Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

This dataset contains the 2024 Primary and General Election Official Results from the Maryland State and Local Offices, as well as Presidential election-related information and webpages.

EPILOGUE: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2Fa5eefdb31428bd5ce99cdf76fa484a63%2Fmap.jpg?generation=1733007717460285&alt=media" alt="">

FINAL UPDATE: It's election night, and the results are coming in. The final update includes the latest poll data from 538, which is from two days ago. Thanks all for following the development of this dataset.

OCTOBER UPDATE: The past month has been typical of the final weeks before the election - rallies, interviews, and advertising. This update includes a transcript of the VP debate between Walz and Vance, and the latest poll summaries.

SEPTEMBER UPDATE: Trump and Harris had their first debate. This update includes the transcript and recent poll results. Also, there was a second attempt to kill former President Trump! No shots fired though on this one. You'll see aerial diagrams of both attempts in the dataset.

https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftse4.mm.bing.net%2Fth%3Fid%3DOIF.edyLiGntLZbwC9fBkg8TsQ%26pid%3DApi&f=1&ipt=a1096b37cf3eced7dff70d362a2c76f8876422f53c47856cadf09f9fa18b367e&ipo=images" alt="debate">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2F0ecedf88421c303e0112734a30de9e29%2Frouth.jpg?generation=1726701011377683&alt=media">

LATE AUGUST UPDATE: The Democratic Party replaced President Biden with his VP, Kamala Harris. It's now Trump v Harris along with one nominee from each of the smaller factions.

https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fmedia.cnn.com%2Fapi%2Fv1%2Fimages%2Fstellar%2Fprod%2F240122181719-trump-kamala-vpx-split-2.jpg%3Fc%3D16x9%26q%3Dw_850%2Cc_fill&f=1&nofb=1&ipt=984b6cf55cf55e1539003ca1c1beaa359625f6e5b08b511b3b018c9d2c959ae5&ipo=imagesg">

https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Chase_Oliver%2C_Jill_Stein_%26_Randall_Terry_%2853866448015%29.jpg/1280px-Chase_Oliver%2C_Jill_Stein_%26_Randall_Terry_%2853866448015%29.jpg">

AUGUST UPDATE: This election season just gets crazier and crazier. You'll see new data related to the assassination attempt on former President Trump. There are transcripts of Secret Service hearings and an annotated image of the rally area.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2F75dd20a00c2ac6d81c6d6e1f83cbd941%2Fdonald-trump-rally-shooting-2024-113.webp?generation=1722800392288670&alt=media">

JULY UPDATE: Added the transcript of the debate between Trump and Biden.

MAY UPDATE: Added some new polls and also a meta-poll assessing the quality of select pollsters.

APRIL UPDATE : The dataset now contains approval ratings for sitting presidents, which includes Biden and Trump.

MARCH UPDATE: As of last week, the presumptive nominees are Joe Biden(D) and Donald Trump(R). They also ran against each other in 2020. Robert F Kennedy Jr is running as an independent.

Presidential elections occur quadrennially in years evenly divisible by 4, on the first Tuesday after November 1. Presidential candidates from the major political parties usually declare their intentions to run as early as the spring of the previous calendar year before the election. The two major parties each nominate one candidate through a process of primary elections and nominating conventions during the election year. (source: Wikipedia)

This dataset contains data on candidates, primary/caucus results, polls, and debate transcripts. Updates and additional data will be added as the landscape develops.

Note: Version 3 of this dataset contains previous coverage of the 2022 Congressional Mid-term Elections.

Description:

This dataset contains comprehensive voting data for the 2024 US elections, focusing on general ballot measures. This information includes voting results from various sources and tracking public opinion about political parties and candidates across states and demographic groups. Each item in the dataset represents a specific poll. Along with detailed information about the dates of the polls. Survey organization, sample size, margin of error, Percentage of respondents supporting each political party or candidates

Key Features:

Poll Date:The date when the poll was conducted.

