{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating CpG ratio for the *Acropora hyacinthus* transcriptome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This workflow calculates CpG ratio, or CpG O/E, for contigs in the *Acropora hyacinthus* [transcriptome](http://palumbi.stanford.edu/data/33496_Ahyacinthus_CoralContigs.fasta.zip). CpG ratio is an estimate of germline DNA methylation.\n",
    "\n",
    "This workflow is an extension of another IPython notebook workflow, `Ahya_blast_anno.ipynb`, that generates an annotation of the same transcriptome. This workflow assumes that you have created the directories and files specified in the annotation workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jd/Documents/Projects/Coral-CpG-ratio-MS/data/Ahya\n"
     ]
    }
   ],
   "source": [
    "cd .data/Ahya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">contig27\n",
      "CAAAATTCCAGCACTCCGTTTTGCATGGTAAACTTGTCTTAGTAGGACACTGTGGAAGATGTACAGCGCAAGACATCACAGTTGCAAGCGCCGACGAACAGCTGTTAAACTCTCCTCTCATATTCTCGAACAAACCAAATATTTCTTCCTCTCTGTTGTTGCTAACCTTTGAATATATGAAGCTGGCATTAGCACAGGACTCAAAGTTTCCGCCGAGCAGTTT\n",
      "\n",
      "number of seqs =\n",
      "33496\n"
     ]
    }
   ],
   "source": [
    "#fasta file\n",
    "!head -2 33496_Ahyacinthus_CoralContigs.fasta\n",
    "!echo \n",
    "!echo number of seqs =\n",
    "!fgrep -c \">\" 33496_Ahyacinthus_CoralContigs.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Converted 33496 FASTA records in 66992 lines to tabular format\r\n",
      "Total sequence length: 17056543\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "#Converting FASTA to tabular format and placing output file in analyses directory\n",
    "!perl -e '$count=0; $len=0; while(<>) {s/\\r?\\n//; s/\\t/ /g; if (s/^>//) { if ($. != 1) {print \"\\n\"} s/ |$/\\t/; $count++; $_ .= \"\\t\";} else {s/ //g; $len += length($_)} print $_;} print \"\\n\"; warn \"\\nConverted $count FASTA records in $. lines to tabular format\\nTotal sequence length: $len\\n\\n\";' \\\n",
    "33496_Ahyacinthus_CoralContigs.fasta > ../../analyses/Ahya/fasta2tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jd/Documents/Projects/Coral-CpG-ratio-MS/analyses/Ahya\n"
     ]
    }
   ],
   "source": [
    "cd ../../analyses/Ahya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig27\t\tCAAAATTCCAGCACTCCGTTTTGCATGGTAAACTTGTCTTAGTAGGACACTGTGGAAGATGTACAGCGCAAGACATCACAGTTGCAAGCGCCGACGAACAGCTGTTAAACTCTCCTCTCATATTCTCGAACAAACCAAATATTTCTTCCTCTCTGTTGTTGCTAACCTTTGAATATATGAAGCTGGCATTAGCACAGGACTCAAAGTTTCCGCCGAGCAGTTT\r\n",
      "contig88\t\tTGTCCTGTGTTAGAGGCCAGCTTCAACCTCTTGCTTTCCCTGTCAGCCGAGTTTTCTTCTCCTTCAATAAGCTGGGATTTTCGATCTCTACTCAATGTTTCCATCAAACACCTGAGAGTTAAATCTGCCAGATAACGAAGAAATCCTCTTGCTAGAATACTTTTCAAAAGCCCTTCTTCATACATTGATCTTATCCCATTGCAAATTGCGTTGG\r\n"
     ]
    }
   ],
   "source": [
    "#Checking header on new tabular format file\n",
    "!head -2 fasta2tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Added column with length of column 2 for 33496 lines.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "#Add column with length of sequence\n",
    "!perl -e '$col = 2;' -e 'while (<>) { s/\\r?\\n//; @F = split /\\t/, $_; $len = length($F[$col]); print \"$_\\t$len\\n\" } warn \"\\nAdded column with length of column $col for $. lines.\\n\\n\";' \\\n",
    "fasta2tab > tab_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   33496  100488 17731523 tab_1\r\n"
     ]
    }
   ],
   "source": [
    "!wc tab_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#The file used to count Cs and Gs will only include the sequence\n",
    "!awk '{print $2}' tab_1 > tab_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#This counts CGs - both cases\n",
    "!echo \"CG\" | awk -F\\[Cc][Gg] '{print NF-1}' tab_2 > CG "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Counts Cs\n",
    "!echo \"C\" | awk -F\\[Cc] '{print NF-1}' tab_2 > C "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Counts Gs\n",
    "!echo \"G\" | awk -F\\[Gg] '{print NF-1}' tab_2 > G "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig27\t\tCAAAATTCCAGCACTCCGTTTTGCATGGTAAACTTGTCTTAGTAGGACACTGTGGAAGATGTACAGCGCAAGACATCACAGTTGCAAGCGCCGACGAACAGCTGTTAAACTCTCCTCTCATATTCTCGAACAAACCAAATATTTCTTCCTCTCTGTTGTTGCTAACCTTTGAATATATGAAGCTGGCATTAGCACAGGACTCAAAGTTTCCGCCGAGCAGTTT\t223\t8\t55\t42\r\n"
     ]
    }
   ],
   "source": [
    "#Combining counts\n",
    "!paste tab_1 \\\n",
    "CG \\\n",
    "C \\\n",
    "G \\\n",
    "> comb\n",
    "!head -1 comb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating CpGo/e based on [Gavery and Roberts (2010)](http://www.biomedcentral.com/1471-2164/11/483)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://eagle.fish.washington.edu/cnidarian/skitch/BMC_Genomics___Full_text___DNA_methylation_patterns_provide_insight_into_epigenetic_regulation_in_the_Pacific_oyster__Crassostrea_gigas__1A0683A5.png\" alt=\"BMC_Genomics___Full_text___DNA_methylation_patterns_provide_insight_into_epigenetic_regulation_in_the_Pacific_oyster__Crassostrea_gigas__1A0683A5.png\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Calculation of CpG o/e\n",
    "!awk '{print $1, \"\\t\", (($4)/($5*$6))*(($3^2)/($3-1))}' comb > ID_CpG  #use ^ instead of ** for exponent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig27 \t 0.775773\r\n",
      "contig88 \t 0.459903\r\n",
      "contig100 \t 0.254614\r\n",
      "contig211 \t 0.885658\r\n",
      "contig405 \t 0.689373\r\n",
      "contig443 \t 1.34126\r\n",
      "contig470 \t 0.323368\r\n",
      "contig503 \t 0.941889\r\n",
      "contig583 \t 0.625727\r\n",
      "contig590 \t 1.21135\r\n"
     ]
    }
   ],
   "source": [
    "!head ID_CpG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now joining CpG to annotation, but first must sort files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig100010\tsp\tQ08174\tPCDH1_HUMAN\t45.12\t82\t42\t1\t4\t240\t536\t617\t2e-14\t72.4\r\n",
      "contig100021_110093_105915\tsp\tP10978\tPOLX_TOBAC\t44.34\t106\t54\t3\t1932\t2243\t774\t876\t4e-14\t81.3\r\n",
      "contig100025\tsp\tQ69ZS8\tKAZRN_MOUSE\t61.76\t68\t26\t0\t2\t205\t450\t517\t5e-21\t90.5\r\n",
      "contig100026\tsp\tQ6ZMW3\tEMAL6_HUMAN\t68.07\t119\t38\t0\t9\t365\t1805\t1923\t2e-49\t  174\r\n",
      "contig100031\tsp\tB0BNG0\tEMC2_RAT\t54.35\t92\t37\t3\t334\t71\t204\t294\t3e-23\t95.5\r\n",
      "contig100040\tsp\tQ9P215\tPOGK_HUMAN\t30.00\t100\t70\t0\t4\t303\t490\t589\t3e-11\t63.5\r\n",
      "contig100055\tsp\tQ32M45\tANO4_HUMAN\t52.33\t86\t41\t0\t259\t2\t291\t376\t7e-19\t85.5\r\n",
      "contig100067\tsp\tQ58EN8\tVP33B_DANRE\t52.50\t40\t17\t1\t138\t257\t94\t131\t4e-07\t50.4\r\n",
      "contig100105\tsp\tA4Q9F1\tTTLL8_MOUSE\t54.79\t146\t61\t1\t20\t442\t525\t670\t2e-47\t  169\r\n",
      "contig100110_36597\tsp\tQ6PDJ1\tCAHD1_MOUSE\t39.02\t164\t93\t3\t24\t503\t675\t835\t1e-33\t  131\r\n"
     ]
    }
   ],
   "source": [
    "#Sorting Ahya Uniprot/Swissprot annotation file. This file was the result of work done in another notebook: Ahya_blast_anno.ipynb\n",
    "!sort Ahya_blastx_uniprot_sql.tab | tail -n +2 > Ahya_blastx_uniprot_sql.tab.sorted\n",
    "!head Ahya_blastx_uniprot_sql.tab.sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig100010\tcell adhesion\r",
      "\r\n",
      "contig100010\tcell-cell signaling\r",
      "\r\n",
      "contig100010\tdevelopmental processes\r",
      "\r\n",
      "contig100021_110093_105915\tDNA metabolism\r",
      "\r\n",
      "contig100021_110093_105915\tprotein metabolism\r",
      "\r\n",
      "contig100025\tdevelopmental processes\r",
      "\r\n",
      "contig100040\tRNA metabolism\r",
      "\r\n",
      "contig100040\tdevelopmental processes\r",
      "\r\n",
      "contig100055\ttransport\r",
      "\r\n",
      "contig100067\tdevelopmental processes\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "#Sorting Ahya GOSlim annotation file. This file was the result of work done in another notebook: Ahya_blast_anno.ipynb\n",
    "!sort Ahya_GOSlim.tab | tail -n +2 > Ahya_GOSlim.sorted\n",
    "!head Ahya_GOSlim.sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig100 \t 0.254614\r\n",
      "contig100001 \t 0.431531\r\n",
      "contig100008 \t 0.276093\r\n",
      "contig100010 \t 0.476931\r\n",
      "contig100021_110093_105915 \t 2.0758\r\n",
      "contig100025 \t 0.299187\r\n",
      "contig100026 \t 1.0599\r\n",
      "contig100030 \t 0.854552\r\n",
      "contig100031 \t 0.64616\r\n",
      "contig100038_111047 \t 1.60515\r\n"
     ]
    }
   ],
   "source": [
    "#Sorting Ahya CpG file\n",
    "!sort ID_CpG > ID_CpG.sorted\n",
    "!head ID_CpG.sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!join ID_CpG.sorted Ahya_blastx_uniprot_sql.tab.sorted | awk '{print $1, \"\\t\", $2}' > Ahya_cpg_anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig100010 \t 0.476931\r\n",
      "contig100021_110093_105915 \t 2.0758\r\n",
      "contig100025 \t 0.299187\r\n",
      "contig100026 \t 1.0599\r\n",
      "contig100031 \t 0.64616\r\n",
      "contig100040 \t 0.558145\r\n",
      "contig100055 \t 0.161543\r\n",
      "contig100067 \t 0.139249\r\n",
      "contig100105 \t 0.582234\r\n",
      "contig100110_36597 \t 0.762749\r\n"
     ]
    }
   ],
   "source": [
    "!head Ahya_cpg_anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!join ID_CpG.sorted Ahya_GOSlim.sorted > Ahya_cpg_GOslim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig100010 0.476931 cell adhesion\r",
      "\r\n",
      "contig100010 0.476931 cell-cell signaling\r",
      "\r\n",
      "contig100010 0.476931 developmental processes\r",
