{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "KcAZ2RHCg_Ze" }, "source": [ "# WPP Tracking\n", "\n", "Track CWV metric pass rate and feature adoption metrics across initiatives." ] }, { "cell_type": "markdown", "source": [ "# Setup" ], "metadata": { "id": "4G2WkwMPzxbT" } }, { "cell_type": "code", "source": [ "project_id = 'wpp-research'" ], "metadata": { "id": "YhCtDqb8AEcY" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qTmLBxDxBAZL" }, "source": [ "### Provide your credentials to the runtime" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SeTJb51SKs_W", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1dbe1889-3919-4c00-f7eb-dfe6f9fe85cb" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Authenticated\n" ] } ], "source": [ "from google.colab import auth\n", "auth.authenticate_user()\n", "print('Authenticated')\n" ] }, { "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 = project_id" ], "metadata": { "id": "YdTgQYtSoOoE" }, "execution_count": null, "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", "latest_dataset = get_first_of_previous_month() # eg. \"2023_06_01\" - datasets are updated monthly, indicate the latest" ], "metadata": { "id": "stNLljYnR355" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "UMKGkkZEPVRu" }, "source": [ "### Enable data table display" ] }, { "cell_type": "code", "execution_count": null, "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": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Helper Functions\n", "\n", "Define helper functions that are used repeatedly throughout the colab.\n", "\n", "`query_cwv_for_technology_over_dates` - Calculate CWV metrics over time for technology\n" ], "metadata": { "id": "7Zntzp87Q0_y" } }, { "cell_type": "markdown", "source": [ "### Helper to show CWV metrics over time for technology" ], "metadata": { "id": "6X4dlDq8ukqL" } }, { "cell_type": "code", "source": [ "from google.cloud import bigquery\n", "from datetime import datetime, timedelta\n", "\n", "client = bigquery.Client(project=project_id)\n", "\n", "def query_cwv_for_technology_over_dates(technology, from_date=None, to_date=None):\n", " \"\"\"\n", " Queries Core Web Vitals metrics from HTTP Archive for a specific technology\n", " over the past 12 months.\n", "\n", " Args:\n", " technology (str): The technology to filter by (e.g., 'React').\n", " from_date (str, optional): The start date in YYYY_MM_DD format.\n", " to_date (str, optional): The end date in YYYY_MM_DD format.\n", " Returns:\n", " A BigQuery query result object containing CWV data.\n", " \"\"\"\n", " # set to_date if not passed\n", " if not to_date:\n", " to_date = latest_dataset\n", "\n", " # set from date if not passed\n", " if not from_date:\n", " from_date = (datetime.strptime(to_date, '%Y_%m_%d').date() - timedelta(days=365)).strftime('%Y_%m_%d')\n", "\n", "\n", " 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", " WITH sites_using_technology AS (\n", " SELECT\n", " page AS origin,\n", " client AS device,\n", " date,\n", " FROM\n", " `httparchive.all.pages`,\n", " UNNEST(technologies) AS technologies\n", " WHERE\n", " technologies.technology = '{technology}'\n", " AND date BETWEEN PARSE_DATE('%Y_%m_%d', '{from_date}') AND PARSE_DATE('%Y_%m_%d', '{to_date}' )\n", " AND is_root_page = TRUE\n", " GROUP BY\n", " page, device, date\n", " ),\n", " cwv_metrics_for_origins AS (\n", " SELECT\n", " cwvs.date AS date,\n", " sites_using_technology.origin AS origin,\n", " # Device phone or tablet as mobile, otherwise desktop.\n", " IF(cwvs.device = 'phone' OR cwvs.device = 'tablet', 'mobile', 'desktop') AS device,\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", " JOIN\n", " sites_using_technology\n", " ON (\n", " sites_using_technology.origin = CONCAT(cwvs.origin, '/') AND\n", " sites_using_technology.date = cwvs.date AND\n", " sites_using_technology.device = IF(cwvs.device = 'phone' OR cwvs.device = 'tablet', 'mobile', cwvs.device)\n", " )\n", " WHERE\n", " cwvs.date BETWEEN PARSE_DATE('%Y_%m_%d', '{from_date}') AND PARSE_DATE('%Y_%m_%d', '{to_date}' )\n", " AND cwvs.device IN ('desktop', 'tablet', 'phone')\n", "\n", " ),\n", "\n", "\n", " cwvs_by_date AS (\n", " SELECT\n", " date,\n", " device,\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", " FROM\n", " cwv_metrics_for_origins\n", " GROUP BY\n", " date, device\n", " ORDER BY\n", " date, device\n", " )\n", " SELECT * FROM cwvs_by_date\n", " \"\"\"\n", "\n", " query_job = client.query(query)\n", " return query_job.result() # Return the results of the query\n", "\n", "# Restrict the query to on the from and to dates, and to the specified from and to versions\n", "def query_cwv_for_technology_compare_dates_by_version(technology, from_date, to_date, from_version, to_version):\n", " \"\"\"\n", " Queries Core Web Vitals metrics from HTTP Archive for a specific technology\n", " over the past 12 months.\n", "\n", " Args:\n", " technology (str): The technology to filter by (e.g., 'React').\n", " from_date (str): The start date in YYYY_MM_DD format.\n", " to_date (str): The end date in YYYY_MM_DD format.\n", " from_version (str): The start version.\n", " to_version (str): The end version.\n", " Returns:\n", " A BigQuery query result object containing CWV data.\n", " \"\"\"\n", " # set to_date if not passed\n", " if not to_date:\n", " to_date = latest_dataset\n", "\n", " # set from date if not passed\n", " if not from_date:\n", " from_date = (datetime.strptime(to_date, '%Y_%m_%d').date() - timedelta(days=365)).strftime('%Y_%m_%d')\n", "\n", "\n", " 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", " WITH sites_using_technology AS (\n", " SELECT\n", " page AS origin,\n", " client AS device,\n", " date,\n", " FROM\n", " `httparchive.all.pages`,\n", " UNNEST(technologies) AS technologies\n", " WHERE\n", " technologies.technology = '{technology}'\n", " AND (\n", " ( date = PARSE_DATE('%Y_%m_%d', '{from_date}') AND REGEXP_EXTRACT(JSON_EXTRACT( JSON_EXTRACT(payload, '$._detected_apps'),'$.{technology}'),r'(\\\\d.\\\\d+.\\\\d+).*') = '{from_version}' ) OR\n", " ( date = PARSE_DATE('%Y_%m_%d', '{to_date}' ) AND REGEXP_EXTRACT(JSON_EXTRACT( JSON_EXTRACT(payload, '$._detected_apps'),'$.{technology}'),r'(\\\\d.\\\\d+.\\\\d+).*') = '{to_version}' )\n", " )\n", " AND is_root_page = TRUE\n", " GROUP BY\n", " page, device, date\n", " ),\n", " cwv_metrics_for_origins AS (\n", " SELECT\n", " cwvs.date AS date,\n", " sites_using_technology.origin AS origin,\n", " # Device phone or tablet as mobile, otherwise desktop.\n", " IF(cwvs.device = 'phone' OR cwvs.device = 'tablet', 'mobile', cwvs.device) AS device,\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", " JOIN\n", " sites_using_technology\n", " ON (\n", " sites_using_technology.origin = CONCAT(cwvs.origin, '/') AND\n", " sites_using_technology.date = cwvs.date AND\n", " sites_using_technology.device = IF(cwvs.device = 'phone' OR cwvs.device = 'tablet', 'mobile', cwvs.device)\n", " )\n", " WHERE\n", " cwvs.date BETWEEN PARSE_DATE('%Y_%m_%d', '{from_date}') AND PARSE_DATE('%Y_%m_%d', '{to_date}' )\n", " AND cwvs.device IN ('desktop', 'tablet', 'phone')\n", " # Restrict to sites that are in both dates\n", " AND CONCAT(cwvs.origin, '/') IN (\n", " SELECT\n", " origin\n", " FROM\n", " sites_using_technology\n", " GROUP BY\n", " origin\n", " HAVING\n", " COUNT(*) = 2\n", " )\n", " ),\n", "\n", "\n", " cwvs_by_date AS (\n", " SELECT\n", " date,\n", " device,\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", " FROM\n", " cwv_metrics_for_origins\n", " GROUP BY\n", " date, device\n", " ORDER BY\n", " date, device\n", " )\n", " SELECT * FROM cwvs_by_date\n", " \"\"\"\n", "\n", " query_job = client.query(query)\n", " return query_job.result() # Return the results of the query" ], "metadata": { "id": "zxFgf3WUQ5IA" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Feature performance analysis helpers" ], "metadata": { "id": "_59x7rfyWRYO" } }, { "cell_type": "code", "source": [ "#@markdown ## Get sites at `after_date` with feature active.\n", "from google.cloud import bigquery\n", "import pandas as pd\n", "\n", "# This limit is used throughout, adjust to increase sample or speed.