Analysis of internet speed measured over two days.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"6\" color=\"#0068b2\"  face=\"cambria\"> <B> Analysis of internet speed measured over two days</B></font>\n",
    "\n",
    "<font size=\"3\" color=\"#000000\"  face=\"cambria\">  Data is gathered to test my internet speed over two days from 2 June 2020 to 4 June 2020. Variables to test are download speed, upload speed, and ping. These are the independent variables for this test being measured against the experiment running time (the dependent variable).\n",
    "    \n",
    "***09/07/2020*** Graphs done - analysis to follow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib.collections import LineCollection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>clock time</th>\n",
       "      <th>running time/mins</th>\n",
       "      <th>time/days</th>\n",
       "      <th>Download speed/Mbps</th>\n",
       "      <th>Upload speed/Mbps</th>\n",
       "      <th>Ping/ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/06/2020</td>\n",
       "      <td>1900</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.57</td>\n",
       "      <td>9.59</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/06/2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>78</td>\n",
       "      <td>0.054167</td>\n",
       "      <td>30.49</td>\n",
       "      <td>9.68</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/06/2020</td>\n",
       "      <td>2136</td>\n",
       "      <td>156</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>21.41</td>\n",
       "      <td>9.53</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/06/2020</td>\n",
       "      <td>2254</td>\n",
       "      <td>234</td>\n",
       "      <td>0.162500</td>\n",
       "      <td>29.31</td>\n",
       "      <td>9.61</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>12</td>\n",
       "      <td>312</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>29.94</td>\n",
       "      <td>9.63</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>230</td>\n",
       "      <td>450</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>30.21</td>\n",
       "      <td>9.67</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>430</td>\n",
       "      <td>570</td>\n",
       "      <td>0.395833</td>\n",
       "      <td>30.55</td>\n",
       "      <td>9.71</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>700</td>\n",
       "      <td>720</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>27.40</td>\n",
       "      <td>9.64</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>900</td>\n",
       "      <td>840</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>23.92</td>\n",
       "      <td>9.65</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>1000</td>\n",
       "      <td>900</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>22.69</td>\n",
       "      <td>9.50</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>1118</td>\n",
       "      <td>978</td>\n",
       "      <td>0.679167</td>\n",
       "      <td>24.08</td>\n",
       "      <td>8.06</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>1236</td>\n",
       "      <td>1056</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>20.68</td>\n",
       "      <td>8.08</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>1354</td>\n",
       "      <td>1134</td>\n",
       "      <td>0.787500</td>\n",
       "      <td>30.56</td>\n",
       "      <td>9.64</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>1512</td>\n",
       "      <td>1212</td>\n",
       "      <td>0.841667</td>\n",
       "      <td>31.12</td>\n",
       "      <td>9.58</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>1630</td>\n",
       "      <td>1290</td>\n",
       "      <td>0.895833</td>\n",
       "      <td>15.67</td>\n",
       "      <td>3.24</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>1748</td>\n",
       "      <td>1368</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>19.91</td>\n",
       "      <td>9.74</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>1906</td>\n",