      "\r\n",
      "contig100021_110093_105915 2.0758 DNA metabolism\r",
      "\r\n",
      "contig100021_110093_105915 2.0758 protein metabolism\r",
      "\r\n",
      "contig100025 0.299187 developmental processes\r",
      "\r\n",
      "contig100040 0.558145 RNA metabolism\r",
      "\r\n",
      "contig100040 0.558145 developmental processes\r",
      "\r\n",
      "contig100055 0.161543 transport\r",
      "\r\n",
      "contig100067 0.139249 developmental processes\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head Ahya_cpg_GOslim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig100010 \t 0.476931 \t cell adhesion\r",
      "  \r\n",
      "contig100010 \t 0.476931 \t cell-cell signaling\r",
      "  \r\n",
      "contig100010 \t 0.476931 \t developmental processes\r",
      "  \r\n",
      "contig100021_110093_105915 \t 2.0758 \t DNA metabolism\r",
      "  \r\n",
      "contig100021_110093_105915 \t 2.0758 \t protein metabolism\r",
      "  \r\n",
      "contig100025 \t 0.299187 \t developmental processes\r",
      "  \r\n",
      "contig100040 \t 0.558145 \t RNA metabolism\r",
      "  \r\n",
      "contig100040 \t 0.558145 \t developmental processes\r",
      "  \r\n",
      "contig100055 \t 0.161543 \t transport\r",
      "   \r\n",
      "contig100067 \t 0.139249 \t developmental processes\r",
      "  \r\n"
     ]
    }
   ],
   "source": [
    "#Putting tabs in between columns\n",
    "!awk '{print $1, \"\\t\", $2, \"\\t\", $3, $4, $5, $6}' Ahya_cpg_GOslim > Ahya_cpg_GOslim.tab\n",
    "!head Ahya_cpg_GOslim.tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now time to plot data using pandas and matplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0    </th>\n",
       "      <td>                         contig100010 </td>\n",
       "      <td> 0.476931</td>\n",
       "      <td>                 cell adhesion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1    </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2    </th>\n",
       "      <td>                         contig100010 </td>\n",
       "      <td> 0.476931</td>\n",
       "      <td>           cell-cell signaling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3    </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4    </th>\n",
       "      <td>                         contig100010 </td>\n",
       "      <td> 0.476931</td>\n",
       "      <td>       developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5    </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6    </th>\n",
       "      <td>           contig100021_110093_105915 </td>\n",
       "      <td> 2.075800</td>\n",
       "      <td>                DNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7    </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8    </th>\n",
       "      <td>           contig100021_110093_105915 </td>\n",
       "      <td> 2.075800</td>\n",
       "      <td>            protein metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9    </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10   </th>\n",
       "      <td>                         contig100025 </td>\n",
       "      <td> 0.299187</td>\n",
       "      <td>       developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12   </th>\n",
       "      <td>                         contig100040 </td>\n",
       "      <td> 0.558145</td>\n",
       "      <td>                RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14   </th>\n",
       "      <td>                         contig100040 </td>\n",
       "      <td> 0.558145</td>\n",
       "      <td>       developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16   </th>\n",
       "      <td>                         contig100055 </td>\n",
       "      <td> 0.161543</td>\n",
       "      <td>                     transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18   </th>\n",
       "      <td>                         contig100067 </td>\n",
       "      <td> 0.139249</td>\n",
       "      <td>       developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20   </th>\n",
       "      <td>                         contig100067 </td>\n",
       "      <td> 0.139249</td>\n",
       "      <td>                     transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22   </th>\n",
       "      <td>                         contig100105 </td>\n",
       "      <td> 0.582234</td>\n",
       "      <td>            protein metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24   </th>\n",
       "      <td>                   contig100110_36597 </td>\n",
       "      <td> 0.762749</td>\n",
       "      <td>    other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26   </th>\n",
       "      <td>                   contig100110_36597 </td>\n",
       "      <td> 0.762749</td>\n",
       "      <td>                     transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28   </th>\n",
       "      <td>                         contig100128 </td>\n",
       "      <td> 0.526529</td>\n",
       "      <td>     other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29   </th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47245</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47246</th>\n",
       "      <td>                          contig99828 </td>\n",
       "      <td> 0.698013</td>\n",
       "      <td>           signal transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47247</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47248</th>\n",
       "      <td>                          contig99828 </td>\n",
       "      <td> 0.698013</td>\n",
       "      <td>               stress response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47249</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47250</th>\n",
       "      <td>                          contig99828 </td>\n",
       "      <td> 0.698013</td>\n",
       "      <td>                     transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47251</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47252</th>\n",
       "      <td>                          contig99856 </td>\n",
       "      <td> 0.311595</td>\n",
       "      <td>     other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47253</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47254</th>\n",
       "      <td>                          contig99903 </td>\n",
       "      <td> 0.579263</td>\n",
       "      <td>     other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47255</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47256</th>\n",
       "      <td>                     contig99913_9827 </td>\n",
       "      <td> 0.601522</td>\n",
       "      <td>     other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47257</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47258</th>\n",
       "      <td>                     contig99913_9827 </td>\n",
       "      <td> 0.601522</td>\n",
       "      <td>                     transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47259</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47260</th>\n",
       "      <td> contig99921_218449_5860_158351_79662 </td>\n",
       "      <td> 0.849566</td>\n",
       "      <td>     other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47261</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47262</th>\n",
       "      <td>                          contig99925 </td>\n",
       "      <td> 0.658814</td>\n",
       "      <td>           signal transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47263</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47264</th>\n",
       "      <td>                          contig99970 </td>\n",
       "      <td> 1.020940</td>\n",
       "      <td>                RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47265</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47266</th>\n",
       "      <td>                          contig99970 </td>\n",
       "      <td> 1.020940</td>\n",
       "      <td>  cell cycle and proliferation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47267</th>\n",
       "      <td>                          contig99970 </td>\n",
       "      <td> 1.020940</td>\n",
       "      <td>       developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47268</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47269</th>\n",
       "      <td>                          contig99970 </td>\n",
       "      <td> 1.020940</td>\n",
       "      <td>    other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47270</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47271</th>\n",
       "      <td>                    contig99996_15114 </td>\n",
       "      <td> 0.692021</td>\n",
       "      <td>                RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47272</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47273</th>\n",
       "      <td>                    contig99996_15114 </td>\n",
       "      <td> 0.692021</td>\n",
       "      <td>    other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47274</th>\n",
       "      <td>                                      </td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                           NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47275 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0         1  \\\n",
       "0                              contig100010   0.476931   \n",
       "1                                                  NaN   \n",