\n", "limit = 50000\n", "\n", "\n", "# Get sites with the feature active on a specific date\n", "def get_sites_with_feature_active(generator_tag, after_date):\n", " client = bigquery.Client(project=project_id)\n", " # 1. Identify Active and Non-Active Sites\n", " active_sites_query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING, generator_tag STRING)\n", " RETURNS STRING\n", " LANGUAGE js\n", " AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith(generator_tag));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", " ''';\n", "\n", "\n", " SELECT\n", " 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 getFeature(payload, '{generator_tag}') != \"\"\n", "\n", "\n", " LIMIT {limit}\n", " \"\"\"\n", "\n", " active_sites_df = client.query(active_sites_query).to_dataframe()\n", " return active_sites_df" ], "metadata": { "id": "ntnEK4tkv7Y1" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Get sites with features active" ], "metadata": { "id": "bC13eOJnR__7" } }, { "cell_type": "code", "source": [ "#@markdown ## Get sites at `after_date` with feature active.\n", "from google.cloud import bigquery\n", "import pandas as pd\n", "\n", "# This limit is used throughout, adjust to increase sample or speed.\n", "limit = 50000\n", "\n", "\n", "# Get sites with the feature active on a specific date\n", "def get_sites_with_features_active(generator_tag1, generator_tag2, after_date):\n", " client = bigquery.Client(project=project_id)\n", " # 1. Identify Active and Non-Active Sites\n", " active_sites_query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING, generator_tag STRING)\n", " RETURNS STRING\n", " LANGUAGE js\n", " AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith(generator_tag));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", " ''';\n", "\n", "\n", " SELECT\n", " 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 getFeature(payload, '{generator_tag1}') != \"\"\n", " AND getFeature(payload, '{generator_tag2}') != \"\"\n", "\n", "\n", " LIMIT {limit}\n", " \"\"\"\n", "\n", " active_sites_df = client.query(active_sites_query).to_dataframe()\n", " return active_sites_df" ], "metadata": { "id": "Gh97AHI8QqPY" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ## Get sites at `after_date` with feature *not* active.\n", "from google.cloud import bigquery\n", "import pandas as pd\n", "\n", "\n", "# Get sites with the feature active on a specific date\n", "def get_sites_with_feature_inactive(generator_tag, after_date):\n", " client = bigquery.Client(project=project_id)\n", " # 1. Identify Active and Non-Active Sites\n", " inactive_sites_query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING, generator_tag STRING)\n", " RETURNS STRING\n", " LANGUAGE js\n", " AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith(generator_tag));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", " ''';\n", "\n", "\n", " SELECT\n", " 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 getFeature(payload, '{generator_tag}') = \"\"\n", "\n", "\n", " LIMIT {limit}\n", " \"\"\"\n", "\n", " inactive_sites_df = client.query(inactive_sites_query).to_dataframe()\n", " return inactive_sites_df" ], "metadata": { "id": "Pqejonkx4rU2" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ## Get sites at `after_date` with feature *not* active.\n", "from google.cloud import bigquery\n", "import pandas as pd\n", "\n", "\n", "# Get sites with the feature active on a specific date\n", "def get_sites_with_features_inactive(generator_tag1, generator_tag2, after_date):\n", " client = bigquery.Client(project=project_id)\n", " # 1. Identify Active and Non-Active Sites\n", " inactive_sites_query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING, generator_tag STRING)\n", " RETURNS STRING\n", " LANGUAGE js\n", " AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith(generator_tag));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", " ''';\n", "\n", "\n", " SELECT\n", " 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 getFeature(payload, '{generator_tag1}') = \"\"\n", " AND getFeature(payload, '{generator_tag2}') = \"\"\n", "\n", "\n", " LIMIT {limit}\n", " \"\"\"\n", "\n", " inactive_sites_df = client.query(inactive_sites_query).to_dataframe()\n", " return inactive_sites_df" ], "metadata": { "id": "I9SXaAtfn3ga" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Helpers to compare before and after, minus baseline" ], "metadata": { "id": "O6jI1ChWXOJs" } }, { "cell_type": "code", "source": [ "#@markdown ## Get sites at `before_date` with feature active.\n", "#@markdown Ideally, this should be 0.\n", "\n", "# Calculate how many of the sites where the feature was active at the after_date\n", "# did not have the feature active at the before_date\n", "from google.cloud import bigquery\n", "import pandas as pd\n", "\n", "\n", "# Get sites from a list with the feature not active on a specific date\n", "def get_active_sites_also_active_at_before_date(generator_tag, before_date, existing_sites):\n", " client = bigquery.Client(project=project_id)\n", "\n", " # Extract the origins from the existing_sites dataframe as a comma separated list\n", " existing_sites_list = ', '.join(f\"'{origin}'\" for origin in existing_sites['origin'])\n", "\n", "\n", " # 1. Identify Active and Non-Active Sites\n", " active_sites_query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING, generator_tag STRING)\n", " RETURNS STRING\n", " LANGUAGE js\n", " AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith(generator_tag));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", " ''';\n", "\n", "\n", " SELECT\n", " page AS origin\n", " FROM\n", " `httparchive.all.pages`,\n", " UNNEST(technologies) AS technologies\n", "\n", " WHERE\n", " technologies.technology = 'WordPress'\n", " AND date = PARSE_DATE('%Y-%m-%d', '{before_date}' )\n", " AND is_root_page = TRUE\n", " AND client = 'mobile'\n", " AND page IN ({existing_sites_list})\n", " AND getFeature(payload, '{generator_tag}') != \"\"\n", " LIMIT {limit}\n", " \"\"\"\n", "\n", " active_sites_df = client.query(active_sites_query).to_dataframe()\n", " return active_sites_df" ], "metadata": { "id": "u-VvZMmfSBJ8" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ## Get sites at `before_date` with feature active.\n", "#@markdown Ideally, this should be 0.\n", "\n", "# Calculate how many of the sites where the feature was active at the after_date\n", "# did not have the feature active at the before_date\n", "from google.cloud import bigquery\n", "import pandas as pd\n", "\n", "\n", "# Get sites from a list with the feature not active on a specific date\n", "def get_active_sites_also_active_at_before_date_features(generator_tag1, generator_tag2, before_date, existing_sites):\n", " client = bigquery.Client(project=project_id)\n", "\n", " # Extract the origins from the existing_sites dataframe as a comma separated list\n", " existing_sites_list = ', '.join(f\"'{origin}'\" for origin in existing_sites['origin'])\n", "\n", "\n", " # 1. Identify Active and Non-Active Sites\n", " active_sites_query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING, generator_tag STRING)\n", " RETURNS STRING\n", " LANGUAGE js\n", " AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith(generator_tag));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", " ''';\n", "\n", "\n", " SELECT\n", " page AS origin\n", " FROM\n", " `httparchive.all.pages`,\n", " UNNEST(technologies) AS technologies\n", "\n", " WHERE\n", " technologies.technology = 'WordPress'\n", " AND date = PARSE_DATE('%Y-%m-%d', '{before_date}' )\n", " AND is_root_page = TRUE\n", " AND client = 'mobile'\n", " AND page IN ({existing_sites_list})\n", " AND getFeature(payload, '{generator_tag1}') != \"\"\n", " AND getFeature(payload, '{generator_tag2}') != \"\"\n", " LIMIT {limit}\n", " \"\"\"\n", "\n", " active_sites_df = client.query(active_sites_query).to_dataframe()\n", " return active_sites_df" ], "metadata": { "id": "i2DT6JXgPlDO" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "iqE8_d5kKddc" } }, { "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\n", "\n", "\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", " except NotFound:\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", " client = bigquery.Client(project=project_id)\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", " 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_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", " 