{"id":15805302,"url":"https://github.com/lsys/forestplot","last_synced_at":"2025-04-07T12:09:10.372Z","repository":{"id":41802456,"uuid":"510013191","full_name":"LSYS/forestplot","owner":"LSYS","description":"A Python package to make publication-ready but customizable coefficient plots.","archived":false,"fork":false,"pushed_at":"2024-07-28T07:20:29.000Z","size":8791,"stargazers_count":111,"open_issues_count":6,"forks_count":10,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-10-06T02:07:44.816Z","etag":null,"topics":["coefficientplot","data-science","data-visualization","dataviz","forestplot","matplotlib","python","visualization"],"latest_commit_sha":null,"homepage":"http://forestplot.rtfd.io","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LSYS.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"citation.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-07-03T12:11:42.000Z","updated_at":"2024-10-01T03:43:57.000Z","dependencies_parsed_at":"2023-12-14T04:25:07.355Z","dependency_job_id":"e28b4c79-bc53-4c43-86c2-4e104e5b62ca","html_url":"https://github.com/LSYS/forestplot","commit_stats":{"total_commits":169,"total_committers":3,"mean_commits":"56.333333333333336","dds":"0.023668639053254448","last_synced_commit":"b7591e3be7004f9962fd7cd7f91aa4cac91c45fc"},"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LSYS%2Fforestplot","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LSYS%2Fforestplot/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LSYS%2Fforestplot/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LSYS%2Fforestplot/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LSYS","download_url":"https://codeload.github.com/LSYS/forestplot/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247648977,"owners_count":20972945,"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":["coefficientplot","data-science","data-visualization","dataviz","forestplot","matplotlib","python","visualization"],"created_at":"2024-10-05T02:08:02.859Z","updated_at":"2025-04-07T12:09:10.347Z","avatar_url":"https://github.com/LSYS.png","language":"Jupyter Notebook","readme":"\u003cdiv id=\"top\"\u003e\u003c/div\u003e \n\u003ch1 align=\"center\" \u003e\n Forestplot\n\u003c/h1\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://pypi.org/project/forestplot\"\u003e\n \u003cimg alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/forestplot?label=Python\u0026logo=python\u0026logoColor=white\"\u003e\n \u003c/a\u003e\u003cbr\u003e\n \u003cb\u003eEasy API for forest plots.\u003c/b\u003e\u003cbr\u003e\n A Python package to make publication-ready but customizable forest plots.\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/main.png\"\u003e\u003c/p\u003e\n\n-----------------------------------------------------------\nThis package makes publication-ready forest plots easy to make out-of-the-box. Users provide a `dataframe` (e.g. from a spreadsheet) where rows correspond to a variable/study with columns including estimates, variable labels, and lower and upper confidence interval limits.\nAdditional options allow easy addition of columns in the `dataframe` as annotations in the plot.\n\n\u003c!---------------------- Project shields ----------------------\u003e\n\n| | |\n| --- | --- |\n| Release | [![PyPI](https://img.shields.io/pypi/v/forestplot?color=blue\u0026label=PyPI\u0026logo=pypi\u0026logoColor=white)](https://pypi.org/project/forestplot/) [![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/forestplot?logo=conda-forge\u0026logoColor=white)](https://anaconda.org/conda-forge/forestplot) [![GitHub release (latest by date)](https://img.shields.io/github/v/release/lsys/forestplot?color=blue\u0026label=Latest%20release)](https://github.com/LSYS/forestplot/releases) |\n| Status | [![CI](https://github.com/LSYS/forestplot/actions/workflows/CI.yml/badge.svg)](https://github.com/LSYS/forestplot/actions/workflows/CI.yml) [![Notebooks](https://github.com/LSYS/forestplot/actions/workflows/nb.yml/badge.svg)](https://github.com/LSYS/forestplot/actions/workflows/nb.yml) |\n| Coverage | [![Codecov](https://img.shields.io/codecov/c/github/lsys/forestplot?logo=codecov\u0026logoColor=white\u0026label=codecov)](https://app.codecov.io/gh/LSYS/forestplot) |\n| Python | [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/forestplot?label=Python%203.6%2B\u0026logo=python\u0026logoColor=white)](https://pypi.org/project/forestplot/) |\n| Docs | [![Read the Docs (version)](https://img.shields.io/readthedocs/forestplot/stable?label=docs\u0026logo=readthedocs\u0026logoColor=white)](https://forestplot.readthedocs.io/en/latest/?badge=latest) [![DocLinks](https://github.com/LSYS/forestplot/actions/workflows/links.yml/badge.svg)](https://github.com/LSYS/forestplot/actions/workflows/links.yml)|\n| Meta | [![GitHub](https://img.shields.io/github/license/lsys/forestplot?color=purple\u0026label=License)](https://choosealicense.com/licenses/mit/) [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat\u0026labelColor=ef8336)](https://pycqa.github.io/isort/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![types - Mypy](https://img.shields.io/badge/types-Mypy-blue.svg)](https://github.com/python/mypy) [![DOI](https://zenodo.org/badge/510013191.svg)](https://zenodo.org/badge/latestdoi/510013191) |\n| Binder| [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/lsys/forestplot/main?labpath=examples%2Freadme-examples.ipynb) |\n\n\u003c!---------------------- TABLE OF CONTENT ----------------------\u003e\n# Table of Contents[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#table-of-contents)\n\u003cdetails open\u003e\u003csummary\u003e\u003cb\u003eshow/hide\u003c/b\u003e\u003c/summary\u003e\u003cp\u003e\n\n\u003e - [Installation](#installation)\n\u003e - [Quick Start](#quick-start)\n\u003e - [Some Examples with Customizations](#some-examples-with-customizations)\n\u003e - [Gallery and API Options](#gallery-and-api-options)\n\u003e - [Multi-models](#multi-models)\n\u003e - [Known Issues](#known-issues)\n\u003e - [Background and Additional Resources](#background-and-additional-resources)\n\u003e - [Contributing](#contributing)\n\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\n\u003c!------------------------- INSTALLATION -------------------------\u003e\n## Installation[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#installation)\n\nInstall from PyPI\u003cbr\u003e\n [![PyPI](https://img.shields.io/pypi/v/forestplot?color=blue\u0026label=PyPI\u0026logo=pypi\u0026logoColor=white)](https://pypi.org/project/forestplot/)\n```bash\npip install forestplot\n```\n\nInstall from conda-forge\u003cbr\u003e\n[![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/forestplot?logo=conda-forge\u0026logoColor=white)](https://anaconda.org/conda-forge/forestplot)\n```bash\nconda install forestplot\n```\n\nInstall from source\u003cbr\u003e\n[![GitHub release (latest by date)](https://img.shields.io/github/v/release/lsys/forestplot?color=blue\u0026label=Latest%20release)](https://github.com/LSYS/forestplot/releases)\u003cbr\u003e\n```bash\ngit clone https://github.com/LSYS/forestplot.git\ncd forestplot\npip install .\n```\n\nDeveloper installation\u003cbr\u003e\n```bash\ngit clone https://github.com/LSYS/forestplot.git\ncd forestplot\npip install -r requirements_dev.txt\n\nmake lint\nmake test\n```\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!------------------------- QUICK START -------------------------\u003e\n## Quick Start[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#quick-start)\n\n```python\nimport forestplot as fp\n\ndf = fp.load_data(\"sleep\") # companion example data\ndf.head(3)\n```\n| | var | r | moerror | label | group | ll | hl | n | power | p-val |\n|---:|:---------|-----------:|----------:|:--------------------------|:--------------|------:|------:|----:|---------:|----------:|\n| 0 | age | 0.0903729 | 0.0696271 | in years | age | 0.02 | 0.16 | 706 | 0.671578 | 0.0163089 |\n| 1 | black | -0.0270573 | 0.0770573 | =1 if black | other factors | -0.1 | 0.05 | 706 | 0.110805 | 0.472889 |\n| 2 | clerical | 0.0480811 | 0.0719189 | =1 if clerical worker | occupation | -0.03 | 0.12 | 706 | 0.247768 | 0.201948 |\n\n(* This is a toy example of how certain factors correlate with the amount of sleep one gets. See the [notebook that generates the data](https://nbviewer.org/github/LSYS/forestplot/blob/main/examples/get-sleep.ipynb).)