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code/matplotlib/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace
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code/matplotlib/gallery_jupyter/statistics/.ipynb_checkpoints/bxp-checkpoint.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "\n", | ||
| "# Separate calculation and plotting of boxplots\n", | ||
| "\n", | ||
| "Drawing a `~.axes.Axes.boxplot` for a given data set, consists of two main operations,\n", | ||
| "that can also be used separately:\n", | ||
| "\n", | ||
| "1. Calculating the boxplot statistics: `matplotlib.cbook.boxplot_stats`\n", | ||
| "2. Drawing the boxplot: `matplotlib.axes.Axes.bxp`\n", | ||
| "\n", | ||
| "Thus, ``ax.boxplot(data)`` is equivalent to ::\n", | ||
| "\n", | ||
| " stats = cbook.boxplot_stats(data)\n", | ||
| " ax.bxp(stats)\n", | ||
| "\n", | ||
| "All styling keyword arguments are identical between `~.axes.Axes.boxplot` and\n", | ||
| "`~.axes.Axes.bxp`, and they are passed through from `~.axes.Axes.boxplot` to\n", | ||
| "`~.axes.Axes.bxp`. However, the *tick_labels* parameter of `~.axes.Axes.boxplot`\n", | ||
| "translates to a generic *labels* parameter in `.boxplot_stats`, because the labels are\n", | ||
| "data-related and attached to the returned per-dataset dictionaries.\n", | ||
| "\n", | ||
| "The following code demonstrates the equivalence between the two methods.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "jupyter": { | ||
| "outputs_hidden": false | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "from matplotlib import cbook\n", | ||
| "\n", | ||
| "np.random.seed(19680801)\n", | ||
| "data = np.random.randn(20, 3)\n", | ||
| "\n", | ||
| "fig, (ax1, ax2) = plt.subplots(1, 2)\n", | ||
| "\n", | ||
| "# single boxplot call\n", | ||
| "ax1.boxplot(data, tick_labels=['A', 'B', 'C'],\n", | ||
| " patch_artist=True, boxprops={'facecolor': 'bisque'})\n", | ||
| "\n", | ||
| "# separate calculation of statistics and plotting\n", | ||
| "stats = cbook.boxplot_stats(data, labels=['A', 'B', 'C'])\n", | ||
| "ax2.bxp(stats, patch_artist=True, boxprops={'facecolor': 'bisque'})" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Using the separate functions allows to pre-calculate statistics, in case you need\n", | ||
| "them explicitly for other purposes, or to reuse the statistics for multiple plots.\n", | ||
| "\n", | ||
| "Conversely, you can also use the `~.axes.Axes.bxp` function directly, if you already\n", | ||
| "have the statistical parameters:\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "jupyter": { | ||
| "outputs_hidden": false | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "fig, ax = plt.subplots()\n", | ||
| "\n", | ||
| "stats = [\n", | ||
| " dict(med=0, q1=-1, q3=1, whislo=-2, whishi=2, fliers=[-4, -3, 3, 4], label='A'),\n", | ||
| " dict(med=0, q1=-2, q3=2, whislo=-3, whishi=3, fliers=[], label='B'),\n", | ||
| " dict(med=0, q1=-3, q3=3, whislo=-4, whishi=4, fliers=[], label='C'),\n", | ||
| "]\n", | ||
| "\n", | ||
| "ax.bxp(stats, patch_artist=True, boxprops={'facecolor': 'bisque'})\n", | ||
| "\n", | ||
| "plt.show()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| ".. tags:: plot-type: speciality, domain: statistics\n", | ||
| "\n", | ||
| ".. admonition:: References\n", | ||
| "\n", | ||
| " The use of the following functions, methods, classes and modules is shown\n", | ||
| " in this example:\n", | ||
| "\n", | ||
| " - `matplotlib.axes.Axes.bxp`\n", | ||
| " - `matplotlib.axes.Axes.boxplot`\n", | ||
| " - `matplotlib.cbook.boxplot_stats`\n", | ||
| "\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.18" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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