Seaborn Experiments.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:63d3c66a85ce74420158f64d16acaa71db34a471ed52ea294a7f9122418a9256"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "sns.set(style=\"ticks\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rs = np.random.RandomState(4)\n",
      "\n",
      "pos = rs.randint(-1, 2, (20, 5)).cumsum(axis=1)\n",
      "pos -= pos[:, 0, np.newaxis]\n",
      "step = np.tile(range(5), 20)\n",
      "walk = np.repeat(range(20), 5)\n",
      "\n",
      "df = pd.DataFrame(np.c_[pos.flat, step, walk],\n",
      "                  columns=[\"position\", \"step\", \"walk\"])\n",
      "\n",
      "grid = sns.FacetGrid(df, col=\"walk\", hue=\"walk\", col_wrap=5, size=1.5)\n",
      "grid.map(plt.axhline, y=0, ls=\":\", c=\".5\")\n",
      "grid.map(plt.plot, \"step\", \"position\", marker=\"o\", ms=4)\n",
      "grid.set(xticks=np.arange(5), yticks=[-3, 3],\n",
      "         xlim=(-.5, 4.5), ylim=(-3.5, 3.5))\n",
      "grid.fig.tight_layout(w_pad=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4Yx4iOqcC/8jFx0qp2wAncGuEty+EEEKIMIn0RM39wKWR3KYQQgghIiPSIxVW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       "text": [
        "<matplotlib.figure.Figure at 0x112db76d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>position</th>\n",
        "      <th>step</th>\n",
        "      <th>walk</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>-1</td>\n",
        "      <td> 4</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>-1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 2</td>\n",
        "      <td> 4</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>-1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>-1</td>\n",
        "      <td> 2</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>-1</td>\n",
        "      <td> 3</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>-1</td>\n",
        "      <td> 4</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>70</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>71</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>72</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>73</th>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>74</th>\n",
        "      <td>-1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>76</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77</th>\n",
        "      <td>-1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>78</th>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79</th>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>81</th>\n",
        "      <td>-1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>82</th>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>83</th>\n",
        "      <td>-1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>84</th>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>85</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86</th>\n",
        "      <td>-1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>87</th>\n",
        "      <td>-2</td>\n",
        "      <td> 2</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>88</th>\n",
        "      <td>-2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>89</th>\n",
        "      <td>-1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>90</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>91</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>92</th>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>93</th>\n",
        "      <td>-1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>94</th>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>95</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96</th>\n",
        "      <td>-1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97</th>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>98</th>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99</th>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>100 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "    position  step  walk\n",
        "0          0     0     0\n",
        "1          1     1     0\n",
        "2          1     2     0\n",
        "3          1     3     0\n",
        "4          0     4     0\n",
        "5          0     0     1\n",
        "6          1     1     1\n",
        "7          1     2     1\n",
        "8          2     3     1\n",
        "9          1     4     1\n",
        "10         0     0     2\n",
        "11         1     1     2\n",
        "12         1     2     2\n",
        "13         0     3     2\n",
        "14        -1     4     2\n",
        "15         0     0     3\n",
        "16        -1     1     3\n",
        "17         0     2     3\n",
        "18         1     3     3\n",
        "19         2     4     3\n",
        "20         0     0     4\n",
        "21        -1     1     4\n",
        "22        -1     2     4\n",
        "23        -1     3     4\n",
        "24        -1     4     4\n",
        "25         0     0     5\n",
        "26         0     1     5\n",
        "27         1     2     5\n",
        "28         0     3     5\n",
        "29         1     4     5\n",
        "..       ...   ...   ...\n",
        "70         0     0    14\n",
        "71         0     1    14\n",
        "72         1     2    14\n",
        "73         0     3    14\n",
        "74        -1     4    14\n",
        "75         0     0    15\n",
        "76         0     1    15\n",
        "77        -1     2    15\n",
        "78         0     3    15\n",
        "79         0     4    15\n",
        "80         0     0    16\n",
        "81        -1     1    16\n",
        "82         0     2    16\n",
        "83        -1     3    16\n",
        "84         0     4    16\n",
        "85         0     0    17\n",
        "86        -1     1    17\n",
        "87        -2     2    17\n",
        "88        -2     3    17\n",
        "89        -1     4    17\n",
        "90         0     0    18\n",
        "91         0     1    18\n",
        "92         0     2    18\n",
        "93        -1     3    18\n",
        "94         0     4    18\n",
        "95         0     0    19\n",
        "96        -1     1    19\n",
        "97         0     2    19\n",
        "98         1     3    19\n",
        "99         0     4    19\n",
        "\n",
        "[100 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}