\n\n\u003cdetails\u003e\u003csummary\u003e\u003ci\u003eThe example input dataframe above have 4 key columns\u003c/i\u003e\u003c/summary\u003e\n\n | Column | Description | Required |\n |:----------|:------------------------------------------------|:----------|\n | `var` | Variable label | \u0026check; |\n | `r` | Correlation coefficients (estimates to plot) | \u0026check; |\n | `label` | Variable labels | \u0026check; |\n | `group` | Variable grouping labels | |\n | `ll` | Conf. int. *lower limits* | |\n | `hl` | Containing the conf. int. *higher limits* | |\n | `n` | Sample size | |\n | `power` | Statistical power | |\n | `p-val` | P-value | |\n\n (See [Gallery and API Options](#gallery-and-api-options) for more details on required and optional arguments.) \n\u003c/details\u003e\n\nMake the forest plot\n```python\nfp.forestplot(df, # the dataframe with results data\n estimate=\"r\", # col containing estimated effect size \n ll=\"ll\", hl=\"hl\", # columns containing conf. int. lower and higher limits\n varlabel=\"label\", # column containing variable label\n ylabel=\"Confidence interval\", # y-label title\n xlabel=\"Pearson correlation\", # x-label title\n )\n```\n\u003cp align=\"left\"\u003e\u003cimg width=\"75%\" src=\"https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/vanilla.png\"\u003e\u003c/p\u003e\n\nSave the plot\n```python\nplt.savefig(\"plot.png\", bbox_inches=\"tight\")\n```\n\u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\u003c!------------------ EXAMPLES of CUSTOMIZATIONS ------------------\u003e\n## Some Examples With Customizations[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#some-examples-with-customizations)\n\n1. Add variable groupings, add group order, and sort by estimate size.\n```python\nfp.forestplot(df, # the dataframe with results data\n estimate=\"r\", # col containing estimated effect size \n ll=\"ll\", hl=\"hl\", # columns containing conf. int. lower and higher limits \n varlabel=\"label\", # column containing variable label\n capitalize=\"capitalize\", # Capitalize labels\n groupvar=\"group\", # Add variable groupings \n # group ordering\n group_order=[\"labor factors\", \"occupation\", \"age\", \"health factors\", \n \"family factors\", \"area of residence\", \"other factors\"],\n sort=True # sort in ascending order (sorts within group if group is specified) \n )\n```\n\u003cp align=\"left\"\u003e\u003cimg width=\"75%\" src=\"https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/group-grouporder-sort.png\"\u003e\u003c/p\u003e\n\n2. Add p-values on the right and color alternate rows gray\n```python\nfp.forestplot(df, # the dataframe with results data\n estimate=\"r\", # col containing estimated effect size \n ll=\"ll\", hl=\"hl\", # columns containing conf. int. lower and higher limits\n varlabel=\"label\", # column containing variable label\n capitalize=\"capitalize\", # Capitalize labels\n groupvar=\"group\", # Add variable groupings \n # group ordering\n group_order=[\"labor factors\", \"occupation\", \"age\", \"health factors\", \n \"family factors\", \"area of residence\", \"other factors\"],\n sort=True, # sort in ascending order (sorts within group if group is specified) \n pval=\"p-val\", # Column of p-value to be reported on right\n color_alt_rows=True, # Gray alternate rows\n ylabel=\"Est.(95% Conf. Int.)\", # ylabel to print\n **{\"ylabel1_size\": 11} # control size of printed ylabel\n )\n```\n\n\u003cp align=\"left\"\u003e\u003cimg width=\"80%\" src=\"https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/group-grouporder-pvalue-sort-colorrows.png\"\u003e\u003c/p\u003e\n\n\n3. Customize annotations and make it a table\n```python\nfp.forestplot(df, # the dataframe with results data\n estimate=\"r\", # col containing estimated effect size \n ll=\"ll\", hl=\"hl\", # lower \u0026 higher limits of conf. int.