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adamsilverstein/performant-translations-colab.ipynb

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Performant Translations Colab
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performant-translations-colab.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/adamsilverstein/5caf45f73a39e3f51fa1d0a8082c6f1b/performant-translations-colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KcAZ2RHCg_Ze"
},
"source": [
"## Measure the impact of performant translations\n",
"\n",
"This colab will measure two distinct impacts\n",
"\n",
"* Core feature: impact on X for sites that are Y, before and after they enabled the feature\n",
"* Plugin: impact on sites that enabled the Performant Translations plugin\n",
"\n"
]
},
{
"cell_type": "markdown",
"source": [
"## Setup"
],
"metadata": {
"id": "4G2WkwMPzxbT"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "qTmLBxDxBAZL"
},
"source": [
"### Provide your credentials to the runtime"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "SeTJb51SKs_W",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8def6cfd-36f9-461b-e8df-9ce282fda22d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Authenticated\n"
]
}
],
"source": [
"from google.colab import auth\n",
"auth.authenticate_user()\n",
"print('Authenticated')\n",
"project_id = 'wpp-research'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "goQQ96EDKs_7"
},
"source": [
"### Declare the Cloud project ID which will be used throughout this notebook\n",
"\n"
]
},
{
"cell_type": "code",
"source": [
"from google.cloud.bigquery import magics\n",
"# Update with your own Google Cloud Platform project name\n",
"magics.context.project = 'wpp-research'"
],
"metadata": {
"id": "YdTgQYtSoOoE"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Add a helper to get the latest dataset"
],
"metadata": {
"id": "yV85Ec6A9FED"
}
},
{
"cell_type": "code",
"source": [
"from datetime import datetime, timedelta\n",
"\n",
"def get_first_of_previous_month():\n",
" today = datetime.now()\n",
" first_day_previous_month = datetime(today.year, today.month - 1, 1) if today.month > 1 else datetime(today.year - 1, 12, 1)\n",
" return first_day_previous_month.strftime('%Y_%m_%d')\n",
"\n",
"dataset = get_first_of_previous_month() # eg. \"2023_06_01\" - datasets are updated monthly, indicate the latest"
],
"metadata": {
"id": "stNLljYnR355"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "UMKGkkZEPVRu"
},
"source": [
"### Optional: Enable data table display\n",
"\n",
"Colab includes the ``google.colab.data_table`` package that can be used to display large pandas dataframes as an interactive data table.\n",
"It can be enabled with:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "LMNA-vBHPyHz"
},
"outputs": [],
"source": [
"%load_ext google.colab.data_table"
]
},
{
"cell_type": "code",
"source": [
"from google.colab import data_table\n",
"data_table.enable_dataframe_formatter()"
],
"metadata": {
"id": "JlBfb2k3JpRS"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Queries"
],
"metadata": {
"id": "2FfbQTTPMspu"
}
},
{
"cell_type": "markdown",
"source": [
"### TTFB by localization status\n",
"From wpp-research::sql/2024/01/ttfb-localized-sites.sql"
],
"metadata": {
"id": "ouqXcVW2vh7N"
}
},
{
"cell_type": "code",
"source": [
"from google.cloud import bigquery\n",
"\n",
"client = bigquery.Client(project=project_id)\n",
"\n",
"query = f\"\"\"\n",
"CREATE TEMP FUNCTION\n",
" IS_GOOD(good FLOAT64,\n",
" needs_improvement FLOAT64,\n",
" poor FLOAT64)\n",
" RETURNS BOOL AS ( SAFE_DIVIDE(good, good + needs_improvement + poor) >= 0.75 );\n",
"CREATE TEMP FUNCTION\n",
" IS_NON_ZERO(good FLOAT64,\n",
" needs_improvement FLOAT64,\n",
" poor FLOAT64)\n",
" RETURNS BOOL AS ( good + needs_improvement + poor > 0 );\n",
"CREATE TEMP FUNCTION\n",
" IS_LOCALIZED(lang STRING)\n",
" RETURNS BOOL AS ( lang IS NOT NULL\n",
" AND lang != \"en\"\n",
" AND lang != \"en-us\" );\n",
"WITH\n",
" pages AS (\n",
" SELECT\n",
" client,\n",
" IS_LOCALIZED(REPLACE(TRIM(LOWER(JSON_VALUE(JSON_VALUE(payload, '$._almanac'), '$.html_node.lang'))), '_', '-' )) AS is_localized,\n",
" page AS url,\n",
" FROM\n",
" `httparchive.all.pages`,\n",
" UNNEST(technologies) AS t\n",
" WHERE\n",
" date = '2023-12-01'\n",
" AND is_root_page\n",
" AND t.technology = 'WordPress' ),\n",
" devices AS (\n",
" SELECT\n",
" CONCAT(origin, '/') AS url,\n",
" IF\n",
" (device = 'desktop', 'desktop', 'mobile') AS client,\n",
" IS_NON_ZERO(fast_ttfb,\n",
" avg_ttfb,\n",
" slow_ttfb) AS any_ttfb,\n",
" IS_GOOD(fast_ttfb,\n",
" avg_ttfb,\n",
" slow_ttfb) AS good_ttfb\n",
" FROM\n",
" `chrome-ux-report.materialized.device_summary`\n",
" WHERE\n",
" date = CAST(\"2023-12-01\" AS DATE)\n",
" AND device IN ('desktop',\n",
" 'tablet',\n",
" 'phone') )\n",
"SELECT\n",
" client,\n",
" is_localized,\n",
" SAFE_DIVIDE(COUNTIF(good_ttfb), COUNTIF(any_ttfb)) AS ttfb_passing_rate\n",
"FROM\n",
" devices\n",
" JOIN\n",
" pages\n",
" USING\n",
" (client,\n",
" url)\n",
"GROUP BY\n",
" is_localized,\n",
" client\n",
"ORDER BY\n",
" is_localized ASC,\n",
" client ASC\n",
"\"\"\"\n",
"\n",
"localize_or_not = client.query(query).to_dataframe()\n"
],
"metadata": {
"id": "XYbol66evJE1"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"localize_or_not.head(1000)"
],
"metadata": {
"id": "LPXobb8QvkSG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"outputId": "ac72865f-da3d-4a99-cf75-473d0bfe8340"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" client is_localized ttfb_passing_rate\n",
"0 desktop False 0.307445\n",
"1 mobile False 0.196011\n",
"2 desktop True 0.331216\n",
"3 mobile True 0.254033"
],
"text/html": [
"\n",
" <div id=\"df-b7a065f7-3b3b-4317-9ea4-e33c4f36e5e8\" class=\"colab-df-container\">\n",
" <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>client</th>\n",
" <th>is_localized</th>\n",
" <th>ttfb_passing_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.307445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.196011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.331216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.254033</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7a065f7-3b3b-4317-9ea4-e33c4f36e5e8')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-b7a065f7-3b3b-4317-9ea4-e33c4f36e5e8 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-b7a065f7-3b3b-4317-9ea4-e33c4f36e5e8');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-b86b1875-2571-4b83-8a52-eef747d8cfdd\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b86b1875-2571-4b83-8a52-eef747d8cfdd')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
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" }\n",
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" border-color: transparent;\n",
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" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-b86b1875-2571-4b83-8a52-eef747d8cfdd button');\n",
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"\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "localize_or_not",
"summary": "{\n \"name\": \"localize_or_not\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"client\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"mobile\",\n \"desktop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_localized\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 2,\n \"samples\": [\n true,\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ttfb_passing_rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.060166357541579896,\n \"min\": 0.1960105308950741,\n \"max\": 0.3312159014712447,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.1960105308950741,\n 0.2540327301518636\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
},
