pandas_dataframe_rendering_options.ipynb

{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Pandas Dataframe Rendering Options",
     "level": 1
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "I've tried out quite a few options for displaying dataframes as tables in notebooks and nbconverted pdfs described at various points on stackoverflow. \n\nBelow is an incomplete list of my impressions of each option. \n\nAt the end is code for a new-ish alternative: create a custom class with customized `_repr_html_` and `_repr_latex_` methods. The advantages of this are that it is extremely simple, shouldn't require too much custom fiddling for each use, but could easily be customized for more specific (multiple) use case. \n\nSome discussion of something like this in the SO post [here](http://stackoverflow.com/questions/20685635/pandas-dataframe-as-latex-or-html-table-nbconvert)"
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Setup",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Importage"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "import pandas as pd\nimport numpy as np\nimport os\nfrom wand.image import Image as WImage # (for viewing PDF results of nbconvert)\nfrom IPython.display import Latex",
     "prompt_number": 23,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Create an example dataframe"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "dat = np.random.randn(10,10)\ncols = ['col%s' %s for s in np.arange(1,11)]\nrows = ['row%s' %s for s in np.arange(1,11)]\ndf = pd.DataFrame(data=dat, columns=cols, index=rows)",
     "prompt_number": 24,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Native rendering",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Pros*\n\nblah blah\n\n*Cons*\n\nblah blah"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "# Example code",
     "prompt_number": 25,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "df",
     "prompt_number": 26,
     "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>col1</th>\n      <th>col2</th>\n      <th>col3</th>\n      <th>col4</th>\n      <th>col5</th>\n      <th>col6</th>\n      <th>col7</th>\n      <th>col8</th>\n      <th>col9</th>\n      <th>col10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>row1</th>\n      <td>-1.202136</td>\n      <td>-0.480558</td>\n      <td> 0.582788</td>\n      <td>-1.656705</td>\n      <td>-0.915293</td>\n      <td> 0.614987</td>\n      <td> 1.078665</td>\n      <td> 0.715520</td>\n      <td> 0.731975</td>\n      <td> 0.133179</td>\n    </tr>\n    <tr>\n      <th>row2</th>\n      <td> 0.517846</td>\n      <td> 0.053090</td>\n      <td>-0.587714</td>\n      <td> 1.008842</td>\n      <td>-0.120580</td>\n      <td> 0.116634</td>\n      <td> 0.938874</td>\n      <td>-0.570899</td>\n      <td> 1.544524</td>\n      <td> 0.310006</td>\n    </tr>\n    <tr>\n      <th>row3</th>\n      <td> 1.412077</td>\n      <td> 0.513327</td>\n      <td> 0.979082</td>\n      <td>-0.368265</td>\n      <td> 2.039693</td>\n      <td> 3.208406</td>\n      <td> 1.178527</td>\n      <td>-0.350981</td>\n      <td>-1.584484</td>\n      <td> 0.018533</td>\n    </tr>\n    <tr>\n      <th>row4</th>\n      <td>-2.515969</td>\n      <td>-0.944538</td>\n      <td> 0.540484</td>\n      <td> 0.894848</td>\n      <td> 1.658475</td>\n      <td>-0.781964</td>\n      <td>-0.391155</td>\n      <td>-1.172419</td>\n      <td> 0.939206</td>\n      <td> 0.497390</td>\n    </tr>\n    <tr>\n      <th>row5</th>\n      <td>-0.194632</td>\n      <td>-0.685372</td>\n      <td>-2.319909</td>\n      <td> 0.951289</td>\n      <td>-2.206009</td>\n      <td> 0.245230</td>\n      <td>-0.401082</td>\n      <td>-1.509288</td>\n      <td> 1.529092</td>\n      <td>-0.225724</td>\n    </tr>\n    <tr>\n      <th>row6</th>\n      <td> 1.101476</td>\n      <td> 0.401250</td>\n      <td>-0.830789</td>\n      <td> 1.668242</td>\n      <td>-0.707730</td>\n      <td>-1.594333</td>\n      <td> 0.271916</td>\n      <td> 0.302317</td>\n      <td>-0.908913</td>\n      <td>-0.385076</td>\n    </tr>\n    <tr>\n      <th>row7</th>\n      <td> 0.044887</td>\n      <td> 0.996410</td>\n      <td>-0.208828</td>\n      <td> 0.148348</td>\n      <td> 1.263482</td>\n      <td>-0.491838</td>\n      <td> 0.743221</td>\n      <td> 0.736697</td>\n      <td>-1.322639</td>\n      <td>-0.141409</td>\n    </tr>\n    <tr>\n      <th>row8</th>\n      <td>-1.020418</td>\n      <td>-0.627410</td>\n      <td> 1.143973</td>\n      <td>-0.701307</td>\n      <td> 1.560556</td>\n      <td>-0.853806</td>\n      <td>-0.871930</td>\n      <td> 0.921137</td>\n      <td> 0.483304</td>\n      <td>-0.076432</td>\n    </tr>\n    <tr>\n      <th>row9</th>\n      <td>-0.363402</td>\n      <td>-0.268809</td>\n      <td>-0.340043</td>\n      <td> 0.329142</td>\n      <td>-0.070143</td>\n      <td>-0.853087</td>\n      <td>-0.258099</td>\n      <td>-0.271687</td>\n      <td> 2.034606</td>\n      <td>-0.296724</td>\n    </tr>\n    <tr>\n      <th>row10</th>\n      <td>-1.410353</td>\n      <td> 0.502400</td>\n      <td>-1.056651</td>\n      <td>-0.485771</td>\n      <td>-0.451451</td>\n      <td>-0.001718</td>\n      <td>-1.225560</td>\n      <td> 1.435823</td>\n      <td>-0.510913</td>\n      <td> 1.175397</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "prompt_number": 26,
       "text": "           col1      col2      col3      col4      col5      col6      col7  \\\nrow1  -1.202136 -0.480558  0.582788 -1.656705 -0.915293  0.614987  1.078665   \nrow2   0.517846  0.053090 -0.587714  1.008842 -0.120580  0.116634  0.938874   \nrow3   1.412077  0.513327  0.979082 -0.368265  2.039693  3.208406  1.178527   \nrow4  -2.515969 -0.944538  0.540484  0.894848  1.658475 -0.781964 -0.391155   \nrow5  -0.194632 -0.685372 -2.319909  0.951289 -2.206009  0.245230 -0.401082   \nrow6   1.101476  0.401250 -0.830789  1.668242 -0.707730 -1.594333  0.271916   \nrow7   0.044887  0.996410 -0.208828  0.148348  1.263482 -0.491838  0.743221   \nrow8  -1.020418 -0.627410  1.143973 -0.701307  1.560556 -0.853806 -0.871930   \nrow9  -0.363402 -0.268809 -0.340043  0.329142 -0.070143 -0.853087 -0.258099   \nrow10 -1.410353  0.502400 -1.056651 -0.485771 -0.451451 -0.001718 -1.225560   \n\n           col8      col9     col10  \nrow1   0.715520  0.731975  0.133179  \nrow2  -0.570899  1.544524  0.310006  \nrow3  -0.350981 -1.584484  0.018533  \nrow4  -1.172419  0.939206  0.497390  \nrow5  -1.509288  1.529092 -0.225724  \nrow6   0.302317 -0.908913 -0.385076  \nrow7   0.736697 -1.322639 -0.141409  \nrow8   0.921137  0.483304 -0.076432  \nrow9  -0.271687  2.034606 -0.296724  \nrow10  1.435823 -0.510913  1.175397  "