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", "\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", " ]" ], "metadata": { "id": "WVcPDbM75Sz_" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Breakout by device" ], "metadata": { "id": "nmMTyZvMKRgw" } }, { "cell_type": "code", "source": [ "#@markdown ## Get CWV changes for a set of sites between before and after dates\n", "#@markdown ### Broken out by device\n", "#@markdown `get_cwv_change_before_after_by_device`\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_mobile\n", "#@markdown * pct_eligible_origins_with_good_cwv_change_mobile,\n", "#@markdown * pct_eligible_origins_with_good_lcp_change_mobile,\n", "#@markdown * pct_eligible_origins_with_good_cls_change_mobile,\n", "#@markdown * pct_eligible_origins_with_good_inp_change_mobile,\n", "#@markdown * pct_eligible_origins_with_good_cwv_change_desktop,\n", "#@markdown * pct_eligible_origins_with_good_lcp_change_desktop,\n", "#@markdown * pct_eligible_origins_with_good_cls_change_desktop,\n", "#@markdown * pct_eligible_origins_with_good_inp_change_desktop\n", "\n" ], "metadata": { "id": "heT91UXwKUWi" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "import pdb\n", "# Get the CWV change before/after for sites\n", "def get_cwv_change_before_after_by_device(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", " except NotFound:\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", " client = bigquery.Client(project=project_id)\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", " device,\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", " 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_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", " GROUP BY\n", " date, device\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 and device columns)\n", " # mobile data\n", " cwv_after_mobile = cwv_data[(cwv_data['date'] == pd.to_datetime(after_date)) & (cwv_data['device'] == 'mobile')]\n", " cwv_before_mobile = cwv_data[(cwv_data['date'] == pd.to_datetime(before_date)) & (cwv_data['device'] == 'mobile')]\n", "\n", " # desktop data\n", " cwv_after_desktop = cwv_data[(cwv_data['date'] == pd.to_datetime(after_date)) & (cwv_data['device'] == 'desktop')]\n", " cwv_before_desktop = cwv_data[(cwv_data['date'] == pd.to_datetime(before_date)) & (cwv_data['device'] == 'desktop')]\n", "\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_mobile = cwv_after_mobile['pct_eligible_origins_with_good_cwv'].values[0] - cwv_before_mobile['pct_eligible_origins_with_good_cwv'].values[0]\n", " pct_eligible_origins_with_good_lcp_change_mobile = cwv_after_mobile['pct_eligible_origins_with_good_lcp'].values[0] - cwv_before_mobile['pct_eligible_origins_with_good_lcp'].values[0]\n", " pct_eligible_origins_with_good_cls_change_mobile = cwv_after_mobile['pct_eligible_origins_with_good_cls'].values[0] - cwv_before_mobile['pct_eligible_origins_with_good_cls'].values[0]\n", " pct_eligible_origins_with_good_inp_change_mobile = cwv_after_mobile['pct_eligible_origins_with_good_inp'].values[0] - cwv_before_mobile['pct_eligible_origins_with_good_inp'].values[0]\n", "\n", " pct_eligible_origins_with_good_cwv_change_desktop = cwv_after_desktop['pct_eligible_origins_with_good_cwv'].values[0] - cwv_before_desktop['pct_eligible_origins_with_good_cwv'].values[0]\n", " pct_eligible_origins_with_good_lcp_change_desktop = cwv_after_desktop['pct_eligible_origins_with_good_lcp'].values[0] - cwv_before_desktop['pct_eligible_origins_with_good_lcp'].values[0]\n", " pct_eligible_origins_with_good_cls_change_desktop = cwv_after_desktop['pct_eligible_origins_with_good_cls'].values[0] - cwv_before_desktop['pct_eligible_origins_with_good_cls'].values[0]\n", " pct_eligible_origins_with_good_inp_change_desktop = cwv_after_desktop['pct_eligible_origins_with_good_inp'].values[0] - cwv_before_desktop['pct_eligible_origins_with_good_inp'].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_mobile,\n", " pct_eligible_origins_with_good_lcp_change_mobile,\n", " pct_eligible_origins_with_good_cls_change_mobile,\n", " pct_eligible_origins_with_good_inp_change_mobile,\n", " pct_eligible_origins_with_good_cwv_change_desktop,\n", " pct_eligible_origins_with_good_lcp_change_desktop,\n", " pct_eligible_origins_with_good_cls_change_desktop,\n", " pct_eligible_origins_with_good_inp_change_desktop,\n", " cwv_before_mobile,\n", " cwv_after_mobile,\n", " cwv_before_desktop,\n", " cwv_after_desktop\n", " ]" ], "metadata": { "id": "EkvnjJrKOH5O" }, "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", " 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", " }" ], "metadata": { "id": "gF8s3t5L6Vjg" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ## Calculate impact minus baseline by device\n", "\n", "# calculate the impact by subracting the baseline from the change\n", "def calculate_feature_impact_by_device(change, baseline):\n", " cwv_change_mobile = change[0] - baseline[0]\n", " lcp_change_mobile = change[1] - baseline[1]\n", " cls_change_mobile = change[2] - baseline[2]\n", " inp_change_mobile = change[3] - baseline[3]\n", " cwv_change_desktop = change[4] - baseline[4]\n", " lcp_change_desktop = change[5] - baseline[5]\n", " cls_change_desktop = change[6] - baseline[6]\n", " inp_change_desktop = change[7] - baseline[7]\n", " return {\n", " 'pct_good_cwv_change_mobile': \"{:.2%}\".format(cwv_change_mobile),\n", " 'pct_good_lcp_change_mobile': \"{:.2%}\".format(lcp_change_mobile),\n", " 'pct_good_cls_change_mobile': \"{:.2%}\".format(cls_change_mobile),\n", " 'pct_good_inp_change_mobile': \"{:.2%}\".format(inp_change_mobile),\n", " 'pct_good_cwv_change_desktop': \"{:.2%}\".format(cwv_change_desktop),\n", " 'pct_good_lcp_change_desktop': \"{:.2%}\".format(lcp_change_desktop),\n", " 'pct_good_cls_change_desktop': \"{:.2%}\".format(cls_change_desktop),\n", " 'pct_good_inp_change_desktop': \"{:.2%}\".format(inp_change_desktop)\n", " }" ], "metadata": { "id": "1Dh14Oa1Pb2-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ## Measure feature performance impact minus baseline\n", "#@markdown\n", "#@markdown ### Note:\n", "#@markdown * This helper combines device data.\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", " also_active_at_before = (count_active_at_from_date['origin'] / count_active_sites['origin']) * 100\n", " else:\n", " also_active_at_before = 0\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['also_active_at_before'] = \"{:.2%}\".format(also_active_at_before)\n", "\n", " return final_results" ], "metadata": { "id": "JL0yN8uu76cq" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ## Measure feature performance impact minus baseline\n", "#@markdown ### Breakout by device\n", "def query_cwv_compare_feature_to_baseline_by_device(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", " also_active_at_before = (count_active_at_from_date['origin'] / count_active_sites['origin']) * 100\n", " else:\n", " also_active_at_before = 0\n", "\n", " # Calculate CWV change\n", " cwv_changes_active_sites = get_cwv_change_before_after_by_device(active_sites, before_date, after_date )\n", " cwv_changes_inactive_sites = get_cwv_change_before_after_by_device(inactive_sites, before_date, after_date )\n", "\n", " final_results = calculate_feature_impact_by_device(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['also_active_at_before'] = \"{:.2%}\".format(also_active_at_before)\n", " final_results['before_date'] = before_date\n", " final_results['after_date'] = after_date\n", " final_results['generator_tag'] = generator_tag\n", " final_results['cwv_changes_active_sites'] = cwv_changes_active_sites\n", " final_results['cwv_changes_inactive_sites'] = cwv_changes_inactive_sites\n", "\n", " return final_results" ], "metadata": { "id": "UqtzgMvZSpiE" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ## Measure feature performance impact minus baseline\n", "#@markdown ### Breakout by device\n", "def query_cwv_compare_feature_to_baseline_by_device_multiple_tags(generator_tag1, generator_tag2, before_date, after_date):\n", "\n", " active_sites = get_sites_with_features_active(generator_tag1, generator_tag2, after_date)\n", " count_active_sites = active_sites.count()\n", " inactive_sites = get_sites_with_features_inactive(generator_tag1, generator_tag2, 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_features(generator_tag1, generator_tag2, 