2014_12_19a_interactive_horizontal_bar_chart_w_mpld3.ipynb

{"nbformat": 3, "worksheets": [{"cells": [{"cell_type": "code", "language": "python", "outputs": [{"output_type": "stream", "stream": "stdout", "text": "Fri Dec 19 10:35:23 PST 2014\r\n"}], "collapsed": false, "prompt_number": 1, "input": "!date\nimport numpy as np, pandas as pd, matplotlib.pyplot as plt, mpld3, seaborn as sns\n%matplotlib inline", "metadata": {"trusted": true}}, {"source": "# The Horizontal Bar Chart\n\nA figure that I often want is the \"horizontal bar chart\", a way to represent one or two columns of a table visually for deep inspection.  Each row of the table often has a descriptive name, and there are comparisons within columns and between rows, as well as within rows between columns that I think might be interesting.\n\nHere is how it might be done with `mpld3`, and how it might be done better:", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>site</th>\n      <th>module</th>\n      <th>gs_code34</th>\n      <th>gs_text34</th>\n      <th>va34</th>\n      <th>gs_code46</th>\n      <th>gs_text46</th>\n      <th>va46</th>\n      <th>gs_code55</th>\n      <th>gs_text55</th>\n      <th>...</th>\n      <th>word_woman</th>\n      <th>word_womb</th>\n      <th>word_worri</th>\n      <th>word_wors</th>\n      <th>word_worsen</th>\n      <th>word_worst</th>\n      <th>word_wound</th>\n      <th>word_xray</th>\n      <th>word_yellow</th>\n      <th>newid</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> Mexico</td>\n      <td> Adult</td>\n      <td> K71</td>\n      <td>                   Cirrhosis</td>\n      <td>  6</td>\n      <td> K71</td>\n      <td>                   Cirrhosis</td>\n      <td>  8</td>\n      <td> K71</td>\n      <td>                   Cirrhosis</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>     AP</td>\n      <td> Adult</td>\n      <td> G40</td>\n      <td>                    Epilepsy</td>\n      <td> 12</td>\n      <td> G40</td>\n      <td>                    Epilepsy</td>\n      <td> 16</td>\n      <td> G40</td>\n      <td>                    Epilepsy</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>     AP</td>\n      <td> Adult</td>\n      <td> J12</td>\n      <td>                   Pneumonia</td>\n      <td> 26</td>\n      <td> J12</td>\n      <td>                   Pneumonia</td>\n      <td> 37</td>\n      <td> J12</td>\n      <td>                   Pneumonia</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Mexico</td>\n      <td> Adult</td>\n      <td> J33</td>\n      <td>                        COPD</td>\n      <td>  8</td>\n      <td> J33</td>\n      <td>                        COPD</td>\n      <td> 10</td>\n      <td> J33</td>\n      <td>                        COPD</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>     UP</td>\n      <td> Adult</td>\n      <td> I21</td>\n      <td> Acute Myocardial Infarction</td>\n      <td> 17</td>\n      <td> I21</td>\n      <td> Acute Myocardial Infarction</td>\n      <td> 23</td>\n      <td> I21</td>\n      <td> Acute Myocardial Infarction</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 5</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 946 columns</p>\n</div>", "metadata": {}, "prompt_number": 3, "text": "     site module gs_code34                    gs_text34  va34 gs_code46  \\\n0  Mexico  Adult       K71                    Cirrhosis     6       K71   \n1      AP  Adult       G40                     Epilepsy    12       G40   \n2      AP  Adult       J12                    Pneumonia    26       J12   \n3  Mexico  Adult       J33                         COPD     8       J33   \n4      UP  Adult       I21  Acute Myocardial Infarction    17       I21   \n\n                     gs_text46  va46 gs_code55                    gs_text55  \\\n0                    Cirrhosis     8       K71                    Cirrhosis   \n1                     Epilepsy    16       G40                     Epilepsy   \n2                    Pneumonia    37       J12                    Pneumonia   \n3                         COPD    10       J33                         COPD   \n4  Acute Myocardial Infarction    23       I21  Acute Myocardial Infarction   \n\n   ...   word_woman word_womb word_worri word_wors word_worsen word_worst  \\\n0  ...            