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/54ded79905fc55e4/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"desktop\",\nfalse,\n{\n 'v': 0.30744503527151634,\n 'f': \"0.30744503527151634\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"mobile\",\nfalse,\n{\n 'v': 0.1960105308950741,\n 'f': \"0.1960105308950741\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"desktop\",\ntrue,\n{\n 'v': 0.3312159014712447,\n 'f': \"0.3312159014712447\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"mobile\",\ntrue,\n{\n 'v': 0.2540327301518636,\n 'f': \"0.2540327301518636\",\n }]],\n columns: [[\"number\", \"index\"], [\"string\", \"client\"], [\"string\", \"is_localized\"], [\"number\", \"ttfb_passing_rate\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-63573e67-a73c-47f4-bed5-b5cbb8f8d072\">\n <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-63573e67-a73c-47f4-bed5-b5cbb8f8d072')\"\n title=\"Suggest charts\"\n style=\"display:none;\">\n \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n width=\"24px\">\n <g>\n <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n </g>\n</svg>\n </button>\n \n<style>\n .colab-df-quickchart {\n --bg-color: #E8F0FE;\n --fill-color: #1967D2;\n --hover-bg-color: #E2EBFA;\n --hover-fill-color: #174EA6;\n --disabled-fill-color: #AAA;\n --disabled-bg-color: #DDD;\n }\n\n [theme=dark] .colab-df-quickchart {\n --bg-color: #3B4455;\n --fill-color: #D2E3FC;\n --hover-bg-color: #434B5C;\n --hover-fill-color: #FFFFFF;\n --disabled-bg-color: #3B4455;\n --disabled-fill-color: #666;\n }\n\n .colab-df-quickchart {\n background-color: var(--bg-color);\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: var(--fill-color);\n height: 32px;\n padding: 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: var(--hover-bg-color);\n box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: var(--button-hover-fill-color);\n }\n\n .colab-df-quickchart-complete:disabled,\n .colab-df-quickchart-complete:disabled:hover {\n background-color: var(--disabled-bg-color);\n fill: var(--disabled-fill-color);\n box-shadow: none;\n }\n\n .colab-df-spinner {\n border: 2px solid var(--fill-color);\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n animation:\n spin 1s steps(1) infinite;\n }\n\n @keyframes spin {\n 0% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n border-left-color: var(--fill-color);\n }\n 20% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 30% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n border-right-color: var(--fill-color);\n }\n 40% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 60% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n }\n 80% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-bottom-color: var(--fill-color);\n }\n 90% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n }\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const quickchartButtonEl =\n document.querySelector('#' + key + ' button');\n quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n quickchartButtonEl.classList.add('colab-df-spinner');\n try {\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n } catch (error) {\n console.error('Error during call to suggestCharts:', error);\n }\n quickchartButtonEl.classList.remove('colab-df-spinner');\n quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n }\n (() => {\n let quickchartButtonEl =\n document.querySelector('#df-63573e67-a73c-47f4-bed5-b5cbb8f8d072 button');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n })();\n </script>\n</div>`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n "
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "h59eYHfTwRWH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### TTFB passing rate 10/1/24"
],
"metadata": {
"id": "HBejguopxinI"
}
},
{
"cell_type": "code",
"source": [
"from google.cloud import bigquery\n",
"\n",
"client = bigquery.Client(project=project_id)\n",
"\n",
"query = f\"\"\"\n",
"CREATE TEMP FUNCTION\n",
" IS_GOOD(good FLOAT64,\n",
" needs_improvement FLOAT64,\n",
" poor FLOAT64)\n",
" RETURNS BOOL AS ( SAFE_DIVIDE(good, good + needs_improvement + poor) >= 0.75 );\n",
"CREATE TEMP FUNCTION\n",
" IS_NON_ZERO(good FLOAT64,\n",
" needs_improvement FLOAT64,\n",
" poor FLOAT64)\n",
" RETURNS BOOL AS ( good + needs_improvement + poor > 0 );\n",
"CREATE TEMP FUNCTION\n",
" IS_LOCALIZED(lang STRING)\n",
" RETURNS BOOL AS ( lang IS NOT NULL\n",
" AND lang != \"en\"\n",
" AND lang != \"en-us\" );\n",
"WITH\n",
" pages AS (\n",
" SELECT\n",
" client,\n",
" IS_LOCALIZED(REPLACE(TRIM(LOWER(JSON_VALUE(JSON_VALUE(payload, '$._almanac'), '$.html_node.lang'))), '_', '-' )) AS is_localized,\n",
" page AS url,\n",
" FROM\n",
" `httparchive.all.pages`,\n",
" UNNEST(technologies) AS t\n",
" WHERE\n",
" date = '2024-10-01'\n",
" AND is_root_page\n",
" AND t.technology = 'WordPress' ),\n",
" devices AS (\n",
" SELECT\n",
" CONCAT(origin, '/') AS url,\n",
" IF\n",
" (device = 'desktop', 'desktop', 'mobile') AS client,\n",
" IS_NON_ZERO(fast_ttfb,\n",
" avg_ttfb,\n",
" slow_ttfb) AS any_ttfb,\n",
" IS_GOOD(fast_ttfb,\n",
" avg_ttfb,\n",
" slow_ttfb) AS good_ttfb\n",
" FROM\n",
" `chrome-ux-report.materialized.device_summary`\n",
" WHERE\n",
" date = CAST(\"2024-10-01\" AS DATE)\n",
" AND device IN ('desktop',\n",
" 'tablet',\n",
" 'phone') )\n",
"SELECT\n",
" client,\n",
" is_localized,\n",
" SAFE_DIVIDE(COUNTIF(good_ttfb), COUNTIF(any_ttfb)) AS ttfb_passing_rate\n",
"FROM\n",
" devices\n",
" JOIN\n",
" pages\n",
" USING\n",
" (client,\n",
" url)\n",
"GROUP BY\n",
" is_localized,\n",
" client\n",
"ORDER BY\n",
" is_localized ASC,\n",
" client ASC\n",
"\"\"\"\n",
"\n",
"localize_or_not_24 = client.query(query).to_dataframe()\n"
],
"metadata": {
"id": "_sVCe4dsxg3X"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"localize_or_not_24.head(1000)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "ScLf4yeNxt7M",
"outputId": "5de5095e-4dfc-4ca8-cdbf-229e2ffb6d4c"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" client is_localized ttfb_passing_rate\n",
"0 desktop False 0.316738\n",
"1 mobile False 0.221321\n",
"2 desktop True 0.336921\n",
"3 mobile True 0.270966"
],
"text/html": [
"\n",
" <div id=\"df-b33f8c02-f83d-4deb-b851-8235f565486c\" class=\"colab-df-container\">\n",
" <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",
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"\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>client</th>\n",
" <th>is_localized</th>\n",
" <th>ttfb_passing_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.316738</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.221321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.336921</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.270966</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
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" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
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"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
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" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-b33f8c02-f83d-4deb-b851-8235f565486c button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-b33f8c02-f83d-4deb-b851-8235f565486c');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-14419aa8-36b0-457e-b0d0-30d0bc7c044c\">\n",
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" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
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" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
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" }\n",
"\n",
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" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
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" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
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" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-14419aa8-36b0-457e-b0d0-30d0bc7c044c button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "localize_or_not_24",
"summary": "{\n \"name\": \"localize_or_not_24\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"client\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"mobile\",\n \"desktop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_localized\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 2,\n \"samples\": [\n true,\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ttfb_passing_rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05146614745586329,\n \"min\": 0.22132055966815736,\n \"max\": 0.3369209730338933,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.22132055966815736,\n 0.270965663613118\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
},
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},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# Compare absolute change between localize_or_not and localize_or_not_24\n",
"merged_df = pd.merge(localize_or_not, localize_or_not_24, on=['client', 'is_localized'], suffixes=('_2023', '_2024'))\n",
"merged_df['ttfb_passing_rate_absolute_change'] = merged_df['ttfb_passing_rate_2024'] - merged_df['ttfb_passing_rate_2023']\n",
"merged_df.head(1000)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "SKyf9JDCxwWA",
"outputId": "97ce42db-2568-403a-a19b-e10d06a4edd3"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" client is_localized ttfb_passing_rate_2023 ttfb_passing_rate_2024 \\\n",
"0 desktop False 0.307445 0.316738 \n",
"1 mobile False 0.196011 0.221321 \n",
"2 desktop True 0.331216 0.336921 \n",
"3 mobile True 0.254033 0.270966 \n",
"\n",
" ttfb_passing_rate_absolute_change \n",
"0 0.009293 \n",
"1 0.025310 \n",
"2 0.005705 \n",
"3 0.016933 "
],