\n varlabel=\"label\", # column containing the varlabels to be printed on far left\n capitalize=\"capitalize\", # Capitalize labels\n pval=\"p-val\", # column containing p-values to be formatted\n annote=[\"n\", \"power\", \"est_ci\"], # columns to report on left of plot\n annoteheaders=[\"N\", \"Power\", \"Est. (95% Conf. Int.)\"], # ^corresponding headers\n rightannote=[\"formatted_pval\", \"group\"], # columns to report on right of plot \n right_annoteheaders=[\"P-value\", \"Variable group\"], # ^corresponding headers\n xlabel=\"Pearson correlation coefficient\", # x-label title\n table=True, # Format as a table\n )\n```\n\n\u003cp align=\"left\"\u003e\u003cimg width=\"85%\" src=\"https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/leftannote-rightannote-table.png\"\u003e\u003c/p\u003e\n\n4. Strip down all bells and whistle\n```python\nfp.forestplot(df, # the dataframe with results data\n estimate=\"r\", # col containing estimated effect size \n ll=\"ll\", hl=\"hl\", # lower \u0026 higher limits of conf. int.\n varlabel=\"label\", # column containing the varlabels to be printed on far left\n capitalize=\"capitalize\", # Capitalize labels\n ci_report=False, # Turn off conf. int. reporting\n flush=False, # Turn off left-flush of text\n **{'fontfamily': 'sans-serif'} # revert to sans-serif \n )\n``` \n\u003cp align=\"left\"\u003e\u003cimg width=\"50%\" src=\"https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/vcoefplot.png\"\u003e\u003c/p\u003e\n\n5. Example with more customizations\n```python\nfp.forestplot(df, # the dataframe with results data\n estimate=\"r\", # col containing estimated effect size \n ll=\"ll\", hl=\"hl\", # lower \u0026 higher limits of conf. int.\n varlabel=\"label\", # column containing the varlabels to be printed on far left\n capitalize=\"capitalize\", # Capitalize labels\n pval=\"p-val\", # column containing p-values to be formatted\n annote=[\"n\", \"power\", \"est_ci\"], # columns to report on left of plot\n annoteheaders=[\"N\", \"Power\", \"Est. (95% Conf. Int.)\"], # ^corresponding headers\n rightannote=[\"formatted_pval\", \"group\"], # columns to report on right of plot \n right_annoteheaders=[\"P-value\", \"Variable group\"], # ^corresponding headers\n groupvar=\"group\", # column containing group labels\n group_order=[\"labor factors\", \"occupation\", \"age\", \"health factors\", \n \"family factors\", \"area of residence\", \"other factors\"], \n xlabel=\"Pearson correlation coefficient\", # x-label title\n xticks=[-.4,-.2,0, .2], # x-ticks to be printed\n sort=True, # sort estimates in ascending order\n table=True, # Format as a table\n # Additional kwargs for customizations\n **{\"marker\": \"D\", # set maker symbol as diamond\n \"markersize\": 35, # adjust marker size\n \"xlinestyle\": (0, (10, 5)), # long dash for x-reference line \n \"xlinecolor\": \"#808080\", # gray color for x-reference line\n \"xtick_size\": 12, # adjust x-ticker fontsize\n } \n )\n```\n\u003cp align=\"left\"\u003e\u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/main.png\"\u003e\u003c/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003e\u003ci\u003eAnnotations arguments allowed include:\u003c/i\u003e\u003c/summary\u003e\n \n * `ci_range`: Confidence interval range (e.g. `(-0.39 to -0.25)`).\n * `est_ci`: Estimate and CI (e.g. `-0.32(-0.39 to -0.25)`).\n * `formatted_pval`: Formatted p-values (e.g. `0.01**`).