Polling Organization: The name of the organization that conducted the poll.

Sample Size: The number of respondents in the poll.

Margin of Error: The statistical margin of error for the poll results.

Party/Candidate Support: Percentage of respondents who support each political party or candidate.

State/Demographics: Geographic and demographic breakdowns of the polling data.

Use Cases:

Analyzing trends in public opinion leading up to the 2024 U.S. elections. Comparing support for different political parties and candidates over time. Studying the impact of key events on voter preferences. Informing political strategies and campaign planning.

This dataset provides detailed county-level returns for U.S. presidential general elections, compiled by Dave Leip’s Atlas of U.S. Presidential Elections. For each election year included, the dataset is distributed as an Excel workbook (.xlsx) with multiple worksheets and accompanied by machine-readable CSV files for additional administrative levels (county, congressional district, state). There are two codebooks for the this data collection describing variable names and meanings: one for the Congressional District level data and the other for County level data.The Excel workbook contains:Candidates – names and party ballot listings by state.Vote Data by State – statewide vote totals for each candidate, with boundary identifiers (FIPS codes).Vote Data by County – county-level vote totals for all states and the District of Columbia, with FIPS codes.Vote Data by Town – town-level results for New England states (ME, MA, CT, RI, VT, NH), with FIPS codes.Graphs – pie charts summarizing results by state and nationally.Party – statewide vote strength of major parties.Statistics – summary statistics including closest races, maxima, and other aggregate indicators.Data Sources – documentation of sources used to compile the dataset.For the 2016, 2020, and 2024 elections, additional Excel workbooks and CSV files are provided at the congressional district (CD) level, containing:Vote Data by Congressional District – vote totals by district for each candidate, with FIPS codes. Includes detailed allocations for counties that span multiple congressional districts.Data Sources – documentation of sources used to compile the dataset.Candidates – candidate names and national party ballot listings.Notes – state-level notes describing data compilation details.

According to exit polling in ten key states of the 2024 presidential election in the United States, 46 percent of voters with a 2023 household income of 30,000 U.S. dollars or less reported voting for Donald Trump. In comparison, 51 percent of voters with a total family income of 100,000 to 199,999 U.S. dollars reported voting for Kamala Harris.

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.

This file shows all election related data by state and county (i.e. total votes, Republican votes, Democratic votes, Republican voting percentage, Democratic voting percentage) for both the 2020 and 2024 U.S. Presidential Elections.

This study contains files of Presidential election votes by State, County, and Town for each U.S. Presidential election year from 1964-2020. From Dave Leip, Atlas of U.S. Presidential Elections. Note: MIT posted similar publicly available data beginning with 1976 at https://doi.org/10.7910/DVN/42MVDX

Information available in each dataset

If you want to know what each Presidential Election dataset contains before downloading it, for easy reference, the CCSS Data Services team prepared a spreadsheet summarizing the contents of each dataset. You can view them in this Summary of contents and codebooks spreadsheet.

The summary spreadsheet contains the following: 1. A matrix table summarizing the information available in each Presidential election dataset 2. Codebook describing the variables in the Presidential Election vote data at the State level 3. Codebook describing the variables in the Presidential Election vote data at the County level 4. Codebook describing the variables in the Presidential Election vote data at the Town level 5. A matrix table listing the statistics and graphs included in each Presidential election dataset

Labels of the variables in the State, County, and Town data, as well as a description of each tab in the dataset, are also available here: https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_national.html

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php has additional years of data available going back to 1912 but at a fee.

Sometimes the files are updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS Data Discovery and Replication Services. These files were last checked for updates in June 2024.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the CCSS Data and Reproduction Archive Version number in your citations for the full dataset.