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"summary": "{\n \"name\": \"merged_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"client\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"mobile\",\n \"desktop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_localized\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 2,\n \"samples\": [\n true,\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ttfb_passing_rate_2023\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.060166357541579896,\n \"min\": 0.1960105308950741,\n \"max\": 0.3312159014712447,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.1960105308950741,\n 0.2540327301518636\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ttfb_passing_rate_2024\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05146614745586329,\n \"min\": 0.22132055966815736,\n \"max\": 0.3369209730338933,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.22132055966815736,\n 0.270965663613118\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ttfb_passing_rate_absolute_change\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.008700471229457914,\n \"min\": 0.0057050715626486,\n \"max\": 0.025310028773083276,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.025310028773083276,\n 0.016932933461254407\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
},
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/54ded79905fc55e4/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"desktop\",\nfalse,\n{\n 'v': 0.30744503527151634,\n 'f': \"0.30744503527151634\",\n },\n{\n 'v': 0.31673812458898926,\n 'f': \"0.31673812458898926\",\n },\n{\n 'v': 0.009293089317472913,\n 'f': \"0.009293089317472913\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"mobile\",\nfalse,\n{\n 'v': 0.1960105308950741,\n 'f': \"0.1960105308950741\",\n },\n{\n 'v': 0.22132055966815736,\n 'f': \"0.22132055966815736\",\n },\n{\n 'v': 0.025310028773083276,\n 'f': \"0.025310028773083276\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"desktop\",\ntrue,\n{\n 'v': 0.3312159014712447,\n 'f': \"0.3312159014712447\",\n },\n{\n 'v': 0.3369209730338933,\n 'f': \"0.3369209730338933\",\n },\n{\n 'v': 0.0057050715626486,\n 'f': \"0.0057050715626486\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"mobile\",\ntrue,\n{\n 'v': 0.2540327301518636,\n 'f': \"0.2540327301518636\",\n },\n{\n 'v': 0.270965663613118,\n 'f': \"0.270965663613118\",\n },\n{\n 'v': 0.016932933461254407,\n 'f': \"0.016932933461254407\",\n }]],\n columns: [[\"number\", \"index\"], [\"string\", \"client\"], [\"string\", \"is_localized\"], [\"number\", \"ttfb_passing_rate_2023\"], [\"number\", \"ttfb_passing_rate_2024\"], [\"number\", \"ttfb_passing_rate_absolute_change\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-a0a9f9e5-ff93-45bb-8cbb-a8ceb5846f04\">\n <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a0a9f9e5-ff93-45bb-8cbb-a8ceb5846f04')\"\n title=\"Suggest charts\"\n style=\"display:none;\">\n \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n width=\"24px\">\n <g>\n <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n </g>\n</svg>\n </button>\n \n<style>\n .colab-df-quickchart {\n --bg-color: #E8F0FE;\n --fill-color: #1967D2;\n --hover-bg-color: #E2EBFA;\n --hover-fill-color: #174EA6;\n --disabled-fill-color: #AAA;\n --disabled-bg-color: #DDD;\n }\n\n [theme=dark] .colab-df-quickchart {\n --bg-color: #3B4455;\n --fill-color: #D2E3FC;\n --hover-bg-color: #434B5C;\n --hover-fill-color: #FFFFFF;\n --disabled-bg-color: #3B4455;\n --disabled-fill-color: #666;\n }\n\n .colab-df-quickchart {\n background-color: var(--bg-color);\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: var(--fill-color);\n height: 32px;\n padding: 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: var(--hover-bg-color);\n box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: var(--button-hover-fill-color);\n }\n\n .colab-df-quickchart-complete:disabled,\n .colab-df-quickchart-complete:disabled:hover {\n background-color: var(--disabled-bg-color);\n fill: var(--disabled-fill-color);\n box-shadow: none;\n }\n\n .colab-df-spinner {\n border: 2px solid var(--fill-color);\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n animation:\n spin 1s steps(1) infinite;\n }\n\n @keyframes spin {\n 0% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n border-left-color: var(--fill-color);\n }\n 20% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 30% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n border-right-color: var(--fill-color);\n }\n 40% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 60% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n }\n 80% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-bottom-color: var(--fill-color);\n }\n 90% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n }\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const quickchartButtonEl =\n document.querySelector('#' + key + ' button');\n quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n quickchartButtonEl.classList.add('colab-df-spinner');\n try {\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n } catch (error) {\n console.error('Error during call to suggestCharts:', error);\n }\n quickchartButtonEl.classList.remove('colab-df-spinner');\n quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n }\n (() => {\n let quickchartButtonEl =\n document.querySelector('#df-a0a9f9e5-ff93-45bb-8cbb-a8ceb5846f04 button');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n })();\n </script>\n</div>`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n "
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"source": [
"## TTFB score changes"
],
"metadata": {
"id": "UwijSnIfyoLw"
}
},
{
"cell_type": "code",
"source": [
"from google.cloud import bigquery\n",
"\n",
"client = bigquery.Client(project=project_id)\n",
"\n",
"query = f\"\"\"\n",
"CREATE TEMP FUNCTION\n",
" IS_LOCALIZED(lang STRING)\n",
" RETURNS BOOL AS ( lang IS NOT NULL\n",
" AND lang != \"en\"\n",
" AND lang != \"en-us\" );\n",
"WITH\n",
" pages AS (\n",
" SELECT\n",
" client,\n",
" IS_LOCALIZED(REPLACE(TRIM(LOWER(JSON_VALUE(JSON_VALUE(payload, '$._almanac'), '$.html_node.lang'))), '_', '-' )) AS is_localized,\n",
" page AS url,\n",
" FROM\n",
" `httparchive.all.pages`,\n",
" UNNEST(technologies) AS t\n",
" WHERE\n",
" date = '2023-11-01'\n",
" AND is_root_page\n",
" AND t.technology = 'WordPress' ),\n",
" devices AS (\n",
" SELECT\n",
" CONCAT(origin, '/') AS url,\n",
" IF\n",
" (device = 'desktop', 'desktop', 'mobile') AS client,\n",
" p75_ttfb AS ttfb\n",
" FROM\n",
" `chrome-ux-report.materialized.device_summary`\n",
" WHERE\n",
" date = CAST(\"2023-11-01\" AS DATE)\n",
" AND device IN ('desktop',\n",
" 'tablet',\n",
" 'phone') )\n",
"SELECT\n",
" client,\n",
" is_localized,\n",
" AVG(ttfb) AS avg_ttfb\n",
"FROM\n",
" devices\n",
" JOIN\n",
" pages\n",
" USING\n",
" (client,\n",
" url)\n",
"GROUP BY\n",
" is_localized,\n",
" client\n",
"ORDER BY\n",
" is_localized ASC,\n",
" client ASC\n",
"\"\"\"\n",
"\n",
"localize_or_not_ttfb_2023 = client.query(query).to_dataframe()\n"
],
"metadata": {
"id": "SPRQqBEzyr0H"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"source": [
"localize_or_not_ttfb_2023.head(1000)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "NyM1v1Qm1SdV",
"outputId": "b890bf70-8ad4-4ef2-f4db-ed087bfee06e"
},
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" client is_localized avg_ttfb\n",
"0 desktop False 1343.236099\n",
"1 mobile False 1639.110170\n",
"2 desktop True 1376.459262\n",
"3 mobile True 1577.732006"
],
"text/html": [
"\n",
" <div id=\"df-093afb80-a97c-42bc-aec0-5776aaff88a6\" class=\"colab-df-container\">\n",
" <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>client</th>\n",
" <th>is_localized</th>\n",
" <th>avg_ttfb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>1343.236099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>1639.110170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>1376.459262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>1577.732006</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
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" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
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" fill: #1967D2;\n",
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" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-093afb80-a97c-42bc-aec0-5776aaff88a6 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-093afb80-a97c-42bc-aec0-5776aaff88a6');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-f31937a6-ef61-4cb8-bdfc-1332a46400c9\">\n",
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" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
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" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
