedia.
Race Type	Whether it was a “regular” or “special” election. Supplied by Ballotpedia.
Race Primary Election Date	The date on which the primary was held. Supplied by Ballotpedia.
Primary Status	Whether the candidate lost (“Lost”) the primary or won/advanced to a runoff (“Advanced”). Supplied by Ballotpedia.
Primary Runoff Status	“None” if there was no runoff; “On the Ballot” if the candidate advanced to a runoff but it hasn’t been held yet; “Advanced” if the candidate won the runoff; “Lost” if the candidate lost the runoff. Supplied by Ballotpedia.
General Status	“On the Ballot” if the candidate won the primary or runoff and has advanced to November; otherwise, “None.” Supplied by Ballotpedia.
Primary %	The percentage of the vote received by the candidate in his or her primary. In states that hold runoff elections, we looked only at the first round (the regular primary). In states that hold all-party primaries (e.g., California), a candidate’s primary percentage is the percentage of the total Democratic vote they received. Unopposed candidates and candidates nominated by convention (not primary) are given a primary percentage of 100 but were excluded from our analysis involving vote share. Numbers come from official results posted by the secretary of state or local elections authority; if those were unavailable, we used unofficial election results from the New York Times.
Won Primary	“Yes” if the candidate won his or her primary and has advanced to November; “No” if he or she lost.

dem_candidates.csv includes:

Column	Description
Gender	“Male” or “Female.” Supplied by Ballotpedia.
Partisan Lean	The FiveThirtyEight partisan lean of the district or state in which the election was held. Partisan leans are calculated by finding the average difference between how a state or district voted in the past two presidential elections and how the country voted overall, with 2016 results weighted 75 percent and 2012 results weighted 25 percent.
Race	“White” if we identified the candidate as non-Hispanic white; “Nonwhite” if we identified the candidate as Hispanic and/or any nonwhite race; blank if we could not identify the candidate’s race or ethnicity. To determine race and ethnicity, we checked each candidate’s website to see if he or she identified as a certain race. If not, we spent no more than two minutes searching online news reports for references to the candidate’s race.
Veteran?	If the candidate’s website says that he or she served in the armed forces, we put “Yes.” If the website is silent on the subject (or explicitly says he or she didn’t serve), we put “No.” If the field was left blank, no website was available.
LGBTQ?	If the candidate’s website says that he or she is LGBTQ (including indirect references like to a same-sex partner), we put “Yes.” If the website is silent on the subject (or explicitly says he or she is straight), we put “No.” If the field was left blank, no website was available.
Elected Official?	We used Ballotpedia, VoteSmart and news reports to research whether the candidate had ever held elected office before, at any level. We put “Yes” if the candidate has held elected office before and “No” if not.
Self-Funder?	We used Federal Election Committee fundraising data (for federal candidates) and state campaign-finance data (for gubernatorial candidates) to look up how much each candidate had invested in his or her own campaign, through either donations or loans. We put “Yes” if the candidate donated or loaned a cumulative $400,000 or more to his or her own campaign before the primary and “No” for all other candidates.
STEM?	If the candidate identifies on his or her website that he or she has a background in the fields of science, technology, engineering or mathematics, we put “Yes.” If not, we put “No.” If the field was left blank, no website was available.
Obama Alum?	We put “Yes” if the candidate mentions working for the Obama administration or campaign on his or her website, or if the candidate shows up on this list of Obama administration members and campaign hands running for office. If not, we put “No.”
Dem Party Support?	“Yes” if the candidate was placed on the DCCC’s Red to Blue list before the primary, was endorsed by the DSCC before the primary, or if the DSCC/DCCC aired pre-primary ads in support of the candidate. (Note: according to the DGA’s press secretary, the DGA does not get involved in primaries.) “No” if the candidate is running against someone for whom one of the above things is true, or if one of those groups specifically anti-endorsed or spent money to attack the candidate. If those groups simply did not weigh in on the race, we left the cell blank.
Emily Endorsed?	“Yes” if the candidate was endorsed by Emily’s List before the primary. “No” if the candidate is running against an Emily-endorsed candidate or if Emily’s List specifically anti-endorsed or spent money to attack the candidate. If Emily’s List simply did not weigh in on the race, we left the cell blank.
Gun Sense Candidate?	“Yes” if the candidate received the Gun Sense Candidate Distinction from Moms Demand Action/Everytown for Gun Safety before the primary, according to media reports or the candidate’s website. “No” if the candidate is running against an candidate with the distinction. If Moms Demand Action simply did not weigh in on the race, we left the cell blank.
Biden Endorsed?	“Yes” if the candidate was endorsed by Joe Biden before the primary. “No” if the candidate is running against a Biden-endorsed candidate or if Biden specifically anti-endorsed the candidate. If Biden simply did not weigh in on the race, we left the cell blank.
Warren Endorsed?	“Yes” if the candidate was endorsed by Elizabeth Warren before the primary. “No” if the candidate is running against a Warren-endorsed candidate or if Warren specifically anti-endorsed the candidate. If Warren simply did not weigh in on the race, we left the cell blank.
Sanders Endorsed?	“Yes” if the candidate was endorsed by Bernie Sanders before the primary. “No” if the candidate is running against a Sanders-endorsed candidate or if Sanders specifically anti-endorsed the candidate. If Sanders simply did not weigh in on the race, we left the cell

Last July 28th, 2024, Venezuela elected a new president. The election was carried out through over 30k polling places across the country. The main dataset (elecciones_venezuela_24.csv) contains the data collected from electoral observers. The data was scraped from the actual digitalized tallies produced along the process.