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Modified native rendering",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Pros*\n\nblah blah\n\n*Cons*\n\nblah blah\n\n"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "# Direct - ERRORS\n#Latex(df.to_latex(float_format=f1))",
     "prompt_number": 27,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "#Replace the new lines - ERRORS\n# #ltx = df.to_latex(float_format=f1).replace('\\n', ' ')\n#Latex(ltx)",
     "prompt_number": 28,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "#Replace with hlines - OK\n#ltx = df.to_latex(float_format=f1).replace('\\\\toprule', '\\\\hline')\\\n#                                  .replace('\\\\midrule', '\\\\hline')\\\n#                                 .replace('\\\\bottomrule', '\\\\hline')\n#Latex(ltx)",
     "prompt_number": 29,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "#Replace with hlines and new lines - GIVES SAME RESULT AS REPLACE WITH HLINES\n#ltx = df.to_latex(float_format=f1).replace('\\n', ' ')\\\n#                                  .replace('\\\\toprule', '\\\\hline')\\\n#                                  .replace('\\\\midrule', '\\\\hline')\\\n#                                 .replace('\\\\bottomrule', '\\\\hline')\n#Latex(ltx)",
     "prompt_number": 30,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Write .tex file and convert to png externally",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Pros*\n\nblah blah\n\n*Cons*\n\nblah blah\n\n"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "# Example code",
     "prompt_number": 31,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Matplotlib figure",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Pros*\n\nblah blah\n\n*Cons*\n\nblah blah\n\n"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "# Example code",
     "prompt_number": 32,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "D3J figure",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Pros*\n\nblah blah\n\n*Cons*\n\nblah blah\n\n"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "# Example code",
     "prompt_number": 33,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Ipy_table",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Pros*\n\nblah blah\n\n*Cons*\n\nblah blah\n\n"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "# Example code",
     "prompt_number": 34,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Custom display class\n",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Pros*\n\nblah blah\n\n*Cons*\n\nblah blah\n\n"
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Code"
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Custom dataframe display class"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "class jg_df(object):\n    \n  def __init__(self,s):\n        \n    #self._repr_html_ = s._repr_html_\n    self._h = s.to_html()\n    \n    def fff(x): return '%1.2f' % x  # Float format function\n    self._ltx = s.to_latex(float_format=fff).replace('\\\\toprule', '\\\\hline')\\\n                            .replace('\\\\midrule', '\\\\hline')\\\n                            .replace('\\\\bottomrule', '\\\\hline')\n            \n  def _repr_html_(self):\n    #return self._h\n    return '<center>{0}</center>'.format(self._h)\n    \n  def _repr_latex_(self):\n    return self._ltx\n    #Latex(self._ltx) \n    \n  @property \n  def latex(self):\n    return Latex(self._repr_latex_())\n ",
