udit-k/TN2026_AC_Elections_Results

Tamil Nadu AC May 2026 — scraped CSV layout

This repository distributes CSV extracts of published Tamil Nadu Assembly Constituency results (“TN”, state code S22) under the folders below. Paths are relative to the project root.

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Disclaimer and affiliation

This project is not affiliated with or endorsed by the Election Commission of India (ECI). Published electoral totals remain official products of ECI (see NOTICE). Anything extracted here is provided as is, without warranty of correctness or completeness for any purpose—including journalism, litigation, or campaign messaging—and should be checked against results.eci.gov.in before reliance.

Scraping can stop working whenever URLs or HTML change; snapshot CSVs may drift unless regenerated.

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License

Documentation in this repository (for example README.md) is released under the MIT License.

Bundled or regenerated CSV extracts derive from official publications as noted in NOTICE; cite both this repository (when publishing derivatives you built here) and the ECI portal as the originating governmental source.

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Citation

Suggested wording:

Derived counts assembled using TN-2026-ECI-Scraping (paste your repository URL here), extracting tables published by the Election Commission of India at https://results.eci.gov.in/.

Replace with your GitHub URL / Zenodo DOI if you mint tags or archival deposits.

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Methodology and known limitations

• Figures were extracted from ECI-published HTML; some non-Latin or malformed header/name rows were dropped during consolidation (English-focused sanity checks).
• Statewide CSV: trailing col_* columns mirror stray glued markup fragments (“party wise state trends…”). Prefer documented snake_case columns for joins (see below).
• Party naming: abbreviations differ across vote-share exports vs wins CSV vs candidate exports—normalize explicitly before merges (party_abbr, full strings).
• Constituency label: wins CSV uses NAME(const_no); statewise uses bare names plus **const_no**—always align joins on const_no (1–234).

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Folder overview

Folder	Role
TN_AC2026_general_statistics/	State-level summaries: constituency grid, vote share, seat totals, merged wins, AC number ↔ name
TN_AC2026_constituency_wins_by_party/	One CSV per party that won at least one seat (same row shape as the merged wins file)
TN_AC2026_candidates_by_ac/	One CSV per assembly constituency (001 … 234): full candidate list and votes

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TN_AC2026_general_statistics/

TN_AC2026_statewise_by_constituency.csv

Row grain: one constituency.

Column (main)	Meaning
constituency	Constituency name (English; normalized in scrape)
const_no	Assembly constituency number 1–234
leading_candidate, leading_party, trailing_candidate, trailing_party	Leading / runner-up from the statewise grid
margin, round, status	Margin, counting round fraction (e.g. 31/31), declaration status

Additional columns (col_9, col_10, …) come from extra cells in the published HTML table (party-wise trend snippets, etc.). Prefer the snake_case columns above for analysis; treat trailing col_* as optional / noisy.

TN_AC2026_constituency_no_to_name.csv

Row grain: one AC number.

Column	Meaning
const_no	1–234
constituency_name	Name aligned with the statewise scrape (deduped by const_no, sorted)

This file is derived from the statewise CSV whenever that scrape succeeds.

TN_AC2026_vote_share_by_party.csv

Row grain: one party (state-wide vote share).

Column	Meaning
Party	Short party label as on the site (often abbreviation-style)
Vote %	Percentage string (e.g. 21.21%)
Total Votes	Integer vote total

TN_AC2026_party_seat_totals.csv

Row grain: one party.

Column	Meaning
party_abbr	Abbreviation used on detail pages (e.g. TVK, DMK)
party_name	Long name including abbreviation suffix
seats_won	Seats won

TN_AC2026_all_party_constituency_wins.csv

Row grain: one seat won (concatenation of all per-party win files).

Column	Meaning
party_name, party_abbr	Party
S.No	Serial within that party’s list
Constituency	String like TIRUTTANI(3) — name + (const_no)
Winning Candidate, Total Votes, Margin, Status	Win stats

Extracting const_no for joins: parse the trailing parenthetical, e.g. TIRUTTANI(3) → 3.

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TN_AC2026_constituency_wins_by_party/

Files: TN_AC2026_constituency_wins_<ABBR>.csv (e.g. TN_AC2026_constituency_wins_ADMK.csv).

Same columns as TN_AC2026_all_party_constituency_wins.csv, but only rows for that party.

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TN_AC2026_candidates_by_ac/

Files: TN_AC2026_ac_<NNN>_candidates.csv where <NNN> is zero-padded const_no (001 … 234).

Row grain: one candidate in that constituency.

Column	Meaning
const_no	Same numeric AC id (matches statewise const_no)
page_title, source_url	Provenance
sn	Ballot order / serial on the page
candidate, party	Candidate and party
evm_votes, postal_votes, total_votes	Votes
%_of_votes	Share in that constituency

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How to join datasets

Canonical constituency key: const_no

Use const_no (integer 1–234) whenever possible:

• Statewise ↔ AC name map: statewise["const_no"] = map_df["const_no"].
• Statewise ↔ per-AC candidates: statewise["const_no"] = candidates["const_no"].
• Merged / per-party wins ↔ others: add a column by parsing Constituency:

import pandas as pd

wins = pd.read_csv("TN_AC2026_general_statistics/TN_AC2026_all_party_constituency_wins.csv")
wins["const_no"] = wins["Constituency"].str.extract(r"\((\d+)\)$").astype(int)

names = pd.read_csv("TN_AC2026_general_statistics/TN_AC2026_constituency_no_to_name.csv")
wins_named = wins.merge(names, on="const_no", how="left")

Constituency names differ by file

• Statewise / map: plain name (e.g. TIRUTTANI).
• Wins CSVs: NAME(AC_NO) in Constituency.

Do not rely on string equality between those two; join on const_no (or parse wins as above).

Party identifiers differ by table

• Seat totals: party_abbr, full party_name.
• Vote share: short Party column — may not exactly equal party_abbr; fuzzy matching or a manual alias map may be needed for strict merges.
• Candidates: full party name text per candidate row.

Typical pattern: merge seat totals to wins on party_abbr, or on normalized party_name after stripping suffix noise.

Load all candidate files into one table

from pathlib import Path

paths = sorted(Path("TN_AC2026_candidates_by_ac").glob("TN_AC2026_ac_*_candidates.csv"))
cand_all = pd.concat([pd.read_csv(p) for p in paths], ignore_index=True)

state = pd.read_csv("TN_AC2026_general_statistics/TN_AC2026_statewise_by_constituency.csv")
combined = cand_all.merge(state, on="const_no", how="left", suffixes=("", "_statewise"))