This data is public, according to Venezuela's electoral law, meaning that it is available for everyone to analyze.

Kudos to Jose Guerra for sharing the original dataset.

The main dataset has the following columns:

• COD_EDO. State code
• EDO. State name (i.e. Falcon State, Miranda State)
• COD_MUN. Municipality code
• MUN. Municipality name
• COD_PAR. County code
• PAR. County name (Parroquia)
• CENTRO. Electoral center/place
• MESA. Polling place. Each polling place corresponds to one electoral machine
• RE. Electoral registry. Number of people registered to vote in a give polling place
• VOTOS_VALIDOS. Valid casted vote
• VOTOS_NULOS. Non valid vote
• EG. Number of votes for candidate Edmundo Gonzalez
• NM. Number of votes for candidate Nicolas Maduro
• LM. Number of votes for candidate Luis Martinez
• JABE. Number of votes for candidate Javier Bertucci
• JOBR. Number of votes for candidate Jose Brito
• AE. Number of votes for candidate Antonio Ecarri
• CF. Number of votes for candidate Claudio Fermin
• DC. Number of votes for candidate Daniel Ceballos
• EM. Number of votes for candidate Enrique Marquez
• BERA. Number of votes for candidate Benjamin Rausseo

The Voter Participation indicator presents voter turnout in Champaign County as a percentage, calculated using two different methods.

In the first method, the voter turnout percentage is calculated using the number of ballots cast compared to the total population in the county that is eligible to vote. In the second method, the voter turnout percentage is calculated using the number of ballots cast compared to the number of registered voters in the county.

Since both methods are in use by other agencies, and since there are real differences in the figures that both methods return, we have provided the voter participation rate for Champaign County using each method.

Voter participation is a solid illustration of a community’s engagement in the political process at the federal and state levels. One can infer a high level of political engagement from high voter participation rates.

The voter participation rate calculated using the total eligible population is consistently lower than the voter participation rate calculated using the number of registered voters, since the number of registered voters is smaller than the total eligible population.

There are consistent trends in both sets of data: the voter participation rate, no matter how it is calculated, shows large spikes in presidential election years (e.g., 2008, 2012, 2016, 2020) and smaller spikes in intermediary even years (e.g., 2010, 2014, 2018, 2022). The lowest levels of voter participation can be seen in odd years (e.g., 2015, 2017, 2019, 2021, 2023).

This data primarily comes from the election results resources on the Champaign County Clerk website. Election results resources from Champaign County include the number of ballots cast and the number of registered voters. The results are published frequently, following each election.

Data on the total eligible population for Champaign County was sourced from the U.S. Census Bureau, using American Community Survey (ACS) 1-Year Estimates for each year starting in 2005, when the American Community Survey was created. The estimates are released annually by the Census Bureau.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because this data is not available for Champaign County, the eligible voting population for 2020 is not included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Population by Sex and Population Under 18 Years by Age.

Sources: Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (10 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (5 October 2023).; Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (7 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; Champaign County Clerk Election History; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (6 March 2017).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).

This data-set by the Electoral Integrity Project evaluates the integrity of the US presidential election held on 8 November 2016. Based on a survey collecting the views of US-based political scientists, this research provides independent and reliable evidence to assess whether the 2016 presidential election met international standards of electoral integrity. The survey asks respondents to evaluate how the presidential election on 8 November 2016 was conducted in each of the 50 US states plus the District of Columbia. The study collects 49 indicators to measure electoral integrity. These indicators are clustered to evaluate eleven stages in the electoral cycle as well as generating an overall summary Perception of Electoral Integrity (PEI) 100-point index and comparative ranking of US states. The datasets are available for analysis at two levels: STATE-level (51 cases); and EXPERT-level (726 cases). Each dataset can be downloaded in STATA, SPSS, CSV and R formats. PEI_US_1.0 compiles the responses of 726 experts, representing an overall response rate of 18.9%. The study is conducted by Pippa Norris, Alessandro Nai and Max Grömping for the Electoral Integrity Project based at the Universities of Sydney and Harvard.