       "      <td>1446</td>\n",
       "      <td>1.004167</td>\n",
       "      <td>23.92</td>\n",
       "      <td>9.53</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>2024</td>\n",
       "      <td>1524</td>\n",
       "      <td>1.058333</td>\n",
       "      <td>20.66</td>\n",
       "      <td>9.46</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>2142</td>\n",
       "      <td>1602</td>\n",
       "      <td>1.112500</td>\n",
       "      <td>20.65</td>\n",
       "      <td>9.67</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3/06/2020</td>\n",
       "      <td>2300</td>\n",
       "      <td>1680</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>21.14</td>\n",
       "      <td>9.62</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>18</td>\n",
       "      <td>1758</td>\n",
       "      <td>1.220833</td>\n",
       "      <td>26.98</td>\n",
       "      <td>9.69</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>230</td>\n",
       "      <td>1890</td>\n",
       "      <td>1.312500</td>\n",
       "      <td>29.13</td>\n",
       "      <td>9.72</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>430</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.395833</td>\n",
       "      <td>30.61</td>\n",
       "      <td>9.67</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>700</td>\n",
       "      <td>2160</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>29.72</td>\n",
       "      <td>9.68</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>900</td>\n",
       "      <td>2280</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>23.55</td>\n",
       "      <td>9.59</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>954</td>\n",
       "      <td>2334</td>\n",
       "      <td>1.620833</td>\n",
       "      <td>22.87</td>\n",
       "      <td>9.49</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>1112</td>\n",
       "      <td>2412</td>\n",
       "      <td>1.675000</td>\n",
       "      <td>20.68</td>\n",
       "      <td>9.67</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>1230</td>\n",
       "      <td>2490</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>24.47</td>\n",
       "      <td>9.70</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>1348</td>\n",
       "      <td>2568</td>\n",
       "      <td>1.783333</td>\n",
       "      <td>27.00</td>\n",
       "      <td>9.62</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>1506</td>\n",
       "      <td>2646</td>\n",
       "      <td>1.837500</td>\n",
       "      <td>26.27</td>\n",
       "      <td>8.58</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>1624</td>\n",
       "      <td>2724</td>\n",
       "      <td>1.891667</td>\n",
       "      <td>30.04</td>\n",
       "      <td>9.60</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>1742</td>\n",
       "      <td>2802</td>\n",
       "      <td>1.945833</td>\n",
       "      <td>27.18</td>\n",
       "      <td>9.55</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4/06/2020</td>\n",
       "      <td>1900</td>\n",
       "      <td>2880</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.48</td>\n",
       "      <td>9.34</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day  clock time  running time/mins  time/days  Download speed/Mbps  \\\n",
       "0   2/06/2020        1900                  0   0.000000                20.57   \n",
       "1   2/06/2020        2018                 78   0.054167                30.49   \n",
       "2   2/06/2020        2136                156   0.108333                21.41   \n",
       "3   2/06/2020        2254                234   0.162500                29.31   \n",
       "4   3/06/2020          12                312   0.216667                29.94   \n",
       "5   3/06/2020         230                450   0.312500                30.21   \n",
       "6   3/06/2020         430                570   0.395833                30.55   \n",
       "7   3/06/2020         700                720   0.500000                27.40   \n",