     "prompt_number": 35,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Example"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "jg_df(df)",
     "prompt_number": 36,
     "outputs": [
      {
       "latex": "\\begin{tabular}{lrrrrrrrrrr}\n\\hline\n{} &  col1 &  col2 &  col3 &  col4 &  col5 &  col6 &  col7 &  col8 &  col9 &  col10 \\\\\n\\hline\nrow1  & -1.20 & -0.48 &  0.58 & -1.66 & -0.92 &  0.61 &  1.08 &  0.72 &  0.73 &   0.13 \\\\\nrow2  &  0.52 &  0.05 & -0.59 &  1.01 & -0.12 &  0.12 &  0.94 & -0.57 &  1.54 &   0.31 \\\\\nrow3  &  1.41 &  0.51 &  0.98 & -0.37 &  2.04 &  3.21 &  1.18 & -0.35 & -1.58 &   0.02 \\\\\nrow4  & -2.52 & -0.94 &  0.54 &  0.89 &  1.66 & -0.78 & -0.39 & -1.17 &  0.94 &   0.50 \\\\\nrow5  & -0.19 & -0.69 & -2.32 &  0.95 & -2.21 &  0.25 & -0.40 & -1.51 &  1.53 &  -0.23 \\\\\nrow6  &  1.10 &  0.40 & -0.83 &  1.67 & -0.71 & -1.59 &  0.27 &  0.30 & -0.91 &  -0.39 \\\\\nrow7  &  0.04 &  1.00 & -0.21 &  0.15 &  1.26 & -0.49 &  0.74 &  0.74 & -1.32 &  -0.14 \\\\\nrow8  & -1.02 & -0.63 &  1.14 & -0.70 &  1.56 & -0.85 & -0.87 &  0.92 &  0.48 &  -0.08 \\\\\nrow9  & -0.36 & -0.27 & -0.34 &  0.33 & -0.07 & -0.85 & -0.26 & -0.27 &  2.03 &  -0.30 \\\\\nrow10 & -1.41 &  0.50 & -1.06 & -0.49 & -0.45 & -0.00 & -1.23 &  1.44 & -0.51 &   1.18 \\\\\n\\hline\n\\end{tabular}\n",
       "text": "<__main__.jg_df at 0x494b1d0>",
       "html": "<center><table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>col1</th>\n      <th>col2</th>\n      <th>col3</th>\n      <th>col4</th>\n      <th>col5</th>\n      <th>col6</th>\n      <th>col7</th>\n      <th>col8</th>\n      <th>col9</th>\n      <th>col10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>row1</th>\n      <td>-1.202136</td>\n      <td>-0.480558</td>\n      <td> 0.582788</td>\n      <td>-1.656705</td>\n      <td>-0.915293</td>\n      <td> 0.614987</td>\n      <td> 1.078665</td>\n      <td> 0.715520</td>\n      <td> 0.731975</td>\n      <td> 0.133179</td>\n    </tr>\n    <tr>\n      <th>row2</th>\n      <td> 0.517846</td>\n      <td> 0.053090</td>\n      <td>-0.587714</td>\n      <td> 1.008842</td>\n      <td>-0.120580</td>\n      <td> 0.116634</td>\n      <td> 0.938874</td>\n      <td>-0.570899</td>\n      <td> 1.544524</td>\n      <td> 0.310006</td>\n    </tr>\n    <tr>\n      <th>row3</th>\n      <td> 1.412077</td>\n      <td> 0.513327</td>\n      <td> 0.979082</td>\n      <td>-0.368265</td>\n      <td> 2.039693</td>\n      <td> 3.208406</td>\n      <td> 1.178527</td>\n      <td>-0.350981</td>\n      <td>-1.584484</td>\n      <td> 0.018533</td>\n    </tr>\n    <tr>\n      <th>row4</th>\n      <td>-2.515969</td>\n      <td>-0.944538</td>\n      <td> 0.540484</td>\n      <td> 0.894848</td>\n      <td> 1.658475</td>\n      <td>-0.781964</td>\n      <td>-0.391155</td>\n      <td>-1.172419</td>\n      <td> 0.939206</td>\n      <td> 0.497390</td>\n    </tr>\n    <tr>\n      <th>row5</th>\n      <td>-0.194632</td>\n      <td>-0.685372</td>\n      <td>-2.319909</td>\n      <td> 0.951289</td>\n      <td>-2.206009</td>\n      <td> 0.245230</td>\n      <td>-0.401082</td>\n      <td>-1.509288</td>\n      <td> 1.529092</td>\n      <td>-0.225724</td>\n    </tr>\n    <tr>\n      <th>row6</th>\n      <td> 1.101476</td>\n      <td> 0.401250</td>\n      <td>-0.830789</td>\n      <td> 1.668242</td>\n      <td>-0.707730</td>\n      <td>-1.594333</td>\n      <td> 0.271916</td>\n      <td> 0.302317</td>\n      <td>-0.908913</td>\n      <td>-0.385076</td>\n    </tr>\n    <tr>\n      <th>row7</th>\n      <td> 0.044887</td>\n      <td> 0.996410</td>\n      <td>-0.208828</td>\n      <td> 0.148348</td>\n      <td> 1.263482</td>\n      <td>-0.491838</td>\n      <td> 0.743221</td>\n      <td> 0.736697</td>\n      <td>-1.322639</td>\n      <td>-0.141409</td>\n    </tr>\n    <tr>\n      <th>row8</th>\n      <td>-1.020418</td>\n      <td>-0.627410</td>\n      <td> 1.143973</td>\n      <td>-0.701307</td>\n      <td> 1.560556</td>\n      <td>-0.853806</td>\n      <td>-0.871930</td>\n      <td> 0.921137</td>\n      <td> 0.483304</td>\n      <td>-0.076432</td>\n    </tr>\n    <tr>\n      <th>row9</th>\n      <td>-0.363402</td>\n      <td>-0.268809</td>\n      <td>-0.340043</td>\n      <td> 0.329142</td>\n      <td>-0.070143</td>\n      <td>-0.853087</td>\n      <td>-0.258099</td>\n      <td>-0.271687</td>\n      <td> 2.034606</td>\n      <td>-0.296724</td>\n    </tr>\n    <tr>\n      <th>row10</th>\n      <td>-1.410353</td>\n      <td> 0.502400</td>\n      <td>-1.056651</td>\n      <td>-0.485771</td>\n      <td>-0.451451</td>\n      <td>-0.001718</td>\n      <td>-1.225560</td>\n      <td> 1.435823</td>\n      <td>-0.510913</td>\n      <td> 1.175397</td>\n    </tr>\n  </tbody>\n</table></center>",
       "prompt_number": 36,
       "output_type": "pyout",
       "metadata": {}
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Can take a look at the latex representation as well"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "jg_df(df).latex",
     "prompt_number": 37,
     "outputs": [
      {
       "latex": "\\begin{tabular}{lrrrrrrrrrr}\n\\hline\n{} &  col1 &  col2 &  col3 &  col4 &  col5 &  col6 &  col7 &  col8 &  col9 &  col10 \\\\\n\\hline\nrow1  & -1.20 & -0.48 &  0.58 & -1.66 & -0.92 &  0.61 &  1.08 &  0.72 &  0.73 &   0.13 \\\\\nrow2  &  0.52 &  0.05 & -0.59 &  1.01 & -0.12 &  0.12 &  0.94 & -0.57 &  1.54 &   0.31 \\\\\nrow3  &  1.41 &  0.51 &  0.98 & -0.37 &  2.04 &  3.21 &  1.18 & -0.35 & -1.58 &   0.02 \\\\\nrow4  & -2.52 & -0.94 &  0.54 &  0.89 &  1.66 & -0.78 & -0.39 & -1.17 &  0.94 &   0.50 \\\\\nrow5  & -0.19 & -0.69 & -2.32 &  0.95 & -2.21 &  0.25 & -0.40 & -1.51 &  1.53 &  -0.23 \\\\\nrow6  &  1.10 &  0.40 & -0.83 &  1.67 & -0.71 & -1.59 &  0.27 &  0.30 & -0.91 &  -0.39 \\\\\nrow7  &  0.04 &  1.00 & -0.21 &  0.15 &  1.26 & -0.49 &  0.74 &  0.74 & -1.32 &  -0.14 \\\\\nrow8  & -1.02 & -0.63 &  1.14 & -0.70 &  1.56 & -0.85 & -0.87 &  0.92 &  0.48 &  -0.08 \\\\\nrow9  & -0.36 & -0.27 & -0.34 &  0.33 & -0.07 & -0.85 & -0.26 & -0.27 &  2.03 &  -0.30 \\\\\nrow10 & -1.41 &  0.50 & -1.06 & -0.49 & -0.45 & -0.00 & -1.23 &  1.44 & -0.51 &   1.18 \\\\\n\\hline\n\\end{tabular}\n",
       "prompt_number": 37,
       "text": "<IPython.core.display.Latex at 0x4946790>",
       "metadata": {},
       "output_type": "pyout"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Nbconvert",
     "level": 2