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" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-f31937a6-ef61-4cb8-bdfc-1332a46400c9 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "localize_or_not_ttfb_2023",
"summary": "{\n \"name\": \"localize_or_not_ttfb_2023\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"client\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"mobile\",\n \"desktop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_localized\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 2,\n \"samples\": [\n true,\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"avg_ttfb\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 146.31503388460007,\n \"min\": 1343.2360994654653,\n \"max\": 1639.110169663145,\n \"num_unique_values\": 4,\n \"samples\": [\n 1639.110169663145,\n 1577.7320062324136\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
},
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/54ded79905fc55e4/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"desktop\",\nfalse,\n{\n 'v': 1343.2360994654653,\n 'f': \"1343.2360994654653\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"mobile\",\nfalse,\n{\n 'v': 1639.110169663145,\n 'f': \"1639.110169663145\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"desktop\",\ntrue,\n{\n 'v': 1376.4592624179481,\n 'f': \"1376.4592624179481\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"mobile\",\ntrue,\n{\n 'v': 1577.7320062324136,\n 'f': \"1577.7320062324136\",\n }]],\n columns: [[\"number\", \"index\"], [\"string\", \"client\"], [\"string\", \"is_localized\"], [\"number\", \"avg_ttfb\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-3fe086b8-d963-429a-9514-2f5507e5902b\">\n <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3fe086b8-d963-429a-9514-2f5507e5902b')\"\n title=\"Suggest charts\"\n style=\"display:none;\">\n \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n width=\"24px\">\n <g>\n <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n </g>\n</svg>\n </button>\n \n<style>\n .colab-df-quickchart {\n --bg-color: #E8F0FE;\n --fill-color: #1967D2;\n --hover-bg-color: #E2EBFA;\n --hover-fill-color: #174EA6;\n --disabled-fill-color: #AAA;\n --disabled-bg-color: #DDD;\n }\n\n [theme=dark] .colab-df-quickchart {\n --bg-color: #3B4455;\n --fill-color: #D2E3FC;\n --hover-bg-color: #434B5C;\n --hover-fill-color: #FFFFFF;\n --disabled-bg-color: #3B4455;\n --disabled-fill-color: #666;\n }\n\n .colab-df-quickchart {\n background-color: var(--bg-color);\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: var(--fill-color);\n height: 32px;\n padding: 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: var(--hover-bg-color);\n box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: var(--button-hover-fill-color);\n }\n\n .colab-df-quickchart-complete:disabled,\n .colab-df-quickchart-complete:disabled:hover {\n background-color: var(--disabled-bg-color);\n fill: var(--disabled-fill-color);\n box-shadow: none;\n }\n\n .colab-df-spinner {\n border: 2px solid var(--fill-color);\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n animation:\n spin 1s steps(1) infinite;\n }\n\n @keyframes spin {\n 0% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n border-left-color: var(--fill-color);\n }\n 20% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 30% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n border-right-color: var(--fill-color);\n }\n 40% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 60% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n }\n 80% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-bottom-color: var(--fill-color);\n }\n 90% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n }\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const quickchartButtonEl =\n document.querySelector('#' + key + ' button');\n quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n quickchartButtonEl.classList.add('colab-df-spinner');\n try {\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n } catch (error) {\n console.error('Error during call to suggestCharts:', error);\n }\n quickchartButtonEl.classList.remove('colab-df-spinner');\n quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n }\n (() => {\n let quickchartButtonEl =\n document.querySelector('#df-3fe086b8-d963-429a-9514-2f5507e5902b button');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n })();\n </script>\n</div>`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n "
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"from google.cloud import bigquery\n",
"\n",
"client = bigquery.Client(project=project_id)\n",
"\n",
"query = f\"\"\"\n",
"CREATE TEMP FUNCTION\n",
" IS_LOCALIZED(lang STRING)\n",
" RETURNS BOOL AS ( lang IS NOT NULL\n",
" AND lang != \"en\"\n",
" AND lang != \"en-us\" );\n",
"WITH\n",
" pages AS (\n",
" SELECT\n",
" client,\n",
" IS_LOCALIZED(REPLACE(TRIM(LOWER(JSON_VALUE(JSON_VALUE(payload, '$._almanac'), '$.html_node.lang'))), '_', '-' )) AS is_localized,\n",
" page AS url,\n",
" FROM\n",
" `httparchive.all.pages`,\n",
" UNNEST(technologies) AS t\n",
" WHERE\n",
" date = '2024-10-01'\n",
" AND is_root_page\n",
" AND t.technology = 'WordPress' ),\n",
" devices AS (\n",
" SELECT\n",
" CONCAT(origin, '/') AS url,\n",
" IF\n",
" (device = 'desktop', 'desktop', 'mobile') AS client,\n",
" p75_ttfb AS ttfb\n",
" FROM\n",
" `chrome-ux-report.materialized.device_summary`\n",
" WHERE\n",
" date = CAST(\"2024-10-01\" AS DATE)\n",
" AND device IN ('desktop',\n",
" 'tablet',\n",
" 'phone') )\n",
"SELECT\n",
" client,\n",
" is_localized,\n",
" AVG(ttfb) AS avg_ttfb\n",
"FROM\n",
" devices\n",
" JOIN\n",
" pages\n",
" USING\n",
" (client,\n",
" url)\n",
"GROUP BY\n",
" is_localized,\n",
" client\n",
"ORDER BY\n",
" is_localized ASC,\n",
" client ASC\n",
"\"\"\"\n",
"\n",
"localize_or_not_ttfb_2024 = client.query(query).to_dataframe()\n"
],
"metadata": {
"id": "IZs2WmxT1FvT"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"localize_or_not_ttfb_2024.head(1000)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "ixyepIm-1VNl",
"outputId": "b3e033f9-7ddb-40c9-d0c5-82ed18114152"
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" client is_localized avg_ttfb\n",
"0 desktop False 1329.573209\n",
"1 mobile False 1597.053311\n",
"2 desktop True 1352.784210\n",
"3 mobile True 1532.340842"
],
"text/html": [
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" .dataframe tbody tr th:only-of-type {\n",
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" <th></th>\n",
" <th>client</th>\n",
" <th>is_localized</th>\n",
" <th>avg_ttfb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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"\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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" document.querySelector('#df-a36e99d1-725a-45bd-a2a9-755673b411f1 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-a36e99d1-725a-45bd-a2a9-755673b411f1');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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"<div id=\"df-53d97e88-730e-4c4b-9c18-addc374bb088\">\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
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" .colab-df-quickchart {\n",
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" }\n",
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" fill: var(--button-hover-fill-color);\n",
" }\n",
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" border-bottom-color: var(--fill-color);\n",
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" }\n",
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" }\n",
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" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
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" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-53d97e88-730e-4c4b-9c18-addc374bb088 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "localize_or_not_ttfb_2024",
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},
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},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"source": [
"### Absolute TTFB change 11/23 -> 10/24"
],
"metadata": {
"id": "RvwaN0FY3Syf"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# Compare absolute change between localize_or_not and localize_or_not_24\n",
"merged_df = pd.merge(localize_or_not_ttfb_2023, localize_or_not_ttfb_2024, on=['client', 'is_localized'], suffixes=('_2023', '_2024'))\n",
"merged_df['ttfb_p75_absolute_change'] = merged_df['avg_ttfb_2024'] - merged_df['avg_ttfb_2023']\n",
"merged_df.head(1000)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "54xzjpMB1Xat",
"outputId": "107c001b-d3a3-4228-c2ad-974439fe79c7"
},
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" client is_localized avg_ttfb_2023 avg_ttfb_2024 \\\n",