       "2                              contig100010   0.476931   \n",
       "3                                                  NaN   \n",
       "4                              contig100010   0.476931   \n",
       "5                                                  NaN   \n",
       "6                contig100021_110093_105915   2.075800   \n",
       "7                                                  NaN   \n",
       "8                contig100021_110093_105915   2.075800   \n",
       "9                                                  NaN   \n",
       "10                             contig100025   0.299187   \n",
       "11                                                 NaN   \n",
       "12                             contig100040   0.558145   \n",
       "13                                                 NaN   \n",
       "14                             contig100040   0.558145   \n",
       "15                                                 NaN   \n",
       "16                             contig100055   0.161543   \n",
       "17                                                 NaN   \n",
       "18                             contig100067   0.139249   \n",
       "19                                                 NaN   \n",
       "20                             contig100067   0.139249   \n",
       "21                                                 NaN   \n",
       "22                             contig100105   0.582234   \n",
       "23                                                 NaN   \n",
       "24                       contig100110_36597   0.762749   \n",
       "25                                                 NaN   \n",
       "26                       contig100110_36597   0.762749   \n",
       "27                                                 NaN   \n",
       "28                             contig100128   0.526529   \n",
       "29                                                 NaN   \n",
       "...                                      ...       ...   \n",
       "47245                                              NaN   \n",
       "47246                           contig99828   0.698013   \n",
       "47247                                              NaN   \n",
       "47248                           contig99828   0.698013   \n",
       "47249                                              NaN   \n",
       "47250                           contig99828   0.698013   \n",
       "47251                                              NaN   \n",
       "47252                           contig99856   0.311595   \n",
       "47253                                              NaN   \n",
       "47254                           contig99903   0.579263   \n",
       "47255                                              NaN   \n",
       "47256                      contig99913_9827   0.601522   \n",
       "47257                                              NaN   \n",
       "47258                      contig99913_9827   0.601522   \n",
       "47259                                              NaN   \n",
       "47260  contig99921_218449_5860_158351_79662   0.849566   \n",
       "47261                                              NaN   \n",
       "47262                           contig99925   0.658814   \n",
       "47263                                              NaN   \n",
       "47264                           contig99970   1.020940   \n",
       "47265                                              NaN   \n",
       "47266                           contig99970   1.020940   \n",
       "47267                           contig99970   1.020940   \n",
       "47268                                              NaN   \n",
       "47269                           contig99970   1.020940   \n",
       "47270                                              NaN   \n",
       "47271                     contig99996_15114   0.692021   \n",
       "47272                                              NaN   \n",
       "47273                     contig99996_15114   0.692021   \n",
       "47274                                              NaN   \n",
       "\n",
       "                                   2  \n",
       "0                      cell adhesion  \n",
       "1                                NaN  \n",
       "2                cell-cell signaling  \n",
       "3                                NaN  \n",
       "4            developmental processes  \n",
       "5                                NaN  \n",
       "6                     DNA metabolism  \n",
       "7                                NaN  \n",
       "8                 protein metabolism  \n",
       "9                                NaN  \n",
       "10           developmental processes  \n",
       "11                               NaN  \n",
       "12                    RNA metabolism  \n",
       "13                               NaN  \n",
       "14           developmental processes  \n",
       "15                               NaN  \n",
       "16                         transport  \n",
       "17                               NaN  \n",
       "18           developmental processes  \n",
       "19                               NaN  \n",
       "20                         transport  \n",
       "21                               NaN  \n",
       "22                protein metabolism  \n",
       "23                               NaN  \n",
       "24        other biological processes  \n",
       "25                               NaN  \n",
       "26                         transport  \n",
       "27                               NaN  \n",
       "28         other metabolic processes  \n",
       "29                               NaN  \n",
       "...                              ...  \n",
       "47245                            NaN  \n",
       "47246            signal transduction  \n",
       "47247                            NaN  \n",
       "47248                stress response  \n",
       "47249                            NaN  \n",
       "47250                      transport  \n",
       "47251                            NaN  \n",
       "47252      other metabolic processes  \n",
       "47253                            NaN  \n",
       "47254      other metabolic processes  \n",
       "47255                            NaN  \n",
       "47256      other metabolic processes  \n",
       "47257                            NaN  \n",
       "47258                      transport  \n",
       "47259                            NaN  \n",
       "47260      other metabolic processes  \n",
       "47261                            NaN  \n",
       "47262            signal transduction  \n",
       "47263                            NaN  \n",
       "47264                 RNA metabolism  \n",
       "47265                            NaN  \n",
       "47266   cell cycle and proliferation  \n",
       "47267        developmental processes  \n",
       "47268                            NaN  \n",
       "47269     other biological processes  \n",
       "47270                            NaN  \n",
       "47271                 RNA metabolism  \n",
       "47272                            NaN  \n",
       "47273     other biological processes  \n",
       "47274                            NaN  \n",
       "\n",
       "[47275 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jData = pd.read_table('Ahya_cpg_GOslim.tab', header=None)\n",
    "jData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SKFKPyobug9clJU0GXrZ9Stl11wtJ7jjuG8wIx6uaQRB0gEJVs+kZBvxe0rKk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104897510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jData.groupby(2)[1].mean().plot(kind='barh', color=list('myb'))\n",
    "plt.axis([0.7, 0.9, -1, 14])\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0    </th>\n",
       "      <td>                         contig100010 </td>\n",
       "      <td> 0.476931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1    </th>\n",
       "      <td>           contig100021_110093_105915 </td>\n",
       "      <td> 2.075800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2    </th>\n",
       "      <td>                         contig100025 </td>\n",
       "      <td> 0.299187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3    </th>\n",
       "      <td>                         contig100026 </td>\n",
       "      <td> 1.059900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4    </th>\n",
       "      <td>                         contig100031 </td>\n",
       "      <td> 0.646160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5    </th>\n",
       "      <td>                         contig100040 </td>\n",
       "      <td> 0.558145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6    </th>\n",
       "      <td>                         contig100055 </td>\n",
       "      <td> 0.161543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7    </th>\n",
       "      <td>                         contig100067 </td>\n",
       "      <td> 0.139249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8    </th>\n",
       "      <td>                         contig100105 </td>\n",
       "      <td> 0.582234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9    </th>\n",
       "      <td>                   contig100110_36597 </td>\n",
       "      <td> 0.762749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10   </th>\n",
       "      <td>                         contig100118 </td>\n",
