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        "\n# Separate calculation and plotting of boxplots\n\nDrawing a `~.axes.Axes.boxplot` for a given data set, consists of two main operations,\nthat can also be used separately:\n\n1. Calculating the boxplot statistics: `matplotlib.cbook.boxplot_stats`\n2. Drawing the boxplot: `matplotlib.axes.Axes.bxp`\n\nThus, ``ax.boxplot(data)`` is equivalent to ::\n\n    stats = cbook.boxplot_stats(data)\n    ax.bxp(stats)\n\nAll 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`\ntranslates to a generic *labels* parameter in `.boxplot_stats`, because the labels are\ndata-related and attached to the returned per-dataset dictionaries.\n\nThe following code demonstrates the equivalence between the two methods.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom matplotlib import cbook\n\nnp.random.seed(19680801)\ndata = np.random.randn(20, 3)\n\nfig, (ax1, ax2) = plt.subplots(1, 2)\n\n# single boxplot call\nax1.boxplot(data, tick_labels=['A', 'B', 'C'],\n            patch_artist=True, boxprops={'facecolor': 'bisque'})\n\n# separate calculation of statistics and plotting\nstats = cbook.boxplot_stats(data, labels=['A', 'B', 'C'])\nax2.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\nthem explicitly for other purposes, or to reuse the statistics for multiple plots.\n\nConversely, you can also use the `~.axes.Axes.bxp` function directly, if you already\nhave the statistical parameters:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fig, ax = plt.subplots()\n\nstats = [\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\nax.bxp(stats, patch_artist=True, boxprops={'facecolor': 'bisque'})\n\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ".. tags:: plot-type: specialty, 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"
      ]
    }
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