       "8   3/06/2020         900                840   0.583333                23.92   \n",
       "9   3/06/2020        1000                900   0.625000                22.69   \n",
       "10  3/06/2020        1118                978   0.679167                24.08   \n",
       "11  3/06/2020        1236               1056   0.733333                20.68   \n",
       "12  3/06/2020        1354               1134   0.787500                30.56   \n",
       "13  3/06/2020        1512               1212   0.841667                31.12   \n",
       "14  3/06/2020        1630               1290   0.895833                15.67   \n",
       "15  3/06/2020        1748               1368   0.950000                19.91   \n",
       "16  3/06/2020        1906               1446   1.004167                23.92   \n",
       "17  3/06/2020        2024               1524   1.058333                20.66   \n",
       "18  3/06/2020        2142               1602   1.112500                20.65   \n",
       "19  3/06/2020        2300               1680   1.166667                21.14   \n",
       "20  4/06/2020          18               1758   1.220833                26.98   \n",
       "21  4/06/2020         230               1890   1.312500                29.13   \n",
       "22  4/06/2020         430               2010   1.395833                30.61   \n",
       "23  4/06/2020         700               2160   1.500000                29.72   \n",
       "24  4/06/2020         900               2280   1.583333                23.55   \n",
       "25  4/06/2020         954               2334   1.620833                22.87   \n",
       "26  4/06/2020        1112               2412   1.675000                20.68   \n",
       "27  4/06/2020        1230               2490   1.729167                24.47   \n",
       "28  4/06/2020        1348               2568   1.783333                27.00   \n",
       "29  4/06/2020        1506               2646   1.837500                26.27   \n",
       "30  4/06/2020        1624               2724   1.891667                30.04   \n",
       "31  4/06/2020        1742               2802   1.945833                27.18   \n",
       "32  4/06/2020        1900               2880   2.000000                25.48   \n",
       "\n",
       "    Upload speed/Mbps  Ping/ms  \n",
       "0                9.59       31  \n",
       "1                9.68       10  \n",
       "2                9.53        5  \n",
       "3                9.61       17  \n",
       "4                9.63       19  \n",
       "5                9.67       12  \n",
       "6                9.71       14  \n",
       "7                9.64       16  \n",
       "8                9.65       15  \n",
       "9                9.50       16  \n",
       "10               8.06       53  \n",
       "11               8.08       75  \n",
       "12               9.64       14  \n",
       "13               9.58       16  \n",
       "14               3.24       15  \n",
       "15               9.74       17  \n",
       "16               9.53       11  \n",
       "17               9.46       16  \n",
       "18               9.67       15  \n",
       "19               9.62       16  \n",
       "20               9.69       15  \n",
       "21               9.72       12  \n",
       "22               9.67       11  \n",
       "23               9.68       14  \n",
       "24               9.59       16  \n",
       "25               9.49       15  \n",
       "26               9.67       13  \n",
       "27               9.70       16  \n",
       "28               9.62       46  \n",
       "29               8.58       13  \n",
       "30               9.60       13  \n",
       "31               9.55       13  \n",
       "32               9.34       16  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract data from csv file to Pandas DataFrame.\n",
    "df = pd.read_csv(r'C:\\Users\\fergi\\Documents\\data entry csv.csv')\n",
    "\n",
    "# Remove NaN values.\n",
    "df = df.dropna(axis='columns')\n",
    "\n",