       "      <td> 0.245657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11   </th>\n",
       "      <td>                         contig100128 </td>\n",
       "      <td> 0.526529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12   </th>\n",
       "      <td>                         contig100132 </td>\n",
       "      <td> 0.710240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13   </th>\n",
       "      <td>                          contig10014 </td>\n",
       "      <td> 0.398674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14   </th>\n",
       "      <td>                          contig10017 </td>\n",
       "      <td> 0.297622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15   </th>\n",
       "      <td>                         contig100183 </td>\n",
       "      <td> 0.533699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16   </th>\n",
       "      <td>                   contig100194_45147 </td>\n",
       "      <td> 0.689714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17   </th>\n",
       "      <td>                         contig100209 </td>\n",
       "      <td> 0.686760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18   </th>\n",
       "      <td>                         contig100227 </td>\n",
       "      <td> 0.205884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19   </th>\n",
       "      <td>                         contig100237 </td>\n",
       "      <td> 0.125373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20   </th>\n",
       "      <td>                         contig100251 </td>\n",
       "      <td> 0.980997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21   </th>\n",
       "      <td>                         contig100252 </td>\n",
       "      <td> 0.268736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22   </th>\n",
       "      <td>                         contig100259 </td>\n",
       "      <td> 0.785491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23   </th>\n",
       "      <td>                  contig100308_100284 </td>\n",
       "      <td> 0.869684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24   </th>\n",
       "      <td>                         contig100321 </td>\n",
       "      <td> 0.747259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25   </th>\n",
       "      <td>                         contig100328 </td>\n",
       "      <td> 0.761890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26   </th>\n",
       "      <td>                         contig100330 </td>\n",
       "      <td> 0.585245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27   </th>\n",
       "      <td>                         contig100341 </td>\n",
       "      <td> 0.625205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28   </th>\n",
       "      <td>                  contig100347_112826 </td>\n",
       "      <td> 0.843350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29   </th>\n",
       "      <td>                         contig100349 </td>\n",
       "      <td> 0.524223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11563</th>\n",
       "      <td>                          contig99614 </td>\n",
       "      <td> 0.516533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11564</th>\n",
       "      <td>                          contig99638 </td>\n",
       "      <td> 0.244230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11565</th>\n",
       "      <td>                          contig99649 </td>\n",
       "      <td> 0.291315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11566</th>\n",
       "      <td>                          contig99670 </td>\n",
       "      <td> 0.511823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11567</th>\n",
       "      <td>                          contig99674 </td>\n",
       "      <td> 0.552142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11568</th>\n",
       "      <td>                    contig99689_83153 </td>\n",
       "      <td> 1.043430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11569</th>\n",
       "      <td>                          contig99694 </td>\n",
       "      <td> 0.368911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11570</th>\n",
       "      <td>                          contig99698 </td>\n",
       "      <td> 0.584653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11571</th>\n",
       "      <td>                          contig99714 </td>\n",
       "      <td> 0.914334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11572</th>\n",
       "      <td>                          contig99748 </td>\n",
       "      <td> 0.861369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11573</th>\n",
       "      <td>                          contig99754 </td>\n",
       "      <td> 0.967096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11574</th>\n",
       "      <td>                          contig99766 </td>\n",
       "      <td> 0.238253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11575</th>\n",
       "      <td>                          contig99771 </td>\n",
       "      <td> 0.586749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11576</th>\n",
       "      <td>                          contig99778 </td>\n",
       "      <td> 0.822174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11577</th>\n",
       "      <td>                          contig99780 </td>\n",
       "      <td> 0.754110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11578</th>\n",
       "      <td>                          contig99784 </td>\n",
       "      <td> 0.368969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11579</th>\n",
       "      <td>                          contig99804 </td>\n",
       "      <td> 1.013420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11580</th>\n",
       "      <td>      contig99808_211682_218627_80123 </td>\n",
       "      <td> 0.852763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11581</th>\n",
       "      <td>                          contig99810 </td>\n",
       "      <td> 0.599800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11582</th>\n",
       "      <td>             contig99816_208470_81450 </td>\n",
       "      <td> 0.905370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11583</th>\n",
       "      <td>                   contig99826_145307 </td>\n",
       "      <td> 0.670821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11584</th>\n",
       "      <td>                          contig99828 </td>\n",
       "      <td> 0.698013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11585</th>\n",
       "      <td>                          contig99851 </td>\n",
       "      <td> 0.252815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11586</th>\n",
       "      <td>                          contig99856 </td>\n",
       "      <td> 0.311595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11587</th>\n",
       "      <td>                          contig99903 </td>\n",
       "      <td> 0.579263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11588</th>\n",
       "      <td>                     contig99913_9827 </td>\n",
       "      <td> 0.601522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11589</th>\n",
       "      <td> contig99921_218449_5860_158351_79662 </td>\n",
       "      <td> 0.849566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11590</th>\n",
       "      <td>                          contig99925 </td>\n",
       "      <td> 0.658814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11591</th>\n",
       "      <td>                          contig99970 </td>\n",
       "      <td> 1.020940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11592</th>\n",
       "      <td>                    contig99996_15114 </td>\n",
       "      <td> 0.692021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11593 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0         1\n",
       "0                              contig100010   0.476931\n",
       "1                contig100021_110093_105915   2.075800\n",
       "2                              contig100025   0.299187\n",
       "3                              contig100026   1.059900\n",
       "4                              contig100031   0.646160\n",
       "5                              contig100040   0.558145\n",
       "6                              contig100055   0.161543\n",
       "7                              contig100067   0.139249\n",
       "8                              contig100105   0.582234\n",
       "9                        contig100110_36597   0.762749\n",
       "10                             contig100118   0.245657\n",
       "11                             contig100128   0.526529\n",
       "12                             contig100132   0.710240\n",
       "13                              contig10014   0.398674\n",
       "14                              contig10017   0.297622\n",
       "15                             contig100183   0.533699\n",
       "16                       contig100194_45147   0.689714\n",
       "17                             contig100209   0.686760\n",
       "18                             contig100227   0.205884\n",
       "19                             contig100237   0.125373\n",
       "20                             contig100251   0.980997\n",
       "21                             contig100252   0.268736\n",
       "22                             contig100259   0.785491\n",
