{"id":25198538,"url":"https://github.com/jpcurada/exploralytics","last_synced_at":"2026-02-05T21:02:37.966Z","repository":{"id":270544122,"uuid":"910707641","full_name":"JpCurada/exploralytics","owner":"JpCurada","description":"A python package for creating intermediate plotly visualizations","archived":false,"fork":false,"pushed_at":"2025-01-12T15:57:55.000Z","size":71,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-28T11:26:05.973Z","etag":null,"topics":["data","eda","plotly","python","visualization"],"latest_commit_sha":null,"homepage":"https://pypi.org/project/exploralytics/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JpCurada.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-01-01T06:23:49.000Z","updated_at":"2025-02-22T10:21:41.000Z","dependencies_parsed_at":"2025-05-09T00:55:14.704Z","dependency_job_id":"9426a1fd-3379-4c18-9775-7a0a32cd5c0b","html_url":"https://github.com/JpCurada/exploralytics","commit_stats":null,"previous_names":["jpcurada/exploralytics"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/JpCurada/exploralytics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JpCurada%2Fexploralytics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JpCurada%2Fexploralytics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JpCurada%2Fexploralytics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JpCurada%2Fexploralytics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JpCurada","download_url":"https://codeload.github.com/JpCurada/exploralytics/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JpCurada%2Fexploralytics/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29134233,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-05T20:50:26.975Z","status":"ssl_error","status_checked_at":"2026-02-05T20:49:26.082Z","response_time":65,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data","eda","plotly","python","visualization"],"created_at":"2025-02-10T03:17:32.496Z","updated_at":"2026-02-05T21:02:36.756Z","avatar_url":"https://github.com/JpCurada.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Exploralytics\n\n[![PyPI Latest Release](https://img.shields.io/pypi/v/exploralytics.svg)](https://pypi.org/project/exploralytics/)\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue.svg)](https://www.linkedin.com/in/jpcurada/)\n[![GitHub](https://img.shields.io/badge/GitHub-Follow-black.svg)](https://github.com/JpCurada)\n\n## What is it?\nA Python toolkit that streamlines the creation of Plotly visualizations for exploratory data analysis (EDA). Built to simplify the visualization workflow, Exploralytics provides an intuitive interface for creating common EDA plots like histograms, correlation matrices, and bar charts with consistent styling and formatting.\n\n \u003e I created this to simplify my own workflow, but other data professionals might find it useful too.\n\n## Main Features\nCreate sophisticated data visualizations with minimal code. Key features include:\n\n- **Histogram Grid**: Analyze distributions of multiple numerical variables\n- **Correlation Analysis**: \n  - Full correlation matrix heatmap\n  - Target-focused correlation analysis\n- **Bar Charts**: \n  - Horizontal bar plots with customizable highlighting\n  - Dot plots with connecting lines\n- **Consistent Styling**: Unified look across all visualizations\n- **Customization Options**: Colors, dimensions, templates, and more\n\n## Installation\n\nRequires Python 3.9 or newer.\n\nUsing pip:\n```bash\npip install exploralytics\n```\n\nOr install from source:\n```bash\ngit clone https://github.com/jpcurada/exploralytics.git\ncd exploralytics\npip install -e .\n```\n\n## Usage Examples\n\n### Basic Usage\n\n```python\nfrom exploralytics.visualize import Visualizer\nimport pandas as pd\n\n# Initialize visualizer with custom styling\nviz = Visualizer(\n    color=\"#94C973\",  # Custom color\n    height=768,       # Plot height\n    width=1366,       # Plot width\n    template=\"simple_white\"  # Plotly template\n)\n\n# Create histogram grid\nfig = viz.plot_histograms(\n    df,\n    title='Distribution Analysis',\n    subtitle='Histogram of numerical variables',\n    num_cols=2,\n    show_mean=True,\n    show_median=True\n)\nfig.show()\n\n# Create correlation heatmap\nfig = viz.plot_correlation_map(\n    df,\n    title='Correlation Analysis',\n    subtitle='Relationship between variables'\n)\nfig.show()\n```\n\n### Advanced Features\n\n```python\n# Target-specific correlation analysis\nfig = viz.plot_correlation_with_target(\n    df,\n    target_column='sales',\n    title='Feature Importance',\n    subtitle='Correlation with sales'\n)\n\n# Horizontal bar plot with highlights\nfig = viz.plot_hbar(\n    df,\n    x_col='category',\n    y_col='value',\n    highlight_top_n=(3, '#2E75B6'),  # Highlight top 3 in blue\n    highlight_low_n=(2, '#FF9999')   # Highlight bottom 2 in red\n)\n\n# Dot plot with reference line\nfig = viz.plot_dot(\n    df,\n    x_col='category',\n    y_col='metric',\n    add_hline_at=('Average', 75.5),\n    top_n=10\n)\n```\n\n## Customization Options\n\nThe `Visualizer` class accepts several parameters for customization:\n\n```python\nviz = Visualizer(\n    color=\"#94C973\",                    # Default color for plot elements\n    height=768,                         # Plot height in pixels\n    width=1366,                         # Plot width in pixels\n    template=\"simple_white\",            # Plotly template\n    colorscale=px.colors.diverging.Earth,  # Color scale for heatmaps\n    texts_font_style=\"Arial\",           # Font family\n    title_bold=True                     # Bold titles\n)\n```\n\n## Dependencies\n- pandas \u003e= 1.3.0\n- plotly \u003e= 5.0.0\n- numpy \u003e= 1.20.0\n\n## Development\n\nWant to contribute? Here's how:\n\n1. Fork the repository\n2. Create a feature branch\n```bash\ngit checkout -b feature/new-feature\n```\n3. Make your changes\n4. Submit a pull request\n\n## License\nBSD License\n\n## Support\nFor bugs, questions, or suggestions, please [open an issue](https://github.com/jpcurada/exploralytics/issues) on GitHub.\n\n---\nCreated and maintained by John Paul Curada. Contributions welcome!\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjpcurada%2Fexploralytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjpcurada%2Fexploralytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjpcurada%2Fexploralytics/lists"}