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", " also_active_at_before = (count_active_at_from_date['origin'] / count_active_sites['origin']) * 100\n", " else:\n", " also_active_at_before = 0\n", "\n", " # Calculate CWV change\n", " cwv_changes_active_sites = get_cwv_change_before_after_by_device(active_sites, before_date, after_date )\n", " cwv_changes_inactive_sites = get_cwv_change_before_after_by_device(inactive_sites, before_date, after_date )\n", "\n", " final_results = calculate_feature_impact_by_device(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['also_active_at_before'] = \"{:.2%}\".format(also_active_at_before)\n", " final_results['before_date'] = before_date\n", " final_results['after_date'] = after_date\n", " final_results['generator_tag1'] = generator_tag1\n", " final_results['generator_tag2'] = generator_tag2\n", " final_results['cwv_changes_active_sites'] = cwv_changes_active_sites\n", " final_results['cwv_changes_inactive_sites'] = cwv_changes_inactive_sites\n", "\n", " return final_results" ], "metadata": { "id": "zRWWFk1jQOMd" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Helper use examples" ], "metadata": { "id": "tRAH6Wr0moIo" } }, { "cell_type": "code", "source": [ "elementor_data = query_cwv_for_technology_over_dates('Elementor')\n", "elementor_data.to_dataframe()" ], "metadata": { "id": "whbQMo9ia6HY", "outputId": "578f5c37-ce5d-4716-f756-552a56c27347", "colab": { "base_uri": "https://localhost:8080/", "height": 304 } }, "execution_count": null, "outputs": [ { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0melementor_data\u001b[0m \u001b[0;34m=\u001b[0m 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all.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1676\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_job_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1677\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1678\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/api_core/retry/retry_unary.py\u001b[0m in \u001b[0;36mretry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maximum\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/job/query.py\u001b[0m in \u001b[0;36mis_job_done\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1643\u001b[0m \u001b[0;31m# jobs.getQueryResults hangs as long as it can to ensure we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1644\u001b[0m \u001b[0;31m# know when the query has finished as soon as possible.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1645\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reload_query_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mreload_query_results_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1647\u001b[0m \u001b[0;31m# Even if the query is finished now according to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/job/query.py\u001b[0m in \u001b[0;36m_reload_query_results\u001b[0;34m(self, retry, timeout, page_size)\u001b[0m\n\u001b[1;32m 1441\u001b[0m \u001b[0mtransport_timeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m self._query_results = self._client._get_query_results(\n\u001b[0m\u001b[1;32m 1444\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjob_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0mretry\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/client.py\u001b[0m in \u001b[0;36m_get_query_results\u001b[0;34m(self, job_id, retry, project, timeout_ms, location, timeout, page_size)\u001b[0m\n\u001b[1;32m 2022\u001b[0m \u001b[0;31m# QueryJob.result()). So we don't need to poll here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2023\u001b[0m \u001b[0mspan_attributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"path\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2024\u001b[0;31m resource = self._call_api(\n\u001b[0m\u001b[1;32m 2025\u001b[0m \u001b[0mretry\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2026\u001b[0m \u001b[0mspan_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"BigQuery.getQueryResults\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/client.py\u001b[0m in \u001b[0;36m_call_api\u001b[0;34m(self, retry, span_name, span_attributes, job_ref, headers, **kwargs)\u001b[0m\n\u001b[1;32m 831\u001b[0m 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predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msleep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msleep_generator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misawaitable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 146\u001b[0m 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542\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 587\u001b[0m }\n\u001b[1;32m 588\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m 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669\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 788\u001b[0m \u001b[0;31m# Make the request on the HTTPConnection object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m response = self._make_request(\n\u001b[0m\u001b[1;32m 790\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[0;31m# Receive the response from the server\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 536\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 537\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0;31m# Get the response from http.client.HTTPConnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1373\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1375\u001b[0;31m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1376\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1377\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 280\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.10/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 706\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 707\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1301\u001b[0m \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1302\u001b[0m self.__class__)\n\u001b[0;32m-> 1303\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1304\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1305\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1157\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1158\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1160\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1161\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "code", "source": [ "active_sites = get_sites_with_feature_active('speculation-rules', '2024-11-01')\n", "active_sites.head(5)\n", "\n", "# count active sites\n", "count_active_sites = active_sites.count()\n", "print(count_active_sites)" ], "metadata": { "id": "XMUE3rbvwpIr" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "get_sites_with_features_active('embed-optimizer', 'optimization-detective', '2024-09-01')" ], "metadata": { "id": "tmX9uC1NQ19C" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "inactive_sites = get_sites_with_feature_inactive('speculation-rules', '2024-08-01')\n", "inactive_sites.head(5)\n", "\n", "# count inactive sites\n", "count_inactive_sites = inactive_sites.count()\n", "print(count_inactive_sites)" ], "metadata": { "id": "Sw5e-p6z479N", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "099caf84-dcf3-4d8f-998f-0ba63ec38c76" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "origin 50000\n", "dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "sites = get_sites_with_features_inactive( 'embed-optimizer', 'optimization-detective', '2024-09-01')\n", "sites.head(500)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 669 }, "id": "tOZen3o4Dbw4", "outputId": "bbebd31d-e2f4-4598-edbc-168e4d6b1e78" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " origin\n", "0 https://mrd.cash/\n", "1 https://www.handipet.org/\n", "2 https://www.mimasaka-no-kuni.jp/\n", "3 https://soundcity.com.ar/\n", "4 https://ciclismoarg.com.ar/\n", ".. ...\n", "495 https://ivadi.es/\n", "496 https://meuesquematizado.com.br/\n", "497 https://safemedicinedrop.com/\n", "498 https://visittrollaskagi.is/\n", "499 https://www.plazahyundai.com/\n", "\n", "[500 rows x 1 columns]" ], "text/html": [ "\n", "
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origin
0https://mrd.cash/
1https://www.handipet.org/
2https://www.mimasaka-no-kuni.jp/
3https://soundcity.com.ar/
4https://ciclismoarg.com.ar/
......
