repo-statistics 0.5.1

Calculate metrics and statistics for source-code repositories.

Repo Statistics

Calculate collaboration, code, and social metrics and statistics for a source-code repository.

Metrics Available

See full table for additional information

Metric Family	Metrics	Description
Development Activity Pattern Metrics	commit_entropy, commit_variation, commit_frac, lines_changed_entropy, lines_changed_variation	Metrics measuring regularity and consistency of development effort over time, calculated at both weekly and monthly intervals to identify sustained engagement patterns versus bursty development
Development Episode Characteristics	median_commit_span, mean_commit_span, std_commit_span, median_no_commit_span, mean_no_commit_span, std_no_commit_span	Metrics describing the temporal structure of active and inactive development periods, characterizing sustained work episodes and dormancy gaps
Contributor Engagement Patterns	stable_contributors_count, transient_contributors_count, median_contribution_span_days, mean_contribution_span_days, normalized_median_span, normalized_mean_span	Metrics characterizing contributor stability and engagement duration, distinguishing between sustained community members and episodic contributors
Contributor Distribution Metrics	unique_contributors_count, contributor_absence_factor_code, contributor_absence_factor_all, contributor_specialization, specialists_contributor_count, generalists_contributor_count, contributor_change_count, contributor_same_count	Metrics examining how development effort and knowledge are distributed among contributors, including bus factor analysis and specialist/generalist patterns
Repository Timeline Metrics	initial_commit_datetime, most_recent_commit_datetime, most_recent_substantial_commit_datetime, to_most_recent_commit_duration_days, to_most_recent_substantial_commit_duration_days	Basic temporal metadata for development history analysis, tracking project age, activity status, and lifetime of meaningful development
Development Activity Volume	commits_count, non_bot_commits_count, coding_commits_count, source_lines_of_code, source_lines_of_comments	Metrics quantifying overall development activity and effort, distinguishing between human and automated contributions and measuring codebase size
Community Engagement Metrics	stars_count, forks_count, watchers_count, open_issues_count	Metrics reflecting community interest and participation through GitHub features, indicating broader impact and active engagement
Release Management Metrics	semver_tags_count, non_semver_tags_count, total_tags_count	Metrics related to versioning and release practices, measuring adoption of formal release management conventions
Repository Classification Metadata	repo_primary_language, repo_classification, file_extensions_set	Descriptive metadata for filtering and comparative analysis, characterizing project type and technical composition
Documentation and Best Practices	repo_linter_license_file_exists, repo_linter_readme_file_exists, repo_linter_readme_references_license, repo_linter_changelog_file_exists, repo_linter_contributing_file_exists, repo_linter_code_of_conduct_file_exists, repo_linter_code_of_conduct_file_contains_email, repo_linter_security_file_exists, repo_linter_support_file_exists, repo_linter_test_directory_exists, repo_linter_integrates_with_ci, repo_linter_github_issue_template_exists, repo_linter_github_pull_request_template_exists, repo_linter_binaries_not_present	Binary indicators of documentation files and development practices supporting sustainability, including core documentation, community guidelines, and development infrastructure
Gini Coefficients (experimental)	commit_gini_coefficient, lines_changed_gini_coefficient, contributor_commit_gini, contributor_lines_gini, commit_size_gini, time_between_commits_gini	Alternative inequality measures using Gini coefficients to complement existing sustainability indicators, measuring distribution equality across temporal and contributor dimensions
Commit Pattern Metrics	commit_size_entropy, commit_size_variation, time_between_commits_entropy, time_between_commits_variation	Metrics analyzing commit sizing and timing patterns using entropy and variation measures to characterize development rhythm and consistency
Advanced Sustainability Indicators	documentation_to_code_ratio, contributor_retention_rate, releases_per_year, knowledge_concentration_risk, simple_code_churn_rate	Higher-level metrics for comprehensive sustainability assessment combining multiple dimensions including documentation quality, contributor retention, release cadence, knowledge distribution, and code volatility

Usage

Single Repository Processing

import json

from repo_statistics import analyze_repository

# Repo Path can be a local path or remote
repo_metrics = analyze_repository(
    repo_path="https://github.com/bioio-devs/bioio",
)

with open("example-repo-metrics.json", "w") as f:
    json.dump(repo_metrics, f, indent=4)

# It is recommended to provide a GitHub API token
# unless you disable "platform" metrics
repo_metrics = analyze_repository(
    repo_path="https://github.com/bioio-devs/bioio",
    # Provide a token
    # github_token="ABC",
    # Or disable platform metrics gathering
    compute_platform_metrics=False,
)

# Nearly every portion of metrics can be disable independent from one another
repo_metrics = analyze_repository(
    repo_path="https://github.com/bioio-devs/bioio",
    compute_timeseries_metrics=True,
    compute_contributor_stability_metrics=False,
    compute_contributor_absence_factor=True,
    compute_contributor_distribution_metrics=False,
    compute_repo_linter_metrics=False,
    compute_tag_metrics=True,
    compute_platform_metrics=False,
)

# By default, all time-periods are considered
# However, you can provide also provide a "start_datetime" and/or "end_datetime"
# TODO: Temporarily disabled
# repo_metrics = analyze_repository(
#     repo_path="https://github.com/bioio-devs/bioio",
#     start_datetime="2025-01-01",
#     end_datetime="2026-01-01",
#     compute_platform_metrics=False,
# )

# We also ignore bot changes by default by looking for
# "[bot]" account naming in commit information
# This can be disabled, or, changed as well
repo_metrics = analyze_repository(
    repo_path="https://github.com/bioio-devs/bioio",
    # Keep all bots by ignoring name checks
    bot_name_indicates=None,
    # Keep all bots by ignoring email checks
    bot_email_indicators=None,
    compute_platform_metrics=False,
)

Multiple Repository Processing

from repo_statistics import analyze_repositories, DEFAULT_COILED_KWARGS

analyze_repos_results = analyze_repositories(
    repo_paths=[
        "https://github.com/bioio-devs/bioio",
        "https://github.com/bioio-devs/bioio-ome-zarr",
        "https://github.com/evamaxfield/aws-grobid",
        "https://github.com/evamaxfield/rs-graph",
        "https://github.com/evamaxfield/repo-statistics",
    ],

    # Has built in batching and caching to avoid re-processing repositories
    cache_results_path="repo-metrics-results.parquet",
    cache_errors_path="repo-metrics-errors.parquet",
    batch_size=4,
    # Or as a proportion of the total number of repositories
    # batch_size=0.1,
    # By default, we will use cached results before re-processing
    # This will drop repositories already in the cache and only process new ones
    # To re-process all repositories
    # ignore_cached_results=True,

    # Provide multiple tokens as strings in a list
    # github_tokens=["ghp_exampletoken1", "ghp_exampletoken2"],
    # Or can provide a gh-tokens file path
    # github_tokens=".github-tokens.yml",

    # By default, will process repositories one at a time
    # Can enable multithreading with the following options
    use_multithreading=True,
    n_threads=4,
    # Or, can use Coiled for distributed processing
    # use_coiled=True,
    # coiled_kwargs=DEFAULT_COILED_KWARGS,
    
    # All other keyword arguments are passed to analyze_repository
    # For example, to skip computing repo linter metrics
    # compute_repo_linter_metrics=False,
)

# Provides back an object with results and errors DataFrames
analyze_repos_results.metrics_df
analyze_repos_results.errors_df