Process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import fastplot\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "\n",
    "OUT_FILE = 'tests_windows.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ping_ms</th>\n",
       "      <th>download_mbps</th>\n",
       "      <th>upload_mbps</th>\n",
       "      <th>organization</th>\n",
       "      <th>server_ip</th>\n",
       "      <th>sponsor</th>\n",
       "      <th>city</th>\n",
       "      <th>browser</th>\n",
       "      <th>iteration</th>\n",
       "      <th>time</th>\n",
       "      <th>platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>939.89</td>\n",
       "      <td>825.69</td>\n",
       "      <td>Consorzio Top Ix - Torino E Piemonte Exchange ...</td>\n",
       "      <td>194.116.85.68</td>\n",
       "      <td>BBBell</td>\n",
       "      <td>Turin</td>\n",
       "      <td>chrome</td>\n",
       "      <td>0</td>\n",
       "      <td>1.570185e+09</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>941.87</td>\n",
       "      <td>871.97</td>\n",
       "      <td>Consorzio Top Ix - Torino E Piemonte Exchange ...</td>\n",
       "      <td>194.116.85.68</td>\n",
       "      <td>BBBell</td>\n",
       "      <td>Turin</td>\n",
       "      <td>chrome</td>\n",
       "      <td>1</td>\n",
       "      <td>1.570186e+09</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>915.75</td>\n",
       "      <td>940.98</td>\n",
       "      <td>Consorzio Top Ix - Torino E Piemonte Exchange ...</td>\n",
       "      <td>194.116.85.68</td>\n",
       "      <td>BBBell</td>\n",
       "      <td>Turin</td>\n",
       "      <td>chrome</td>\n",
       "      <td>2</td>\n",
       "      <td>1.570186e+09</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>937.63</td>\n",
       "      <td>830.52</td>\n",
       "      <td>Consorzio Top Ix - Torino E Piemonte Exchange ...</td>\n",
       "      <td>194.116.85.68</td>\n",
       "      <td>BBBell</td>\n",
       "      <td>Turin</td>\n",
       "      <td>firefox</td>\n",
       "      <td>0</td>\n",
       "      <td>1.570185e+09</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>928.69</td>\n",
       "      <td>831.93</td>\n",
       "      <td>Consorzio Top Ix - Torino E Piemonte Exchange ...</td>\n",
       "      <td>194.116.85.68</td>\n",
       "      <td>BBBell</td>\n",
       "      <td>Turin</td>\n",
       "      <td>firefox</td>\n",
       "      <td>1</td>\n",
       "      <td>1.570186e+09</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ping_ms  download_mbps  upload_mbps  \\\n",
       "0       1         939.89       825.69   \n",
       "1       0         941.87       871.97   \n",