495https://ivadi.es/
496https://meuesquematizado.com.br/
497https://safemedicinedrop.com/
498https://visittrollaskagi.is/
499https://www.plazahyundai.com/
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500 rows × 1 columns

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\"440\",\n },\n\"https://www.mh-ordi.at/\"],\n [{\n 'v': 441,\n 'f': \"441\",\n },\n\"https://iyibilgiler.wordpress.com/\"],\n [{\n 'v': 442,\n 'f': \"442\",\n },\n\"https://restaurantrosas.nl/\"],\n [{\n 'v': 443,\n 'f': \"443\",\n },\n\"https://febi.iainpalopo.ac.id/\"],\n [{\n 'v': 444,\n 'f': \"444\",\n },\n\"https://www.wacker1930.de/\"],\n [{\n 'v': 445,\n 'f': \"445\",\n },\n\"https://metropostnews.com/\"],\n [{\n 'v': 446,\n 'f': \"446\",\n },\n\"https://labastiglia.com/\"],\n [{\n 'v': 447,\n 'f': \"447\",\n },\n\"https://rushd.sch.id/\"],\n [{\n 'v': 448,\n 'f': \"448\",\n },\n\"https://laviklinic.co.il/\"],\n [{\n 'v': 449,\n 'f': \"449\",\n },\n\"https://garaz-nowoczesny.pl/\"],\n [{\n 'v': 450,\n 'f': \"450\",\n },\n\"https://nirvanaandspa.com/\"],\n [{\n 'v': 451,\n 'f': \"451\",\n },\n\"https://navarro.gob.ar/\"],\n [{\n 'v': 452,\n 'f': \"452\",\n },\n\"https://www.griffincdjrvaldosta.com/\"],\n [{\n 'v': 453,\n 'f': \"453\",\n },\n\"https://tempo-tv.com/\"],\n [{\n 'v': 454,\n 'f': \"454\",\n },\n\"https://uranai.news/\"],\n [{\n 'v': 455,\n 'f': \"455\",\n },\n\"https://thebroughton.co.uk/\"],\n [{\n 'v': 456,\n 'f': \"456\",\n },\n\"https://www.sutliffvolkswagen.com/\"],\n [{\n 'v': 457,\n 'f': \"457\",\n },\n\"https://togetheralive.be/\"],\n [{\n 'v': 458,\n 'f': \"458\",\n },\n\"https://batutairenginiams.lt/\"],\n [{\n 'v': 459,\n 'f': \"459\",\n },\n\"https://www.thorntonbrothers.net/\"],\n [{\n 'v': 460,\n 'f': \"460\",\n },\n\"https://activehistoryteacher.com/\"],\n [{\n 'v': 461,\n 'f': \"461\",\n },\n\"https://powervacamerica.com/\"],\n [{\n 'v': 462,\n 'f': \"462\",\n },\n\"https://www.goeters.nl/\"],\n [{\n 'v': 463,\n 'f': \"463\",\n },\n\"https://hostys.uprwssp.org/\"],\n [{\n 'v': 464,\n 'f': \"464\",\n },\n\"https://iptvlovers.com/\"],\n [{\n 'v': 465,\n 'f': \"465\",\n },\n\"https://www.radiologie-luton-reims.fr/\"],\n [{\n 'v': 466,\n 'f': \"466\",\n },\n\"https://g2r.com.br/\"],\n [{\n 'v': 467,\n 'f': \"467\",\n },\n\"https://brilliantasmit.staff.unri.ac.id/\"],\n [{\n 'v': 468,\n 'f': \"468\",\n },\n\"https://bad-bunny.flowhot.cc/\"],\n [{\n 'v': 469,\n 'f': \"469\",\n },\n\"https://dearbillypatterns.com/\"],\n [{\n 'v': 470,\n 'f': \"470\",\n },\n\"https://green-card-news.com/\"],\n [{\n 'v': 471,\n 'f': \"471\",\n },\n\"https://devon-tools.ru/\"],\n [{\n 'v': 472,\n 'f': \"472\",\n },\n\"https://www.radirna.cz/\"],\n [{\n 'v': 473,\n 'f': \"473\",\n },\n\"https://carasnortes.com/\"],\n [{\n 'v': 474,\n 'f': \"474\",\n },\n\"https://prodibio.com/\"],\n [{\n 'v': 475,\n 'f': \"475\",\n },\n\"https://idees-store.gr/\"],\n [{\n 'v': 476,\n 'f': \"476\",\n },\n\"https://serenitytalks.com/\"],\n [{\n 'v': 477,\n 'f': \"477\",\n },\n\"https://www.casadeyemanya.com/\"],\n [{\n 'v': 478,\n 'f': \"478\",\n },\n\"https://greenpuddle.net/\"],\n [{\n 'v': 479,\n 'f': \"479\",\n },\n\"http://www.fluirautomacao.com.br/\"],\n [{\n 'v': 480,\n 'f': \"480\",\n },\n\"https://www.ptplusvip.com/\"],\n [{\n 'v': 481,\n 'f': \"481\",\n },\n\"https://medicoop.com.do/\"],\n [{\n 'v': 482,\n 'f': \"482\",\n },\n\"https://www.benevit.at/\"],\n [{\n 'v': 483,\n 'f': \"483\",\n },\n\"https://slaskiecentrumosteopatii.pl/\"],\n [{\n 'v': 484,\n 'f': \"484\",\n },\n\"https://campervanbruno.es/\"],\n [{\n 'v': 485,\n 'f': \"485\",\n },\n\"https://amancialandrealty.com/\"],\n [{\n 'v': 486,\n 'f': \"486\",\n },\n\"https://andes-travel.cl/\"],\n [{\n 'v': 487,\n 'f': \"487\",\n },\n\"https://www.textilesmontecid.com/\"],\n [{\n 'v': 488,\n 'f': \"488\",\n },\n\"https://infinitybouncepark.com/\"],\n [{\n 'v': 489,\n 'f': \"489\",\n },\n\"http://www.motu-yamamoto.com/\"],\n [{\n 'v': 490,\n 'f': \"490\",\n },\n\"https://www.playground-kids.com.ua/\"],\n [{\n 'v': 491,\n 'f': \"491\",\n },\n\"https://lavoixdelesperance.org/\"],\n [{\n 'v': 492,\n 'f': \"492\",\n },\n\"https://www.gbs-mozaiek.be/\"],\n [{\n 'v': 493,\n 'f': \"493\",\n },\n\"https://semi-sa.com.ar/\"],\n [{\n 'v': 494,\n 'f': \"494\",\n },\n\"https://onlinebingo.co.uk/\"],\n [{\n 'v': 495,\n 'f': \"495\",\n },\n\"https://ivadi.es/\"],\n [{\n 'v': 496,\n 'f': \"496\",\n },\n\"https://meuesquematizado.com.br/\"],\n [{\n 'v': 497,\n 'f': \"497\",\n },\n\"https://safemedicinedrop.com/\"],\n [{\n 'v': 498,\n 'f': \"498\",\n },\n\"https://visittrollaskagi.is/\"],\n [{\n 'v': 499,\n 'f': \"499\",\n },\n\"https://www.plazahyundai.com/\"]],\n columns: [[\"number\", \"index\"], [\"string\", \"origin\"]],\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
\n \n \n\n\n \n
`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n " }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "code", "source": [ "previously_not_active = get_active_sites_also_active_at_before_date('speculation-rules', '2024-05-01', active_sites)\n", "previously_not_active.head(5)\n", "\n", "# count active sites\n", "count_previously_not_active = previously_not_active.count()\n", "print(count_previously_not_active)" ], "metadata": { "id": "2f60oM9FTQAU", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "937b5fe1-9d08-47fc-a175-d1ceda81a5c4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "origin 2960\n", "dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "cwv_changes_active_sites = get_cwv_change_before_after(active_sites, '2024-03-01', '2024-08-01' )\n", "print(cwv_changes_active_sites)" ], "metadata": { "id": "g7DrwD1Z6kcB", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f584f309-8303-49e6-ae3a-939f2b864123" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[0.06232002113378332, 0.06346816102047553, 0.03573906737828336, 0.044142435735356034]\n" ] } ] }, { "cell_type": "code", "source": [ "cwv_changes_active_sites_by_device = get_cwv_change_before_after_by_device(active_sites, '2024-03-01', '2024-08-01' )\n", "print(cwv_changes_active_sites_by_device)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Yl99_75aLbMr", "outputId": "ba349d86-1aa2-4d87-8027-556472c1068c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[0.07083488781153219, 0.06909267366979688, 0.03922572086938758, 