       "23                      contig100308_100284   0.869684\n",
       "24                             contig100321   0.747259\n",
       "25                             contig100328   0.761890\n",
       "26                             contig100330   0.585245\n",
       "27                             contig100341   0.625205\n",
       "28                      contig100347_112826   0.843350\n",
       "29                             contig100349   0.524223\n",
       "...                                      ...       ...\n",
       "11563                           contig99614   0.516533\n",
       "11564                           contig99638   0.244230\n",
       "11565                           contig99649   0.291315\n",
       "11566                           contig99670   0.511823\n",
       "11567                           contig99674   0.552142\n",
       "11568                     contig99689_83153   1.043430\n",
       "11569                           contig99694   0.368911\n",
       "11570                           contig99698   0.584653\n",
       "11571                           contig99714   0.914334\n",
       "11572                           contig99748   0.861369\n",
       "11573                           contig99754   0.967096\n",
       "11574                           contig99766   0.238253\n",
       "11575                           contig99771   0.586749\n",
       "11576                           contig99778   0.822174\n",
       "11577                           contig99780   0.754110\n",
       "11578                           contig99784   0.368969\n",
       "11579                           contig99804   1.013420\n",
       "11580       contig99808_211682_218627_80123   0.852763\n",
       "11581                           contig99810   0.599800\n",
       "11582              contig99816_208470_81450   0.905370\n",
       "11583                    contig99826_145307   0.670821\n",
       "11584                           contig99828   0.698013\n",
       "11585                           contig99851   0.252815\n",
       "11586                           contig99856   0.311595\n",
       "11587                           contig99903   0.579263\n",
       "11588                      contig99913_9827   0.601522\n",
       "11589  contig99921_218449_5860_158351_79662   0.849566\n",
       "11590                           contig99925   0.658814\n",
       "11591                           contig99970   1.020940\n",
       "11592                     contig99996_15114   0.692021\n",
       "\n",
       "[11593 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To plot density curve, must use CpG with original annotation\n",
    "CpG = pd.read_table('Ahya_cpg_anno', header=None)\n",
    "CpG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.3, 1.7, 0, 1.7]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ffxJXArvHzcvM+Jeiv7MMqfycrosi+lPiBOBrcfNFws2Dz+b5HSlK5dj0Sdo6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\nIUCMTQPjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10804e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pandas density plot\n",
    "CpG[1].plot(kind='kde', linewidth=3);\n",
    "plt.axis([-0.3, 1.7, 0, 1.7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now looking only at differentially expressed contigs <Ahya_diff_contigs>, joining via common contig IDs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file \"Ahya_diff_contigs\" is a text file that lists the 484 contigs that were differentially expressed\n",
    "between control and heated corals, regardless of source population. The contig IDs are the same as those \n",
    "in the transcriptome above. The file was obtained from an [Excel file]((http://www.pnas.org/content/suppl/2013/01/02/1210224110.DCSupplemental/sd01.xlsx) that was downloaded\n",
    "from the supplementary info page at http://www.pnas.org/content/110/4/1387.abstract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig83050\n",
      "contig180146_147773\n",
      "contig216267\n",
      "contig118470_120199_147241\n",
      "contig211162\n",
      "contig181073\n",
      "contig145010\n",
      "contig189881_190487_185006\n",
      "contig185261\n",
      "contig184253_153996_125963\n",
      "     484     484    7941 Ahya_diff_contigs\n"
     ]
    }
   ],
   "source": [
    "!head -10 Ahya_diff_contigs\n",
    "!wc Ahya_diff_contigs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!sort <Ahya_diff_contigs> Ahya_diff_contigs.sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig100302_114262_202031 0.908145\n",
      "contig100349 0.524223\n",
      "contig102770 1.2155\n",
      "contig103080_193887 0.661096\n",
      "contig104395_153016 0.885662\n",
      "contig105632_159216 2.26543\n",
      "contig105645 1.45804\n",
      "contig105949 0.973447\n",
      "contig107034 0.727932\n",
      "contig107336 0.719959\n",
      "     484     968   12122 Ahya_diff_cpg\n"
     ]
    }
   ],
   "source": [
    "#Joining with CpG O/E data\n",
    "!join Ahya_diff_contigs.sorted ID_CpG.sorted > Ahya_diff_cpg   | awk '{print $1, \"\\t\", $2}' \n",
    "!head Ahya_diff_cpg\n",
    "!wc Ahya_diff_cpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contig100349 \t 0.524223\n",
      "contig103080_193887 \t 0.661096\n",
      "contig104395_153016 \t 0.885662\n",
      "contig105632_159216 \t 2.26543\n",
      "contig105949 \t 0.973447\n",
      "contig107034 \t 0.727932\n",
      "contig110172 \t 0.729807\n",
      "contig110751 \t 1.08054\n",
      "contig112007_150291_111307 \t 1.03768\n",
      "contig112463 \t 0.627976\n",
      "     278     556    7800 Ahya_diff_cpg_anno\n"
     ]
    }
   ],
   "source": [
    "#Joining with annotation file\n",
    "!join Ahya_diff_cpg Ahya_blastx_uniprot_sql.tab.sorted | awk '{print $1, \"\\t\", $2}' > Ahya_diff_cpg_anno\n",
    "!head Ahya_diff_cpg_anno\n",
    "!wc Ahya_diff_cpg_anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  \n",
      " \n",
      "  \n",
      " \n",
      " \n",
      "  \n",
      "  \n",
      " \n",
      "  \n",
      "   \n",
      "     698    3081   37965 Ahya_diff_cpg_GOslim\n"
     ]
    }
   ],
   "source": [
    "#Joining with GOslim annotation file\n",
    "!join Ahya_diff_cpg Ahya_GOSlim.sorted | awk '{print $1, \"\\t\", $2, \"\\t\", $3, $4, $5, $6}' > Ahya_diff_cpg_GOslim\n",
    "!head Ahya_diff_cpg_GOslim\n",
    "!wc Ahya_diff_cpg_GOslim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>Column1</th>\n",
       "      <th>Column2</th>\n",
       "      <th>GOslim_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0   </th>\n",
       "      <td> contig100302_114262_202031</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                              NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1   </th>\n",
       "      <td>               contig100349</td>\n",
       "      <td> 0.524223</td>\n",
       "      <td>                   RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2   </th>\n",
       "      <td>               contig100349</td>\n",
       "      <td> 0.524223</td>\n",
       "      <td>                  stress response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3   </th>\n",
       "      <td>               contig100349</td>\n",
       "      <td> 0.524223</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4   </th>\n",
       "      <td>               contig100349</td>\n",
       "      <td> 0.524223</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5   </th>\n",
       "      <td>               contig102770</td>\n",
       "      <td> 1.215500</td>\n",
       "      <td>                   RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6   </th>\n",
       "      <td>               contig102770</td>\n",
       "      <td> 1.215500</td>\n",
       "      <td>                        transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7   </th>\n",
       "      <td>               contig102770</td>\n",
       "      <td> 1.215500</td>\n",
       "      <td>               protein metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8   </th>\n",
       "      <td>               contig102770</td>\n",
       "      <td> 1.215500</td>\n",
       "      <td>     cell cycle and proliferation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9   </th>\n",
       "      <td>               contig102770</td>\n",
       "      <td> 1.215500</td>\n",
       "      <td>              signal transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10  </th>\n",
       "      <td>               contig102770</td>\n",
       "      <td> 1.215500</td>\n",
       "      <td>          developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11  </th>\n",
       "      <td>               contig102770</td>\n",
       "      <td> 1.215500</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12  </th>\n",
       "      <td>               contig102770</td>\n",
       "      <td> 1.215500</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13  </th>\n",
       "      <td>        contig103080_193887</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                              NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14  </th>\n",
       "      <td>        contig104395_153016</td>\n",
       "      <td> 0.885662</td>\n",
       "      <td>                   RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15  </th>\n",
       "      <td>        contig104395_153016</td>\n",
       "      <td> 0.885662</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16  </th>\n",