       "2       2         915.75       940.98   \n",
       "3       1         937.63       830.52   \n",
       "4       1         928.69       831.93   \n",
       "\n",
       "                                        organization      server_ip sponsor  \\\n",
       "0  Consorzio Top Ix - Torino E Piemonte Exchange ...  194.116.85.68  BBBell   \n",
       "1  Consorzio Top Ix - Torino E Piemonte Exchange ...  194.116.85.68  BBBell   \n",
       "2  Consorzio Top Ix - Torino E Piemonte Exchange ...  194.116.85.68  BBBell   \n",
       "3  Consorzio Top Ix - Torino E Piemonte Exchange ...  194.116.85.68  BBBell   \n",
       "4  Consorzio Top Ix - Torino E Piemonte Exchange ...  194.116.85.68  BBBell   \n",
       "\n",
       "    city  browser  iteration          time platform  \n",
       "0  Turin   chrome          0  1.570185e+09  Windows  \n",
       "1  Turin   chrome          1  1.570186e+09  Windows  \n",
       "2  Turin   chrome          2  1.570186e+09  Windows  \n",
       "3  Turin  firefox          0  1.570185e+09  Windows  \n",
       "4  Turin  firefox          1  1.570186e+09  Windows  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for f in glob(\"test*/*/result.json\"):\n",
    "    j = json.load(open(f,\"r\"))\n",
    "    data.append(j)\n",
    "df = pd.DataFrame(data)    \n",
    "df[\"download_mbps\"] = df[\"download_mbps\"].astype(float)\n",
    "df[\"upload_mbps\"] = df[\"upload_mbps\"].astype(float)\n",
    "df.to_csv(OUT_FILE, index=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nc6/yJBLA0REoLASe93RVoVg2OS4OePZMdZuDA/dN5OlToOL67iYmQLt2gFwOpKUBNR0KkZ+fj4sXL2LDhg0q7SwdOnSAnZ0drl27BoBbUaz8qmcSiQReXl74+++/lWXXrl1D//79lYkAgPIOoCbHo2RAGqrYWODQIWDPTjkKdQGRUMR3SLygZFBNAQHAqlWqZePGAQcOAI8fAx4e6nUUoxonTwYuX1bd9tNPwPjxQGAgUHEqJW9v4NQpLoEEBAArV9Ys1qysLMjlcnz88cf4+OOP1bY/evQIurq6Gntc2djYqPycnJyMdu3aqe1Xvm51jkdIQ5WSAjRrBoh1i1EkBAwoGZCqzJzJLYtXnkTCvbdsyd1qVmbfPs13BgAwejTwxhuq2xRfwo2MuOPWlJmZGQQCAT799FO8/fbbatstLS2xf/9+PFVM1VhOamqqys92dnYa9ytfVp3jEdJQRUcDrVoBKC7m7gx0DfgOiReUDKrJzq7yxzUGBmWPhDTp0KHybVZW3EsTobDmj4gAwMjICG+88QZiY2PxxRdfaNynZ8+eyM7ORnh4uPJRUVZWFsLDw1Uu3j169EBAQAByc3OVj4oiIiKQnZ1do+MR0lBdugT4+QEoKkKRLiDSpTsDokXWr18PLy8vjBkzBu+++y4kEgkeP36MM2fOYMqUKfD19UW3bt0wbtw4rF27FmZmZlizZo3aDLIff/wxtm/fjsGDB2PRokWQSqVYuXIlLCwsoKOjU+3j9evXr55/A4RUz4oVXBsdiouRpw8Y6RvxHRIvqGuplurVqxf+/PNP5OXlYcqUKfDz88Pnn38OsViMtm3bQiAQ4NixY/Dw8MDMmTMxa9YsvP3222qPeezs7HDixAnk5ubinXfewVdffYWtW7dCLBarNCK/6HiENFQTJgCvvw6guBi5+kAzfZMX1tFGAsaazrJgOTk5MDU1RXZ2Nk1U9wri4+PRsWNH7N27F5MmTeI7HEJeWlYWcPQo8NZbgCQ1Grq/umGP8xJMG/0VbzHxdZ2ix0TkhZYuXYrOnTujefPmuHfvHtasWYPmzZtj5MiRfIdGyCu5exeYOhW4dQuwgAz3twDmYT58h8ULSgbkhYqLi7FkyRKkpKTA0NAQ/fr1w/r165VrQhDSWP3zD/duYwMIHpfAQQrAyJzXmPhCbQbkhTZu3IikpCQUFRVBKpXi6NGjGsceENLYfP89oKvL9drLyEvDu+8Af+ff5zssXlAyIIQ0WVIp8O673H8XF+XjqRgoEfIbE1/oMREhpMn64IOycUB2MMHZHwF8qWE6gSaAkgEhpMkqPxWMrKgARXqAWE8PAv5C4g09JiKENEkFBcBvvwGKFXcvS6NhvAyIz3/Ib2A8oWRACGmSHjwAhg0Dbt/mfi4sKQAAGBg2zTFIlAwIIU3Sw+c3AM2bc+8FxfkAAAPDpjkCmZIBIaRJ+vtvwNgYaNOG+7mw5HkyENHcRETLbN68Ga1bt4ZQKMTbb78NgUCAX3/9tdY+PzMzE8OHD4dEIoFAIFBbMpOQhuzxY265S8V8i7kleQBoojpexcfHw9fXF0ZGRrC2tsb8+fNRUFBQrbqZmZmYPXs27OzsYGBggPbt2yMgIKCOI2747ty5A39/f4wbNw4XLlzA2rVrERkZqbKy2atav349IiIisH//fkRGRsLT07PWPpuQumZqCvTsWfZzVkkuTIoAXZ2m2cmS97OWSqXw8vKCvb09goKCkJaWBn9/f2RkZODAgQNV1s3Ly4OnpycMDQ3xzTffwNraGgkJCSgpKamn6BuuO3fuAABmzJiBNor74BcoLi6Grq6uytTUVYmNjUXnzp1p0XvSKH3+uerPz+QFMJY1iO/H/GA8+/rrr5lYLGZPnz5Vlh08eJABYDExMVXWXbp0KXNycmL5+fnVOlZ2djYDwLKzs18p5oZu0qRJDIDKa+/evQwAO3LkiHI/e3t7NmfOHLZu3TrWunVrpqOjw9LS0hhjjMXExLChQ4eyZs2aMbFYzPz8/Njdu3eVdSt+fvl/SiEhIczd3Z2JRCJmY2PDZs+ezXJzc5Xb58+fz8zMzNijR4+UZZGRkUwoFLLvvvuuLn81hDDGGEtJYezCBcaKi8vKFq/szdr4C/kL6jm+rlO8J4O+ffuyoUOHqpQVFhYykUjENmzYUGVdGxsbtmbNmmofq6kkg7t377I1a9YwACw4OJhFRkayq1evakwGtra2rG/fviwkJIT99ttvLC8vjyUmJjIzMzPWp08fFhwczI4ePcp69OjB7O3tWWFhIWOMu3j37t2bde3alUVGRrLIyEjGGGPHjh1jAoGAjR49moWFhbFt27YxExMTNmDAAOVxCwoKmLOzMxswYAArLS1leXl5rG3btszHx6d+f1Gkydq5kzGBgLGcnLKyr5f3Z29ON+QvqOeabDKwsrJiixcvVit3cXFh06ZNq7TevXv3GAC2Y8cONnjwYKavr8/Mzc3Z7NmzK71TaCrJgDHGjhw5wgCw+/fvM8YYu3//vsZkYGlpyZ49e6ZSd+LEiczR0ZEVFBQoy9LS0piRkZHKN/fBgwczT09Plbpdu3ZlPXv2VCn7+eefGQAWERGhLLt+/TrT09Nj33zzDZs1axYzNzdn//777yueNSHV89ZbjFX4p8vYggWMdejARzgq+LpO8d5mkJWVpbbUIgBIJBJkZmZWWi8lJQUAsGjRIowaNQphYWGIiYnB0qVLUVxcjN27d1daNyoqSjn9sp2dHeyqsdBwcm4ykvOSq9yntWlrWIotkZ6fjofZD9HNjlsYOS49Ds9KnlVZ19XaFfpCfdzLugd5qRztLNpBXipH2rM02Jm8xELI1dSvXz+IxWKVstOnT+Pdd9+Frq4uZDIZAO7v0aVLF1y7dq3Sz8rLy0NUVBTWr1+vUj5q1ChMnDgRFy5cUC5/6eHhgRUrVmDRokUoLi7G4cOH0VzR4ZuQOnb7NjB8eIXC4mJAX5+XeBoC3pMBAAgE6jOBMMY0liuUlpYCAJydnfHDDz8AAAYMGICSkhIsWrQIq1evhq2trca65Xu9fPbZZ1i5cuULYwy4EYBV51dVuc/ut3ZjerfpOHrnKGYcnwH2GbeI3ORjk3H58eUq6z76+BFaNmuJxb8vRk5RDk6NP4VnJc8QcCMAK/u9OL6XZW1trVaWnp6OLVu2YMuWLWrbDA0NK/0sqVQKxpja711XVxcWFhZqyf3dd9/FZ599hubNm2O42v+ZhNSN5GTg3j3AxUW1fLz4BArfSEftdb5uXHhPBhKJBFlZWWrlUqkUzs7OldYzN+cWoKjYVdLLywulpaWIjY2tNBmcP39e5c6gOmZ6zMTQDlX3mmlt2hoA8HbHt5V3BQCwb9i+F94ZWBtxF+W1A9dCXioHABjpGWGmx8xqxfeyNCVcc3NzDB48GLNnz1bbZmJS+ehMMzMzCAQCpKamqpTLZDJkZGQo/2YAl8ynTZuGDh06ICkpCatXr8bnFbt3EFIHsrMBT0+gVy/V8tG59ihJa7q9iXhPBs7OzoiNjVUpKyoqQmJiIqZOnVppPScnJ+hruKVjz5d0rqp7pLu7e43XFrUzsav24xpLsSUsxZbKnztYdqj2cdpIyrqBCnWEdfqIqDIDBw5EdHQ0unbtCqGw+pO7Gxsbw93dHYGBgfD391eWBwUFQSaT4T//+Y+ybMOGDbhy5QquX7+OiIgILFiwAEOGDEHP8h2/CakDHTsC586plw/NtgWyZPUeT0PBexr08/PD2bNnkaGYOhBASEgIioqK4OfnV2k9fX19DBo0CGfPnlUpP3v2LHR1deFS8R6QVNuqVauQkJAAHx8fBAYG4vz58zh8+DBmz56NQ4cOVVl35cqVuHr1KsaOHYuTJ09i+/bteP/99zFgwABle8E///yDFStW4PPPP4ebmxvmzZsHT09PTJgwodqDDQl5WefPAwkJ6uVn9B7hlqSw/gNqKKrb0nz48GGWmZlZ6y3YWVlZrEWLFqx3797s5MmT7Mcff2SWlpZs3LhxKvtNnTqVCYWqfYCvXLnC9PT02IQJE9ipU6fY5s2bmVgsZh999JHGY1FvIs3jDDSJj49no0ePZhYWFkwkEjEHBwc2ceJEFh0drdxHU28ixhgLDg5m7u7uTF9fn1lbW6uMMygqKmKdO3dmvXr1YnK5XFnn0aNHzMzMjM2dO7cWzp6Qyjk5MbZokXp5l0+asdmz7es9nooafNdSHR0dduXKFeXPcrmctWvXjt2+ffuVg4iLi2Pe3t5MLBYzS0tLNm/ePLXuoYqBVBWdPn2aeXh4MH19fWZnZ8cWL17MisuPJCmnKSUDQohmxsaMaRrC1H6xmC2Y067+A6qgwXctZc+fxZf/+e7duygsfPXbqvbt2+PUqVNV7rNv3z7s27dPrXzQoEEYNGjQK8dACNF++flAXh5gY6O+rUAgh6FAr/6DaiB4bzMghJD6kpbGvWvoUY0CHTkMdUT1G1ADQsmAENJk5OcD7duXLWhTXoFOKQx1aNBZtRw6dAh//vknAK6fuEAgwMGDB3GuQj8tgUCAjz/+uNaCJISQ2uDiAsTFqZczxlAgLG3SdwYCVrExoBLVndYY4JKBXC5/6aDqSk5ODkxNTZGdnV3jcQaEkMavtLRsMZvyimRFMPjSAPueeWPSuqrbL+saX9epat8ZKKZ/IISQxsrNDTAyAq5eVS1XzBBgpFv5dCvajtoMCCFNRno6oGkGGh2BDhb8YwIX3fof8d9QvNR0FL///jsiIyORkpICgUAAW1tb9OrVq1aXVCSEkNokkwFPnwJDhqhvMzMww4Y/xYB7i/oPrIGoUTKIjo7G6NGjERcXpzbuQCAQwMXFBYGBgVVOMEcIIXxITQUY09yTKDUvFYkW+eipJ+R/wjaeVPsxUWZmJry9vSGVSrFt2zYkJCSgsLAQBQUFSEhIwLfffov09HR4e3trnIWUEEL4lJkJmJtrTgYn7p5A79G5kOvToLMX2r59O4qLi3H16lV88MEHyllDRSIRnJycMGfOHFy5cgUFBQXYuXNnXcZMCCE15uYGZGQAXbuqbxvpPBIxO3UhEonVNzYR1U4GJ0+exKxZs9CyZctK92ndujVmzZqF0NDQWgmOEELqg4meEZxTZE16pbNqJ4M7d+6gV8XVIDTo1asX4jSN6iCEEB6tWQMMGKB520+39mG5FwBR0x10Vu1kkJ2drbJSVWXMzc2RnZ39SkERQkhtS0jgpqPQ5ELSBZx2At0ZVIdcLq/WqlcNdfQxIaRpe/JEc+MxAOQUStGsCE06GdSoF9WCBQtgZmZW5T5SqfSVAiKEkLrw5AnwfLE9NdmF2ZQMqrtj3759IRAIkJubW+V+QqEQffv2feXACCGkNv37L9CikjFlOUU5sKFkUD0VZyYlhJDGJDS0isdERbkwLUSTTgbVbjO4d+8enJycquw2GhoaCicnJ+pNRAhpcN54A7C317wtpyS3yT8mqnYy2LhxIxwdHTF48OBK9xk8eDDatWuHjRs31kpwhBBSG+7cARYuBCqbHCGnJI+SQXV3DA0NxeTJk1+434QJE3DmzJlXiYkQQmrVX38BGzdqXsuAMYackmdcMqBxBi+WnJwMBweHF+5nb2+PJ0+evEpMhBBSq5KTAbEY0LRWDAPD+ZbL8VY86M6gOkxMTJCmWE26CmlpaTAxMalREPHx8fD19YWRkRGsra0xf/58FBQU1OgzQkJCIBAI4OrqWqN6hBDtFx8PODgAAoH6Nh2BDvrotkHzXFTeqNAEVDsZ9OjRA4cOHXrhfocOHUKPHj2qHYBUKoWXlxdyc3MRFBSEDRs24ODBg5gxY0a1P6OgoAD+/v6wsbGpdh1CSNNx7RrQvbvmbSl5Kfgk7QAeSnSa9J1BtbuWzps3D0OGDMGKFSuwcuVKtdHIcrkcq1atQnBwMP73v/9VO4CAgABkZWUhKioKlpaWXFC6uhg3bhyWLVtWrbURvvrqK7Ru3RqOjo64fv16tY9NCGka3noLqGxqtZyiHAQX3MJ4MwO01nTr0EQIWMVVaqqwfPlyrFmzBra2thg4cCBatWoFgUCAhw8f4vfff0dqaio+/fRTrF69utoBeHp6wszMDMeOHVOWFRUVwdTUFF9++SUWLIf4w0YAACAASURBVFhQZf3ExER07twZly5dwubNm3H9+nVER0dr3JevhaYJIQ3c558DO3ZwjQs84+s6VaPpKL744gv06dMHGzZswK+//orCwkIAgIGBAfr06YO9e/fCx8enRgHExsZi6tSpKmWKNRJiY2NfWH/+/PmYOHEiunTpUqPjEkKahrw84OpV7jFRpdfW/HyuhbkJq/EKb76+vvD19YVcLkdGRgYYY7C0tKzWJHaaZGVlaZzvSCKRIDMzs8q6x48fx6VLlxAfH1+jY0ZFRcHY2BgAYGdnBztNK2QTQrRCfDw3dfX164CHh/r2jZc2Yrv+DiSKW9d/cA1ItRuQKxIKhbC2toaNjc1LJwIFgYbndIwxjeUKhYWF+Oijj7Bq1SplW0N1eXp6wsPDAx4eHggICKhxvISQxkPxnbKyGfgzCzIhZ6WAkVH9BdUA8b72s0Qi0bhmslQqrbLxeMuWLdDR0cHYsWOVM6UWFxejtLQUUqkUYrEY+pX0DDh//rzKnQEhRHtlZHDvFhaat+cU5aCZTEiPifgOwNnZWa1toKioCImJiWptCeXduXMHd+/ehZWVldo2iUSCHTt2YNasWRrruru7UwMyIU1ERgagqwtUNvwppzgHppQM+E8Gfn5+WL16NTIyMmDxPHWHhISgqKgIfn5+ldZbsmSJ2vQYX3/9NeLi4rB37160b9++LsMmhDQSOjpAp06aB5wBz9cyKBY0+cdENepaWhekUilcXV3h4OCAFStWIC0tDf7+/vDx8cGBAweU+02bNg379++HTCar9LMmT55MXUsJITVi8IUBxj5shr0lg4G9e/kOh7fr1Es3INcWMzMzhIeHw8jICCNGjIC/vz/Gjh2L3bt3q+wnl8tpOU1CSK2SlcpQJC9C8xxU/hypieD9zqA+0Z0BIU3P8OGAtTWgqePgvzn/ouXmlvjfaSsM7v8+8MUX9R9gBU32zoAQQurSrVuARKJ5W15xHrrYdEGb5ELA1LR+A2tgKBkQQrRWfj6QlAR06KB5ewfLDoiaeQvOSc+qGJ7cNPDem4gQQupKQgL33rGj5u0FJQUwKCiBoLSUkgHfARBCSF1RJIPK7gzGh4xHcU4WjgNAE58Cn5IBIURrjRgBRERUPhXFnB5zUPr3XwAiAFvbeo2toaFkQAjRWjo6QL9+lW/3cvQCbj6fDqeJ3xlQAzIhRGstXAjs26d5W3p+Oj4++TEeZ9znCqg3ESGEaKeQEKCyZVFuJt/ElitbkJLzhJuKQrdpPyihZEAI0UoXLwL37gFt2qhvK5YX472g9wAAnfOMmvxdAUDJgBCipa5d497Hj1ff9v3N75FRkIGRziOhn0NjDABKBoQQLZWZCbRooT4ZqaxUhtlhswEAh985DGRn050BKBkQQrTU3LnAyZPq5RH3IwAA3/h+A6GOEMjKAjQsvdvUNO0WE0KI1rK25l4VRT6OBACMcxvHFSQnVz5EuQmhOwNCiFaaMgU4e1a93NbYFmM6jYGF+Pk6mDk5dGcASgaEEC309Ck3vkCx/nF573u8j1/e+aWsICeHGpBByYAQooUePuTenZxUy2WlMoTfD4fKMi7Z2ZQMQMmAEKKFjh/n3lu0UC0PSwjDgB8H4MDfz5fUTUsDcnOpNxGoAZkQooWuX+dWsazYgDy0w1DEzY1DO/N2XMGxY9x7t271G2ADRMmAEKJ19uwBHjzgJqpTeJL7BJceXcLgdoMhEAi4wkuXAH19SgagZEAI0UK2tqozUqc9S0OLTdwzo8xPMmGoZ8ht+PdfwMuLhwgbngbRZhAfHw9fX18YGRnB2toa8+fPR0FBQZV1cnJysHLlSrz22mswMzODlZUVfH19cfPmzXqKmhDSEH3xBbBmjWrZkdtHAAAfv/4xJIblFkSOiwPc3esxuoaL92QglUrh5eWF3NxcBAUFYcOGDTh48CBmzJhRZb2HDx8iICAAAwcOxOHDh7F3717I5XL06tWLEgIhTVRREbB1K/D336rliVmJcJI4YZPPprLCggKu21H79vUbZAPF+2OigIAAZGVlISoqCpaWlgAAXV1djBs3DsuWLYOzs7PGeo6OjkhMTIRYLFaWDRw4EG3atMHWrVuxd+/eeomfENJw7NnDdRCaOLGsTFYqw7ar2/B2x7dVdw4O5t47daq/ABsw3u8MwsLCMHDgQGUiAICRI0dCJBIhLCys0npGRkYqiQAADAwM4OzsjCdPntRZvISQhokxYMcObqlLP7+y8hMJJ1BSWoL+Dv1VKygWSO7Ro/6CbMB4TwaxsbFq3/5FIhGcnJwQW9mqFJV49uwZbt26VendBCFEe92/DyQlAR98oFp+8i43W93M7jNVNyQkAH36AIqeRU0c74+JsrKyYKZhXhCJRILMzMwafdby5cuRn5+PuXPnVrlfVFQUjI2NAQB2dnaws7Or0XEIIQ1PmzbAkyfqU1bnFOeghUkL6AgqfPdNSABcXesvwAaO9zsDAGV9fsthjGksr8zPP/+MLVu2YNOmTWjbtm2V+3p6esLDwwMeHh4ICAiocbyEkIaFMeDOHUBPT3VsAQB8P/R7RE6LVK+QkAC0a1d/QTZwvN8ZSCQSZGVlqZVLpdJqP+45c+YMpkyZgkWLFmH27Nkv3P/8+fMqdwaEkMbt0iXuic/p08CgQWXl0kIpdHV00cq0lWqFjAxAKgVe8MWxKeH9zsDZ2VmtbaCoqAiJiYnVSgZXr17FiBEjMGrUKKxdu7Zax3R3d0e3bt3QrVs3SgaENHJZWVwicHICBgxQ3bY5cjPab9XQdXT9eu6d7gyUeE8Gfn5+OHv2LDLKzTUbEhKCoqIi+JXvEqBBbGws/Pz80Lt3b+zdu7dGj5UIIdph4ULufd069UdE4zuPx95hFbqZHz0KBAUBdnZA5871E2QjIGAqc7nWP6lUCldXVzg4OGDFihVIS0uDv78/fHx8cODAAeV+06ZNw/79+yGTyQAAaWlp6N69O0pKSnDgwAEYlWs1EolE6Nq1q9qxcnJyYGpqiuzsbDSjKWsJafRSUrhr+vTpwO7dqtt+i/sNThIndLIuN47g2jWgZ0/uvw8fBkaPrr9gq4mv6xTvbQZmZmYIDw/HvHnzMGLECIjFYowdO1btkY9cLodcLlf+HBMTg0ePHgHgBpuVZ29vjwcPHtR57IQQftnacgvZVBhyhBJ5CYb9MgzzX5uPLb5byjb88Qf3/uQJl0WIEu93BvWJ7gwI0R5ZWdwXfW9v9W3f3/we049Pxy8jf8EY1zFlG95+m5ur4t69+gu0hvi6TvHeZkAIITUllwPTpgE+PkBEhOq2zIJMHIo+BAAY1nFY2YasLODECaBly3qMtPHg/TERIYTU1PLlQEgI98i/f7lZJpKkSXD4xgEAMKbTGBjoGpRtnDgRKC7mGhiIGkoGhJBGRS7neob27Qvs2qW6be1Frq1xw6ANeN/jfdWNly9zkxaVn8WOKFEyIIQ0aKWlQHQ0UFICiERAq1bA+PHc4/+KSxfHZ8TD3tQeC3otUN2wbx+Qng5MnVpvcTc2lAwIIQ1WWhp30Y98PptE+/bcejQ//KA+pgAAFvVaVLaKmcLVq8CUKVyFvn3rPuhGipIBIaTB2rWLSwSTJgEffcTdGQCaE4G8VI4BbQZAV6fcZS0zs2wswb17gJVV3QfdSFEyIIQ0KE+fAhcvAr6+3NoEnTsDQ4e+uJ7Ldhc4mjkieEwwxHrPBx6sXcvNa71gAWBvX7eBN3I0zoAQ0mAkJABduwLPngGvvcbdFbxolpnw++Ho1aoXMgsy8UfSH3jX9V1uA2OAtTXQrBmQmFj3wdcSGmdACGnylizhEsGhQ8CZMy9OBIG3AzHgxwEY9NMgNDdpXpYIAODbb7lG43feqdugtQTdGRBCGgTGAImEeyykmDWiKvJSOfRW64GBIXVhKqyNrFV3aNeOm7xIKgWEwroJug7QnQEhpEk6fZobSQxwHX9++aV69eIz4sHAcGjkIfVEIJeXtRU0okTAJ2pAJoTworQUyMkBxo7legcJBFzX0eqIz4jHyMCRAADftr7qO1y7xg1M6N27FiPWbpQMCCH1LikJcHMDcnO5n8PDq1/36bOn6LCtAwCgr31fmBlUWEP9ypWyJODuXgvRNg2UDAgh9erJE24UcUAAN3dcp06Ap2f1699JvwMAODLqCEY4j1DdyBjw4Yfcbcfu3TSuoAYoGRBC6kV+Prdoff/+wOPH3OOhmsjIz8DP//yMEc4jcHv2bbhYuagfYMQIruFh1iyakK6GqAGZEFKn5HJg6VLAyAjw8ODaCZKTa/45qc9SseD0AuQV56kngvh47gCnTnELIm/bVjvBNyGUDAghtU4xf1BhIZcMDhwAHB2BnTuBmJjqNxQDQMT9CPge8IWLlQtCxoSggyXXXoDLl7m5rJcv5xogAG5swYUL1IPoJdBjIkJIrZDLgVWruGvxuXNcWUgIcPw48HyF2hphjOG7a99h3ol5yrLBFq9zo4lTU8saiR0cgObNgZkzgXnzNH8YeSFKBoSQlxYYyF38J04EzM2B0FAgIwOYMIGbWK5z55p9nqxUhv1R+/Gs5BmWhS9DXnEeACB8YjjXJtC6Nfeu8McfwH/+U4tn1HRRMiCkiZPLgYMHuWtsnz6Aqyv3CL5id08bG2D4cO6/d+7kHsvfvg3o6wNvvgn06AEEB3PrzOvrv1wsR+8cxfTj02Eg5FYom+L8HrZ5ruUmnjtxggty+3bA2Zmbd8jF5QWfSKqNNQBxcXHMx8eHicViZmVlxT788EOWn59frbr79u1jHTp0YCKRiHXq1IkFBgZWum92djYDwLKzs2srdEIalKIixi5eZOzhQ+7n9HTGLlwoe02axNi+fdy206cZc3NjzMyMMa5PJmPffcdtO3CAMaFQ9dW7d9lxhELG9PUZmzaNsZycV49bJpexf+//zc7MHMh8xoPl6aEsqIqval4bGiu+rlO83xlIpVJ4eXnB3t4eQUFBSEtLg7+/PzIyMnDgwIEq6/7666+YPHkylixZAm9vbxw9ehRjxoyBqakpvL296+kMCOFHbi5gYsL999OnXDvqmTPA/fvAF18Ay5YBly6pT//cpg33bmUF9OvH/Xf79sDcuWX7jBvHvSojk9Ug0HLTnxXKCrH6j9XKxz//af0fvOPyDjZfWIfgQ//FpQAZXm9uCaPDuzR/lpMTYGioeRt5JbxPVLd27Vp8/vnnSEpKgqWlJQDg559/xrhx4xATEwNnZ+dK6zo7O8PNzQ2BgYHKMh8fH2RnZ+Py5ctq+8fHx6NDhw6Ii4tD+5p0Z2iEkpOTERAQgJkzZ8LOzo7vcOpcdc43Ohq4eZN7vl1aCnz1lfo+c+YAZmbA0aPcI5Dy+vblHk/fvQscPqy6zdwc+OAD7r83buR60ZQ3ZQrXxnn6NDdTQnndu3Nz8zx5Auzdq7rNwICbXgfgHs1kZAC5ubn49ddUZGc74ulTrteMhQW3jkv37sCgQVwdCwsgLw/499+yzxMKuWSgaXGYV5KWxmUeAN+lh8HTqBNcDe3hcmMq7gizlLux57OQtswGzAqBsdHApxeA21bAzu7AVs+vgU8+UZmutKn9W+brOsV7MvD09ISZmRmOHTumLCsqKoKpqSm+/PJLLFiwQGO9+/fvo02bNggODsZwxYNMAPv378eUKVOQlpamTC4Kf/zxBzw9PXH+/Hn01fLl727evAkPDw/cuHED3bp1q9XPzsnheoso/uW4u3PrhkREAJMnq35r3LaNe878ww/AihWqn9O/P9flMDtb86Pf6GhuFsuJE4GzZ1W3rVwJzJgB/PYbdxEuKSnG06dPYWVlBQ8PfZw4we1nb18Wz5MnZcsmyuXcxbmiq1e5OrNmcT1hylu8GPD3B37/Xf1bc5s2ZUszdujATZRZ3okTQLdu3Lf33btVt02fDnz5JXDrFregS3mmptzze4DrPHP3LiCTlSAzMxN9+4pw/ryZ8vONjV+tLTUhIwH3pfcxqM0gCAQCXHl8BUb6RnC1dkVecR4uPbqkVicsIQwnbwaCpSRjXwjwxmPA6FPgm5PA9JvAERdA2kwfeLdsamlroQmGGXZVD8DKSuMqNnX5b7kh4us6xftjotjYWEytsEi1SCSCk5MTYmNjq6wHQO3OwcXFBYwx3LlzB3369Kn9gF+CTAY8eKBaZmwM2Npyc2klJanXaduWe3/8WP1bpmK9juxs7vGAQnExdxctEgFnz5oBOIXZs9tCMQvuG29wXf+k0rKVAMsLCeHG7SxdCty4obpt7lzu/9Pff+cGeSrmlAGA77/n1hmPi+MGEy1dWtbNW3Eerq5cz7/ynJy4d5FIfZuiHOAaJxWfo6DoVu7kxNV98iQdAQEBGDFiFjw8yq7yM2ZwdwEKo0Zx70Ih1zuxMjt3ci9NBg5UrZuSl4KCkgLcf/4F+GS5m9L8kny0NW8Lka4IgbcDca/rUaSm/szF9tsMJOcl4y8AQ7gi9NiseqyQMSEA9LD096UYvt4CC3stRETIIbzz6yTouLlhyFYjlf3XRj3//Ri3wVdt3kdJqQzDo5fDv9VoeEm64X/pl7DzyW8azys0kwu81DMCEAjw8c056ChujR86Lsaj+9fgk7RIYz1dOfDRHcBy0WfAuHl4BgD+3LZRAPePVU9P8y+TNBi8J4OsrCyYmZmplUskEmRmZlZZD4BaXYlEAgAa6ypugqKiopRltra2sLW1rXngNbB8ObB1q2rZ6NHcN8R797iVnSrKzubehw8Hrl9X3RYQwH3R2rMHWLhQdVvnzty3drk8G8ATGBpmwej59UIo5L7V5+ZCWVZebi73jVlXV327TMbVLSkBBgwAvLyAIUO4bUZG3LYRI7iXgYFq3ZwcoGNH7lVRTg73/tFH6tsUxxw8WH2bom6rVlzdqKi7CAhYjffeGwh3d2Pl5374YeXHrA0xT2Pwxp43qtynjaQNbs26hcJnhSgtLEXO8wDkhXKwwqpvzHNycqAn1IOsQIYSlCAnJweWCxbi9W4Arv+DymrLnl5Gzh8/o0QAsBFAwbXLyHkIFLYBmIZ/bwDgB+D9G0DOyv4QAPhRDOiW3kZO4QlYC4DbxuAyr6Kh4jkrkQlE294FbG2h8VdbUMC9XlJeXp7yPac2/3gN1LNnzwCUXa/qTb02V2ugq6vLvv76a7XyXr16sREjRlRa78CBAwwAS0lJUSmPj49nANhvv/2mVufGjRsMAL3oRS96NfjXjRs3Xv0CWwO83xlIJBLlt/zypFJplY3HijuArKws2NjYqNQrv708d3d33LhxA7m5uRA8b6CqjzsDQgipLsYYcnNz0VxTo1Yd4j0ZODs7q7UNFBUVITExUa0toWI9gGs76Fju+UNMTAwEAoFKmYKOjk6TaIAihDRupqam9X5M3ieq8/Pzw9mzZ5GRkaEsCwkJQVFREfz8/Cqt5+joiI4dO+JwhT5+hw4dQs+ePdV6EhFCCKkc711LpVIpXF1d4eDggBUrVigHnfn4+KgMOps2bRr2798PWbl+i0eOHMGYMWOwdOlSDBo0CMeOHcM333yDkydP0qAzQgipiXptoahEXFwc8/b2ZmKxmFlaWrJ58+apTUcxadIkVj7cvXv3amx0GTZsmEq96kxXUVxczJYsWcJsbW2ZoaEh69evH/vrr7/q5mRf0Z49e1jnzp2ZSCRiVlZW7K233lLZri3n6+npWWnD2qFDh5T7acv5hoSEsJ49ezITExNmbW3Nhg8fzu7cuaO2nzac7/Hjx1nXrl2Zvr4+a9myJfvvf//LZDKZ2n6hoaHM3d2diUQi5uTkxL5TzJVRwfr165m9vT0TiUSse/fuLCIioo7PoHIJCQls5syZrEuXLkwoFLJOnTpp3K82/47Jycls9OjRzMTEhJmamrIJEyawjIyMGsfeIJLBy1Akg5MnT7LIyEjl66FiUhbG2JEjRxgAtmTJEhYeHs4+/PBDJhAI2KlTp1Q+a86cOczExITt2rWLnT59mg0cOJBZWlqy5OTk+j6tKn322WesWbNmbO3atezcuXMsODiYvf/++8rt2nS+t2/fVvm7RkZGsjFjxjBdXV329OlTxpj2nO+ZM2eYQCBgEyZMYKdPn2aBgYHMxcWFtWzZUmV+Gm0438jISKajo8Pee+89dvLkSbZp0yZmaGjIFixYoLLfpUuXmK6uLps6dSoLDw9nq1evZjo6Omz37t0q+61fv57p6emx9evXs7Nnz7J3332XGRgYsL///rs+T0vp6NGjrGXLlmzkyJHMzc1NYzKozb9jSUkJ69KlC3N2dmZHjx5lR44cYY6OjqxPnz6stLS0RrE3+mSguDBo0rFjRzZq1CiVMm9vb/baa68pf378+DETCoUq3zpycnKYhYUFW7x4ce0H/pJiYmKYUChU+wdTnjadryaOjo7Mz89P+bO2nO+0adOYg4ODyv+8V65cYQBYWFiYskwbztfHx4d169ZNpUxxQS/fTdzX15f17NlTZb8ZM2YwOzs7JpfLGWOMFRYWMlNTU7Zo0SLlPjKZjDk7O7MxY8bU4VlUThEbY9zTDE3JoDb/jr/88gsDwKKjo5VlFy9eZADYiRMnahS71iaDe/fuMQAsODhYpXzfvn1MIBAo6/3www8MgNpt1eTJk5mrq2vdBP8SPvnkE9auXbtKt2vb+Vak+Ad+8OBBxph2ne/EiROZm5ubSllcXBwDwEJDQxlj2nO+1tbWbOnSpSpl//zzDwPA9u/fzxjjLvL6+vps06ZNKvudO3eOAWDXr19njDEWHh7