"0 desktop False 1343.236099 1329.573209 \n",
"1 mobile False 1639.110170 1597.053311 \n",
"2 desktop True 1376.459262 1352.784210 \n",
"3 mobile True 1577.732006 1532.340842 \n",
"\n",
" ttfb_p75_absolute_change \n",
"0 -13.662891 \n",
"1 -42.056859 \n",
"2 -23.675053 \n",
"3 -45.391164 "
],
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},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"source": [
"### Relative TTFB change 11/23 -> 10/24"
],
"metadata": {
"id": "HIqq35Yo3YKS"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# Compare relative change between localize_or_not and localize_or_not_24\n",
"merged_df = pd.merge(localize_or_not_ttfb_2023, localize_or_not_ttfb_2024, on=['client', 'is_localized'], suffixes=('_2023', '_2024'))\n",
"merged_df['ttfb_p75_relative_change'] = ( merged_df['avg_ttfb_2024'] - merged_df['avg_ttfb_2023'] ) / merged_df['avg_ttfb_2023']\n",
"merged_df.head(1000)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "LLi7f3t-26k8",
"outputId": "579cecce-49a2-4578-dc82-b6f8febc4ae2"
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" client is_localized avg_ttfb_2023 avg_ttfb_2024 \\\n",
"0 desktop False 1343.236099 1329.573209 \n",
"1 mobile False 1639.110170 1597.053311 \n",
"2 desktop True 1376.459262 1352.784210 \n",
"3 mobile True 1577.732006 1532.340842 \n",
"\n",
" ttfb_p75_relative_change \n",
"0 -0.010172 \n",
"1 -0.025658 \n",
"2 -0.017200 \n",
"3 -0.028770 "
],
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"summary": "{\n \"name\": \"merged_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"client\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"mobile\",\n \"desktop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_localized\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 2,\n \"samples\": [\n true,\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"avg_ttfb_2023\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 146.31503388460007,\n \"min\": 1343.2360994654653,\n \"max\": 1639.110169663145,\n \"num_unique_values\": 4,\n \"samples\": [\n 1639.110169663145,\n 1577.7320062324136\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"avg_ttfb_2024\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 132.06524602108334,\n \"min\": 1329.5732088181987,\n \"max\": 1597.0533109308935,\n \"num_unique_values\": 4,\n \"samples\": [\n 1597.0533109308935,\n 1532.340842273954\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ttfb_p75_relative_change\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.00841734495697065,\n \"min\": -0.02876988219745421,\n \"max\": -0.010171622585711929,\n \"num_unique_values\": 4,\n \"samples\": [\n -0.025658347749068344,\n -0.02876988219745421\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
},
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/54ded79905fc55e4/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"desktop\",\nfalse,\n{\n 'v': 1343.2360994654653,\n 'f': \"1343.2360994654653\",\n },\n{\n 'v': 1329.5732088181987,\n 'f': \"1329.5732088181987\",\n },\n{\n 'v': -0.010171622585711929,\n 'f': \"-0.010171622585711929\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"mobile\",\nfalse,\n{\n 'v': 1639.110169663145,\n 'f': \"1639.110169663145\",\n },\n{\n 'v': 1597.0533109308935,\n 'f': \"1597.0533109308935\",\n },\n{\n 'v': -0.025658347749068344,\n 'f': \"-0.025658347749068344\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"desktop\",\ntrue,\n{\n 'v': 1376.4592624179481,\n 'f': \"1376.4592624179481\",\n },\n{\n 'v': 1352.7842097040073,\n 'f': \"1352.7842097040073\",\n },\n{\n 'v': -0.01719996614527644,\n 'f': \"-0.01719996614527644\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"mobile\",\ntrue,\n{\n 'v': 1577.7320062324136,\n 'f': \"1577.7320062324136\",\n },\n{\n 'v': 1532.340842273954,\n 'f': \"1532.340842273954\",\n },\n{\n 'v': -0.02876988219745421,\n 'f': \"-0.02876988219745421\",\n }]],\n columns: [[\"number\", \"index\"], [\"string\", \"client\"], [\"string\", \"is_localized\"], [\"number\", \"avg_ttfb_2023\"], [\"number\", \"avg_ttfb_2024\"], [\"number\", \"ttfb_p75_relative_change\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-cddbec09-9ce4-4fff-861b-84484b9ebc43\">\n <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cddbec09-9ce4-4fff-861b-84484b9ebc43')\"\n title=\"Suggest charts\"\n style=\"display:none;\">\n \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n width=\"24px\">\n <g>\n <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n </g>\n</svg>\n </button>\n \n<style>\n .colab-df-quickchart {\n --bg-color: #E8F0FE;\n --fill-color: #1967D2;\n --hover-bg-color: #E2EBFA;\n --hover-fill-color: #174EA6;\n --disabled-fill-color: #AAA;\n --disabled-bg-color: #DDD;\n }\n\n [theme=dark] .colab-df-quickchart {\n --bg-color: #3B4455;\n --fill-color: #D2E3FC;\n --hover-bg-color: #434B5C;\n --hover-fill-color: #FFFFFF;\n --disabled-bg-color: #3B4455;\n --disabled-fill-color: #666;\n }\n\n .colab-df-quickchart {\n background-color: var(--bg-color);\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: var(--fill-color);\n height: 32px;\n padding: 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: var(--hover-bg-color);\n box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: var(--button-hover-fill-color);\n }\n\n .colab-df-quickchart-complete:disabled,\n .colab-df-quickchart-complete:disabled:hover {\n background-color: var(--disabled-bg-color);\n fill: var(--disabled-fill-color);\n box-shadow: none;\n }\n\n .colab-df-spinner {\n border: 2px solid var(--fill-color);\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n animation:\n spin 1s steps(1) infinite;\n }\n\n @keyframes spin {\n 0% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n border-left-color: var(--fill-color);\n }\n 20% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 30% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n border-right-color: var(--fill-color);\n }\n 40% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 60% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n }\n 80% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-bottom-color: var(--fill-color);\n }\n 90% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n }\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const quickchartButtonEl =\n document.querySelector('#' + key + ' button');\n quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n quickchartButtonEl.classList.add('colab-df-spinner');\n try {\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n } catch (error) {\n console.error('Error during call to suggestCharts:', error);\n }\n quickchartButtonEl.classList.remove('colab-df-spinner');\n quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n }\n (() => {\n let quickchartButtonEl =\n document.querySelector('#df-cddbec09-9ce4-4fff-861b-84484b9ebc43 button');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n })();\n </script>\n</div>`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n "
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "markdown",
"source": [
"## Localized sites over time"
],
"metadata": {
"id": "FXfHNeQr9T9g"
}
},
{
"cell_type": "code",
"source": [
"from google.cloud import bigquery\n",
"\n",
"client = bigquery.Client(project=project_id)\n",
"\n",
"query = f\"\"\"\n",
"CREATE TEMP FUNCTION\n",
" IS_GOOD(good FLOAT64,\n",
" needs_improvement FLOAT64,\n",
" poor FLOAT64)\n",
" RETURNS BOOL AS ( SAFE_DIVIDE(good, good + needs_improvement + poor) >= 0.75 );\n",
"CREATE TEMP FUNCTION\n",
" IS_NON_ZERO(good FLOAT64,\n",
" needs_improvement FLOAT64,\n",
" poor FLOAT64)\n",
" RETURNS BOOL AS ( good + needs_improvement + poor > 0 );\n",
"CREATE TEMP FUNCTION\n",
" IS_LOCALIZED(lang STRING)\n",
" RETURNS BOOL AS ( lang IS NOT NULL\n",
" AND lang != \"en\"\n",
" AND lang != \"en-us\" );\n",
"WITH\n",
" pages AS (\n",
" SELECT\n",
" client,\n",
" IS_LOCALIZED(REPLACE(TRIM(LOWER(JSON_VALUE(JSON_VALUE(payload, '$._almanac'), '$.html_node.lang'))), '_', '-' )) AS is_localized,\n",
" page AS url,\n",
" date\n",
" FROM\n",
" `httparchive.all.pages`,\n",
" UNNEST(technologies) AS t\n",
" WHERE\n",
" date > CAST(\"2023-12-01\" AS DATE)\n",
" AND is_root_page\n",
" AND t.technology = 'WordPress' ),\n",
" devices AS (\n",
" SELECT\n",
" date,\n",
" CONCAT(origin, '/') AS url,\n",
" IF\n",
" (device = 'desktop', 'desktop', 'mobile') AS client,\n",
" IS_NON_ZERO(fast_ttfb,\n",
" avg_ttfb,\n",
" slow_ttfb) AS any_ttfb,\n",
" IS_GOOD(fast_ttfb,\n",
" avg_ttfb,\n",
" slow_ttfb) AS good_ttfb\n",
" FROM\n",
" `chrome-ux-report.materialized.device_summary`\n",
" WHERE\n",
" date > CAST(\"2023-12-01\" AS DATE)\n",
" AND device IN ('desktop',\n",
" 'tablet',\n",
" 'phone') )\n",
"SELECT\n",
" date,\n",
" client,\n",
" is_localized,\n",
" SAFE_DIVIDE(COUNTIF(good_ttfb), COUNTIF(any_ttfb)) AS ttfb_passing_rate\n",
