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

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.

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.

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.

Historical Reported Registration and Voting Rates for US Presidential Elections 1980-2024. Data from tables 5a, 5b, 9, and 10

Details the statistics of the Electoral College vote and popular vote over time in both the Democratic Party and the Republican Party. Also shows the voting population participation trend over time.

This data set consists of all Fulton County Election results from April 2012 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.

This dataset provides a comprehensive breakdown of votes for the 2024 Indonesian presidential election, organized by province. It includes vote counts for each of the three main presidential candidates: Anies Baswedan, Prabowo Subianto, and Ganjar Pranowo. Each row represents a province in Indonesia, showing the respective vote totals for each candidate, allowing for a detailed analysis of regional voting patterns across the country. This data is valuable for political analysts, researchers, and others interested in examining the geographical distribution of support for each candidate. By offering insights into voting trends, this dataset can aid in understanding regional political dynamics and voter preferences within Indonesia.

this graph was created in eci.gov.in:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F72741c56b74a7b94eba89cf4b4dbf581%2Fgraph1.png?generation=1720477489892805&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fff074501c79871293842195582d736fb%2Fgraph2.png?generation=1720477494668588&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff24ad545dd7e5ac3a3e22e1515d33f41%2Fgraph3.png?generation=1720477499963162&alt=media" alt="">

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!

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 119th Congressional Districts dataset reflects boundaries from January 3rd, 2025 from the United States Census Bureau (USCB), and the attributes are updated every Sunday from the United States House of Representatives and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Information for each member of Congress is appended to the Census Congressional District shapefile using information from the Office of the Clerk, U.S. House of Representatives' website https://clerk.house.gov/xml/lists/MemberData.xml and its corresponding XML file. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. This dataset also includes 9 geographies for non-voting at large delegate districts, resident commissioner districts, and congressional districts that are not defined. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 119th Congress is seated from January 3, 2025 through January 3, 2027. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the CDs to cover all of the state or state equivalent area. In these areas with no CDs defined, the code "ZZ" has been assigned, which is treated as a single CD for purposes of data presentation. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by May 31, 2024. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529006

Dataset for General Election 2024

Plots :

1. Difference between EVM Votes counted and EVM votes polled across PCs 🔗
2. Victory Margins and all related information 🔗

Files : 1. eci_data_2024.csv - Raw Data scraped from Election Commission Of India Results 2024 2. phase_data.xlsx - Election Commission of India Press Releases (Phase 1 - 5 , Phase 6 and Phase 7) 3. GE India 2024 - Reconciled Data (Difference of EVM Votes counts and EVM Votes Polled, Victory Margins)

This dataset was collected from university students using a random sampling technique, ensuring a representative sample. It comprises 16 columns, incorporating both numerical and categorical variables, to provide a comprehensive view of the data. This dataset is particularly valuable for students of public policy, political science, and sociology, offering a robust foundation for analyzing electoral behaviors. The primary objective of this dataset is to facilitate a descriptive study of the voting patterns observed in the 2024 election, enabling detailed statistical analysis, data mining, and hypothesis testing. By utilizing advanced analytics and machine learning techniques, researchers can uncover intricate patterns and correlations within the dataset, contributing to a deeper understanding of the socio-political landscape.

Indian General Elections 2024 Dataset This dataset provides comprehensive details of the Indian General Elections 2024, encompassing results for all constituencies across the country. It includes data on candidates, political parties, vote counts, and winning margins, offering valuable insights for political analysis, academic research, and data-driven decision-making.

Source: https://results.eci.gov.in/PcResultGenJune2024/index.htm Link: Indian General Elections 2024 Results Data Features: Constituency Information: Name, state, and constituency code Candidate Details: Name, party affiliation, and vote count Election Results: Winning candidate, party, and vote margins This dataset is an invaluable resource for understanding the dynamics of the 2024 Indian General Elections, providing a granular view of electoral outcomes and trends across the nation.

CSV which shows you the voter count, breakdown of votes, turnout stats and win margin of each constituency.

Note that NaN is used in each constituency that did not have 13 candidates standing. This is to have every candidate available in one place, but obviously having NaNs is a bit annoying.

All sourced from the BBC News website.

TO DO: Add Geospatial data either here or in another csv.

2024 US Electoral College votes available as extracted from Wikipedia.

This is basically the same as the 2024 dataset I made last week, I wanted to store this here because again I couldn't find a good breakdown elsewhere.

The same columns can be found here as in that. Additionally, it's worth noting again that the NaNs are because I wanted to put every candidate in this dataframe, but not every constituency has the same number of candidates competing.

Sources from BBC News (https://www.bbc.com/news/politics/constituencies). It also has the 2015/2017 data but I've not decided to parse them (yet!)