Fixing GitHub Regressions Errors with Private Datasets in Python

In a Python data science community I participate in, a developer recently encountered a roadblock when moving their project from a public repository to a private one. They reported that their automated regression tests—specifically those using the github-regressions pattern to compare data outputs—suddenly began failing with 404 Not Found errors or Authentication Required exceptions.

The error typically surfaces when a Python script tries to fetch a “baseline” dataset from a private GitHub repository to compare against the current run’s results.

The Original Question

“I’m using a Python script to track model regressions by comparing current output to a CSV stored on GitHub. It worked perfectly while the repo was public. Now that I’ve switched to a private repository, my script fails to download the baseline dataset. I’m getting a urllib.error.HTTPError: HTTP Error 404: Not Found even though the URL is correct. How do I authenticate my Python environment to access this private dataset?”

The Immediate Fix

If you are using requests or pandas to pull a raw file from a private GitHub repository, you cannot use the standard “Raw” URL (e.g., raw.githubusercontent.com/...) without an Authorization Header containing a Personal Access Token (PAT).

Version: Python 3.9+, requests 2.28+ (Illustrative example — verify in your environment)

import os
import requests
import pandas as pd
from io import StringIO

def get_private_dataset(repo_owner, repo_name, file_path, github_token):
    # Construct the API URL, not the raw.githubusercontent URL
    url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/contents/{file_path}"

    headers = {
        "Authorization": f"token {github_token}",
        "Accept": "application/vnd.github.v3.raw", # This header is key for raw content
    }

    response = requests.get(url, headers=headers)

    if response.status_status == 200:
        return pd.read_csv(StringIO(response.text))
    else:
        raise Exception(f"Failed to fetch data: {response.status_code} {response.text}")

# Usage
# Ensure GITHUB_TOKEN is set in your Environment Variables
token = os.getenv("GITHUB_TOKEN")
df = get_private_dataset("my-org", "private-repo", "data/baseline.csv", token)

Detailed Explanation: Why the 404 Happens

When a repository is public, raw.githubusercontent.com serves files via simple HTTP GET requests. However, for private repositories, GitHub intentionally returns a 404 Not Found instead of a 403 Forbidden. This is a security measure to prevent “repository existence leakage”—not even confirming the file exists unless you are authenticated.

To fix this, you must switch from the “Raw” URL to the GitHub REST API. By using the application/vnd.github.v3.raw header, the API bypasses the standard JSON metadata response and returns the literal bytes of the file, which pandas or csv modules can then parse directly.

Alternative Solution: Using Git-based Authentication (CI/CD)

If your regressions are running inside a GitHub Action, you don’t need to manually handle API calls. You can leverage the filesystem by checking out the repository correctly.

Version: GitHub Actions v4, Python 3.11 (Illustrative example — verify in your environment)

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
        with:
          # This allows the runner to see the private repo files locally
          token: ${{ secrets.GITHUB_TOKEN }}

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'

      - name: Run Regressions
        run: |
          # Now your script can access the file as a local path
          python scripts/check_regressions.py --baseline ./data/baseline_metrics.csv

Why this works: The actions/checkout step handles the authentication layer for you. It places the private files directly into the runner’s workspace. Your Python code then treats the dataset as a local file (e.g., open('data.csv')) rather than a network resource.

Edge Cases and Common Pitfalls

1. Fine-Grained Personal Access Tokens

GitHub now recommends Fine-Grained PATs over “Classic” tokens. If your fix isn’t working, ensure your token has the “Contents: Read” permission specifically for the repository containing the dataset. A classic token with only repo scope will work, but it is less secure.

2. Handling Large Datasets (Git LFS)

If your private dataset is larger than 100MB, it is likely stored via Git LFS (Large File Storage). The api.github.com method described above will only return the LFS pointer (a small text file with a hash) rather than the actual data.

Fix: You must use the “Checkout” method in your CI/CD and ensure LFS is enabled:
```
- name: Checkout Code
  uses: actions/checkout@v4
  with:
    lfs: true
```

3. Rate Limiting

The GitHub API has a rate limit (typically 5,000 requests per hour for authenticated users). If your regression suite performs hundreds of fetches in a loop, you may get a 403 Rate Limit Exceeded.

Fix: Download the dataset once at the start of your test session and cache it locally rather than fetching it for every individual test case.

Does this work with private datasets on GitLab or Bitbucket? The logic is similar, but the headers and API endpoints differ. GitLab uses the PRIVATE-TOKEN header and the /projects/:id/repository/files/:path/raw endpoint.

Can I use a SSH key instead of a PAT? Yes, particularly for local development or Docker containers. You would mount your id_rsa key and use Git-specific libraries like GitPython to pull the file, or simply rely on git clone via SSH. PATs are generally preferred for simple script-based data fetching in cloud environments because they are easier to scope and revoke.