From be5b5a05803346c0a757e5a16ff9c22c9ee740f4 Mon Sep 17 00:00:00 2001 From: Dom Date: Thu, 24 Feb 2022 10:28:47 +0000 Subject: [PATCH] Update docs (#91) * Update docs * Add notebook links * Update Iris example --- README.md | 2 +- docs/02-user-guide/quick-start.md | 1 - docs/02-user-guide/tutorial-notebooks.md | 17 +++++++------ docs/examples/iris-report.ipynb | 32 ++++++++++++++++-------- 4 files changed, 32 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 7198103..7a5ee1b 100644 --- a/README.md +++ b/README.md @@ -111,7 +111,7 @@ Examples -------- Iris Report - [Webpage](https://domvwt.github.io/esparto/examples/iris-report.html) | -[PDF](https://domvwt.github.io/esparto/examples/iris-report.pdf) +[PDF](https://domvwt.github.io/esparto/examples/iris-report.pdf) | [Notebook](https://github.com/domvwt/esparto/blob/main/docs/examples/iris-report.ipynb)
diff --git a/docs/02-user-guide/quick-start.md b/docs/02-user-guide/quick-start.md index 1d55198..b529c46 100644 --- a/docs/02-user-guide/quick-start.md +++ b/docs/02-user-guide/quick-start.md @@ -75,7 +75,6 @@ del page.section_title.row_title[-1] ```python del page.section_title.row_title.column_two -page.tree() ``` ``` diff --git a/docs/02-user-guide/tutorial-notebooks.md b/docs/02-user-guide/tutorial-notebooks.md index 3061f2a..54baf6f 100644 --- a/docs/02-user-guide/tutorial-notebooks.md +++ b/docs/02-user-guide/tutorial-notebooks.md @@ -2,17 +2,18 @@ ## Data Analysis -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/domvwt/esparto/blob/main/docs/examples/iris-report.ipynb) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/domvwt/esparto/main?filepath=docs%2Fexamples%2Firis-report.ipynb) -[![GitHub](https://img.shields.io/badge/view%20on-GitHub-lightgrey)](https://github.com/domvwt/esparto/blob/main/docs/examples/iris-report.ipynb) +The Iris dataset is one of the best known datasets in statistics. +In this example we put together a simple data analysis report using: -This notebook shows how we can put together a simple data analysis report. +* Text content with markdown formatting +* Pandas DataFrames +* Plots from Matplotlib -* Text content with Markdown formatting -* Converting a Pandas DataFrame to a table -* Adding plots from Matplotlib +[Webpage](../examples/iris-report.html) | [PDF](../examples/iris-report.pdf) -Output: [Webpage](../examples/iris-report.html) | [PDF](../examples/iris-report.pdf) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/domvwt/esparto/blob/main/docs/examples/iris-report.ipynb) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/domvwt/esparto/main?filepath=docs%2Fexamples%2Firis-report.ipynb) +[![GitHub](https://img.shields.io/badge/view%20on-GitHub-lightgrey)](https://github.com/domvwt/esparto/blob/main/docs/examples/iris-report.ipynb) ---- diff --git a/docs/examples/iris-report.ipynb b/docs/examples/iris-report.ipynb index 293d956..5864623 100644 --- a/docs/examples/iris-report.ipynb +++ b/docs/examples/iris-report.ipynb @@ -7,14 +7,23 @@ }, "source": [ "# Iris Report\n", - "The iris dataset is one of the best known datasets in statistics.\n", - "This example notebook shows how we can put together a simple data analysis report.\n", + "The Iris dataset is one of the best known datasets in statistics.\n", + "In this example we put together a simple data analysis report using:\n", "\n", - "\n", - "Specifically we will look at\n", "* Text content with markdown formatting\n", - "* Converting a Pandas DataFrame to a table\n", - "* Adding plots from Matplotlib" + "* Pandas DataFrames\n", + "* Plots from Matplotlib\n", + "\n", + "
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + "

" ] }, { @@ -755,7 +764,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -2967,10 +2976,13 @@ "provenance": [] }, "hide_input": false, + "interpreter": { + "hash": "dae0b3f1c5ff3f49207376eae2b2b5320aa00799c3d6c67399bd6ef505d44226" + }, "kernelspec": { - "display_name": "py38", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py38" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2982,7 +2994,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.3" }, "toc": { "base_numbering": 1,