Context

Tamil Nadu 2021 State Assembly Elections - Dataset contains Constituency wise contestant, polling details & Winning details

Content

Tamil Nadu 2021 State Assembly Elections - Event date -6-April-2021 Results out on - 2 May-2021

Data File - Tamil_Nadu_State_Elections_2021_Constituency_Metadata.csv

• Constituency - Demographic location name in Tamil Nadu ( India )
• District - District on which Constituency belongs to ( Probably District may contain 6-8 Constituency )
• Reserved. - Holds it belongs to SC/ST or General
• Lok_sabha_constituency - Lok Sabha is a national election constituency ( Probably each Lok Sabha constituency have 6 State Constituency )
• State_Name - State Name in India - "Tamil Nadu"

Data File - Tamil_Nadu_State_Elections_2021_Details.csv

• Constituency - Demographic location name in Tamil Nadu ( India )
• Candidate - Name of the Candidate who contested in the Constituency
• Party - Name of the party the Candidate belongs to
• EVM_Votes - No. of Electoral Voting Machine Votes
• Postal_Votes - No. of Votes from Postal
• Total_Votes - Total Votes for the candidate
• %_of_Votes - Percentage of votes the candidate get in his constituency
• Tot_Constituency_votes_polled - Total no. of votes polled in the constituency
• Tot_votes_by_parties - Total votes the party got in all the constituency
• Winning_votes - Total votes declated as win
• Win_Lost_Flag 🎊 - True or False

General Information 👍 -

Total State Election Constituency in Tamil Nadu - 234 Total Lok Sabha Election Constituency in Tamil Nadu - 39

Acknowledgements

Thanks 🤩👐to https://www.elections.tn.gov.in/Elections.aspx for detailed information

Inspiration

Tamil Nadu is one of the highly industrialized states in India, It Contributes 10-15 % of India's GDP. 2021 State elections date help to understand the voting pattern, People of TN have given mandate to which party EDA, Data Visualisation & ML Modeling

This dataset contains information about the parties from UK, Italy, Germany, Spain, and Poland that ran ads on Facebook during the 2019 EU parliamentary elections. For each party, we manually associated the corresponding Facebook page that ran the ads. We also report the PopuList tags as per https://popu-list.org.

"2019 EU elections parties.csv" contains aggregate data:

• State: the country of the party
• Party: acronym of the party
• Name: extended name of the party
• Percentage: share of votes in the election
• Facebook page_name: name of the Facebook page that ran the ads
• Facebook page_id: id of the Facebook page that ran the ads
• populist: binary label indicating whether the party is populist according to PopuList
• farright: binary label indicating whether the party is far right according to PopuList
• farleft: binary label indicating whether the party is far left according to PopuList
• eurosceptic: binary label indicating whether the party is eurosceptic according to PopuList
• found on populist: binary label indicating whether the party is on any list on PopuList
• seat: whether the party got a seat in any previous national election (selection criterion from PopuList)

"FB ads impressions cost populist demographic votes" contains detailed data:

• ad_id: Facebook ad id
• page_id: id of the Facebook page who published the ad
• page_name: name of the Facebook page who published the ad
• ad_creative_body: text of the ad
• ad_creative_*: other properties of the ad (caption, links, title)
• ad_delivery_start_time,ad_delivery_stop_time: time of beginning and end of the ad campaign
• funding_entity: declared party who financed the ad
• impressions_min, impressions_max: range of impressions for the ad
• spend_min, spend_max: range of expenditure for the ad
• ad_url: URL of the ad
• state: state of the party
• impression_mean, spend_mean: average number of impressions and expenditure
• imp_per_day: number of impressions per day
• populist, farright, farleft, eurosceptic, popu-list: binary tags from PopuList (refer to the description above)
• female_*, male_*, unknown_*: fraction of impressions for each demographic bucket (defined as gender_age)
• party: name of the party
• percentage: share of votes in the election

This dataset provides the number of votes and the percentage of the first preference vote won by each of the parties in the 2016 federal election. The data also includes the first preferences swing by party - a comparison of the percentage of first preference votes for each party compared to the percentage of first preference votes received at the previous federal election.

For more information please visit the Australian Electoral Commission.

Please note:

• AURIN has combined and re-structured the original state level data for "First preferences by candidate by polling place".

• AURIN has spatially enabled the data using locations of polling places.

• AURIN has calculated the polling booth vote percentages for each party and included them in this dataset.

• Where multiple independent candidates were running for election in the same seat, their votes have been summed in this dataset and their swing percentages have been excluded.

• A first preference vote is where the voter has given that party's candidates a number 1 on the ballot paper.

This dataset provides the number of votes and the percentage of the first preference vote won by each of the parties in the 2019 federal election. The data also includes the first preferences swing by party - a comparison of the percentage of first preference votes for each party compared to the percentage of first preference votes received at the previous federal election.

For more information please visit the Australian Electoral Commission.

Please note:

• AURIN has combined and re-structured the original state level data for "First preferences by candidate by polling place".

• AURIN has spatially enabled the data using locations of polling places.

• AURIN has calculated the polling place vote percentages for each party and included them in this dataset.

• Where multiple independent candidates were running for election in the same seat, their votes have been summed and their swing percentages have been excluded from this dataset.

• A first preference vote is where the voter has given that party's candidates a number 1 on the ballot paper.

• These results are not final.