0         0          0         0           0          0   \n1  ...            0         0          0         0           0          0   \n2  ...            0         0          0         0           0          0   \n3  ...            0         0          0         0           0          0   \n4  ...            0         0          0         0           0          0   \n\n  word_wound word_xray word_yellow newid  \n0          0         0           0     1  \n1          0         0           0     2  \n2          0         0           0     3  \n3          0         0           0     4  \n4          0         0           0     5  \n\n[5 rows x 946 columns]"}], "collapsed": false, "prompt_number": 3, "input": "# get some of my favorite data\ndf = pd.read_csv('http://ghdx.healthdata.org/sites/default/files/'\n                 'record-attached-files/IHME_PHMRC_VA_DATA_ADULT_Y2013M09D11_0.csv',\n                low_memory=False)\ndf.head()", "metadata": {"trusted": true}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>Field Site</th>\n      <th>AP</th>\n      <th>Bohol</th>\n      <th>Dar</th>\n      <th>Mexico</th>\n      <th>Pemba</th>\n      <th>UP</th>\n      <th>Mean</th>\n      <th>Max</th>\n      <th>Min</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AIDS</th>\n      <td> 136</td>\n      <td>   0</td>\n      <td> 203</td>\n      <td> 120</td>\n      <td>  0</td>\n      <td>  43</td>\n      <td>  83.666667</td>\n      <td> 203</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Acute Myocardial Infarction</th>\n      <td> 101</td>\n      <td> 116</td>\n      <td>   3</td>\n      <td>  76</td>\n      <td>  0</td>\n      <td> 104</td>\n      <td>  66.666667</td>\n      <td> 116</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Asthma</th>\n      <td>  21</td>\n      <td>   6</td>\n      <td>  12</td>\n      <td>   1</td>\n      <td>  6</td>\n      <td>   1</td>\n      <td>   7.833333</td>\n      <td>  21</td>\n      <td>  1</td>\n    </tr>\n    <tr>\n      <th>Bite of Venomous Animal</th>\n      <td>  31</td>\n      <td>   1</td>\n      <td>   0</td>\n      <td>   1</td>\n      <td>  0</td>\n      <td>  33</td>\n      <td>  11.000000</td>\n      <td>  33</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Breast Cancer</th>\n      <td>   3</td>\n      <td>  28</td>\n      <td>  90</td>\n      <td>  69</td>\n      <td>  0</td>\n      <td>   5</td>\n      <td>  32.500000</td>\n      <td>  90</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>COPD</th>\n      <td>  25</td>\n      <td>   4</td>\n      <td>   2</td>\n      <td>  63</td>\n      <td>  0</td>\n      <td>  77</td>\n      <td>  28.500000</td>\n      <td>  77</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Cervical Cancer</th>\n      <td>   3</td>\n      <td>   9</td>\n      <td> 108</td>\n      <td>  31</td>\n      <td>  0</td>\n      <td>   4</td>\n      <td>  25.833333</td>\n      <td> 108</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Cirrhosis</th>\n      <td>  51</td>\n      <td>  39</td>\n      <td>  27</td>\n      <td> 133</td>\n      <td>  0</td>\n      <td>  63</td>\n      <td>  52.166667</td>\n      <td> 133</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Colorectal Cancer</th>\n      <td>   7</td>\n      <td>  24</td>\n      <td>  33</td>\n      <td>  35</td>\n      <td>  0</td>\n      <td>   0</td>\n      <td>  16.500000</td>\n      <td>  35</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Diabetes</th>\n      <td>  88</td>\n      <td>  77</td>\n      <td>  59</td>\n      <td> 105</td>\n      <td> 38</td>\n      <td>  47</td>\n      <td>  69.000000</td>\n      <td> 105</td>\n      <td> 38</td>\n    </tr>\n    <tr>\n      <th>Diarrhea/Dysentery</th>\n      <td>  77</td>\n      <td>  27</td>\n      <td>  37</td>\n      <td>  18</td>\n      <td> 21</td>\n      <td>  48</td>\n      <td>  38.000000</td>\n      <td>  77</td>\n      <td> 18</td>\n    </tr>\n    <tr>\n      <th>Drowning</th>\n      <td>  32</td>\n      <td>   4</td>\n      <td>   9</td>\n      <td>   0</td>\n      <td> 28</td>\n      <td>  33</td>\n      <td>  17.666667</td>\n      <td>  33</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Epilepsy</th>\n      <td>  30</td>\n      <td>   5</td>\n      <td>   6</td>\n      <td>   2</td>\n      <td>  0</td>\n      <td>   5</td>\n      <td>   8.000000</td>\n      <td>  30</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Esophageal Cancer</th>\n      <td>   1</td>\n      <td>   5</td>\n      <td>  34</td>\n      <td>   0</td>\n      <td>  0</td>\n      <td>   0</td>\n      <td>   6.666667</td>\n      <td>  34</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Falls</th>\n      <td>  32</td>\n      <td>  38</td>\n      <td>  11</td>\n      <td>  36</td>\n      <td> 26</td>\n      <td>  30</td>\n      <td>  28.833333</td>\n      <td>  38</td>\n      <td> 11</td>\n    </tr>\n    <tr>\n      <th>Fires</th>\n      <td>  35</td>\n      <td>   7</td>\n      <td>  25</td>\n      <td>  18</td>\n      <td>  6</td>\n      <td>  31</td>\n      <td>  20.333333</td>\n      <td>  35</td>\n      <td>  6</td>\n    </tr>\n    <tr>\n      <th>Homicide</th>\n      <td>  31</td>\n      <td>  35</td>\n      <td>  30</td>\n      <td>  37</td>\n      <td>  3</td>\n      <td>  31</td>\n      <td>  27.833333</td>\n      <td>  37</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>Leukemia/Lymphomas</th>\n      <td>   7</td>\n      <td>  15</td>\n      <td>  59</td>\n      <td>  72</td>\n      <td>  0</td>\n      <td>   3</td>\n      <td>  26.000000</td>\n      <td>  72</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Lung Cancer</th>\n      <td>   5</td>\n      <td>  23</td>\n      <td>  11</td>\n      <td>  59</td>\n      <td>  0</td>\n      <td>   8</td>\n      <td>  17.666667</td>\n      <td>  59</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Malaria</th>\n      <td>  29</td>\n      <td>   0</td>\n      <td>  34</td>\n      <td>   0</td>\n      <td>  2</td>\n      <td>  35</td>\n      <td>  16.666667</td>\n      <td>  35</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Maternal</th>\n      <td>  71</td>\n      <td>  41</td>\n      <td> 135</td>\n      <td>  39</td>\n      <td> 46</td>\n      <td> 136</td>\n      <td>  78.000000</td>\n      <td> 136</td>\n      <td> 39</td>\n    </tr>\n    <tr>\n      <th>Other Cardiovascular Diseases</th>\n      <td>  76</td>\n      <td>  78</td>\n      <td> 112</td>\n      <td>  81</td>\n      <td>  0</td>\n      <td>  69</td>\n      <td>  69.333333</td>\n      <td> 112</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Other Infectious Diseases</th>\n      <td>  34</td>\n      <td>  51</td>\n      <td>  37</td>\n      <td>  37</td>\n      <td>  3</td>\n      <td> 101</td>\n      <td>  43.833333</td>\n      <td> 101</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>Other Injuries</th>\n      <td>  31</td>\n      <td>   7</td>\n      <td>  23</td>\n      <td>   2</td>\n      <td>  3</td>\n      <td>  37</td>\n      <td>  17.166667</td>\n      <td>  37</td>\n      <td>  2</td>\n    </tr>\n    <tr>\n      <th>Other Non-communicable Diseases</th>\n      <td>  90</td>\n      <td> 125</td>\n      <td> 171</td>\n      <td> 122</td>\n      <td> 54</td>\n      <td>  37</td>\n      <td>  99.833333</td>\n      <td> 171</td>\n      <td> 37</td>\n    </tr>\n    <tr>\n      <th>Pneumonia</th>\n      <td>  55</td>\n      <td> 142</td>\n      <td>  95</td>\n      <td> 102</td>\n      <td> 41</td>\n      <td> 105</td>\n      <td>  90.000000</td>\n      <td> 142</td>\n      <td> 41</td>\n    </tr>\n    <tr>\n      <th>Poisonings</th>\n      <td>  30</td>\n      <td>   3</td>\n      <td>   8</td>\n      <td>   9</td>\n      <td>  1</td>\n      <td>  35</td>\n      <td>  14.333333</td>\n      <td>  35</td>\n      <td>  1</td>\n    </tr>\n    <tr>\n      <th>Prostate Cancer</th>\n      <td>   1</td>\n      <td>   4</td>\n      <td>  32</td>\n      <td>  11</td>\n      <td>  0</td>\n      <td>   0</td>\n      <td>   8.000000</td>\n      <td>  32</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Renal Failure</th>\n      <td> 102</td>\n      <td>  68</td>\n      <td>  49</td>\n      <td>  92</td>\n      <td>  0</td>\n      <td> 105</td>\n      <td>  69.333333</td>\n      <td> 105</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Road Traffic</th>\n      <td>  39</td>\n      <td>  55</td>\n      <td>  31</td>\n      <td>  34</td>\n      <td> 11</td>\n      <td>  32</td>\n      <td>  33.666667</td>\n      <td>  