    "# Echo DataFrame.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['2/06/2020', '2/06/2020', '2/06/2020', '2/06/2020', '3/06/2020',\n",
       "        '3/06/2020', '3/06/2020', '3/06/2020', '3/06/2020', '3/06/2020',\n",
       "        '3/06/2020', '3/06/2020', '3/06/2020', '3/06/2020', '3/06/2020',\n",
       "        '3/06/2020', '3/06/2020', '3/06/2020', '3/06/2020', '3/06/2020',\n",
       "        '4/06/2020', '4/06/2020', '4/06/2020', '4/06/2020', '4/06/2020',\n",
       "        '4/06/2020', '4/06/2020', '4/06/2020', '4/06/2020', '4/06/2020',\n",
       "        '4/06/2020', '4/06/2020', '4/06/2020'],\n",
       "       [1900, 2018, 2136, 2254, 12, 230, 430, 700, 900, 1000, 1118, 1236,\n",
       "        1354, 1512, 1630, 1748, 1906, 2024, 2142, 2300, 18, 230, 430,\n",
       "        700, 900, 954, 1112, 1230, 1348, 1506, 1624, 1742, 1900],\n",
       "       [0, 78, 156, 234, 312, 450, 570, 720, 840, 900, 978, 1056, 1134,\n",
       "        1212, 1290, 1368, 1446, 1524, 1602, 1680, 1758, 1890, 2010, 2160,\n",
       "        2280, 2334, 2412, 2490, 2568, 2646, 2724, 2802, 2880],\n",
       "       [0.0, 0.054166667, 0.108333333, 0.1625, 0.21666666699999998,\n",
       "        0.3125, 0.39583333299999995, 0.5, 0.583333333, 0.625,\n",
       "        0.6791666670000001, 0.7333333329999999, 0.7875, 0.841666667,\n",
       "        0.895833333, 0.95, 1.004166667, 1.058333333, 1.1125, 1.166666667,\n",
       "        1.220833333, 1.3125, 1.3958333330000001, 1.5, 1.5833333330000001,\n",
       "        1.6208333330000002, 1.675, 1.7291666669999999, 1.783333333,\n",
       "        1.8375, 1.891666667, 1.9458333330000002, 2.0],\n",
       "       [20.57, 30.49, 21.41, 29.31, 29.94, 30.21, 30.55, 27.4, 23.92,\n",
       "        22.69, 24.08, 20.68, 30.56, 31.12, 15.67, 19.91, 23.92, 20.66,\n",
       "        20.65, 21.14, 26.98, 29.13, 30.61, 29.72, 23.55, 22.87, 20.68,\n",
       "        24.47, 27.0, 26.27, 30.04, 27.18, 25.48],\n",
       "       [9.59, 9.68, 9.53, 9.61, 9.63, 9.67, 9.71, 9.64, 9.65, 9.5, 8.06,\n",
       "        8.08, 9.64, 9.58, 3.24, 9.74, 9.53, 9.46, 9.67, 9.62, 9.69, 9.72,\n",
       "        9.67, 9.68, 9.59, 9.49, 9.67, 9.7, 9.62, 8.58, 9.6, 9.55, 9.34],\n",
       "       [31, 10, 5, 17, 19, 12, 14, 16, 15, 16, 53, 75, 14, 16, 15, 17,\n",
       "        11, 16, 15, 16, 15, 12, 11, 14, 16, 15, 13, 16, 46, 13, 13, 13,\n",
       "        16]], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create NumPy 2D array from Pandas DataFrame.\n",
    "arr = pd.DataFrame.to_numpy(df.T)\n",
    "\n",
    "# Echo array.\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Extract relevant 1D arrays with required data from arr and convert to data type 'float 64' to avoid errors in data mapping.\n",
    "clock_time = arr[1].astype('float64')\n",
    "time_days = arr[3].astype('float64')\n",
    "d_speed = arr[4].astype('float64')\n",
    "u_speed = arr[5].astype('float64')\n",
    "ping = arr[6].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.        , 20.57      ],\n",
       "        [ 0.05416667, 30.49      ]],\n",
       "\n",
       "       [[ 0.05416667, 30.49      ],\n",
       "        [ 0.10833333, 21.41      ]],\n",
       "\n",
       "       [[ 0.10833333, 21.41      ],\n",
       "        [ 0.1625    , 29.31      ]],\n",
       "\n",
       "       [[ 0.1625    , 29.31      ],\n",
       "        [ 0.21666667, 29.94      ]],\n",
       "\n",
       "       [[ 0.21666667, 29.94      ],\n",
       "        [ 0.3125    , 30.21      ]],\n",
       "\n",
       "       [[ 0.3125    , 30.21      ],\n",
       "        [ 0.39583333, 30.55      ]],\n",
       "\n",
       "       [[ 0.39583333, 30.55      ],\n",
       "        [ 0.5       , 27.4       ]],\n",
       "\n",
       "       [[ 0.5       , 27.4       ],\n",
       "        [ 0.58333333, 23.92      ]],\n",
       "\n",
       "       [[ 0.58333333, 23.92      ],\n",
       "        [ 0.625     , 22.69      ]],\n",
       "\n",
       "       [[ 0.625     , 22.69      ],\n",
       "        [ 0.67916667, 24.08      ]],\n",
       "\n",