\n \n To confirm what processed `columns` are available as annotations, you can do:\n \n ```python\n processed_df, ax = fp.forestplot(df, \n ... # other arguments here\n return_df=True # return processed dataframe with processed columns\n )\n processed_df.head(3)\n ```\n \n | | label | group | n | r | CI95% | p-val | BF10 | power | var | hl | ll | moerror | formatted_r | formatted_ll | formatted_hl | ci_range | est_ci | formatted_pval | formatted_n | formatted_power | formatted_est_ci | yticklabel | formatted_formatted_pval | formatted_group | yticklabel2 |\n|---:|:---------------------|:--------------|----:|-----------:|:--------------|------------:|----------:|--------:|:-------|------:|------:|----------:|--------------:|---------------:|---------------:|:-----------------|:----------------------|:-----------------|--------------:|------------------:|:----------------------|:------------------------------------------------------------------|:---------------------------|:------------------|:-----------------------|\n| 0 | Mins worked per week | Labor factors | 706 | -0.321384 | [-0.39 -0.25] | 1.99409e-18 | 1.961e+15 | 1 | totwrk | -0.25 | -0.39 | 0.0686165 | -0.32 | -0.39 | -0.25 | (-0.39 to -0.25) | -0.32(-0.39 to -0.25) | 0.0*** | 706 | 1 | -0.32(-0.39 to -0.25) | Mins worked per week 706 1.0 -0.32(-0.39 to -0.25) | 0.0*** | Labor factors | 0.0*** Labor factors |\n| 1 | Years of schooling | Labor factors | 706 | -0.0950039 | [-0.17 -0.02] | 0.0115515 | 1.137 | 0.72 | educ | -0.02 | -0.17 | 0.0749961 | -0.1 | -0.17 | -0.02 | (-0.17 to -0.02) | -0.10(-0.17 to -0.02) | 0.01** | 706 | 0.72 | -0.10(-0.17 to -0.02) | Years of schooling 706 0.72 -0.10(-0.17 to -0.02) | 0.01** | Labor factors | 0.01** Labor factors |\n \n\u003c/details\u003e\n\u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\u003c!------------------- Multi-models -------------------\u003e\n## Multi-models[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#multi-models)\n\nFor coefficient plots where each variable can have multiple estimates (each `model` has one).\n\n```python\nimport forestplot as fp\n\ndf_mmodel = pd.read_csv(\"../examples/data/sleep-mmodel.csv\").query(\n \"model=='all' | model=='young kids'\"\n)\ndf_mmodel.head(3)\n```\n\n| | var | coef | se | T | pval | r2 | adj_r2 | ll | hl | model | group | label |\n|---:|:------|-----------:|---------:|----------:|---------:|---------:|-----------:|-----------:|--------:|:-----------|:--------------|:------------|\n| 0 | age | 0.994889 | 1.96925 | 0.505213 | 0.613625 | 0.127289 | 0.103656 | -2.87382 | 4.8636 | all | age | in years |\n| 3 | age | 22.634 | 15.4953 | 1.4607 | 0.149315 | 0.178147 | -0.0136188 | -8.36124 | 53.6293 | young kids | age | in years |\n| 4 | black | -84.7966 | 82.1501 | -1.03222 | 0.302454 | 0.127289 | 0.103656 | -246.186 | 76.5925 | all | other factors | =1 if black |\n\n```python\nfp.mforestplot(\n dataframe=df_mmodel,\n estimate=\"coef\",\n ll=\"ll\",\n hl=\"hl\",\n varlabel=\"label\",\n capitalize=\"capitalize\",\n model_col=\"model\",\n color_alt_rows=True,\n groupvar=\"group\",\n table=True,\n rightannote=[\"var\", \"group\"],\n right_annoteheaders=[\"Source\", \"Group\"],\n xlabel=\"Coefficient (95% CI)\",\n modellabels=[\"Have young kids\", \"Full sample\"],\n xticks=[-1200, -600, 0, 600],\n mcolor=[\"#CC6677\", \"#4477AA\"],\n # Additional kwargs for customizations\n **{\n \"markersize\": 30,\n # override default vertical offset between models (0.0 to 1.0)\n \"offset\": 0.35, \n \"xlinestyle\": (0, (10, 5)), # long dash for x-reference line\n \"xlinecolor\": \".8\", # gray color for x-reference line\n },\n)\n```\n\u003cp align=\"left\"\u003e\u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/LSYS/forestplot/mplot-dev/docs/images/multimodel.png\"\u003e\u003c/p\u003e\n\nPlease note: This module is still experimental. See [this jupyter notebook](https://nbviewer.org/github/LSYS/forestplot/blob/mplot-dev/examples/test-multmodel-sleep.ipynb) for more examples and tweaks.\n\n\u003c!------------------- GALLERY AND API OPTIONS -------------------\u003e\n## Gallery and API Options[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#gallery-and-api-options)\n\n[![Notebooks](https://github.com/LSYS/forestplot/actions/workflows/nb.yml/badge.svg)](https://github.com/LSYS/forestplot/actions/workflows/nb.yml)\n\nCheck out [this jupyter notebook](https://nbviewer.org/github/LSYS/forestplot/blob/main/examples/readme-examples.ipynb) for a gallery variations of forest plots possible out-of-the-box.\nThe table below shows the list of arguments users can pass in.\nMore fined-grained control for base plot options (eg font sizes, marker colors) can be inferred from the [example notebook gallery](https://nbviewer.org/github/LSYS/forestplot/blob/main/examples/readme-examples.ipynb). \n\n\n| Option | Description | Required |\n|:-------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:---|\n| `dataframe` | Pandas dataframe where rows are variables (or studies for meta-analyses) and columns include estimated effect sizes, labels, and confidence intervals, etc. | \u0026check; |\n| `estimate` | Name of column in `dataframe` containing the *estimates*. | \u0026check; |\n| `varlabel` | Name of column in `dataframe` containing the *variable labels* (study labels if meta-analyses). | \u0026check; |\n| `ll` | Name of column in `dataframe` containing the conf. int. *lower limits*. | |\n| `hl` | Name of column in `dataframe` containing the conf. int. *higher limits*. | |\n| `logscale` | If True, make the x-axis log scale. Default is False. | |\n| `capitalize` | How to capitalize strings. Default is None. One of \"capitalize\", \"title\", \"lower\", \"upper\", \"swapcase\". | |\n| `form_ci_report` | If True (default), report the estimates and confidence interval beside the variable labels. | |\n| `ci_report` | If True (default), format the confidence interval as a string. | |\n| `groupvar` | Name of column in `dataframe` containing the variable *grouping labels*. | |\n| `group_order` | List of group labels indicating the order of groups to report in the plot. | |\n| `annote` | List of columns to add as annotations on the left-hand side of the plot. | |\n| `annoteheaders` | List of column headers for the left-hand side annotations. | |\n| `rightannote` | List of columns to add as annotations on the right-hand side of the plot. | |\n| `right_annoteheaders` | List of column headers for the right-hand side annotations. | |\n| `pval` | Name of column in `dataframe` containing the p-values. | |\n| `starpval` | If True (default), format p-values with stars indicating statistical significance. | |\n| `sort` | If True, sort variables by `estimate` values in ascending order. | |\n| `sortby` | Name of column to sort by. Default is `estimate`. | |\n| `flush` | If True (default), left-flush variable labels and annotations. | |\n| `decimal_precision` | Number of decimal places to print. (Default = 2) | |\n| `figsize` | Tuple indicating core figure size. Default is (4, 8) | |\n| `xticks` | List of xticklabels to print on x-axis. | |\n| `ylabel` | Y-label title. | |\n| `xlabel` | X-label title. | |\n| `color_alt_rows` | If True, shade out alternating rows in gray. | |\n| `preprocess` | If True (default), preprocess the `dataframe` before plotting. | |\n| `return_df` | If True, returned the preprocessed `dataframe`. | |\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\u003c!------------------------ KNOWN ISSUES ------------------------\u003e\n## Known Issues[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#known-issues)\n\n* Variable labels coinciding with group variables may lead to unexpected formatting issues in the graph.\n* Left-flushing of annotations relies on the `monospace` font.\n* Plot may give strange behavior for few rows of data (six rows or fewer. [see this issue](https://github.com/LSYS/forestplot/issues/52))\n* Plot can get cluttered with too many variables/rows (~30 onwards) \n* Not tested with PyCharm (#80) nor Google Colab (#110).\n* Duplicated `varlabel` may lead to unexpected results (see #76, #81). `mplot` for grouped models could be useful for such cases (see #59, WIP).\n\u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\u003c!