OALCbN2+q7Ldy5UpmYmJS42/GtU1TMqjtv+PEiRNZ586d1Y7t4ODA5s6dW6N4ee9N9Ko6deoEoVCINm3a4KuvvoJcLgdQvekqFPvZ2NjA3Nxcbb+4uDiUlp/LgEeXL1+Gm5sbVq9eDWtra+jr68PT01M5mlrbzrein3/+GWKxGMOGDQOgXec7bdo0xMbGYuvWrZBKpXjw4AEWLlwIZ2dnDBgwAID2nG9hYSH0Kyx2IHo+74jiHBMTE1FcXKzxXMvvp3iv2I3cxcUFubm5+Lf8DH0NRG3/HWNjYzWOx3JxcalyOh9NGm0ysLOzw6pVq/Djjz/ixIkT8PPzw/LlyzF//nwA1Z+uoqrpMEpKSpRD4fmWkpKC06dP4+DBg9i5cyeCg4ORn5+PQYMGQSqVat35lieTyXDkyBEMGzYMRs/nydCm8+3bty9CQkKwbNkySCQSODo6IjExEadPn1ZeKLXlfNu3b4+rV6+qlClmGC5/DkD1zlUkEsGwwpTWVU1Jw7fa/ju+7HQ+mvA+6Oxl+fj4wMfHR/mzt7c3DA0NsXnzZixbtkxZLig3FS4A5Xwf5csr7lPZfnwqLS1FXl4egoKC0KlTJwCAh4cHHB0dsWvXLrRo0QKA9pxveWfOnEFaWhree+89tW3acL6XLl3C+PHjMXXqVAwdOhTZ2dlYs2YN3nzzTVy8eBHNFDMNovGf75w5czBlyhR88803mDBhAmJiYrBs2TIIhULoVJhXu7JYG8u5VqU2/46V7VfT82+0dwaajB49GnK5HFFRUSrTVZRXcbqKqqbD0NPTU34T5Zu5uTlsbGyUiQDg7o46duyI27dva935lvfzzz/DwsJCJflr0/l++OGH8PLywpYtW+Dl5YXhw4cjLCwM8fHx2LNnDwDtOd9JkybB398fCxcuhIWFBQYMGIBZs2bB3NxcOS1MZeeq+Ln8uRYWFqKwwrS+VU1Jw7fa/jtWtV9Nz1+rkgErN36u/HQV5VWcrsLZ2RlpaWlqt1QxMTHo0KGD2rcVvlQ2TxNjDDo6Olp3vgoFBQU4duwYRo0aBb1y0yBr0/nGxMTA3d1dpczKygrNmzdHYmIiAO05X4FAgI0bNyI9PR1//fUXUlNTMWPGDDx9+hSvv/46AMDJyQn6+voazxUo+11U9TsxMTFR3i03JLX9d9Q0nY9iv6rmdtOoRs3NDZy/vz8TCoXKfrgdO3ZU62Lm4+OjsQvXjh07lGW5ubkNpiuegqJv8j///KMse/z4MdPX12ebN29mjGnX+Soous798ccfatu05Xw7duzIhg4dqlKWnJzM9PT02Lp161T204bzrWjFihXMwcFBZeCZr68ve/3111X2mzlzpsaupeXPSyaTMRcXF966lpZXVdfS2vo7/vLLL0wgELCYmBhlWWRkZNPqWurt7c3Wrl3LQkNDWWhoKJs5cyYTCATso48+Uu4TGBjIBAIB+/TTT1lERAT76KOPKh3c0axZM7Z79252+vRp5u3tzSwsLBrEIB0FmUzGunXrxtq1a8cOHz7MQkJCWNeuXVmLFi1YXl4eY0y7zldh6NChrHXr1hq7CWrL+W7dupUBYHPmzFEOOnN3d2cSiYQ9efJEuZ82nO+VK1fYunXr2OnTp9mxY8fYtGnTmL6+Pjt79qzKfopBZ9OnT2cRERHsiy++qHLQ2YYNG1h4eDh77733eB109uzZM3bkyBF25MgR1q9fP9aqVSvlz2lpaYyx2v07Kgadubi4sGPHjrGgoCDWpk2bpjXo7MMPP2Tt2rVjhoaGTCQSMTc3N/bNN9+o/QL27dvH2rdvz/T19ZmLi4vGYd9FRUVs8eLFzMbGhhkYGDBPT08WFRVVX6dSbampqey9995jpqamTCwWszfffFNtygJtOt/MzEymr6/PPvnkk0r30YbzLS0tZQEBAaxLly7MyMiI2djYsLfeekvjBa2xn++tW7fYa6+9xoyNjZmxsTEbMGAAu3TpksZ9Q0NDWZcuXZi+vj5r06YN27Ztm9o+paWlbN26dax169bK6SjCw8Pr+jQqdf/+/UqnUSk/TUZt/h2fPHnCRo0axUxMTFizZs3Y+PHjWXp6eo1j532iOkIIIfzjv/WMEEII7ygZEEIIoWRACCGEkgEhhBBQMiCEEAJKBoQQQkDJgBBCCCgZEEIIASUDQgghoGRACCEElAwIKfG+mwAAAA9JREFUIYSAkgEhhBAA/wfvCdHqnEe88AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 400x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = df.groupby(\"browser\")[\"download_mbps\"].apply(list).reset_index().values\n",
    "fastplot.plot(grouped, None, mode='CDF_multi', legend=True).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = df.groupby(\"browser\")[\"upload_mbps\"].apply(list).reset_index().values\n",
    "fastplot.plot(grouped, None, mode='CDF_multi', legend=True).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}