55</td>\n      <td> 11</td>\n    </tr>\n    <tr>\n      <th>Stomach Cancer</th>\n      <td>   5</td>\n      <td>   5</td>\n      <td>  21</td>\n      <td>  31</td>\n      <td>  0</td>\n      <td>   0</td>\n      <td>  10.333333</td>\n      <td>  31</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Stroke</th>\n      <td> 125</td>\n      <td> 179</td>\n      <td> 103</td>\n      <td> 122</td>\n      <td>  0</td>\n      <td> 101</td>\n      <td> 105.000000</td>\n      <td> 179</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>Suicide</th>\n      <td>  49</td>\n      <td>  16</td>\n      <td>  13</td>\n      <td>  11</td>\n      <td>  2</td>\n      <td>  33</td>\n      <td>  20.666667</td>\n      <td>  49</td>\n      <td>  2</td>\n    </tr>\n    <tr>\n      <th>TB</th>\n      <td> 101</td>\n      <td>  22</td>\n      <td> 103</td>\n      <td>  17</td>\n      <td>  6</td>\n      <td>  27</td>\n      <td>  46.000000</td>\n      <td> 103</td>\n      <td>  6</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 27, "text": "Field Site                        AP  Bohol  Dar  Mexico  Pemba   UP  \\\nAIDS                             136      0  203     120      0   43   \nAcute Myocardial Infarction      101    116    3      76      0  104   \nAsthma                            21      6   12       1      6    1   \nBite of Venomous Animal           31      1    0       1      0   33   \nBreast Cancer                      3     28   90      69      0    5   \nCOPD                              25      4    2      63      0   77   \nCervical Cancer                    3      9  108      31      0    4   \nCirrhosis                         51     39   27     133      0   63   \nColorectal Cancer                  7     24   33      35      0    0   \nDiabetes                          88     77   59     105     38   47   \nDiarrhea/Dysentery                77     27   37      18     21   48   \nDrowning                          32      4    9       0     28   33   \nEpilepsy                          30      5    6       2      0    5   \nEsophageal Cancer                  1      5   34       0      0    0   \nFalls                             32     38   11      36     26   30   \nFires                             35      7   25      18      6   31   \nHomicide                          31     35   30      37      3   31   \nLeukemia/Lymphomas                 7     15   59      72      0    3   \nLung Cancer                        5     23   11      59      0    8   \nMalaria                           29      0   34       0      2   35   \nMaternal                          71     41  135      39     46  136   \nOther Cardiovascular Diseases     76     78  112      81      0   69   \nOther Infectious Diseases         34     51   37      37      3  101   \nOther Injuries                    31      7   23       2      3   37   \nOther Non-communicable Diseases   90    125  171     122     54   37   \nPneumonia                         55    142   95     102     41  105   \nPoisonings                        30      3    8       9      1   35   \nProstate Cancer                    1      4   32      11      0    0   \nRenal Failure                    102     68   49      92      0  105   \nRoad Traffic                      39     55   31      34     11   32   \nStomach Cancer                     5      5   21      31      0    0   \nStroke                           125    179  103     122      0  101   \nSuicide                           49     16   13      11      2   33   \nTB                               101     22  103      17      6   27   \n\nField Site                             Mean  Max  Min  \nAIDS                              83.666667  203    0  \nAcute Myocardial Infarction       66.666667  116    0  \nAsthma                             7.833333   21    1  \nBite of Venomous Animal           11.000000   33    0  \nBreast Cancer                     32.500000   90    0  \nCOPD                              28.500000   77    0  \nCervical Cancer                   25.833333  108    0  \nCirrhosis                         52.166667  133    0  \nColorectal Cancer                 16.500000   35    0  \nDiabetes                          69.000000  105   38  \nDiarrhea/Dysentery                38.000000   