----------------- BACKGROUND AND ADDITIONAL RESOURCES -----------------\u003e\n## Background and Additional Resources[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#background-and-additional-resources)\n\n**More about forest plots**\n\n[Forest plots](https://en.wikipedia.org/wiki/Forest_plot) have many aliases (h/t Chris Alexiuk). Other names include coefplots, coefficient plots, meta-analysis plots, dot-and-whisker plots, blobbograms, margins plots, regression plots, and ropeladder plots. \n\n[Forest plots](https://en.wikipedia.org/wiki/Forest_plot) in the medical and health sciences literature are plots that report results from different studies as a meta-analysis. Markers are centered on the estimated effect and horizontal lines running through each marker depicts the confidence intervals.\n\nThe simplest version of a forest plot has two columns: one for the variables/studies, and the second for the estimated coefficients and confidence intervals.\nThis layout is similar to coefficient plots ([coefplots](http://repec.sowi.unibe.ch/stata/coefplot/getting-started.html)) and is thus useful for more than meta-analyses.\n\n\u003cdetails\u003e\u003csummary\u003e\u003ci\u003eMore resources about forest plots\u003c/i\u003e\u003c/summary\u003e\u003cp\u003e\n\n* [[1]](https://doi.org/10.1038/s41433-021-01867-6) Chang, Y., Phillips, M.R., Guymer, R.H. et al. The 5 min meta-analysis: understanding how to read and interpret a forest plot. Eye 36, 673–675 (2022).\n* [[2]](https://doi.org/10.1136/bmj.322.7300.1479) Lewis S, Clarke M. Forest plots: trying to see the wood and the trees BMJ 2001; 322 :1479 \n\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\n**More about this package**\n\nThe package is lightweight, built on `pandas`, `numpy`, and `matplotlib`.\n\nIt is slightly opinioniated in that the aesthetics of the plot inherits some of my sensibilities about what makes a nice figure.\nYou can however easily override most defaults for the look of the graph. This is possible via `**kwargs` in the `forestplot` API (see [Gallery and API options](#gallery-and-api-options)) and the `matplotlib` API.\n\n**Planned enhancements** include forest plots where each row can have multiple coefficients (e.g. from multiple models). \n\n\u003cdetails\u003e\u003csummary\u003e\u003ci\u003eRelated packages\u003c/i\u003e\u003c/summary\u003e\u003cp\u003e\n\n* [[1]](https://www.stata-journal.com/article.html?article=gr0059) [Stata] Jann, Ben (2014). Plotting regression coefficients and other estimates. The Stata Journal 14(4): 708-737. \n* [[2]](https://www.statsmodels.org/devel/examples/notebooks/generated/metaanalysis1.html) [Python] Meta-Analysis in statsmodels\n* [[3]](https://github.com/seafloor/forestplot) [Python] Matt Bracher-Smith's Forestplot\n* [[4]](https://github.com/fsolt/dotwhisker) [R] Solt, Frederick and Hu, Yue (2021) dotwhisker: Dot-and-Whisker Plots of Regression Results\n* [[5]](https://rpubs.com/mbounthavong/forest_plots_r) [R] Bounthavong, Mark (2021) Forest plots. RPubs by RStudio\n\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!----------------------- CONTRIBUTING -----------------------\u003e\n## Contributing[![](https://raw.githubusercontent.com/LSYS/forestplot/main/docs/images/pin.svg)](#contributing)\n\nContributions are welcome, and they are greatly appreciated!\n\n**Potential ways to contribute:**\n\n* Raise issues/bugs/questions\n* Write tests for missing coverage\n* Add features (see [examples notebook](https://nbviewer.org/github/LSYS/forestplot/blob/main/examples/readme-examples.ipynb) for a survey of existing features)\n* Add example datasets with companion graphs\n* Add your graphs with companion code\n\n**Issues**\n\nPlease submit bugs, questions, or issues you encounter to the [GitHub Issue Tracker](https://github.com/lsys/forestplot/issues).\nFor bugs, please provide a minimal reproducible example demonstrating the problem (it may help me troubleshoot if I have a version of your data).\n\n**Pull Requests**\n\nPlease feel free to open an issue on the [Issue Tracker](https://github.com/lsys/forestplot/issues) if you'd like to discuss potential contributions via PRs.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flsys%2Fforestplot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flsys%2Fforestplot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flsys%2Fforestplot/lists"}