       "      <td>        contig105632_159216</td>\n",
       "      <td> 2.265430</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17  </th>\n",
       "      <td>               contig105645</td>\n",
       "      <td> 1.458040</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18  </th>\n",
       "      <td>               contig105949</td>\n",
       "      <td> 0.973447</td>\n",
       "      <td>                        transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19  </th>\n",
       "      <td>               contig105949</td>\n",
       "      <td> 0.973447</td>\n",
       "      <td> cell organization and biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20  </th>\n",
       "      <td>               contig107034</td>\n",
       "      <td> 0.727932</td>\n",
       "      <td>               protein metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21  </th>\n",
       "      <td>               contig107336</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                              NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22  </th>\n",
       "      <td>               contig110017</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                              NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23  </th>\n",
       "      <td>               contig110172</td>\n",
       "      <td> 0.729807</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24  </th>\n",
       "      <td>               contig110172</td>\n",
       "      <td> 0.729807</td>\n",
       "      <td>              signal transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25  </th>\n",
       "      <td>               contig110751</td>\n",
       "      <td> 1.080540</td>\n",
       "      <td>                        transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26  </th>\n",
       "      <td> contig112007_150291_111307</td>\n",
       "      <td> 1.037680</td>\n",
       "      <td>          developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27  </th>\n",
       "      <td> contig112007_150291_111307</td>\n",
       "      <td> 1.037680</td>\n",
       "      <td>              signal transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28  </th>\n",
       "      <td> contig112007_150291_111307</td>\n",
       "      <td> 1.037680</td>\n",
       "      <td>     cell cycle and proliferation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29  </th>\n",
       "      <td> contig112007_150291_111307</td>\n",
       "      <td> 1.037680</td>\n",
       "      <td>              cell-cell signaling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>                contig92662</td>\n",
       "      <td> 0.295825</td>\n",
       "      <td>                   RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>                contig93670</td>\n",
       "      <td> 1.031360</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>                contig93732</td>\n",
       "      <td> 0.830275</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>                contig93732</td>\n",
       "      <td> 0.830275</td>\n",
       "      <td>          developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>                contig93732</td>\n",
       "      <td> 0.830275</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>                 contig9516</td>\n",
       "      <td> 0.899129</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>         contig96421_189251</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                              NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>                contig96499</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>                              NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>   contig97794_175986_93895</td>\n",
       "      <td> 0.797713</td>\n",
       "      <td>          developmental processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>   contig97794_175986_93895</td>\n",
       "      <td> 0.797713</td>\n",
       "      <td>                   RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>                contig98199</td>\n",
       "      <td> 0.724031</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>                contig98199</td>\n",
       "      <td> 0.724031</td>\n",
       "      <td> cell organization and biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>                contig98199</td>\n",
       "      <td> 0.724031</td>\n",
       "      <td>                            death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>                contig98199</td>\n",
       "      <td> 0.724031</td>\n",
       "      <td>              signal transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>                contig98199</td>\n",
       "      <td> 0.724031</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>                contig98199</td>\n",
       "      <td> 0.724031</td>\n",
       "      <td>                  stress response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>                contig98199</td>\n",
       "      <td> 0.724031</td>\n",
       "      <td>               protein metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>                contig98199</td>\n",
       "      <td> 0.724031</td>\n",
       "      <td>                   RNA metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>                contig98618</td>\n",
       "      <td> 0.660431</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>                contig98618</td>\n",
       "      <td> 0.660431</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>                contig98984</td>\n",
       "      <td> 0.768580</td>\n",
       "      <td>                        transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>                contig98984</td>\n",
       "      <td> 0.768580</td>\n",
       "      <td>        other metabolic processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>                contig98984</td>\n",
       "      <td> 0.768580</td>\n",
       "      <td>                  stress response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>                contig98984</td>\n",
       "      <td> 0.768580</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>                contig99157</td>\n",
       "      <td> 0.511279</td>\n",
       "      <td>                        transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>                contig99523</td>\n",
       "      <td> 0.785016</td>\n",
       "      <td>                    cell adhesion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>                contig99523</td>\n",
       "      <td> 0.785016</td>\n",
       "      <td>     cell cycle and proliferation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>                contig99523</td>\n",
       "      <td> 0.785016</td>\n",
       "      <td>              signal transduction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>                contig99523</td>\n",
       "      <td> 0.785016</td>\n",
       "      <td>               protein metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>                contig99523</td>\n",
       "      <td> 0.785016</td>\n",
       "      <td>       other biological processes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1084 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Column1   Column2                        GOslim_bin\n",
       "0     contig100302_114262_202031       NaN                               NaN\n",
       "1                   contig100349  0.524223                    RNA metabolism\n",
       "2                   contig100349  0.524223                   stress response\n",
       "3                   contig100349  0.524223        other biological processes\n",
       "4                   contig100349  0.524223         other metabolic processes\n",
       "5                   contig102770  1.215500                    RNA metabolism\n",
       "6                   contig102770  1.215500                         transport\n",
       "7                   contig102770  1.215500                protein metabolism\n",
       "8                   contig102770  1.215500      cell cycle and proliferation\n",
       "9                   contig102770  1.215500               signal transduction\n",
       "10                  contig102770  1.215500           developmental processes\n",
       "11                  contig102770  1.215500         other metabolic processes\n",
       "12                  contig102770  1.215500        other biological processes\n",
       "13           contig103080_193887       NaN                               NaN\n",
       "14           contig104395_153016  0.885662                    RNA metabolism\n",
       "15           contig104395_153016  0.885662        other biological processes\n",
       "16           contig105632_159216  2.265430         other metabolic processes\n",
       "17                  contig105645  1.458040        other biological processes\n",
       "18                  contig105949  0.973447                         transport\n",
       "19                  contig105949  0.973447  cell organization and biogenesis\n",
       "20                  contig107034  0.727932                protein metabolism\n",
       "21                  contig107336       NaN                               NaN\n",
       "22                  contig110017       NaN                               NaN\n",
       "23                  contig110172  0.729807         other metabolic processes\n",