This dataset provides county and state-level returns for U.S. Senate general elections, compiled by Dave Leip’s Atlas of U.S. Presidential Elections. For each election year included, the dataset is distributed as an Excel workbook (.xlsx) with multiple worksheets and accompanied by machine-readable CSV files (county and state levels). A codebook for the data collection describing variable names and meanings is provided as an .rtf file.The Excel workbook contains:Candidates – names and party ballot listings by state.Vote Data by State – statewide vote totals for each candidate, with boundary identifiers (FIPS codes).Vote Data by County – county-level vote totals for all states and the District of Columbia, with FIPS codes.Vote Data by Town – town-level results for New England states (ME, MA, CT, RI, VT, NH), with FIPS codes.Graphs – pie charts summarizing results by state and nationally.Party – statewide vote strength of major parties.Statistics – summary statistics including closest races, maxima, and other aggregate indicators.Data Sources – documentation of sources used to compile the dataset.

This dataset provides county and congressional district–level returns for U.S. House of Representatives general elections, compiled by Dave Leip’s Atlas of U.S. Presidential Elections. For each election year included, the dataset is distributed as an Excel workbook (.xlsx) with multiple worksheets, accompanied by machine-readable CSV files at the county, congressional district, and state levels. The codebook for the data collection, describing variable names and meanings, is provided as an .rtf file.The Excel workbook contains:Candidates – names and party ballot listings by state.Vote Data by State – statewide vote totals for each candidate, with boundary identifiers (FIPS codes).Vote Data by County – county-level vote totals for all states and the District of Columbia, with FIPS codes.Vote Data by Town – town-level results for New England states (ME, MA, CT, RI, VT, NH), with FIPS codes.Vote Data by Congressional District – vote totals for all congressional districts nationwide.Graphs – pie charts summarizing results by state and nationally.Party – statewide vote strength of major parties.Statistics – summary statistics including closest races, maxima, and other aggregate indicators.Voter Turnout by State – voting-age population and turnout data by state.Data Sources – documentation of sources used to compile the dataset.

This dataset provides detailed county-level returns for U.S. gubernatorial elections, compiled by Dave Leip’s Atlas of U.S. Presidential Elections. For each election year included, the dataset is distributed as an Excel workbook (.xlsx) with multiple worksheets, accompanied by a machine-readable county-level CSV file, and a state-level CSV file. The codebook for the data collection, describing variable names and meanings, is provided as an .rtf file.The Excel workbook contains:Candidates – names and party ballot listings by state.Vote Data by State – statewide vote totals for each candidate, with boundary identifiers (FIPS codes).Vote Data by County – county-level vote totals for all states and the District of Columbia, with FIPS codes.Vote Data by Town – town-level results for New England states (ME, MA, CT, RI, VT, NH), with FIPS codes.Graphs – pie charts summarizing results by state and nationally.Party – statewide vote strength of major parties.Statistics – summary statistics including closest races, maxima, and other aggregate indicators.Data Sources – documentation of sources used to compile the dataset.

this graph was created in eci.gov.in:

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The Indian General Elections 2024 Dataset offers an extensive and detailed overview of the election results from constituencies across India. This dataset serves as a rich resource for political analysts, researchers, and anyone interested in understanding the intricacies of one of the world's largest democratic exercises.

Overview The dataset covers a wide range of information, capturing the nuances of electoral outcomes and the performance of candidates and political parties. It includes:

Constituency Information Name of Constituency: Detailed information about each constituency's name. State: The state to which each constituency belongs, providing a clear geographical context. Candidate Details Candidate Name: Names of all candidates who contested in the elections. Party Affiliation: Political parties to which the candidates belong, offering insights into the political landscape. Votes: Detailed breakdown of votes received by each candidate, including: EVM (Electronic Voting Machine) Votes: Total number of votes received through EVMs. Postal Votes: Number of postal votes counted for each candidate. Election Results Result Status: The outcome of the election for each candidate, indicating whether they "Won" or "Lost". Significance This dataset is invaluable for several reasons:

Political Analysis: It enables detailed analysis of voting patterns, party performance, and constituency-specific results. Academic Research: Scholars and students can use this dataset for research projects, theses, and dissertations focusing on political science, sociology, and related fields. Data-Driven Decision Making: Political strategists, policymakers, and data scientists can leverage this data to make informed decisions and create predictive models. Applications Data Science Projects: Utilize this dataset to build predictive models, analyze trends, and uncover insights into the electoral process. Dashboard Creation: Create comprehensive dashboards to visualize the election results, making it easier to communicate findings and trends. Comparative Studies: Conduct comparative studies of electoral performance across different states and constituencies.