       "       [[ 0.67916667, 24.08      ],\n",
       "        [ 0.73333333, 20.68      ]],\n",
       "\n",
       "       [[ 0.73333333, 20.68      ],\n",
       "        [ 0.7875    , 30.56      ]],\n",
       "\n",
       "       [[ 0.7875    , 30.56      ],\n",
       "        [ 0.84166667, 31.12      ]],\n",
       "\n",
       "       [[ 0.84166667, 31.12      ],\n",
       "        [ 0.89583333, 15.67      ]],\n",
       "\n",
       "       [[ 0.89583333, 15.67      ],\n",
       "        [ 0.95      , 19.91      ]],\n",
       "\n",
       "       [[ 0.95      , 19.91      ],\n",
       "        [ 1.00416667, 23.92      ]],\n",
       "\n",
       "       [[ 1.00416667, 23.92      ],\n",
       "        [ 1.05833333, 20.66      ]],\n",
       "\n",
       "       [[ 1.05833333, 20.66      ],\n",
       "        [ 1.1125    , 20.65      ]],\n",
       "\n",
       "       [[ 1.1125    , 20.65      ],\n",
       "        [ 1.16666667, 21.14      ]],\n",
       "\n",
       "       [[ 1.16666667, 21.14      ],\n",
       "        [ 1.22083333, 26.98      ]],\n",
       "\n",
       "       [[ 1.22083333, 26.98      ],\n",
       "        [ 1.3125    , 29.13      ]],\n",
       "\n",
       "       [[ 1.3125    , 29.13      ],\n",
       "        [ 1.39583333, 30.61      ]],\n",
       "\n",
       "       [[ 1.39583333, 30.61      ],\n",
       "        [ 1.5       , 29.72      ]],\n",
       "\n",
       "       [[ 1.5       , 29.72      ],\n",
       "        [ 1.58333333, 23.55      ]],\n",
       "\n",
       "       [[ 1.58333333, 23.55      ],\n",
       "        [ 1.62083333, 22.87      ]],\n",
       "\n",
       "       [[ 1.62083333, 22.87      ],\n",
       "        [ 1.675     , 20.68      ]],\n",
       "\n",
       "       [[ 1.675     , 20.68      ],\n",
       "        [ 1.72916667, 24.47      ]],\n",
       "\n",
       "       [[ 1.72916667, 24.47      ],\n",
       "        [ 1.78333333, 27.        ]],\n",
       "\n",
       "       [[ 1.78333333, 27.        ],\n",
       "        [ 1.8375    , 26.27      ]],\n",
       "\n",
       "       [[ 1.8375    , 26.27      ],\n",
       "        [ 1.89166667, 30.04      ]],\n",
       "\n",
       "       [[ 1.89166667, 30.04      ],\n",
       "        [ 1.94583333, 27.18      ]],\n",
       "\n",
       "       [[ 1.94583333, 27.18      ],\n",
       "        [ 2.        , 25.48      ]]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segment parameter in lineCollection(segment) requires a 3d object of shape (N, 2, 2). E.g.: \n",
    "# [ [[1,1],[1,1]] ] is (1, 2, 2). \n",
    "# [ [[1,1],[1,1]] ] , [ [[1,1],[1,1]] ] is (2, 2, 2), and so on.\n",
    "\n",
    "# A function is made here for best practice and for reuseability. Parameters 'independent' and 'dependent' represent the\n",
    "# discrete data points of the independent and dependent variables respectively.\n",
    "def segments(independent , dependent):\n",
    "    \n",
    "    array2d = []\n",
    "    list_tuples = []  \n",
    "    \n",
    "    for i in range( 0 , len(independent) ):\n",
    "        list_tuples.append( (independent[i] , dependent[i]) )\n",
    "    \n",
    "    for i in range( 1 , len(list_tuples)):\n",
    "        array2d.append( [ list_tuples[i-1] , list_tuples[i] ] )\n",
    "    \n",
    "    return np.array(array2d)\n",
    "\n",
    "# Call segment to test with independent and dependent variables time_days and d_speed respectively. Dependent variables can also\n",
    "# be u_speed or ping. \n",
    "segments(time_days , d_speed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### General procedure to create multi-coloured lines ####\n",
    "\n",
    "\n",
    "### PART 1 - Define figure and axes for plotting.\n",
    "\n",
    "# Unpack subplots tuple into fig and axs for ease of plot creation. Function syntax is plt.subplots(*Nrows* , *Mcolumns*, ...).\n",
    "# Syntax may be 'ax' or 'axs' for the creation of subplot axes. Axs means two or more subplots. Individual subplots are\n",
    "# called by using index notation - axs[0], axs[1], etc.\n",
    "# Figsize tuple is measured in inches.\n",