0.08127807033918977, 0.053894219906421015, 0.05999325206027517, 0.026095263217394526, 0.0027941094172748704]\n" ] } ] }, { "cell_type": "code", "source": [ "cwv_changes_inactive_sites = get_cwv_change_before_after(inactive_sites, '2024-03-01', '2024-08-01' )\n", "print(cwv_changes_inactive_sites)" ], "metadata": { "id": "Hv4-fMck7pD6", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "370f9c31-68eb-450f-c691-e5b518de9154" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[0.03842766935669645, 0.03281879625688, 0.016236264348328477, 0.036834856975053176]\n" ] } ] }, { "cell_type": "code", "source": [ "cwv_changes_inactive_sites_by_device = get_cwv_change_before_after_by_device(inactive_sites, '2024-03-01', '2024-08-01' )\n", "print(cwv_changes_inactive_sites_by_device)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ReoWFooEOaFJ", "outputId": "330e1059-3ac9-41e9-8c34-8fd24fe82f6c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[0.04525254428354497, 0.03586424508053565, 0.012105841662442773, 0.06688614771352841, 0.030943523718571342, 0.03481560556441132, 0.01572887912168608, 0.0004040350745310173]\n" ] } ] }, { "cell_type": "code", "source": [ "final_results = calculate_feature_impact(cwv_changes_active_sites, cwv_changes_inactive_sites)\n", "print(final_results)\n" ], "metadata": { "id": "9ZgXf5Je9N4x", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e66ea769-9bca-4b4d-b327-62aa2624d51b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change': '2.39%', 'pct_good_lcp_change': '3.06%', 'pct_good_cls_change': '1.95%', 'pct_good_inp_change': '0.73%'}\n" ] } ] }, { "cell_type": "code", "source": [ "final_results = calculate_feature_impact_by_device(cwv_changes_active_sites_by_device, cwv_changes_inactive_sites_by_device)\n", "print(final_results)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "20c210b9-a94d-467d-d65d-ff3410e0fed0", "id": "9LPwX7qGP3Uw" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '2.56%', 'pct_good_lcp_change_mobile': '3.32%', 'pct_good_cls_change_mobile': '2.71%', 'pct_good_inp_change_mobile': '1.44%', 'pct_good_cwv_change_desktop': '2.30%', 'pct_good_lcp_change_desktop': '2.52%', 'pct_good_cls_change_desktop': '1.04%', 'pct_good_inp_change_desktop': '0.24%'}\n" ] } ] }, { "cell_type": "code", "source": [ "results = query_cwv_compare_feature_to_baseline( 'speculation-rules', '2024-03-01', '2024-08-01' )\n", "print(results)" ], "metadata": { "id": "1fHDFRQVRpfH", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "88d4fef4-3987-489f-c662-412880e9f134" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change': '2.42%', 'pct_good_lcp_change': '3.06%', 'pct_good_cls_change': '1.98%', 'pct_good_inp_change': '0.82%', 'feature_active_sites': 7203, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%'}\n" ] } ] }, { "cell_type": "code", "source": [ "results = query_cwv_compare_feature_to_baseline_by_device( 'speculation-rules', '2024-03-01', '2024-08-01' )\n", "print(results)" ], "metadata": { "id": "68oaX4ByWQL5", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "46cbae13-ba83-4ba1-9858-1a018e5ddcbe" }, "execution_count": null, "outputs": [ { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ "{'pct_good_cwv_change_mobile': '2.60%', 'pct_good_lcp_change_mobile': '3.39%', 'pct_good_cls_change_mobile': '2.75%', 'pct_good_inp_change_mobile': '1.52%', 'pct_good_cwv_change_desktop': '2.39%', 'pct_good_lcp_change_desktop': '2.55%', 'pct_good_cls_change_desktop': '1.09%', 'pct_good_inp_change_desktop': '0.23%', 'feature_active_sites': 7203, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-03-01', 'after_date': '2024-08-01', 'generator_tag': 'speculation-rules', 'cwv_changes_active_sites': [0.07083488781153219, 0.06909267366979688, 0.03922572086938758, 0.08127807033918977, 0.053894219906421015, 0.05999325206027517, 0.026095263217394526, 0.0027941094172748704], 'cwv_changes_inactive_sites': [0.04482710642311871, 0.03515486832303927, 0.011717077581957724, 0.06609143742833012, 0.02997313470714902, 0.034507587443841525, 0.015172879563230546, 0.0005163767055975654]}\n" ] } ] }, { "cell_type": "code", "source": [ "# show results as a table\n", "import pandas as pd\n", "df = pd.DataFrame([results])\n", "df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 160 }, "id": "ch7RupSn_2Vp", "outputId": "b536443f-07ee-425a-c788-be211957564d" }, "execution_count": null, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"df\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"pct_good_cwv_change_mobile\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2.60%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pct_good_lcp_change_mobile\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"3.39%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pct_good_cls_change_mobile\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2.75%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pct_good_inp_change_mobile\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"1.52%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pct_good_cwv_change_desktop\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2.39%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pct_good_lcp_change_desktop\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2.55%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pct_good_cls_change_desktop\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"1.09%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pct_good_inp_change_desktop\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"0.23%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_active_sites\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 7203,\n \"max\": 7203,\n \"num_unique_values\": 1,\n \"samples\": [\n 7203\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"feature_inactive_sites\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 50000,\n \"max\": 50000,\n \"num_unique_values\": 1,\n \"samples\": [\n 