Conclusion

The Indian General Elections 2024 Dataset is a powerful tool for anyone interested in delving into the details of India's electoral process. Its comprehensive nature and the breadth of information it covers make it an essential resource for political analysis, academic research, and data-driven decision-making. Whether you are a researcher, a data scientist, or a political strategist, this dataset provides the necessary details to understand and interpret the dynamics of the 2024 Indian General Elections effectively. Thank you!

This Dataset contains State, Lok Sabha Constituency, its reservation category and Political Party-wise EVM, Postal and Total Votes Polled in General Elections to Lok Sabha 2024

U.S. Presidential Election Constituency Returns (1976-2020)

  Dataset Summary

This dataset contains state-level constituency returns for U.S. presidential elections from 1976 to 2020, compiled by the MIT Election Data Science Lab. The dataset includes 4,287 observations across 15 variables, offering detailed insights into the voting patterns for presidential elections over four decades. The data sources include the biennially published document “Statistics of the… See the full description on the dataset page: https://huggingface.co/datasets/fdaudens/us-presidential-elections-with-electoral-college.

This dataset contains the state-wise election results for the Indian General Election 2024, scraped from the official Election Commission of India (ECI) results website. The data includes information on the number of seats won, by various political parties across different states and union territories.

Data Collection

The data was collected using web scraping techniques employing Selenium and Beautiful Soup. The following steps outline the data collection process:

Accessing the ECI Website: Selenium was used to automate a Chrome browser to access the ECI results page.

Dropdown Selection: The script selected each state from a dropdown menu to extract results for all states and union territories.

Data Extraction: For each state, the election results table was located and its contents were extracted into a list. This included party names, the number of seats won, leading, and total seats.

Data Aggregation: The extracted data was then compiled into a single DataFrame, with an additional column indicating the state for each row of data.

State List Extraction: Beautiful Soup was used to parse the state options from the dropdown menu on the ECI results page to ensure all states and union territories were covered.

David Leip provides election returns from presidential, senatorial, gubernatorial and House races at state, county and precinct level. Data includes names of candidates, parties, popular and electoral vote totals, voter turnout, and more. While some data is available for free on David Leip’s website, MIT researchers have access to more granular data from following elections and years: Presidential Primaries (county level): 2000, 2004, 2008, 2012, 2016, 2020, 2024 Presidential General Elections Results by: State: 1824-2024 County: 1980, 2016, 2020, 2024 Precincts: 1992, 1996, 2016, 2020 Congressional districts: 2016, 2020 Gubernatorial General Election : 2022 House of Representatives (General Election, state, county, congressional districts level): 1992 – 2024 U.S. Senate (General Election, state,county, town level): 2020, 2022, 2024 Registration and Turnout (General Election , state, county level): 1992-2024 DATA AVAILABLE FOR YEARS: 1824-2024 (some coverage gaps)

This dataset captures coded instances of dangerous speech in the 2024 U.S. Presidential Election, developed collaboratively by the Dangerous Speech Project’s U.S. Presidential Election Fellows. Each entry includes hallmarks of dangerous speech, targeted groups, speaker, intended audience, contextual factors, medium, and relative dangerousness. Data are anonymized and drawn from public sources including social media, campaign websites, rallies, and archival tools. This dataset provides a structured resource for analyzing inflammatory rhetoric, misinfromation, and psychological manipulation, and is suitable for research in political communication, media studies, electoral studies, far-right studies, and other related fields.

