From 66d9a52c2edad78d3c64625d30a6a339081a0b0c Mon Sep 17 00:00:00 2001 From: Holtz Yan Date: Mon, 23 Oct 2023 12:02:35 +0200 Subject: [PATCH] fix donut link --- src/notebooks/160-basic-donut-plot.ipynb | 148 +++++++++++------------ 1 file changed, 74 insertions(+), 74 deletions(-) diff --git a/src/notebooks/160-basic-donut-plot.ipynb b/src/notebooks/160-basic-donut-plot.ipynb index 848fad287b..84c10b7880 100644 --- a/src/notebooks/160-basic-donut-plot.ipynb +++ b/src/notebooks/160-basic-donut-plot.ipynb @@ -1,80 +1,80 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The trick to plot a donut chart using matplotlib is to draw a [pie plot](https://python-graph-gallery.com/pie-plot/) and add a white circle in the middle of it. In order to draw a circle, you can use the `Circle()` function of matplotlib. The parameters passed to the function in the example below are:\n", - "* `(x,y)` : center point of the circle\n", - "* `radius` : radius of the circle\n", - "* `color` : color of the circle\n", - "\n", - "In the example, the `add_artist()` function is used to add the white circle on the axes of the pie chart. In order to get the current axes instance on the current figure, the `gca()` function is used and to get the current figure, the `gcf()` function is used." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The trick to plot a donut chart using matplotlib is to draw a [pie plot](https://python-graph-gallery.com/pie-plot/) and add a white circle in the middle of it. In order to draw a circle, you can use the `Circle()` function of matplotlib. The parameters passed to the function in the example below are:\n", + "* `(x,y)` : center point of the circle\n", + "* `radius` : radius of the circle\n", + "* `color` : color of the circle\n", + "\n", + "In the example, the `add_artist()` function is used to add the white circle on the axes of the pie chart. In order to get the current axes instance on the current figure, the `gca()` function is used and to get the current figure, the `gcf()` function is used." + ] + }, { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# library\n", + "import matplotlib.pyplot as plt\n", + " \n", + "# create data\n", + "size_of_groups=[12,11,3,30]\n", + " \n", + "# Create a pie plot\n", + "plt.pie(size_of_groups)\n", + "#plt.show()\n", + " \n", + "# add a white circle at the center\n", + "my_circle=plt.Circle( (0,0), 0.7, color='white')\n", + "p=plt.gcf()\n", + "p.gca().add_artist(my_circle)\n", + "\n", + "# show the graph\n", + "plt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "# library\n", - "import matplotlib.pyplot as plt\n", - " \n", - "# create data\n", - "size_of_groups=[12,11,3,30]\n", - " \n", - "# Create a pie plot\n", - "plt.pie(size_of_groups)\n", - "#plt.show()\n", - " \n", - "# add a white circle at the center\n", - "my_circle=plt.Circle( (0,0), 0.7, color='white')\n", - "p=plt.gcf()\n", - "p.gca().add_artist(my_circle)\n", - "\n", - "# show the graph\n", - "plt.show()" - ] - } - ], - "metadata": { - "chartType": "donut", - "description": "This post aims to describe how to plot a basic donut chart using matplotlib library.", - "family": "partOfAWhole", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "keywords": "python, chart, plot, matplotlib, donut, pie, circle", - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" + ], + "metadata": { + "chartType": "donut", + "description": "This post aims to describe how to plot a basic donut chart using matplotlib library.", + "family": "partOfAWhole", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "keywords": "python, chart, plot, matplotlib, donut, pie, circle", + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "seoDescription": "Plotting a basic donut chart using matplotlib", + "slug": "160-basic-donut-plot", + "title": "Most basic donut plot" }, - "seoDescription": "Plotting a basic donut chart using matplotlib", - "slug": "160-basic-donut-plot", - "title": "Basic donut plot" - }, - "nbformat": 4, - "nbformat_minor": 4 + "nbformat": 4, + "nbformat_minor": 4 }