50000\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"also_active_at_before\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"0.00%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"before_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2024-03-01\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"after_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2024-08-01\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"generator_tag\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"speculation-rules\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cwv_changes_active_sites\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cwv_changes_inactive_sites\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "df" }, "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\"2.60%\",\n\"3.39%\",\n\"2.75%\",\n\"1.52%\",\n\"2.39%\",\n\"2.55%\",\n\"1.09%\",\n\"0.23%\",\n{\n 'v': 7203,\n 'f': \"7203\",\n },\n{\n 'v': 50000,\n 'f': \"50000\",\n },\n\"0.00%\",\n\"2024-03-01\",\n\"2024-08-01\",\n\"speculation-rules\",\n[0.07083488781153219, 0.06909267366979688, 0.03922572086938758, 0.08127807033918977, 0.053894219906421015, 0.05999325206027517, 0.026095263217394526, 0.0027941094172748704],\n[0.04482710642311871, 0.03515486832303927, 0.011717077581957724, 0.06609143742833012, 0.02997313470714902, 0.034507587443841525, 0.015172879563230546, 0.0005163767055975654]]],\n columns: [[\"number\", \"index\"], [\"string\", \"pct_good_cwv_change_mobile\"], [\"string\", \"pct_good_lcp_change_mobile\"], [\"string\", \"pct_good_cls_change_mobile\"], [\"string\", \"pct_good_inp_change_mobile\"], [\"string\", \"pct_good_cwv_change_desktop\"], [\"string\", \"pct_good_lcp_change_desktop\"], [\"string\", \"pct_good_cls_change_desktop\"], [\"string\", \"pct_good_inp_change_desktop\"], [\"number\", \"feature_active_sites\"], [\"number\", \"feature_inactive_sites\"], [\"string\", \"also_active_at_before\"], [\"string\", \"before_date\"], [\"string\", \"after_date\"], [\"string\", \"generator_tag\"], [\"string\", \"cwv_changes_active_sites\"], [\"string\", \"cwv_changes_inactive_sites\"]],\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
\n \n \n\n\n \n
`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n ", "text/html": [ "\n", "
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pct_good_cwv_change_mobilepct_good_lcp_change_mobilepct_good_cls_change_mobilepct_good_inp_change_mobilepct_good_cwv_change_desktoppct_good_lcp_change_desktoppct_good_cls_change_desktoppct_good_inp_change_desktopfeature_active_sitesfeature_inactive_sitesalso_active_at_beforebefore_dateafter_dategenerator_tagcwv_changes_active_sitescwv_changes_inactive_sites
02.60%3.39%2.75%1.52%2.39%2.55%1.09%0.23%7203500000.00%2024-03-012024-08-01speculation-rules[0.07083488781153219, 0.06909267366979688, 0.0...[0.04482710642311871, 0.03515486832303927, 0.0...
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\n" ], "text/plain": [ " pct_good_cwv_change_mobile pct_good_lcp_change_mobile \\\n", "0 2.60% 3.39% \n", "\n", " pct_good_cls_change_mobile pct_good_inp_change_mobile \\\n", "0 2.75% 1.52% \n", "\n", " pct_good_cwv_change_desktop pct_good_lcp_change_desktop \\\n", "0 2.39% 2.55% \n", "\n", " pct_good_cls_change_desktop pct_good_inp_change_desktop \\\n", "0 1.09% 0.23% \n", "\n", " feature_active_sites feature_inactive_sites also_active_at_before \\\n", "0 7203 50000 0.00% \n", "\n", " before_date after_date generator_tag \\\n", "0 2024-03-01 2024-08-01 speculation-rules \n", "\n", " cwv_changes_active_sites \\\n", "0 [0.07083488781153219, 0.06909267366979688, 0.0... \n", "\n", " cwv_changes_inactive_sites \n", "0 [0.04482710642311871, 0.03515486832303927, 0.0... " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "source": [ "# Performance by Feature Queries\n", "\n", "## Notes\n", "* `inactive_to_active_percent` Shows the percent of feature-active sites at the after date that __were also feature-active__ at the before date. Ideally this should be 0, meaning none of the sites had the feature ative at the before date.\n" ], "metadata": { "id": "cLMOaWak2-e3" } }, { "cell_type": "markdown", "source": [ "## Speculative Loading" ], "metadata": { "id": "KSgLpTio3Dpj" } }, { "cell_type": "code", "source": [ "latest_dataset = '2024-09-01'" ], "metadata": { "id": "6mYeQTz-xkyN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ### Compare before/after\n", "#@markdown * `before_date`: a date to analyze where the feature is not enabled\n", "#@markdown * `after_date`: a date to analyze where the feature is enabled\n", "#@markdown * `generator`: the generator tag indicating a feature is installed\n", "#@markdown * `inactive_to_active_percent` Shows the percent of feature-active sites at the after date that were also feature-active at the before date.\n", "\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'speculation-rules'\n", "\n", "results_sl = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_sl)" ], "metadata": { "id": "QWxVV0iZ3afy", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5d398dbc-0184-4425-f635-1e6eee110e03" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '2.04%', 'pct_good_lcp_change_mobile': '2.00%', 'pct_good_cls_change_mobile': '2.00%', 'pct_good_inp_change_mobile': '1.33%', 'pct_good_cwv_change_desktop': '2.25%', 'pct_good_lcp_change_desktop': '2.55%', 'pct_good_cls_change_desktop': '1.59%', 'pct_good_inp_change_desktop': '-0.24%', 'feature_active_sites': 9091, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-03-01', 'after_date': '2024-09-01', 'generator_tag': 'speculation-rules', 'cwv_changes_active_sites': [0.0709573638372546, 0.05973671280401477, 0.03339427392262129, 0.09929041920330417, 0.05443899666326818, 0.06367646788742376, 0.02837772363253499, -0.0026272786983606577], 'cwv_changes_inactive_sites': [0.050594884631586345, 0.03974626020874489, 0.013417786565544043, 0.08595551937012391, 0.031957622667799446, 0.03816732623091601, 0.012432157707554903, -0.00018933025299994188]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Image Prioitizer" ], "metadata": { "id": "vYf6nRjU44pU" } }, { "cell_type": "code", "source": [ "# image-prioritizer\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'image-prioritizer'\n", "\n", "results_ip = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_ip)" ], "metadata": { "id": "Vrk1yAp15u1C", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "bfe6b1b2-d9ca-422a-8e28-7c9a2e2e9d5b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '1.83%', 'pct_good_lcp_change_mobile': '2.63%', 'pct_good_cls_change_mobile': '2.02%', 'pct_good_inp_change_mobile': '1.07%', 'pct_good_cwv_change_desktop': '3.63%', 'pct_good_lcp_change_desktop': '3.64%', 