"FROM\n",
" devices\n",
" JOIN\n",
" pages\n",
" USING\n",
" (date,\n",
" client,\n",
" url)\n",
"GROUP BY\n",
" date,\n",
" is_localized,\n",
" client\n",
"ORDER BY\n",
" date DESC,\n",
" is_localized ASC,\n",
" client ASC\n",
" \"\"\"\n",
"\n",
"localize_or_not_over_time = client.query(query).to_dataframe()\n"
],
"metadata": {
"id": "l0-LQA-O9SdK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"localize_or_not_over_time.head(1000)"
],
"metadata": {
"id": "ZMV3VS9c-EXL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 670
},
"outputId": "4c1cbb48-74f2-4e9e-8145-b18dc57a8628"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" date client is_localized ttfb_passing_rate\n",
"0 2024-10-01 desktop False 0.316738\n",
"1 2024-10-01 mobile False 0.221321\n",
"2 2024-10-01 desktop True 0.336921\n",
"3 2024-10-01 mobile True 0.270966\n",
"4 2024-09-01 desktop False 0.314923\n",
"5 2024-09-01 mobile False 0.216768\n",
"6 2024-09-01 desktop True 0.339019\n",
"7 2024-09-01 mobile True 0.270448\n",
"8 2024-08-01 desktop False 0.314144\n",
"9 2024-08-01 mobile False 0.220314\n",
"10 2024-08-01 desktop True 0.344376\n",
"11 2024-08-01 mobile True 0.275307\n",
"12 2024-07-01 desktop False 0.316714\n",
"13 2024-07-01 mobile False 0.216422\n",
"14 2024-07-01 desktop True 0.346693\n",
"15 2024-07-01 mobile True 0.272067\n",
"16 2024-06-01 desktop False 0.314890\n",
"17 2024-06-01 mobile False 0.213893\n",
"18 2024-06-01 desktop True 0.341167\n",
"19 2024-06-01 mobile True 0.268578\n",
"20 2024-05-01 desktop False 0.315471\n",
"21 2024-05-01 mobile False 0.211562\n",
"22 2024-05-01 desktop True 0.340405\n",
"23 2024-05-01 mobile True 0.268464\n",
"24 2024-04-01 desktop False 0.324895\n",
"25 2024-04-01 mobile False 0.217186\n",
"26 2024-04-01 desktop True 0.341155\n",
"27 2024-04-01 mobile True 0.269643\n",
"28 2024-03-01 desktop False 0.317695\n",
"29 2024-03-01 mobile False 0.215624\n",
"30 2024-03-01 desktop True 0.336752\n",
"31 2024-03-01 mobile True 0.267646\n",
"32 2024-02-01 desktop False 0.316770\n",
"33 2024-02-01 mobile False 0.201628\n",
"34 2024-02-01 desktop True 0.332696\n",
"35 2024-02-01 mobile True 0.255326\n",
"36 2024-01-01 desktop False 0.317181\n",
"37 2024-01-01 mobile False 0.204183\n",
"38 2024-01-01 desktop True 0.337619\n",
"39 2024-01-01 mobile True 0.262194"
],
"text/html": [
"\n",
" <div id=\"df-4978e332-fa00-423d-a711-2d8a62f6ee16\" class=\"colab-df-container\">\n",
" <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>date</th>\n",
" <th>client</th>\n",
" <th>is_localized</th>\n",
" <th>ttfb_passing_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2024-10-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.316738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2024-10-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.221321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2024-10-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.336921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2024-10-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.270966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2024-09-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.314923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2024-09-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.216768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2024-09-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.339019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2024-09-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.270448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2024-08-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.314144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2024-08-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.220314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2024-08-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.344376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2024-08-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.275307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2024-07-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.316714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2024-07-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.216422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2024-07-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.346693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2024-07-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.272067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2024-06-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.314890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2024-06-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.213893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2024-06-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.341167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2024-06-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.268578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2024-05-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.315471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2024-05-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.211562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2024-05-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.340405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2024-05-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.268464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2024-04-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.324895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2024-04-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.217186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2024-04-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.341155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2024-04-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.269643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2024-03-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.317695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2024-03-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.215624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2024-03-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.336752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2024-03-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.267646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>2024-02-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.316770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>2024-02-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.201628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>2024-02-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.332696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>2024-02-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.255326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>2024-01-01</td>\n",
" <td>desktop</td>\n",
" <td>False</td>\n",
" <td>0.317181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>2024-01-01</td>\n",
" <td>mobile</td>\n",
" <td>False</td>\n",
" <td>0.204183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>2024-01-01</td>\n",
" <td>desktop</td>\n",
" <td>True</td>\n",
" <td>0.337619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>2024-01-01</td>\n",
" <td>mobile</td>\n",
" <td>True</td>\n",
" <td>0.262194</td>\n",
" </tr>\n",
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{\n --bg-color: #E8F0FE;\n --fill-color: #1967D2;\n --hover-bg-color: #E2EBFA;\n --hover-fill-color: #174EA6;\n --disabled-fill-color: #AAA;\n --disabled-bg-color: #DDD;\n }\n\n [theme=dark] .colab-df-quickchart {\n --bg-color: #3B4455;\n --fill-color: #D2E3FC;\n --hover-bg-color: #434B5C;\n --hover-fill-color: #FFFFFF;\n --disabled-bg-color: #3B4455;\n --disabled-fill-color: #666;\n }\n\n .colab-df-quickchart {\n background-color: var(--bg-color);\n border: none;\n border-radius: 50%;\n cursor: pointer;\n display: none;\n fill: var(--fill-color);\n height: 32px;\n padding: 0;\n width: 32px;\n }\n\n .colab-df-quickchart:hover {\n background-color: var(--hover-bg-color);\n box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n fill: var(--button-hover-fill-color);\n }\n\n .colab-df-quickchart-complete:disabled,\n .colab-df-quickchart-complete:disabled:hover {\n background-color: var(--disabled-bg-color);\n fill: var(--disabled-fill-color);\n box-shadow: none;\n }\n\n .colab-df-spinner {\n border: 2px solid var(--fill-color);\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n animation:\n spin 1s steps(1) infinite;\n }\n\n @keyframes spin {\n 0% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n border-left-color: var(--fill-color);\n }\n 20% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 30% {\n border-color: transparent;\n border-left-color: var(--fill-color);\n border-top-color: var(--fill-color);\n border-right-color: var(--fill-color);\n }\n 40% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-top-color: var(--fill-color);\n }\n 60% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n }\n 80% {\n border-color: transparent;\n border-right-color: var(--fill-color);\n border-bottom-color: var(--fill-color);\n }\n 90% {\n border-color: transparent;\n border-bottom-color: var(--fill-color);\n }\n }\n</style>\n\n <script>\n async function quickchart(key) {\n const quickchartButtonEl =\n document.querySelector('#' + key + ' button');\n quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n quickchartButtonEl.classList.add('colab-df-spinner');\n try {\n const charts = await google.colab.kernel.invokeFunction(\n 'suggestCharts', [key], {});\n } catch (error) {\n console.error('Error during call to suggestCharts:', error);\n }\n quickchartButtonEl.classList.remove('colab-df-spinner');\n quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n }\n (() => {\n let quickchartButtonEl =\n document.querySelector('#df-687d601f-799f-4eee-bd0c-0d2085b2b1f1 button');\n quickchartButtonEl.style.display =\n google.colab.kernel.accessAllowed ? 