.ipynb_checkpoints/seaborn_color_palettes-checkpoint.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Color Palettes in Seaborn\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Preliminaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = {'date': ['2014-05-01 18:47:05.069722', '2014-05-01 18:47:05.119994', '2014-05-02 18:47:05.178768', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.280592', '2014-05-03 18:47:05.332662', '2014-05-03 18:47:05.385109', '2014-05-04 18:47:05.436523', '2014-05-04 18:47:05.486877'], \n",
      "        'deaths_regiment_1': [34, 43, 14, 15, 15, 14, 31, 25, 62, 41],\n",
      "        'deaths_regiment_2': [52, 66, 78, 15, 15, 5, 25, 25, 86, 1],\n",
      "        'deaths_regiment_3': [13, 73, 82, 58, 52, 87, 26, 5, 56, 75],\n",
      "        'deaths_regiment_4': [44, 75, 26, 15, 15, 14, 54, 25, 24, 72],\n",
      "        'deaths_regiment_5': [25, 24, 25, 15, 57, 68, 21, 27, 62, 5],\n",
      "        'deaths_regiment_6': [84, 84, 26, 15, 15, 14, 26, 25, 62, 24],\n",
      "        'deaths_regiment_7': [46, 57, 26, 15, 15, 14, 26, 25, 62, 41]}\n",
      "df = pd.DataFrame(data, columns = ['date', 'battle_deaths', 'deaths_regiment_1', 'deaths_regiment_2',\n",
      "                                   'deaths_regiment_3', 'deaths_regiment_4', 'deaths_regiment_5',\n",
      "                                   'deaths_regiment_6', 'deaths_regiment_7'])\n",
      "df = df.set_index(df.date)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## View some color palettes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"deep\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a1c1d10>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"muted\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1097b3fd0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"bright\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEzwAQJ7gAQDyBA8AkCd4AIC8D4HnJTEANDUuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a1c5e50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"dark\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4296d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"colorblind\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4f5c10>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"Paired\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8QQPABBP8AAA8QQPABDvDUClKddK8QmWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a688590>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"BuGn\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nT/AAAPEEDwAQT/AAAPEEDwAQ7wlB7ikX5+xiWQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a71d7d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"GnBu\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAgdJREFUeJzt2CFL3VEcxvHzF8s1ralprFkUVgTDFYzCDZbFiy9A1gx7\nCQbb8AVcjBbDBaPgDYJlsBXbWNpsJm07vgGxHY48fD71V550+HKGWmsBAEi21HsAAEBrggcAiCd4\nAIB4ggcAiCd4AIB4ggcAiLf81rHW/3UYNBEA8P6NprPyfH44vHZ7M3iGYan8ffrTZtU7sL7ysdw/\n/uw9o5mND1vl9mHRe0YzO6vjcvH7qveMJr582i+nv657z2jmeHOvHC3ues9o5my8XSaXuW/L/GCr\nfP5+03tGMz++7pa1b/PeM5r4dzIpo+ms94wufN8AAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AA\nAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEE\nDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQ\nT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AA\nAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQb6i19t4AANCUHx4AIJ7g\nAQDiCR4AIJ7gAQDiCR4AIJ7gAQDivQB1kCmUa2aLrgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a71d050>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"OrRd\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDwAQT/AAAPEEDwAQ7z94Kik0F0L6QwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a7a8610>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"PuBu\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nT/AAAPEEDwAQT/AAAPG+AKH6KWDWJ9l3AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a7a37d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"YlGn\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBwCIJ3gAgHiCBwCIJ3gAgHgvzmUpIat7qKMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a857d10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"YlGnBu\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neACAeIIHAIgneACAeIIHAIgneACAeP8BVfMpXYxZExcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a798190>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"YlOrBr\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8X4AdRonTJ5hnRUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a3ec150>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"YlOrRd\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAfdJREFUeJzt2aEuBlAcxuFzfCbRbYKNYIIiuABRsrkG3RTXoJjuGr5N\n+qILEBTBBDbBppNsHDdggu049u556r+88bdzamutAAAkmxs9AACgN8EDAMQTPABAPMEDAMQTPABA\nPMEDAMSb//HaPlqpkz+aAgDwe9O6UQ7aff3u9nPw1Ekpr9Muo/6FpYPSns9Gr+imrhyXdns0ekY3\ndeu8tNn+6Bld1L3L8nmxO3pGN3OHV+XjZGf0jG4mp9flfW9z9IxuFmZ35WV9dfSMbpYfnsrN4tro\nGV1svz2Wad0YPWMIX1oAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDE\nEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwA\nQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzB\nAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDE\nEzwAQDzBAwDEEzwAQDzBAwDEEzwAQDzBAwDEq6210RsAALrywgMAxBM8AEA8wQMAxBM8AEA8wQMA\nxBM8AEC8LxLaJ2J7Ul5DAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a35ca90>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"BrBG\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4badd0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"PiYG\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a625e90>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"PRGn\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4430d0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"PuOr\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aa039d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"RdBu\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aa6e310>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"RdGy\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAEA8wQMAxBM8AEC8b3pZKXZ0h6rgAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ac18950>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"RdYlBu\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAQDiCR4AIJ7gAQDiCR4AIN43Up4p/Z2ROm0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aa69d10>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"RdYlGn\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAIgneACAeIIHAIgneACAeF+ipSmTLsJH9AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a49a510>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.palplot(sns.color_palette(\"Spectral\", 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAgFJREFUeJzt2TEuhUEYheG5N9xKFOxCIVRiASQahUIIHQqLUN1FKNAR\nolBoJCxAVERhFxSiIvHbgCgkY+TkedqvOeWbmV7XdQUAIFm/9QAAgNoEDwAQT/AAAPEEDwAQT/AA\nAPEEDwAQb+Sn4+f7e9cfDP5qCwDAr80Nb8rd3kLvu9uPwdMfDMrD/HKdVf/AzO1ledlebD2jmonD\n6/JxsN56RjWjO6fl836v9Ywq+rPD0r2etZ5RTW98rbx9XLSeUc3Y6Ep5fD5qPaOa6cmtsv900npG\nNbtTG2Xz6rz1jCqOl1bL3PCm9YwmfGkBAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQ\nT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AA\nAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEE\nDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQ\nT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQr9d1XesNAABVeeEBAOIJHgAgnuAB\nAOIJHgAgnuABAOIJHgAg3hfHhSoMpv3nKwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a391a90>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Create a color palette and set it as the current color palette"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flatui = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"]\n",
      "sns.set_palette(flatui)\n",
      "sns.palplot(sns.color_palette())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYpJREFUeJzt2LEtRmEYhuHz89vBAHqFKA1AYgAajUSiNIhSItFoGEDC\nAEpR6A1gB+RYQHTnPsJ1tW/zVHe+fItxHAcAGitzDwD4T0QXICS6ACHRBQiJLkBIdAFCy5+OH++f\n43JttdoC8CdsPp8NL1vni+9uP0Z3ubY6XB0+TLPqFzi+2R22L1/nnjGZp5ON4eLmdu4Zkzk9PBje\n9nfmnjGJ9bvHYXvvaO4Zk3m6vx42n8/mnjEL3wsAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoA\nIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEF\nCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIku\nQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYDQYhzHuTcA/BteugAh0QUIiS5A\nSHQBQqILEBJdgNAXbbQeA7+ba4gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a3a0050>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Set the color of a plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.tsplot([df.deaths_regiment_1, df.deaths_regiment_2, df.deaths_regiment_3, df.deaths_regiment_4,\n",
      "            df.deaths_regiment_5, df.deaths_regiment_6, df.deaths_regiment_7], color=\"#34495e\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10aa2c650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9comevRqEa2AI72zegHc2bxA2rqwo6Osq7uRzIV3tdoQiEU0pSXq9Dv1HDuDT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\nz18PwjUwnPPHXizOGQLBUMUDcCqdxozXj2gkBi7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S4/dRW1dPf380KVfDkyeW8N2vPsxv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\n2pIALNKK0+lkYkkRE0qCNDR02D0cIYRImGSpiLTjzs6WZUVCiLQnAVg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       "text": [
        "<matplotlib.figure.Figure at 0x10aa231d0>"
       ]
      }
     ],
     "prompt_number": 27
    }
   ],
   "metadata": {}
  }
 ]
}