77   18  \nDrowning                          17.666667   33    0  \nEpilepsy                           8.000000   30    0  \nEsophageal Cancer                  6.666667   34    0  \nFalls                             28.833333   38   11  \nFires                             20.333333   35    6  \nHomicide                          27.833333   37    3  \nLeukemia/Lymphomas                26.000000   72    0  \nLung Cancer                       17.666667   59    0  \nMalaria                           16.666667   35    0  \nMaternal                          78.000000  136   39  \nOther Cardiovascular Diseases     69.333333  112    0  \nOther Infectious Diseases         43.833333  101    3  \nOther Injuries                    17.166667   37    2  \nOther Non-communicable Diseases   99.833333  171   37  \nPneumonia                         90.000000  142   41  \nPoisonings                        14.333333   35    1  \nProstate Cancer                    8.000000   32    0  \nRenal Failure                     69.333333  105    0  \nRoad Traffic                      33.666667   55   11  \nStomach Cancer                    10.333333   31    0  \nStroke                           105.000000  179    0  \nSuicide                           20.666667   49    2  \nTB                                46.000000  103    6  "}], "collapsed": false, "prompt_number": 27, "input": "# make a summary table that would like to inspect\ndf['Field Site'] = df.site\ndf['Underlying Cause'] = df.gs_text34\n\ng = df.groupby('Field Site')['Underlying Cause']\n\nt = g.value_counts().unstack(0)\nt = t.fillna(0)\n\nt['Mean'] = t.mean(axis=1)\nt['Max'] = t.max(axis=1)\nt['Min'] = t.min(axis=1)\n\nt", "metadata": {"trusted": true}}, {"source": "# The Comparison\n\nFor demonstration purposes, I plan to compare the distribution of data collected on Pemba Island to the average across all study sites.  This is a health-metrics-specific example, but let's not get into the details.", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "display_data", "metadata": {}, "png": 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RWYGkL6TTVfsAMyNigqTXuov9jN1OpLm5ubvVNsDGTrm1taGh26lbhVIqlVZp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\n4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"text": "<matplotlib.figure.Figure at 0x7f7c00d19750>"}], "collapsed": false, "prompt_number": 28, "input": "t = t.sort('Mean')  # sort the table in a meaningful way\n\nfig = plt.figure(figsize=(12,16))  # make a nice, big figure for the plot\n\ny = np.arange(len(t.index))  # select points on the y-axis for each bar\n\n# do actual plotting\nplt.barh(y+.05, t.Pemba, height=.45, color=sns.color_palette()[0], label='Pemba')\nplt.barh( y+.5, t.Mean, height=.45, xerr=[t.Mean - t.Min, t.Max - t.Mean],  # annoying format for error bars\n         color=sns.color_palette()[1], ecolor='k', \n         label='Cross-site Mean')\n\nplt.axis(xmin=0)  # silly error-bars go below zero, but don't show that\n\nplt.legend(loc=(.5,.1))  # legend uses label values from calls to plt.barh\nplt.yticks(y+.5, t.index)  # label each tick on the y-axis with the corresponding cause\nplt.subplots_adjust(left=.5)  # make sure there is enough room to read the tick labels\n\nplt.xlabel('Verbal Autopsies Collected')  # label the plot so that it is easy to remember what it is in the future\npass  # do something with no output to keep display clean", "metadata": {"trusted": true}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "html": "\n\n<style>\n\n</style>\n\n<div id=\"fig_el217941401705664080165455384614\"></div>\n<script>\nfunction mpld3_load_lib(url, callback){\n  var s = document.createElement('script');\n  s.src = url;\n  s.async = true;\n  s.onreadystatechange = s.onload = callback;\n  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n  document.getElementsByTagName(\"head\")[0].appendChild(s);\n}\n\nif(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n   // already loaded: just create the figure\n   !function(mpld3){\n       \n       mpld3.draw_figure(\"fig_el217941401705664080165455384614\", {\"axes\": [{\"xlim\": [0.0, 250.0], \"yscale\": \"linear\", \"axesbg\": \"none\", \"texts\": [{\"v_baseline\": \"hanging\", 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