'block' : 'none';\n })();\n </script>\n</div>`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n "
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"# prompt: plot the ttfb_passing_rate by date\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"\n",
"df = localize_or_not_over_time.copy()\n",
"\n",
"# Plotting with Seaborn (highly recommended for clarity)\n",
"sns.set_theme(style=\"whitegrid\")\n",
"\n",
"# Create subplots for each client type\n",
"plt.figure(figsize=(12, 6))\n",
"for client_type in df['client'].unique():\n",
" plt.subplot(1, len(df['client'].unique()), list(df['client'].unique()).index(client_type) + 1)\n",
"\n",
" # Filter data for the specific client type\n",
" df_client = df[df['client'] == client_type]\n",
"\n",
" # Plot lines for localized and non-localized\n",
" sns.lineplot(x='date', y='ttfb_passing_rate', hue='is_localized', data=df_client, marker='o', palette=['blue', 'orange'])\n",
"\n",
" # Formatting\n",
" plt.title(f'TTFB Passing Rate - {client_type}')\n",
" plt.xlabel('Date')\n",
" plt.ylabel('TTFB Passing Rate')\n",
" plt.xticks(rotation=45)\n",
" plt.ylim(0, 1) # Set y-axis to 0-1 range\n",
" plt.legend(title='Localized', labels=['No', 'Yes'])\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
],
"metadata": {
"id": "mZNRTFs0-Icm",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 601
},
"outputId": "5212d686-f239-4de5-d405-445f25201a36"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Before / after comparisons"
],
"metadata": {
"id": "QvyFgvmUH9tx"
}
},
{
"cell_type": "code",
"source": [
"#@markdown ## Get the localized site origins.\n",
"localized_sites = get_localized_sites('2024-09-01')\n",
"count_localized_sites = localized_sites.count()\n",
"print(count_localized_sites)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KxXviXQXKOgM",
"outputId": "a0abcc31-2cce-4a4c-df39-29c87bee22c1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"origin 3475800\n",
"dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@markdown ## Get the non-localized site origins.\n",
"non_localized_sites = get_non_localized_sites('2024-09-01')\n",
"count_non_localized_sites = non_localized_sites.count()\n",
"print(count_non_localized_sites)"
],
"metadata": {
"id": "pdF3nqoXIBBG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "df0151d9-c50a-490c-ca3f-d355fce17051"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"origin 2143546\n",
"dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@markdown ## Get the CWV changes for localized sites\n",
"#@markdown This shows how much each metric has changed for localized sites between the before and after dates. Metrics in order:\n",
"#@markdown * pct_eligible_origins_with_good_cwv_change\n",
"#@markdown * pct_eligible_origins_with_good_lcp_change\n",
"#@markdown * pct_eligible_origins_with_good_cls_change\n",
"#@markdown * pct_eligible_origins_with_good_inp_change\n",
"#@markdown * pct_eligible_origins_with_good_ttfb_change\n",
"cwv_changes_localized_sites = get_cwv_change_before_after(localized_sites, '2024-01-01', '2024-09-01' )\n",
"print(cwv_changes_localized_sites)"
],
"metadata": {
"id": "OOGavQ78JRZ9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9df694bd-20ec-4528-d81d-168645c62857"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset wpp-research.temp_dataset already exists.\n",
"[0.04945737967421521, 0.033642347609904144, 0.021581152887447885, 0.07416415274440302, -0.003210042653351619]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@markdown ## Get the CWV changes for non_localized sites\n",
"#@markdown This shows how much each metric has changed for non-localized sites between the before and after dates.\n",
"#@markdown * pct_eligible_origins_with_good_cwv_change\n",
"#@markdown * pct_eligible_origins_with_good_lcp_change\n",
"#@markdown * pct_eligible_origins_with_good_cls_change\n",
"#@markdown * pct_eligible_origins_with_good_inp_change\n",
"#@markdown * pct_eligible_origins_with_good_ttfb_change\n",
"cwv_changes_non_localized_sites = get_cwv_change_before_after(non_localized_sites, '2024-02-01', '2024-07-01' )\n",
"print(cwv_changes_non_localized_sites)"
],
"metadata": {
"id": "SNnFoTNYJebT",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "717c8357-1f64-458c-ea64-a26a8cc735bf"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset wpp-research.temp_dataset already exists.\n",
"[0.03785168110090148, 0.03379085700032103, 0.021574287521065383, 0.03713313843194688, 0.000680431590357411]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@markdown ## Get the difference between localized and non-localized sites\n",
"#@markdown This step calculates the difference in changes for each metric between the localized and non-localized sites.\n",
"final_results = calculate_feature_impact(cwv_changes_localized_sites, cwv_changes_non_localized_sites)\n",
"print(final_results)\n"
],
"metadata": {
"id": "M851TSUcKfbN",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cd3fd24f-cc7f-479d-bf96-c991af7fdda9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'pct_good_cwv_change': '1.16%', 'pct_good_lcp_change': '-0.01%', 'pct_good_cls_change': '0.00%', 'pct_good_inp_change': '3.70%', 'pct_good_ttfb_change': '-0.39%'}\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Helpers"
],
"metadata": {
"id": "m9CDTSoUGvfR"
}
},
{
"cell_type": "code",
"source": [
"#@markdown ## Get sites at `after_date` with localization.\n",
"from google.cloud import bigquery\n",
"import pandas as pd\n",
"\n",
"\n",
"# Get sites with the localization on a specific date\n",
"def get_localized_sites(after_date):\n",
" client = bigquery.Client(project=project_id)\n",
" # 1. Identify localized Sites\n",
" localized_sites_query = f\"\"\"\n",
" CREATE TEMP FUNCTION\n",
" IS_LOCALIZED(lang STRING)\n",
" RETURNS BOOL AS ( lang IS NOT NULL\n",
" AND lang != \"en\"\n",
" AND lang != \"en-us\" );\n",
" SELECT\n",
" DISTINCT page AS origin\n",
" FROM\n",
" `httparchive.all.pages`,\n",
" UNNEST(technologies) AS technologies\n",
" WHERE\n",
" technologies.technology = 'WordPress'\n",
" AND date = PARSE_DATE('%Y-%m-%d', '{after_date}' )\n",
" AND is_root_page = TRUE\n",
" AND client = 'mobile'\n",
" AND IS_LOCALIZED(REPLACE(TRIM(LOWER(JSON_VALUE(JSON_VALUE(payload, '$._almanac'), '$.html_node.lang'))), '_', '-' )) = TRUE\n",
" \"\"\"\n",
"\n",
" localized_sites_df = client.query(localized_sites_query).to_dataframe()\n",
" return localized_sites_df"
],
"metadata": {
"id": "ntnEK4tkv7Y1"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@markdown ## Get sites at `after_date` without localization.\n",
"from google.cloud import bigquery\n",
"import pandas as pd\n",
"\n",
"\n",
"# Get sites with the localization on a specific date\n",
"def get_non_localized_sites(after_date):\n",
" client = bigquery.Client(project=project_id)\n",
" # 1. Identify localized Sites\n",
" localized_sites_query = f\"\"\"\n",
" CREATE TEMP FUNCTION\n",
" IS_LOCALIZED(lang STRING)\n",
" RETURNS BOOL AS ( lang IS NOT NULL\n",
" AND lang != \"en\"\n",
" AND lang != \"en-us\" );\n",
" SELECT\n",
" DISTINCT page AS origin\n",
" FROM\n",
" `httparchive.all.pages`,\n",
" UNNEST(technologies) AS technologies\n",
" WHERE\n",
" technologies.technology = 'WordPress'\n",
" AND date = PARSE_DATE('%Y-%m-%d', '{after_date}' )\n",
" AND is_root_page = TRUE\n",
" AND client = 'mobile'\n",
" AND IS_LOCALIZED(REPLACE(TRIM(LOWER(JSON_VALUE(JSON_VALUE(payload, '$._almanac'), '$.html_node.lang'))), '_', '-' )) = FALSE\n",
