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Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 17 new columns ({'html_url', 'name', 'full_name', 'stars', 'license', 'description', 'owner', 'search_language', 'created_at', 'language', 'pushed_at', 'open_issues', 'watchers', 'topics', 'forks', 'updated_at', 'is_archived'}) and 4 missing columns ({'total_followers', 'country', 'user_count', 'total_repos'}).
This happened while the csv dataset builder was generating data using
hf://datasets/EduDevCommons/GitHub_Global_User_Dataset/github_starred_repos.csv (at revision f9d45da4d2047713989c5032d23e350e4c0e15ac), ['hf://datasets/EduDevCommons/GitHub_Global_User_Dataset@f9d45da4d2047713989c5032d23e350e4c0e15ac/github_country_stats.csv', 'hf://datasets/EduDevCommons/GitHub_Global_User_Dataset@f9d45da4d2047713989c5032d23e350e4c0e15ac/github_starred_repos.csv', 'hf://datasets/EduDevCommons/GitHub_Global_User_Dataset@f9d45da4d2047713989c5032d23e350e4c0e15ac/github_user_repos.csv', 'hf://datasets/EduDevCommons/GitHub_Global_User_Dataset@f9d45da4d2047713989c5032d23e350e4c0e15ac/github_users_by_country.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
full_name: string
name: string
owner: string
description: string
language: string
stars: int64
forks: int64
watchers: int64
open_issues: int64
license: string
created_at: string
updated_at: string
pushed_at: string
topics: string
is_archived: bool
html_url: string
search_language: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2246
to
{'country': Value('string'), 'user_count': Value('int64'), 'total_followers': Value('int64'), 'total_repos': Value('int64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 17 new columns ({'html_url', 'name', 'full_name', 'stars', 'license', 'description', 'owner', 'search_language', 'created_at', 'language', 'pushed_at', 'open_issues', 'watchers', 'topics', 'forks', 'updated_at', 'is_archived'}) and 4 missing columns ({'total_followers', 'country', 'user_count', 'total_repos'}).
This happened while the csv dataset builder was generating data using
hf://datasets/EduDevCommons/GitHub_Global_User_Dataset/github_starred_repos.csv (at revision f9d45da4d2047713989c5032d23e350e4c0e15ac), ['hf://datasets/EduDevCommons/GitHub_Global_User_Dataset@f9d45da4d2047713989c5032d23e350e4c0e15ac/github_country_stats.csv', 'hf://datasets/EduDevCommons/GitHub_Global_User_Dataset@f9d45da4d2047713989c5032d23e350e4c0e15ac/github_starred_repos.csv', 'hf://datasets/EduDevCommons/GitHub_Global_User_Dataset@f9d45da4d2047713989c5032d23e350e4c0e15ac/github_user_repos.csv', 'hf://datasets/EduDevCommons/GitHub_Global_User_Dataset@f9d45da4d2047713989c5032d23e350e4c0e15ac/github_users_by_country.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
country string | user_count int64 | total_followers int64 | total_repos int64 |
|---|---|---|---|
Argentina | 20 | 38,857 | 3,904 |
Australia | 20 | 113,037 | 3,233 |
Brazil | 20 | 327,602 | 2,723 |
Canada | 20 | 168,113 | 27,170 |
Chile | 20 | 17,486 | 53,327 |
China | 20 | 460,024 | 3,265 |
Egypt | 20 | 45,899 | 1,124 |
France | 20 | 133,192 | 3,726 |
Germany | 20 | 197,167 | 1,990 |
Hong Kong | 20 | 15,813 | 1,020 |
India | 20 | 350,104 | 2,134 |
Indonesia | 20 | 65,663 | 5,371 |
Israel | 20 | 35,616 | 3,744 |
Italy | 20 | 113,726 | 5,004 |
Japan | 20 | 194,267 | 5,964 |
Malaysia | 20 | 21,520 | 1,947 |
Mexico | 20 | 27,120 | 2,612 |
Netherlands | 20 | 96,478 | 3,445 |
New Zealand | 20 | 2,178 | 1,785 |
Nigeria | 20 | 28,488 | 1,973 |
Norway | 20 | 52,961 | 4,054 |
Philippines | 20 | 32,736 | 2,138 |
Poland | 20 | 87,858 | 2,645 |
Russia | 20 | 69,603 | 979 |
Saudi Arabia | 20 | 1,475 | 440 |
Singapore | 20 | 275,507 | 3,914 |
South Africa | 20 | 3,512 | 2,776 |
South Korea | 20 | 6,332 | 1,129 |
Spain | 20 | 122,981 | 2,028 |
Sweden | 20 | 106,766 | 5,947 |
Switzerland | 20 | 94,741 | 8,415 |
Taiwan | 20 | 76,645 | 6,115 |
Thailand | 20 | 26,698 | 6,850 |
Turkey | 20 | 97,386 | 1,582 |
UAE | 20 | 22,094 | 4,075 |
Ukraine | 20 | 62,510 | 1,174 |
United Kingdom | 20 | 674 | 528 |
United States | 20 | 1,944 | 1,388 |
Vietnam | 20 | 43,858 | 1,903 |
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This dataset provides a rich, multi‑faceted snapshot of GitHub’s global open‑source ecosystem. It is built by querying the GitHub REST API with a focus on:
Active users per country – the most followed developers in each of 39 countries (up to 20 per country), capturing their public profile information, follower counts, repository counts, and account creation dates.