POLITISKY24 (Political Stance Analysis on Bluesky for 2024) is a first-of-its-kind dataset for stance detection, focused on the 2024 U.S. presidential election. It designed for target-specific user-level stance detection and contains 16,044 user-target stance pairs centered on two key political figures, Kamala Harris and Donald Trump. In addition, this dataset includes detailed metadata, such as complete user posting histories and engagement graphs (likes, reposts, and quotes).

Stance labels were generated using a robust and evaluated pipeline that integrates state-of-the-art Information Retrieval (IR) techniques with Large Language Models (LLMs), offering confidence scores, reasoning explanations, and text spans for each label. With an LLM-assisted labeling accuracy of 81%, POLITISKY24 provides a rich resource for the target-specific stance detection task. This dataset enables the exploration of Bluesky platform, paving the way for deeper insights into political opinions and social discourse, and addressing gaps left by traditional datasets constrained by platform policies.

In the uploaded files:

• The file 'Human_annotation_on_validation_users.csv' contains human-annotated stance labels for 445 validation users toward Trump and Harris, resulting in a total of 890 user-target pairs.
  The labels are divided into four stances: 1 (favor), 2 (against), 3 (neutral), and 4 (unrelated). To simplify the stance annotations provided by the large language model, the "neutral" and "unrelated" categories are combined and represented as "neither."
• The file 'LLM_annotation_on_validation_users.json' contains stance labels annotated by a state-of-the-art LLM for 445 validation users toward Trump and Harris, resulting in a total of 890 user-target pairs. In addition to stance labels, each pair includes an explanation of the reasoning, the source tweets, spans from the source tweets used in the reasoning, and a confidence score.
• The file 'LLM_annotation_on_dataset_users.json' is similar to 'LLM_annotation_on_validation_users.json but is generated for all dataset users excluding the validation set. It provides stance labels for 8,022 users toward Trump and Harris, totaling 16,044 user-target pairs.
• The file 'Main_dataset_for_stance_detection.parquet' contains up to 1,000 recent English-language posts (including both original posts and reposts) from each of the 8,022 + 445 = 8,467 users. This file was used for the stance detection task.
• The file 'Bluesky_dataset_on_us_politics.parquet' is similar to 'Main_dataset_for_stance_detection.parquet', but it contains all posts (including both original posts and reposts) from each of the 8,022 + 445 = 8,467 users.
• The file 'Like_network.parquet' captures users' interactions through likes. Specifically, it contains the number of likes each user has given to original posts made by other users. It includes likes from 8,022 + 445 = 8,467 users, but it is not limited to interactions from these users alone.
• The files 'Repost_network.parquet' and 'Quote_network.parquet' are similar to 'Like_network.parquet', but they capture users' interactions through reposts and quotes, respectively.

Context

On July 28, 2024, Venezuela held its presidential elections. Once the voting was over, the two main candidates, president Nicolás Maduro, and opposition leader Edmundo González Urrutia, declared themselves winners. After several hours, the Venezuelan national electoral council (CNE) proclaimed Maduro as the winner with 51,2% of the votes, followed by González Urrutia with 44.2%. At the same time, the opposition denounced massive electoral fraud, supported by the fact that the CNE did not publish the voting records. The Venezuelan government responded with a counter-complaint, alleging that they had been hacked by external agents.

However, on July 30, the Venezuelan opposition declared having obtained more than 80% of the voting records, and leaked them on an open website. According to the results published there, González Urrutia obtained 67% of the votes, and Maduro obtained 30%. After analyzing the data, many countries have considered them legitimate, and have recognized González Urrutia as the legitimate president-elect.

The URL of the open website is https://resultadosconvzla.com/

Content

This dataset contains the results published in the open website. Each row represents an Electoral Machine, and shows the number of votes obtained for each presidential candidate. Each Electoral Machine was placed in a Voting Center, which belongs to a Parish (County), which in turn belongs to a Municipality, which in turn belongs to a State. The dataset contains this information perfectly categorized.

The last column contains the URL that shows the JPG of the signed voting record of that Electoral Machine.