" \"\"\"\n",
"\n",
" localized_sites_df = client.query(localized_sites_query).to_dataframe()\n",
" return localized_sites_df"
],
"metadata": {
"id": "CiJADzDJIujD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@markdown ## Get CWV changes for a set of sites between before and after dates\n",
"#@markdown\n",
"#@markdown Note: sites_df is a dataframe with an origin column containing the URLs.\n",
"#@markdown\n",
"#@markdown Returns:\n",
"#@markdown * pct_eligible_origins_with_good_cwv_change\n",
"#@markdown * pct_eligible_origins_with_good_lcp_change\n",
"#@markdown * pct_eligible_origins_with_good_cls_change\n",
"#@markdown * pct_eligible_origins_with_good_inp_change\n",
"#@markdown * pct_eligible_origins_with_good_ttfb_change\n",
"#@markdown\n",
"\n",
"\n",
"from google.cloud.exceptions import NotFound\n",
"\n",
"import pdb\n",
"# Get the CWV change before/after for sites\n",
"def get_cwv_change_before_after(sites_df, before_date, after_date):\n",
" client = bigquery.Client(project=project_id)\n",
" # 0. Ensure the 'temp_dataset' dataset exists, or create it\n",
" dataset_id = f\"{project_id}.temp_dataset\"\n",
" try:\n",
" client.get_dataset(dataset_id) # Check if dataset exists\n",
" print(f\"Dataset {dataset_id} already exists.\")\n",
" except NotFound:\n",
" print(f\"Creating dataset {dataset_id}...\")\n",
" dataset = bigquery.Dataset(dataset_id)\n",
" dataset.location = \"US\" # Set the location (adjust if needed)\n",
" client.create_dataset(dataset)\n",
"\n",
" # 1. Create a temporary table with the list of origins\n",
" schema = [bigquery.SchemaField(\"origin\", \"STRING\")]\n",
" job_config = bigquery.LoadJobConfig(schema=schema)\n",
" job = client.load_table_from_dataframe(sites_df, f\"{project_id}.temp_dataset.origins\", job_config=job_config)\n",
" job.result() # Wait for the job to complete\n",
"\n",
"\n",
" # 2. Collect CWV Data and Calculate Baselines\n",
" cwv_query = f\"\"\"\n",
" CREATE TEMP FUNCTION IS_GOOD(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64)\n",
" RETURNS BOOL\n",
" AS (\n",
" good / (good + needs_improvement + poor) >= 0.75\n",
" );\n",
"\n",
" CREATE TEMP FUNCTION IS_NON_ZERO(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64)\n",
" RETURNS BOOL\n",
" AS (\n",
" good + needs_improvement + poor > 0\n",
" );\n",
"\n",
"\n",
" SELECT\n",
" date,\n",
" COUNT(DISTINCT origin) AS origins,\n",
" SAFE_DIVIDE(COUNTIF(good_cwv), COUNTIF(any_lcp AND any_cls)) AS pct_eligible_origins_with_good_cwv,\n",
" SAFE_DIVIDE(COUNTIF(good_lcp), COUNTIF(any_lcp)) AS pct_eligible_origins_with_good_lcp,\n",
" SAFE_DIVIDE(COUNTIF(good_cls), COUNTIF(any_cls)) AS pct_eligible_origins_with_good_cls,\n",
" SAFE_DIVIDE(COUNTIF(good_inp), COUNTIF(any_inp)) AS pct_eligible_origins_with_good_inp,\n",
" SAFE_DIVIDE(COUNTIF(good_ttfb), COUNTIF(any_ttfb)) AS pct_eligible_origins_with_good_ttfb\n",
" FROM\n",
" (\n",
" SELECT\n",
" date AS date,\n",
" origin,\n",
" # Device phone or tablet as mobile, otherwise desktop.\n",
" IF(device = 'phone' OR device = 'tablet', 'mobile', device) AS device,\n",
" IS_NON_ZERO(fast_ttfb, avg_ttfb, slow_ttfb) AS any_ttfb,\n",
" IS_GOOD(fast_ttfb, avg_ttfb, slow_ttfb) AS good_ttfb,\n",
" IS_NON_ZERO(fast_inp, avg_inp, slow_inp) AS any_inp,\n",
" IS_GOOD(fast_inp, avg_inp, slow_inp) AS good_inp,\n",
" IS_NON_ZERO(small_cls, medium_cls, large_cls) AS any_cls,\n",
" IS_GOOD(small_cls, medium_cls, large_cls) AS good_cls,\n",
" IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp) AS any_lcp,\n",
" IS_GOOD(fast_lcp, avg_lcp, slow_lcp) AS good_lcp,\n",
" (IS_GOOD(fast_inp, avg_inp, slow_inp) OR fast_inp IS NULL) AND IS_GOOD(small_cls, medium_cls, large_cls) AND IS_GOOD(fast_lcp, avg_lcp, slow_lcp) AS good_cwv\n",
" FROM\n",
" `chrome-ux-report.materialized.device_summary` AS cwvs\n",
" WHERE\n",
" (\n",
" date = PARSE_DATE('%Y-%m-%d', '{before_date}') OR\n",
" date = PARSE_DATE('%Y-%m-%d', '{after_date}' )\n",
" )\n",
" AND cwvs.device IN ('desktop', 'tablet', 'phone')\n",
" # Restrict to sites that are in both dates\n",
" AND CONCAT(cwvs.origin, '/') IN (SELECT origin FROM `{project_id}.temp_dataset.origins`)\n",
"\n",
" )\n",
" GROUP BY\n",
" date\n",
" ORDER BY\n",
" date\n",
" \"\"\"\n",
"\n",
" cwv_data = client.query(cwv_query).to_dataframe()\n",
"\n",
" # select the data from the after_date (by date column)\n",
" cwv_after = cwv_data[cwv_data['date'] == pd.to_datetime(after_date)]\n",
" cwv_before = cwv_data[cwv_data['date'] == pd.to_datetime(before_date)]\n",
"\n",
" # subtract the 'pct_eligible_origins_with_good_cwv' value at cwv_before from cwv_after\n",
" pct_eligible_origins_with_good_cwv_change = cwv_after['pct_eligible_origins_with_good_cwv'].values[0] - cwv_before['pct_eligible_origins_with_good_cwv'].values[0]\n",
" pct_eligible_origins_with_good_lcp_change = cwv_after['pct_eligible_origins_with_good_lcp'].values[0] - cwv_before['pct_eligible_origins_with_good_lcp'].values[0]\n",
" pct_eligible_origins_with_good_cls_change = cwv_after['pct_eligible_origins_with_good_cls'].values[0] - cwv_before['pct_eligible_origins_with_good_cls'].values[0]\n",
" pct_eligible_origins_with_good_inp_change = cwv_after['pct_eligible_origins_with_good_inp'].values[0] - cwv_before['pct_eligible_origins_with_good_inp'].values[0]\n",
" pct_eligible_origins_with_good_ttfb_change = cwv_after['pct_eligible_origins_with_good_ttfb'].values[0] - cwv_before['pct_eligible_origins_with_good_ttfb'].values[0]\n",
"\n",
" # 3. Clean up: Delete the temporary table (optional but good practice)\n",
" client.delete_table(f\"{project_id}.temp_dataset.origins\")\n",
"\n",
" return [\n",
" pct_eligible_origins_with_good_cwv_change,\n",
" pct_eligible_origins_with_good_lcp_change,\n",
" pct_eligible_origins_with_good_cls_change,\n",
" pct_eligible_origins_with_good_inp_change,\n",
" pct_eligible_origins_with_good_ttfb_change\n",
" ]"
],
"metadata": {
"id": "WVcPDbM75Sz_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@markdown ## Calculate impact minus baseline\n",
"\n",
"# calculate the impact by subracting the baseline from the change\n",
"def calculate_feature_impact(change, baseline):\n",
" cwv_change = change[0] - baseline[0]\n",
" lcp_change = change[1] - baseline[1]\n",
" cls_change = change[2] - baseline[2]\n",
" inp_change = change[3] - baseline[3]\n",
" ttfb_change = change[4] - baseline[4]\n",
" return {\n",
" 'pct_good_cwv_change': \"{:.2%}\".format(cwv_change),\n",
" 'pct_good_lcp_change': \"{:.2%}\".format(lcp_change),\n",
" 'pct_good_cls_change': \"{:.2%}\".format(cls_change),\n",
" 'pct_good_inp_change': \"{:.2%}\".format(inp_change),\n",
" 'pct_good_ttfb_change': \"{:.2%}\".format(ttfb_change)\n",
" }"
],
"metadata": {
"id": "gF8s3t5L6Vjg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@markdown ## Measure feature performance impact minus baseline\n",
"def query_cwv_compare_feature_to_baseline(generator_tag, before_date, after_date):\n",
" active_sites = get_sites_with_feature_active(generator_tag, after_date)\n",
" count_active_sites = active_sites.count()\n",
" inactive_sites = get_sites_with_feature_inactive(generator_tag, after_date)\n",
" count_inactive_sites = inactive_sites.count()\n",
"\n",
" # Count feature active sites that were also feature active at the before date.\n",
" active_at_from_date = get_active_sites_also_active_at_before_date(generator_tag, before_date, active_sites)\n",
" count_active_at_from_date = active_at_from_date.count()\n",
"\n",
" # Calculate the percentage of count_active_at_from_date (which might be 0 so use safe divide)\n",
" if count_active_sites['origin'] > 0:\n",
" percent_of_active_sites_also_active_at_from_date = (count_active_at_from_date['origin'] / count_active_sites['origin']) * 100\n",
" else:\n",
" percent_of_active_sites_also_active_at_from_date = 0\n",
"\n",
" inactive_to_active_percent = 100 - percent_of_active_sites_also_active_at_from_date;\n",
"\n",
" # Calculate CWV change\n",
" cwv_changes_active_sites = get_cwv_change_before_after(active_sites, before_date, after_date )\n",
" cwv_changes_inactive_sites = get_cwv_change_before_after(inactive_sites, before_date, after_date )\n",
"\n",
" final_results = calculate_feature_impact(cwv_changes_active_sites, cwv_changes_inactive_sites)\n",
" final_results['feature_active_sites'] = count_active_sites['origin']\n",
" final_results['feature_inactive_sites'] = count_inactive_sites['origin']\n",
" final_results['inactive_to_active_percent'] = inactive_to_active_percent\n",
"\n",
" return final_results"
],
"metadata": {
"id": "JL0yN8uu76cq"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"colab": {
"provenance": [],
"name": "Performant Translations Colab",
"collapsed_sections": [
"4G2WkwMPzxbT"
],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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