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Datasets:
THULab
/
github_top_projects

Tasks:
Time Series Forecasting
Modalities:
Time-series
Size:
100K<n<1M
Tags:
tsfile
timeseries
time-series
github
trending
repositories
License:
Dataset card Files
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github_top_projects / README.md
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wangdx25
Add TsFile (converted from ronantakizawa/github-top-projects)
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metadata 
license: mit
task_categories:
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - time-series
  - github
  - trending
  - repositories
  - software-engineering
  - popularity
  - format:tsfile
pretty_name: GitHub Trending Projects (TsFile)
size_categories:
  - 100K<n<1M

GitHub Trending Projects (TsFile)

Apache TsFile version of ronantakizawa/github-top-projects.

Overview

423,098 GitHub trending repository entries spanning 12+ years (August 2013 – November 2025), scraped from Wayback Machine snapshots of GitHub's trending page. Captures the evolution of trending repositories over time — software development trends across languages and domains, and popular open-source projects. Multiple intra-day snapshots per repository are collapsed to the latest snapshot per day.

Schema (TsFile structure)

  • Time (INT64, milliseconds) — the snapshot date.
  • repo_owner / name (TAG) — the repository (device dimension).
  • star_count / fork_count (FIELD, FLOAT).
  • rank (FIELD, INT64) — the daily rank.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("github_top_projects.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license