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   "source": [
    "### <center>San Jose State University<br>Department of Applied Data Science<br><br>**DATA 200<br>Computational Programming for Data Analytics**<br><br>Spring 2024<br>Instructor: Ron Mak</center>"
   ]
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  {
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   "id": "3868a95d-9681-463c-b8cb-68a99c838ce2",
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   "source": [
    "# `seaborn` color palettes"
   ]
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    "#### The `color_palette()` function is an interface for generating colors.\n",
    "``` Python\n",
    "seaborn.color_palette(palette, n_colors, desat)\n",
    "```\n",
    "where\n",
    "- #### ***palette*** (optional): The name of the palette, or `None` to return the current palette.\n",
    "- #### ***n_colors*** (optional): The number of colors in the palette. If this number is larger than the number of colors in the palette, the colors are cyled.\n",
    "- #### ***desat*** (optional): The proportion by which to desaturate each color.\n",
    "#### Call `set_palette()` to set the palette for all plots.\n",
    "``` Python\n",
    "seaborn.set_palette(palette, n_colors, desat)\n",
    "```"
   ]
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  {
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   "execution_count": null,
   "id": "5d96df41-f2bf-486b-9708-6c6b69b644a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
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   "id": "2b5154b4-1fbf-4201-a054-1280c6ed4aa1",
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   "source": [
    "for pal in ['deep', 'muted', 'bright', 'pastel', \n",
    "            'dark', 'colorblind']:\n",
    "    print(f'Palette {pal}')\n",
    "    palette = sns.color_palette(pal)\n",
    "    sns.palplot(palette)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12b92e6c-4590-4207-be04-a3bf1d5c3fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb0d214-12de-4df1-b4dd-93dd351679ef",
   "metadata": {},
   "source": [
    "#### Adapted from ***Data Visualization with Python***, by Mario Döbler and Tim Großmann, Packt 2019, ISBN 978-1-78995-646-7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a114258b-fd23-40f2-b54f-13567404afa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional material (c) 2024 by Ronald Mak"
   ]
  }
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