My average internet speed over the last year or so

Average Internet Speed.ipynb

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   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.dates\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data gathering with [speed](https://github.com/n8henrie/speed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"speed.csv\", parse_dates=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Average Download</th>\n",
       "      <th>Trimmed Average Download</th>\n",
       "      <th>Average Upload</th>\n",
       "      <th>Trimmed Average Upload</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Dropped Outliers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-06-01 22:16:00</td>\n",
       "      <td>16.81</td>\n",
       "      <td>16.79</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.86</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-02 15:51:00</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.18</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.15</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-02 16:04:00</td>\n",
       "      <td>6.14</td>\n",
       "      <td>5.90</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.11</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-06-02 17:03:00</td>\n",
       "      <td>15.98</td>\n",
       "      <td>16.04</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.71</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-06-02 18:03:00</td>\n",
       "      <td>15.91</td>\n",
       "      <td>15.93</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.68</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date  Average Download  Trimmed Average Download  \\\n",
       "0 2016-06-01 22:16:00             16.81                     16.79   \n",
       "1 2016-06-02 15:51:00              6.26                      6.18   \n",
       "2 2016-06-02 16:04:00              6.14                      5.90   \n",
       "3 2016-06-02 17:03:00             15.98                     16.04   \n",
       "4 2016-06-02 18:03:00             15.91                     15.93   \n",
       "\n",
       "   Average Upload  Trimmed Average Upload  Runs  Dropped Outliers  \n",
       "0            1.85                    1.86     5              True  \n",
       "1            1.10                    1.15     5              True  \n",
       "2            1.11                    1.11     5              True  \n",
       "3            1.69                    1.71     5              True  \n",
       "4            1.69                    1.68     5              True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `trimmed` columns drop the fasted and lowest speeds for the 5-run test and average the remaining three."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Rolling Mean'] = df[['Trimmed Average Download']].rolling(7, min_periods=3).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
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       "             * 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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use(\"seaborn\")\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "plt.xticks(rotation=-45, horizontalalignment='left')\n",
    "ax.xaxis.set_major_locator(matplotlib.dates.MonthLocator(interval=1))\n",
    "ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%Y-%m'))\n",
    "\n",
    "ax.set_title(\"Average Internet Speed\")\n",
    "\n",
    "ax.annotate(\"Centurylink (ABQ)\", xy=(\"2016-06-15\", 12), xytext=(\"2016-09\", 5), ha=\"center\",\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            )\n",
    "ax.annotate(\"Comcast (ABQ)\", xy=(\"2016-10\", 83), xytext=(\"2016-12\", 83), va=\"center\",\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            )\n",
    "ax.annotate(\"Frontier (Shiprock)\", xy=(\"2017-03\", 8), xytext=(\"2017-03\", 25), ha=\"center\",\n",
    "            arrowprops=dict(facecolor='black', shrink=0.10),\n",
    "            )\n",
    "\n",
    "plt.plot(df['Date'], df['Rolling Mean']);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

speed.csv