    "# Constrained_layer parameter avoids overlapping subplots.\n",
    "fig, axs = plt.subplots(3,1, figsize=(10,10), constrained_layout = True)\n",
    "\n",
    "\n",
    "### PART 2 - Create normalised array for colour mapping.\n",
    "\n",
    "# Data used for colour map must be normalised. In other words, the data must be adjusted into a common scale or range. E.g.:\n",
    "# the list [2,3,4,1] is normalised to [1,2,3,4] for a range of 1 to 4. This is so the colour map can assign colours to values\n",
    "# incrementally. We wish to use the 24 hour clock time for our colour map, this must be normalised as [0,...,2400].\n",
    "# Note: clock_time.min() = 12, clock_time.max() = 2300.\n",
    "norm = plt.Normalize(clock_time.min()-12,clock_time.max()+100)\n",
    "\n",
    "\n",
    "#### PART 3 - Multi-coloured line creation for plotting.\n",
    "\n",
    "## FIRST GRAPH.\n",
    "\n",
    "# Segments parameter is the discrete data of the independent and dependent variables for plotting.\n",
    "# Cmap parameter is the colour map we wish to use to represent 24 hour time on the line (refer documentation).\n",
    "# Norm parameter inserts the normalised clock_time data for the independent variable of segment (time_days) to map.\n",
    "lc = LineCollection(segments = segments(time_days , d_speed), cmap='viridis', norm=norm)\n",
    "\n",
    "# Set_array assigns colour data to the normalised clock_time array as per the parameter cmap in lc.\n",
    "lc.set_array(clock_time)\n",
    "\n",
    "# Plot mapped multicoloured line onto axes for first graph.\n",
    "line = axs[0].add_collection(lc)\n",
    "\n",
    "# Set line width.\n",
    "line.set_linewidth(3)\n",
    "\n",
    "# Set title, x and y axis labels, and x and y axis value limits.\n",
    "axs[0].set_title(r'$\\mathrm{Download\\,speed\\,measured\\,over\\,two\\,days}$')\n",
    "axs[0].set_xlabel(r'$time\\,\\mathrm{/\\,days}$')\n",
    "axs[0].set_ylabel(r'$Download\\,\\mathrm{/\\,Mbps}$')\n",
    "axs[0].set_ylim(10,35)\n",
    "axs[0].set_xlim(0,2)\n",
    "\n",
    "# Insert grid to assist in reading data from the plot. Refer to documentation for arguments.\n",
    "axs[0].grid(color = 'lightblue', linewidth = 0.7, alpha = 0.6)\n",
    "\n",
    "### SECOND GRAPH.\n",
    "lc = LineCollection(segments = segments(time_days , u_speed), cmap='viridis', norm=norm)\n",
    "lc.set_array(clock_time)\n",
    "line = axs[1].add_collection(lc)\n",
    "line.set_linewidth(3)\n",
    "\n",
    "axs[1].set_title(r'$\\mathrm{Upload\\,speed\\,measured\\,over\\,two\\,days}$')\n",
    "axs[1].set_xlabel(r'$time\\,\\mathrm{/\\,days}$')\n",
    "axs[1].set_ylabel(r'$Upload\\,\\mathrm{/\\,Mbps}$')\n",
    "axs[1].set_ylim(2,12)\n",
    "axs[1].set_xlim(0,2)\n",
    "axs[1].grid(color = 'lightblue', linewidth = 0.7, alpha = 0.6)\n",
    "\n",
    "### THIRD GRAPH.\n",
    "lc = LineCollection(segments = segments(time_days , ping), cmap='viridis', norm=norm)\n",
    "lc.set_array(clock_time)\n",
    "line = axs[2].add_collection(lc)\n",
    "line.set_linewidth(3)\n",
    "\n",
    "axs[2].set_title(r'$\\mathrm{Ping\\,measured\\,over\\,two\\,days}$')\n",
    "axs[2].set_xlabel(r'$time\\,\\mathrm{/\\,days}$')\n",
    "axs[2].set_ylabel(r'$Ping\\,\\mathrm{/\\,ms}$')\n",
    "axs[2].set_ylim(0,80)\n",
    "axs[2].set_xlim(0,2)\n",
    "axs[2].grid(color = 'lightblue', linewidth = 0.7, alpha = 0.6)\n",
    "\n",
    "\n",
    "#### PART 4 - Create side colour bar as legend for multi-coloured lines.\n",
    "\n",
    "# Colourbar needs to be mapped to multi-coloured line. Therefore, the mappable parameter in colourbar() requires the assigned \n",
    "# colour data to the normalised clock_time array.\n",
    "cbar = fig.colorbar(mappable=line, ax=axs)\n",
    "\n",
    "# Ticks and ticklabels are automatically assigned by the colourbar function. The spacing of ticks can be changed by the \n",
    "# .set_tick() method and the labels changed by the .set_ticklabels method.\n",
    "cbar.set_ticks([i for i in range(0, 2401, 300)]) #change the tick labels to [0,300,600,900,1200,1500,1800,2100,2400].\n",
