{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Plotting with keywords\n\nSome data structures, like dict, [structured numpy array](https://numpy.org/doc/stable/user/basics.rec.html#structured-arrays)\nor `pandas.DataFrame` provide access to labelled data via string index access\n``data[key]``.\n\nFor these data types, Matplotlib supports passing the whole datastructure via the\n``data`` keyword argument, and using the string names as plot function parameters,\nwhere you'd normally pass in your data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nnp.random.seed(19680801)\n\ndata = {'a': np.arange(50),\n        'c': np.random.randint(0, 50, 50),\n        'd': np.random.randn(50)}\ndata['b'] = data['a'] + 10 * np.random.randn(50)\ndata['d'] = np.abs(data['d']) * 100\n\nfig, ax = plt.subplots()\nax.scatter('a', 'b', c='c', s='d', data=data)\nax.set(xlabel='entry a', ylabel='entry b')\nplt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.14.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}