results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate visual diagrams using results.csv\n",
    "\n",
    "> ⚠️ A `results.csv` file must be present! Run the performance test(s) to generate this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Load results into a dataframe and specify the columns we care about.\"\"\"\n",
    "\n",
    "# Replace results.csv with your results file\n",
    "df = pd.read_csv(\"results.csv\")\n",
    "COLUMNS = [\n",
    "    \"NAME\",\n",
    "    \"QUALITI SCRIPTS DURATION\",\n",
    "    \"FIRST CONTENTFUL PAINT\",\n",
    "    \"PAGE LOAD TIME\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>NAME</th>\n",
       "      <th>QUALITI SCRIPTS DURATION</th>\n",
       "      <th>TOTAL SCRIPTS DURATION</th>\n",
       "      <th>FIRST CONTENTFUL PAINT</th>\n",
       "      <th>PAGE LOAD TIME</th>\n",
       "      <th>TEST ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test_web_performance_metrics[1-10]</td>\n",
       "      <td>2483.0</td>\n",
       "      <td>43498.4</td>\n",
       "      <td>7107.0</td>\n",
       "      <td>9671.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test_web_performance_metrics[2-10]</td>\n",
       "      <td>2141.0</td>\n",
       "      <td>45650.3</td>\n",
       "      <td>6665.0</td>\n",
       "      <td>9575.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test_web_performance_metrics[3-10]</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>27123.6</td>\n",
       "      <td>9376.6</td>\n",
       "      <td>8016.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test_web_performance_metrics[4-10]</td>\n",
       "      <td>2229.9</td>\n",
       "      <td>24608.9</td>\n",
       "      <td>8791.6</td>\n",
       "      <td>7608.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test_web_performance_metrics[5-10]</td>\n",
       "      <td>2527.5</td>\n",
       "      <td>30801.1</td>\n",
       "      <td>8409.5</td>\n",
       "      <td>7332.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 NAME  QUALITI SCRIPTS DURATION  \\\n",
       "0  test_web_performance_metrics[1-10]                    2483.0   \n",
       "1  test_web_performance_metrics[2-10]                    2141.0   \n",
       "2  test_web_performance_metrics[3-10]                    2118.0   \n",
       "3  test_web_performance_metrics[4-10]                    2229.9   \n",
       "4  test_web_performance_metrics[5-10]                    2527.5   \n",
       "\n",
       "   TOTAL SCRIPTS DURATION  FIRST CONTENTFUL PAINT  PAGE LOAD TIME  TEST ID  \n",
       "0                 43498.4                  7107.0          9671.6        0  \n",
       "1                 45650.3                  6665.0          9575.5        1  \n",
       "2                 27123.6                  9376.6          8016.8        2  \n",
       "3                 24608.9                  8791.6          7608.5        3  \n",
       "4                 30801.1                  8409.5          7332.0        4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Add a TEST ID column to use in graphs and display the first 5 rows.\"\"\"\n",
    "\n",
    "df[\"TEST ID\"] = [x for x in range(0, len(df))]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 7200x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Create Series Line Graph with matplotlib.\"\"\"\n",
    "\n",
    "plt.figure(figsize=(100, 100))\n",
    "df[COLUMNS].plot()\n",
    "\n",
    "plt.title(\"Web Performance Metrics\")\n",
    "plt.xlabel(\"Test Case\")\n",
    "plt.ylabel(\"Time (ms)\")\n",
    "plt.legend(loc='best', title='Metrics', bbox_to_anchor=(1.04,1))\n",
    "\n",
    "plt.grid(alpha=0.5)    \n",
    "plt.yticks(range(0, 15000, 2000))\n",
    "plt.xticks(range(0, len(df)))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "TEST ID=%{x}<br>PAGE LOAD TIME=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
         "x": [
          0,
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8,
          9
         ],
         "xaxis": "x",
         "y": [
          9671.600000023842,
          9575.5,
          8016.800000011921,
          7608.5,
          7332,
          7213.899999976158,
          7628.799999982119,
          7349.100000023842,
          6872.59999999404,
          10928.80000001192
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
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   "source": [
    "\"\"\" We can graph on a single data point.\"\"\"\n",
    "import plotly.express as px\n",
    "\n",
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  {
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     },
     "metadata": {},
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   ],
   "source": [
    "\"\"\"We can graph on multiple data points\"\"\"\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='Qualiti Scripts Duraction (ms)', x=df['TEST ID'], y=df['QUALITI SCRIPTS DURATION']),\n",
    "    go.Bar(name='First Contentful Paint (ms)', x=df['TEST ID'], y=df['FIRST CONTENTFUL PAINT']),\n",
    "    go.Bar(name='Page Load Time (ms)', x=df['TEST ID'], y=df['PAGE LOAD TIME']),\n",
    "])\n",
    "\n",
    "fig.update_layout(barmode='group', title_text='Web Performance Metrics')\n",
    "fig.show()"
   ]
  }
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