    "cbar.set_ticklabels(['00:00', '03:00', '06:00', '09:00', '12:00', '15:00', '18:00', '21:00', '24:00'])\n",
    "\n",
    "# Create long axis label for colour bar.\n",
    "cbar.set_label('24 hour time')\n",
    "\n",
    "\n",
    "#### PART 5 - Display figure.\n",
    "\n",
    "# Show subplots.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>clock time</th>\n",
       "      <th>running time/mins</th>\n",
       "      <th>time/days</th>\n",
       "      <th>Download speed/Mbps</th>\n",
       "      <th>Upload speed/Mbps</th>\n",
       "      <th>Ping/ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1280.121212</td>\n",
       "      <td>1469.818182</td>\n",
       "      <td>1.020707</td>\n",
       "      <td>25.420000</td>\n",
       "      <td>9.294848</td>\n",
       "      <td>18.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>674.714373</td>\n",
       "      <td>880.519785</td>\n",
       "      <td>0.611472</td>\n",
       "      <td>4.186983</td>\n",
       "      <td>1.163580</td>\n",
       "      <td>13.742422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>900.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>21.410000</td>\n",
       "      <td>9.530000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1348.000000</td>\n",
       "      <td>1446.000000</td>\n",
       "      <td>1.004167</td>\n",
       "      <td>25.480000</td>\n",
       "      <td>9.620000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1900.000000</td>\n",
       "      <td>2280.000000</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>9.670000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2300.000000</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.120000</td>\n",
       "      <td>9.740000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        clock time  running time/mins  time/days  Download speed/Mbps  \\\n",
       "count    33.000000          33.000000  33.000000            33.000000   \n",
       "mean   1280.121212        1469.818182   1.020707            25.420000   \n",
       "std     674.714373         880.519785   0.611472             4.186983   \n",
       "min      12.000000           0.000000   0.000000            15.670000   \n",
       "25%     900.000000         840.000000   0.583333            21.410000   \n",
       "50%    1348.000000        1446.000000   1.004167            25.480000   \n",
       "75%    1900.000000        2280.000000   1.583333            29.720000   \n",
       "max    2300.000000        2880.000000   2.000000            31.120000   \n",
       "\n",
       "       Upload speed/Mbps    Ping/ms  \n",
       "count          33.000000  33.000000  \n",
       "mean            9.294848  18.666667  \n",
       "std             1.163580  13.742422  \n",
       "min             3.240000   5.000000  \n",
       "25%             9.530000  13.000000  \n",
       "50%             9.620000  15.000000  \n",
       "75%             9.670000  16.000000  \n",
       "max             9.740000  75.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,1,figsize=(10,10), constrained_layout = True)\n",
    "sns.set(style='darkgrid')\n",
    "\n",
    "axs[0].hist(d_speed, [i for i in range(14,40,2)])\n",
    "axs[0].set_xlim(14,32)\n",
    "axs[0].set_ylim(0,8)\n",
    "axs[0].set_title(r'$\\mathrm{Histogram\\,of\\,download\\,speeds}$')\n",
    "axs[0].set_xlabel(r'$Download\\,\\mathrm{/\\,Mbps}$')\n",
    "axs[0].set_ylabel(r'$N$')\n",
    "\n",
    "axs[1].hist(u_speed, [i for i in range(3,11)])\n",
    "axs[1].set_xlim(3,10)\n",
    "axs[1].set_ylim(0,30)\n",
    "axs[1].set_title(r'$\\mathrm{Histogram\\,of\\,upload\\,speeds}$')\n",
    "axs[1].set_xlabel(r'$Upload\\,\\mathrm{/\\,Mbps}$')\n",
    "axs[1].set_ylabel(r'$N$')\n",
    "\n",
    "\n",
    "axs[2].hist(ping, [i for i in range(5,81,5)])\n",
    "axs[2].set_xlim(0,80)\n",
    "axs[2].set_ylim(0,20)\n",
    "axs[2].set_title(r'$\\mathrm{Histogram\\,of\\,ping}$')\n",
    "axs[2].set_xlabel(r'$Ping\\,\\mathrm{/\\,ms}$')\n",
    "axs[2].set_ylabel(r'$N$')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}