{"id":18924014,"url":"https://github.com/koonimaru/omniplot","last_synced_at":"2025-04-15T03:54:58.661Z","repository":{"id":65830915,"uuid":"578075294","full_name":"koonimaru/omniplot","owner":"koonimaru","description":"Statistical analysis, clustering and visualinzing scientific data with hassle free","archived":false,"fork":false,"pushed_at":"2024-11-10T00:17:07.000Z","size":43635,"stargazers_count":24,"open_issues_count":1,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-15T03:54:43.384Z","etag":null,"topics":["data-science","matplotlib","numpy","pandas","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/koonimaru.png","metadata":{"files":{"readme":"README.md","changelog":null,"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}},"created_at":"2022-12-14T07:41:34.000Z","updated_at":"2025-02-01T18:59:36.000Z","dependencies_parsed_at":"2024-02-10T02:36:50.736Z","dependency_job_id":null,"html_url":"https://github.com/koonimaru/omniplot","commit_stats":null,"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/koonimaru%2Fomniplot","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/koonimaru%2Fomniplot/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/koonimaru%2Fomniplot/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/koonimaru%2Fomniplot/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/koonimaru","download_url":"https://codeload.github.com/koonimaru/omniplot/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249003954,"owners_count":21196794,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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-science","matplotlib","numpy","pandas","python"],"created_at":"2024-11-08T11:05:16.844Z","updated_at":"2025-04-15T03:54:58.640Z","avatar_url":"https://github.com/koonimaru.png","language":"Jupyter Notebook","readme":"![Downloads](https://img.shields.io/pypi/dm/omniplot)\n[![PyPI Version](https://img.shields.io/pypi/v/omniplot)](https://pypi.org/project/omniplot/)\n![License](https://img.shields.io/pypi/l/omniplot)\n![omniplot logo][logo-image]\n\n## What is omniplot\n\nomniplot is a python module to draw a scientific plot with hassle free. It mainly focuses on bioinfomatics data.\n\n\u003cimg src=\"images/example13.png\" width=\"340\"/\u003e \u003cimg src=\"images/example2.png\" width=\"190\"/\u003e \u003cimg src=\"images/example3.png\" width=\"270\"/\u003e \u003cbr\u003e\n\u003cimg src=\"images/example12.png\" width=\"200\"/\u003e \u003cimg src=\"images/example10.png\" width=\"300\"/\u003e \u003cimg src=\"images/example15.png\" width=\"210\"/\u003e\u003cbr\u003e\n\u003cimg src=\"images/example5.png\" width=\"400\"/\u003e \u003cimg src=\"images/example6.png\" width=\"260\"/\u003e \u003cbr\u003e\n\u003cimg src=\"images/example11.png\" width=\"600\"/\u003e \u003cbr\u003e\n## Motivation\nAlthough there exist many good python data visualization libraries, such as \n[matplotlib](https://matplotlib.org/), \n[pandas](https://pandas.pydata.org/), \n[seaborn](https://seaborn.pydata.org/), \n[plotly](https://plotly.com/), \n[vedo](https://vedo.embl.es/) and so on,\nstill several kinds of plots cannot be drawn without hassle. This module is aimed to provide convenient \ntools that allow users to draw complex plots, such as a scatter plot with PCA and loadings or clustering analysis in one liner.\n\n## Install\nomniplot best works with python3.8. But, greater python versions may be OK. Please try installation with conda, if something wrong with pip installation.\n\n```bash\npip install cython --user\ngit clone https://github.com/koonimaru/omniplot.git --user\ncd omniplot\npip install .\n```\nor \n\n```bash\npip install cython --user\npip install git+https://github.com/koonimaru/omniplot.git --user\n```\nor\n\n```bash\ngit clone https://github.com/koonimaru/omniplot.git\ncd omniplot\nconda env create -f environment.yml python=3.8\nconda activate omniplot\nconda install ipykernel\nipython kernel install --user --name=omniplot\nconda deactivate\n\n```\nAnd [this](https://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernels-for-different-environments) is how to use conda environment in jupyerlab.\n\nKnown issues:\u003cbr\u003e\nIf you get errors saying \"error: invalid command 'bdist_wheel'\", please try pip install --upgrade pip wheel setuptools\n\n## How to use\nI created jupyter notebooks to demonstrate the usage of omniplot [Link](https://github.com/koonimaru/omniplot/tree/main/ipynb).\nYou can open jupyter notebooks with [jupyter lab](https://jupyterlab.readthedocs.io/en/stable/) or [VScode](https://code.visualstudio.com/).\n\nAnd, I also write articles in [medium](https://medium.com/@koh.onimaru).\n\nA formal documation is under development, but you may want to visit an auto-generated [API](https://koonimaru.github.io/omniplot/). \n\n## Example usage\n```python\nimport pandas as pd\nfrom omniplot import plot as op\nimport seaborn as  sns\nimport matplotlib.pyplot as plt\n\ndf=sns.load_dataset(\"titanic\")\ndf=df[[\"class\",\"embark_town\",\"sex\"]].fillna(\"NA\")\nop.nested_piechart(df, category=[\"class\",\"embark_town\",\"sex\"], title=\"Titanic\", ignore=0.01, show_legend=True,show_values=False,hatch=True,ncols=3)\nplt.show()\n\n```\n## Example usage\n```python\nimport seaborn as sns\nfrom omniplot import plot as op\nimport matplotlib.pyplot as plt\ndf=sns.load_dataset(\"penguins\")\ndf=df.dropna(axis=0)\nres=op.radialtree(df, category=[\"species\",\"island\",\"sex\"])\nplt.show()\n```\n\n\n[logo-image]: images/logo.png\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkoonimaru%2Fomniplot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkoonimaru%2Fomniplot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkoonimaru%2Fomniplot/lists"}