       "24                  contig110172  0.729807               signal transduction\n",
       "25                  contig110751  1.080540                         transport\n",
       "26    contig112007_150291_111307  1.037680           developmental processes\n",
       "27    contig112007_150291_111307  1.037680               signal transduction\n",
       "28    contig112007_150291_111307  1.037680      cell cycle and proliferation\n",
       "29    contig112007_150291_111307  1.037680               cell-cell signaling\n",
       "...                          ...       ...                               ...\n",
       "1054                 contig92662  0.295825                    RNA metabolism\n",
       "1055                 contig93670  1.031360        other biological processes\n",
       "1056                 contig93732  0.830275         other metabolic processes\n",
       "1057                 contig93732  0.830275           developmental processes\n",
       "1058                 contig93732  0.830275        other biological processes\n",
       "1059                  contig9516  0.899129         other metabolic processes\n",
       "1060          contig96421_189251       NaN                               NaN\n",
       "1061                 contig96499       NaN                               NaN\n",
       "1062    contig97794_175986_93895  0.797713           developmental processes\n",
       "1063    contig97794_175986_93895  0.797713                    RNA metabolism\n",
       "1064                 contig98199  0.724031        other biological processes\n",
       "1065                 contig98199  0.724031  cell organization and biogenesis\n",
       "1066                 contig98199  0.724031                             death\n",
       "1067                 contig98199  0.724031               signal transduction\n",
       "1068                 contig98199  0.724031         other metabolic processes\n",
       "1069                 contig98199  0.724031                   stress response\n",
       "1070                 contig98199  0.724031                protein metabolism\n",
       "1071                 contig98199  0.724031                    RNA metabolism\n",
       "1072                 contig98618  0.660431         other metabolic processes\n",
       "1073                 contig98618  0.660431        other biological processes\n",
       "1074                 contig98984  0.768580                         transport\n",
       "1075                 contig98984  0.768580         other metabolic processes\n",
       "1076                 contig98984  0.768580                   stress response\n",
       "1077                 contig98984  0.768580        other biological processes\n",
       "1078                 contig99157  0.511279                         transport\n",
       "1079                 contig99523  0.785016                     cell adhesion\n",
       "1080                 contig99523  0.785016      cell cycle and proliferation\n",
       "1081                 contig99523  0.785016               signal transduction\n",
       "1082                 contig99523  0.785016                protein metabolism\n",
       "1083                 contig99523  0.785016        other biological processes\n",
       "\n",
       "[1084 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ahya_diff = pd.read_table('Ahya_diff_cpg_GOslim')\n",
    "Ahya_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7, 1.1, -1, 14]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hs9GEzvYjOhxBEARBEDSdWMMRDCkaMQ8ZBEEw1GjEd2eMcARBEARB0HSiwxEEbUhZ5nXL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\nf2c+Be9QL74iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10808b4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ahya_diff.groupby(\"GOslim_bin\")[\"Column2\"].mean().plot(kind='barh', color=list('myb'))\n",
    "plt.axis([0.7, 1.1, -1, 14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0  </th>\n",
       "      <td>                                     contig100349 </td>\n",
       "      <td> 0.524223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1  </th>\n",
       "      <td>                              contig103080_193887 </td>\n",
       "      <td> 0.661096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2  </th>\n",
       "      <td>                              contig104395_153016 </td>\n",
       "      <td> 0.885662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3  </th>\n",
       "      <td>                              contig105632_159216 </td>\n",
       "      <td> 2.265430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4  </th>\n",
       "      <td>                                     contig105949 </td>\n",
       "      <td> 0.973447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5  </th>\n",
       "      <td>                                     contig107034 </td>\n",
       "      <td> 0.727932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6  </th>\n",
       "      <td>                                     contig110172 </td>\n",
       "      <td> 0.729807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7  </th>\n",
       "      <td>                                     contig110751 </td>\n",
       "      <td> 1.080540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8  </th>\n",
       "      <td>                       contig112007_150291_111307 </td>\n",
       "      <td> 1.037680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9  </th>\n",
       "      <td>                                     contig112463 </td>\n",
       "      <td> 0.627976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 </th>\n",
       "      <td>                              contig112626_149884 </td>\n",
       "      <td> 0.921502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11 </th>\n",
       "      <td>                       contig112688_170707_146883 </td>\n",
       "      <td> 0.783514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 </th>\n",
       "      <td>         contig112809_149298_131394_104581_171125 </td>\n",
       "      <td> 0.931365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13 </th>\n",
       "      <td>                                     contig113128 </td>\n",
       "      <td> 0.579855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14 </th>\n",
       "      <td>                  contig113501_174692_88537_82393 </td>\n",
       "      <td> 1.220230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15 </th>\n",
       "      <td>                                     contig113836 </td>\n",
       "      <td> 0.504676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 </th>\n",
       "      <td>                  contig115860_4992_195722_171755 </td>\n",
       "      <td> 0.969490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17 </th>\n",
       "      <td>                              contig115890_205214 </td>\n",
       "      <td> 0.924856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18 </th>\n",
       "      <td>                               contig116802_21164 </td>\n",
       "      <td> 1.080490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19 </th>\n",
       "      <td>                                     contig118916 </td>\n",
       "      <td> 0.696836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20 </th>\n",
       "      <td>                                     contig119016 </td>\n",
       "      <td> 0.928775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21 </th>\n",
       "      <td>                                      contig11991 </td>\n",
       "      <td> 0.492810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22 </th>\n",
       "      <td>                               contig12336_105256 </td>\n",
       "      <td> 0.992275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23 </th>\n",
       "      <td>                              contig123587_153243 </td>\n",
       "      <td> 0.818188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24 </th>\n",
       "      <td>                                     contig124917 </td>\n",
       "      <td> 0.572254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25 </th>\n",
       "      <td> contig12867_142623_79520_172896_150105_152164_...</td>\n",
       "      <td> 0.735131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26 </th>\n",
       "      <td>                                     contig132286 </td>\n",
       "      <td> 0.846371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27 </th>\n",
       "      <td>                                     contig132443 </td>\n",
       "      <td> 0.777932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28 </th>\n",
       "      <td>                 contig132966_210546_87461_180132 </td>\n",
       "      <td> 2.841970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29 </th>\n",
       "      <td>                               contig13535_144960 </td>\n",
       "      <td> 0.629115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>                         contig75607_169164_74746 </td>\n",
       "      <td> 1.357380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>                                      contig76115 </td>\n",
       "      <td> 0.673705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>                                contig76620_96728 </td>\n",
       "      <td> 0.983838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>                                      contig77121 </td>\n",