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Copy to citations dir and run nbconvert"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "cwd = '/media/sf_WINDOWS_D_DRIVE/Neurodebian/code/git_repos/DoctoralThesis/Misc'\nfnamestem = 'pandas_dataframe_rendering_options'\nthisfile = os.path.join(cwd, fnamestem+'.ipynb')\n#base_dir = '/media/sf_WINDOWS_D_DRIVE/Neurodebian/code/git_repos/DoctoralThesis/Chapter2'\n#base_file = os.path.join(base_dir, 'table_nbconvert_test_tmp.ipynb')\nnbc_dir = os.path.join(cwd, 'nbconvert_to_latex', 'citations_removecode') #'citations_removecode')\nos.chdir(nbc_dir)\nos.system('cp %s .' %thisfile)\nos.system('ipython nbconvert --to latex --template citations.tplx --post PDF %s' %fnamestem+'.ipynb')          ",
     "prompt_number": 41,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 41,
       "metadata": {},
       "text": "0"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "# Check PDF\n# ---------\nn_pages = 4; all_pages = np.arange(0, n_pages)\npdf_pages = {p+1 : WImage(filename= '%s[%s]' %(fnamestem+'.pdf',str(p))) for p in all_pages}\nos.chdir(cwd)",
     "prompt_number": 42,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Look at pdf",
     "level": 2
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "pdf_pages[1]",
     "prompt_number": 43,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "metadata": {},
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       "text": "<wand.image.Image: 352fb63 'PDF' (612x792)>"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "pdf_pages[2]",
     "prompt_number": 44,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 44,
       "metadata": {},
       "png": 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VtTJL6XwJfDf+ClnomsVZKmnb9Lu0RFIZ6+N52FvXlRzA1+evkPXlKejY7/LY/1IW6HwJ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\ni3Ov58dzkLVcnZ3FKceFzFL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       "text": "<wand.image.Image: 46ad54a 'PDF' (612x792)>"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "pdf_pages[3]",
     "prompt_number": 45,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 45,
       "metadata": {},
       "png": 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VFUK7WaxUrXqzX/7Mivk6papr+Tg2nMiuai0fwSp1Y49VysJpS+s0ht4s1qtP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\nW1psqf29pPGo5URv+wSwXfXfJR5ZXKWjUZ9hu9A+ie2j9zX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       "text": "<wand.image.Image: 4566101 'PDF' (612x792)>"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "pdf_pages[4]",
     "prompt_number": 46,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "metadata": {},
       "png": 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TMqA8IQPKEzKgPCEDyhMyoDwhA8oTMqA8IeNv0G5eegS8bULGb9fmmb30GHjb\nhIzfrA3ZvvQYeOuEjN9t2seXHgJvnZDxW7VpNi89Bt6+f7z0AHjzpi2ZNO/L+I28I+O36mPfZPLS\no+Cta/2lRwDwk7wjA8oTMqA8IQPKEzKgPCEDyhMyoDwhA8oTMqA8IQPKEzKgPCEDyhMyoDwhA8oT\nMqA8IQPKEzKgPCEDyhMyoDwhA8oTMqA8IQPKEzKgPCEDyhMyoDwhA8oTMqA8IQPKEzKgPCEDyhMy\noDwhA8oTMqA8IQPKEzKgPCEDyhMyoDwhA8oTMqA8IQPKEzKgPCEDyvv/qHLCdhD4Y9sAAAAldEVY\ndGRhdGU6Y3JlYXRlADIwMTQtMDEtMjBUMTA6MDI6MTQtMDU6MDBlntq5AAAAJXRFWHRkYXRlOm1v\nZGlmeQAyMDE0LTAxLTIwVDEwOjAyOjE0LTA1OjAwFMNiBQAAABR0RVh0cGRmOlZlcnNpb24AUERG\nLTEuNSAFXAs5AAAAAElFTkSuQmCC\n",
       "text": "<wand.image.Image: 3075fd6 'PDF' (612x792)>"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "",
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