Repositories owned by those users – up to 5 repositories per user, with detailed metadata (stars, forks, language, topics, license, etc.).
Top‑starred repositories by language – for 12 mainstream programming languages, the 15 most starred repositories (stars > 1000), plus a global Top 50 (stars > 5000) across all languages.
All timestamps have been normalised to naive (timezone‑free) format for easy analysis. The dataset is ideal for:
Studying the geographical distribution of GitHub activity and influence.
Analysing trends in popular repositories and language ecosystems.
Building recommendation systems, social network graphs, or developer analytics dashboards.
Exploring correlations between developer metrics (followers, repos, account age) and project popularity.
📁 Included Files File Name Content github_users_by_country.csv User profiles (login, name, location, company, email, bio, public repos, followers, following, created/updated dates, HTML URL, account age in days) github_user_repos.csv Repositories owned by the users above (full name, description, language, stars, forks, watchers, open issues, license, creation/update/push dates, topics, archived flag, plus user login and country) github_starred_repos.csv Famous repositories per language + global top (full name, owner, description, language, stars, forks, watchers, open issues, license, dates, topics, archived, plus the language filter used for collection) github_country_stats.csv Aggregated statistics by country (user count, total followers, total public repos) dataset_summary.json Metadata about the dataset (creation time, number of users, countries, languages, etc.) 🔍 Collection Methodology Data source: GitHub REST API via PyGithub.
Authentication: Personal Access Token (rate limit ~5000 requests/hour).
Countries: 39 countries (major developer hubs across Asia, Europe, Americas, Africa, and Oceania).
User selection: For each country, sorted by follower count descending, taking the top 20.
User repos: Up to 5 most recent repositories per user (determined by API order).
Starred repos: For each of 12 languages (Python, JavaScript, TypeScript, Java, Go, Rust, C++, Ruby, PHP, Swift, Kotlin, Shell), repositories with >1000 stars, sorted by stars, taking the top 15. Plus a separate global query for >5000 stars taking the top 50.
Rate‑limit handling: Exponential backoff with automatic retry on 403 errors.
Data cleaning: All datetime fields are converted to naive (timezone‑neutral) to avoid compatibility issues.
📅 Data Snapshot Creation date: (included in dataset_summary.json)
Total users: ~780 (varies per run)
Total countries: 39
Total user repositories: ~3,700
Total starred repositories: ~230
🔧 Use Cases Geographic analysis: Compare developer density, activity, and influence across regions.
Language popularity: Identify which languages dominate in terms of high‑star projects.
Trend spotting: Monitor growth of individual developers or repositories over time (via account age and update dates).
Network analysis: Build collaboration graphs using user‑repo ownership links.
Recommendation engines: Suggest repositories or users based on similarity metrics.
📋 License & Attribution This dataset is compiled from public GitHub data. Please respect GitHub’s Terms of Service and API Terms. When using this dataset, we kindly request that you cite the original source (GitHub) and, if applicable, link to this dataset description.
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