       "      <td> 0.917984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>                               contig77601_207123 </td>\n",
       "      <td> 0.701044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>                                      contig77727 </td>\n",
       "      <td> 0.876562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>                         contig77755_70614_107573 </td>\n",
       "      <td> 0.823651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>                                      contig77844 </td>\n",
       "      <td> 0.668139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>                        contig78105_108948_169460 </td>\n",
       "      <td> 1.213730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>                                      contig78166 </td>\n",
       "      <td> 0.707433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>                                      contig78169 </td>\n",
       "      <td> 0.765839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>                                contig78655_98713 </td>\n",
       "      <td> 1.089800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>                                      contig78998 </td>\n",
       "      <td> 0.437363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>                               contig79495_132352 </td>\n",
       "      <td> 1.093730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>                               contig79613_159385 </td>\n",
       "      <td> 1.303220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>                                      contig79838 </td>\n",
       "      <td> 0.682852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>                               contig79841_173655 </td>\n",
       "      <td> 1.105070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>                                      contig79914 </td>\n",
       "      <td> 0.453962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>                                      contig80216 </td>\n",
       "      <td> 0.647116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>                                      contig80700 </td>\n",
       "      <td> 1.019690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>                                contig8602_188720 </td>\n",
       "      <td> 7.575420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>                                      contig92386 </td>\n",
       "      <td> 0.702157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>                                      contig92662 </td>\n",
       "      <td> 0.295825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>                                      contig93732 </td>\n",
       "      <td> 0.830275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>                                       contig9516 </td>\n",
       "      <td> 0.899129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>                               contig96421_189251 </td>\n",
       "      <td> 0.356141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>                         contig97794_175986_93895 </td>\n",
       "      <td> 0.797713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>                                      contig98199 </td>\n",
       "      <td> 0.724031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>                                      contig98984 </td>\n",
       "      <td> 0.768580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>                                      contig99523 </td>\n",
       "      <td> 0.785016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>278 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     0         1\n",
       "0                                        contig100349   0.524223\n",
       "1                                 contig103080_193887   0.661096\n",
       "2                                 contig104395_153016   0.885662\n",
       "3                                 contig105632_159216   2.265430\n",
       "4                                        contig105949   0.973447\n",
       "5                                        contig107034   0.727932\n",
       "6                                        contig110172   0.729807\n",
       "7                                        contig110751   1.080540\n",
       "8                          contig112007_150291_111307   1.037680\n",
       "9                                        contig112463   0.627976\n",
       "10                                contig112626_149884   0.921502\n",
       "11                         contig112688_170707_146883   0.783514\n",
       "12           contig112809_149298_131394_104581_171125   0.931365\n",
       "13                                       contig113128   0.579855\n",
       "14                    contig113501_174692_88537_82393   1.220230\n",
       "15                                       contig113836   0.504676\n",
       "16                    contig115860_4992_195722_171755   0.969490\n",
       "17                                contig115890_205214   0.924856\n",
       "18                                 contig116802_21164   1.080490\n",
       "19                                       contig118916   0.696836\n",
       "20                                       contig119016   0.928775\n",
       "21                                        contig11991   0.492810\n",
       "22                                 contig12336_105256   0.992275\n",
       "23                                contig123587_153243   0.818188\n",
       "24                                       contig124917   0.572254\n",
       "25   contig12867_142623_79520_172896_150105_152164_...  0.735131\n",
       "26                                       contig132286   0.846371\n",
       "27                                       contig132443   0.777932\n",
       "28                   contig132966_210546_87461_180132   2.841970\n",
       "29                                 contig13535_144960   0.629115\n",
       "..                                                 ...       ...\n",
       "248                          contig75607_169164_74746   1.357380\n",
       "249                                       contig76115   0.673705\n",
       "250                                 contig76620_96728   0.983838\n",
       "251                                       contig77121   0.917984\n",
       "252                                contig77601_207123   0.701044\n",
       "253                                       contig77727   0.876562\n",
       "254                          contig77755_70614_107573   0.823651\n",
       "255                                       contig77844   0.668139\n",
       "256                         contig78105_108948_169460   1.213730\n",
       "257                                       contig78166   0.707433\n",
       "258                                       contig78169   0.765839\n",
       "259                                 contig78655_98713   1.089800\n",
       "260                                       contig78998   0.437363\n",
       "261                                contig79495_132352   1.093730\n",
       "262                                contig79613_159385   1.303220\n",
       "263                                       contig79838   0.682852\n",
       "264                                contig79841_173655   1.105070\n",
       "265                                       contig79914   0.453962\n",
       "266                                       contig80216   0.647116\n",
       "267                                       contig80700   1.019690\n",
       "268                                 contig8602_188720   7.575420\n",
       "269                                       contig92386   0.702157\n",
       "270                                       contig92662   0.295825\n",
       "271                                       contig93732   0.830275\n",
       "272                                        contig9516   0.899129\n",
       "273                                contig96421_189251   0.356141\n",
       "274                          contig97794_175986_93895   0.797713\n",
       "275                                       contig98199   0.724031\n",
       "276                                       contig98984   0.768580\n",
       "277                                       contig99523   0.785016\n",
       "\n",
       "[278 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ahya_diff2 = pd.read_table('Ahya_diff_cpg_anno', header=None)\n",
    "Ahya_diff2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.3, 1.7, 0, 1.7]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rcw29B1+rfFasCWxbHhI4pX4J88VKNplKnmoBRw2qv27zGWP1nJ+qGodTF0V/1l3xO22N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\nh+GK33Ecp8P4fwQmSz3MaCYuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108264610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pandas density plot\n",
    "Ahya_diff2[1].dropna().plot(kind='kde', linewidth=3);\n",
    "CpG[1].plot(kind='kde', linewidth=3);\n",
    "plt.axis([-0.3, 1.7, 0, 1.7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.736736\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(CpG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.89944\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(Ahya_diff2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}