Human RNA Adenylation Patterns.ipynb

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   "source": [
    "Looking at the poly-A tails of human RNA\n",
    "\n",
    "[FASTA file used](http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/mrna.fa.gz)"
   ]
  },
  {
   "cell_type": "code",
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "from itertools import groupby\n",
    "\n",
    "def rna_adenylation(path):\n",
    "    items = []\n",
    "    for (header, seq) in fasta_iter(path):\n",
    "        index = -1\n",
    "        while(seq[index] == 'a'):\n",
    "            index = index - 1\n",
    "        a_tail_len = index * -1 - 1\n",
    "        items.append((header, [len(seq), a_tail_len]))\n",
    "    return pd.DataFrame.from_items(items, orient=\"index\", columns=['rna_len', 'a_tail_len'])\n",
    "\n",
    "# https://www.biostars.org/p/710/\n",
    "def fasta_iter(fasta_name):\n",
    "    \"\"\"\n",
    "    given a fasta file. yield tuples of header, sequence\n",
    "    \"\"\"\n",
    "    fh = open(fasta_name)\n",
    "    # ditch the boolean (x[0]) and just keep the header or sequence since\n",
    "    # we know they alternate.\n",
    "    faiter = (x[1] for x in groupby(fh, lambda line: line[0] == \">\"))\n",
    "    for header in faiter:\n",
    "        # drop the \">\"\n",
    "        header = header.next()[1:].strip()\n",
    "        # join all sequence lines to one.\n",
    "        seq = \"\".join(s.strip() for s in faiter.next())\n",
    "        yield header, seq"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rna_len</th>\n",
       "      <th>a_tail_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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      "text/plain": [
       "            rna_len  a_tail_len\n",
       "AF001540 1     1781          23\n",
       "AF001541 1     1138           0\n",
       "AF001542 1     2992           0\n",
       "AF001543 1      903           0\n",
       "AF001544 1      434           0\n",
       "AF001545 1      370          38\n",
       "AF001546 1     1142           0\n",
       "AF001547 1     1092           0\n",
       "AF034176 1     7232           1\n",
       "AF038950 1     2384          30"
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     "execution_count": 3,
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   "source": [
    "df = rna_adenylation(\"data/mrna.fa\")\n",
    "df.head(10)"
   ]
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   "execution_count": 4,
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rna_len</th>\n",
       "      <th>a_tail_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.683747e+06</td>\n",
       "      <td>2.683747e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.054898e+02</td>\n",
       "      <td>1.215645e+00</td>\n",
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       "      <th>std</th>\n",
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       "      <td>5.291671e+00</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
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       "      <th>75%</th>\n",
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       "      <td>1.000000e+00</td>\n",
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       "      <th>max</th>\n",
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      "text/plain": [
       "            rna_len    a_tail_len\n",
       "count  2.683747e+06  2.683747e+06\n",
       "mean   2.054898e+02  1.215645e+00\n",
       "std    6.991804e+02  5.291671e+00\n",
       "min    2.000000e+00  0.000000e+00\n",
       "25%    1.900000e+01  0.000000e+00\n",
       "50%    1.900000e+01  0.000000e+00\n",
       "75%    3.200000e+01  1.000000e+00\n",
       "max    2.050120e+05  2.160000e+02"
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "masks = [(df.a_tail_len <= 3),\n",
    "         (df.a_tail_len > 3) & (df.a_tail_len <= 50),\n",
    "         (df.a_tail_len > 50) & (df.a_tail_len <= 100),\n",
    "         (df.a_tail_len > 100) & (df.a_tail_len <= 150),\n",
    "         (df.a_tail_len > 150)]\n",
    "\n",
    "plt.figure(figsize=(12,9))\n",
    "for idx, mask in enumerate(masks):\n",
    "    plt.subplot(len(masks), 1, idx+1)\n",
    "    df[mask][\"a_tail_len\"].plot.hist(bins=50, alpha=0.5, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the RNA didn't have poly-A tails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rna_len</th>\n",
       "      <th>a_tail_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.592318e+06</td>\n",
       "      <td>1592318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.944552e+02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.548029e+02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.447400e+04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rna_len  a_tail_len\n",
       "count  1.592318e+06   1592318.0\n",
       "mean   1.944552e+02         0.0\n",
       "std    6.548029e+02         0.0\n",
       "min    2.000000e+00         0.0\n",
       "25%    1.900000e+01         0.0\n",
       "50%    1.900000e+01         0.0\n",
       "75%    3.200000e+01         0.0\n",
       "max    7.447400e+04         0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.a_tail_len == 0)].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "# Remove a few outliers and put them into a table instead\n",
    "df[(df.rna_len < 20000)].plot.scatter(x='rna_len', y='a_tail_len', figsize=(12,9))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rna_len</th>\n",
       "      <th>a_tail_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HW531608 1</th>\n",
       "      <td>26993</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS329402 1</th>\n",
       "      <td>49020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JB406675 1</th>\n",
       "      <td>28803</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JB406694 1</th>\n",
       "      <td>74474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JB406696 1</th>\n",
       "      <td>39731</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FW339979 1</th>\n",
       "      <td>20881</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB537889 1</th>\n",
       "      <td>21055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF357236 1</th>\n",
       "      <td>20478</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF361486 3</th>\n",
       "      <td>21112</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF414442 2</th>\n",
       "      <td>66765</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF435011 1</th>\n",
       "      <td>21794</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF495910 1</th>\n",
       "      <td>27652</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF495911 1</th>\n",
       "      <td>21779</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AF535142 1</th>\n",
       "      <td>27435</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJ002535 1</th>\n",
       "      <td>20435</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GQ497714 1</th>\n",
       "      <td>54690</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GQ859162 1</th>\n",
       "      <td>22743</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HM801842 1</th>\n",
       "      <td>25332</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JX088243 1</th>\n",
       "      <td>205012</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X83957 1</th>\n",
       "      <td>20881</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X90568 1</th>\n",
       "      <td>81940</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X90569 1</th>\n",
       "      <td>23887</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rna_len  a_tail_len\n",
       "HW531608 1    26993           0\n",
       "CS329402 1    49020           0\n",
       "JB406675 1    28803           0\n",
       "JB406694 1    74474           0\n",
       "JB406696 1    39731           2\n",
       "FW339979 1    20881          12\n",
       "AB537889 1    21055           7\n",
       "AF357236 1    20478           1\n",
       "AF361486 3    21112          37\n",
       "AF414442 2    66765          15\n",
       "AF435011 1    21794          18\n",
       "AF495910 1    27652           0\n",
       "AF495911 1    21779          18\n",
       "AF535142 1    27435           0\n",
       "AJ002535 1    20435           0\n",
       "GQ497714 1    54690           0\n",
       "GQ859162 1    22743           4\n",
       "HM801842 1    25332          16\n",
       "JX088243 1   205012           2\n",
       "X83957 1      20881          12\n",
       "X90568 1      81940          10\n",
       "X90569 1      23887           0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The outliers\n",
    "df[(df.rna_len >= 20000)]"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
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 },
 "nbformat": 4,
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