'pct_good_cls_change_desktop': '2.66%', 'pct_good_inp_change_desktop': '-0.46%', 'feature_active_sites': 3057, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-04-01', 'after_date': '2024-09-01', 'generator_tag': 'image-prioritizer', 'cwv_changes_active_sites': [0.06355639390247644, 0.066255044344453, 0.025530920421860004, 0.08869432502523766, 0.06324726213202053, 0.06865713642895399, 0.034364047044162294, -0.004424134395824675], 'cwv_changes_inactive_sites': [0.04529243087001833, 0.039932468951196454, 0.005380117484358293, 0.07796061197519022, 0.02694167020240129, 0.03227752930248928, 0.007753865162267282, 0.00015203821501585946]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Embed Optimizer\n", "\n", "\n" ], "metadata": { "id": "Q0OKwj0847rt" } }, { "cell_type": "code", "source": [ "# embed-optimizer\n", "before_date = '2024-07-01'\n", "after_date = latest_dataset\n", "generator_tag = 'embed-optimizer'\n", "\n", "results_eo = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_eo)" ], "metadata": { "id": "Deb8H7gl5qYH", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "94442b25-a59c-47bb-f8dd-3c361d36c04e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '-5.69%', 'pct_good_lcp_change_mobile': '-4.23%', 'pct_good_cls_change_mobile': '-0.57%', 'pct_good_inp_change_mobile': '0.11%', 'pct_good_cwv_change_desktop': '-0.83%', 'pct_good_lcp_change_desktop': '-4.91%', 'pct_good_cls_change_desktop': '0.91%', 'pct_good_inp_change_desktop': '-0.37%', 'feature_active_sites': 4908, 'feature_inactive_sites': 50000, 'also_active_at_before': '5906.68%', 'before_date': '2024-07-01', 'after_date': '2024-09-01', 'generator_tag': 'embed-optimizer', 'cwv_changes_active_sites': [-0.041306793074358306, -0.031142749330665964, -0.009310373802833838, 0.03288217754941303, -0.00780285351372112, -0.04850123311220522, 0.010024549239471159, -0.005046772644276998], 'cwv_changes_inactive_sites': [0.01555233539948292, 0.011153826487459773, -0.0036123086688277795, 0.03178291363875008, 0.0005148192916936134, 0.0005865276079072279, 0.0008919736799152256, -0.0013168643303140826]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Modern Image Formats" ], "metadata": { "id": "U0sMVoPs4_Xh" } }, { "cell_type": "code", "source": [ "# webp-uploads\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'webp-uploads'\n", "\n", "results_mi = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_mi)" ], "metadata": { "id": "1tNlKpIU5zVS", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "81a9fd42-9de8-41b9-d1bc-a0491b4efb05" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '1.86%', 'pct_good_lcp_change_mobile': '1.26%', 'pct_good_cls_change_mobile': '2.31%', 'pct_good_inp_change_mobile': '1.57%', 'pct_good_cwv_change_desktop': '2.31%', 'pct_good_lcp_change_desktop': '2.14%', 'pct_good_cls_change_desktop': '1.45%', 'pct_good_inp_change_desktop': '0.15%', 'feature_active_sites': 16148, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-01-01', 'after_date': '2024-09-01', 'generator_tag': 'webp-uploads', 'cwv_changes_active_sites': [0.09053611980768622, 0.06681956845397613, 0.04422785761234971, 0.13775801636582619, 0.053067897464070646, 0.055216839004633256, 0.029493570947297654, 0.003979401208744959], 'cwv_changes_inactive_sites': [0.0719599926198663, 0.05425106123086576, 0.021089317940479635, 0.1220972067245063, 0.030003443405208163, 0.033798004781138036, 0.015025429495077125, 0.002452523250483174]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Enhanced Image Sizes" ], "metadata": { "id": "xT4NB_OY5Jld" } }, { "cell_type": "code", "source": [ "# auto-sizes\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'auto-sizes'\n", "\n", "results_is = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_is)" ], "metadata": { "id": "2dQQhAFC5mnw", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2018309b-32bc-422e-a2c4-d79746a4740a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '0.77%', 'pct_good_lcp_change_mobile': '-0.22%', 'pct_good_cls_change_mobile': '2.33%', 'pct_good_inp_change_mobile': '2.50%', 'pct_good_cwv_change_desktop': '3.02%', 'pct_good_lcp_change_desktop': '1.37%', 'pct_good_cls_change_desktop': '3.14%', 'pct_good_inp_change_desktop': '0.18%', 'feature_active_sites': 5633, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-03-01', 'after_date': '2024-09-01', 'generator_tag': 'auto-sizes', 'cwv_changes_active_sites': [0.06442163880919188, 0.048145394148988996, 0.0325715565983854, 0.11997871774408086, 0.06243687002187076, 0.05522724145910157, 0.041617845644969065, 0.001961164045756192], 'cwv_changes_inactive_sites': [0.05675288022675129, 0.05037639877576028, 0.009301400069805288, 0.09498100979108803, 0.03222344332123689, 0.041554902302256336, 0.010225594823027317, 0.00014161616508323593]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Performant Translations" ], "metadata": { "id": "SyUbptUIQ-R0" } }, { "cell_type": "code", "source": [ "# performant-translations\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'performant-translations'\n", "\n", "results_is = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_is)" ], "metadata": { "id": "RfVL5TDlRA-h" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Web Worker Offloading" ], "metadata": { "id": "dFBb2AfYReqb" } }, { "cell_type": "code", "source": [ "# web-worker-offloading\n", "before_date = '2024-07-01'\n", "after_date = latest_dataset\n", "generator_tag = 'web-worker-offloading'\n", "\n", "results_is = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_is)" ], "metadata": { "id": "Y2GyJeDDRg99", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2ffbe55d-168c-4906-bfac-e281544e2092" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '0.94%', 'pct_good_lcp_change_mobile': '-1.22%', 'pct_good_cls_change_mobile': '5.04%', 'pct_good_inp_change_mobile': '1.29%', 'pct_good_cwv_change_desktop': '0.55%', 'pct_good_lcp_change_desktop': '-1.35%', 'pct_good_cls_change_desktop': '4.56%', 'pct_good_inp_change_desktop': '0.10%', 'feature_active_sites': 601, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-07-01', 'after_date': '2024-11-01', 'generator_tag': 'web-worker-offloading', 'cwv_changes_active_sites': [0.026853975535168162, 0.0001429762897652842, 0.046814342865609704, 0.04866273605058535, 0.005261578120310084, -0.007205877183732334, 0.039323973650941535, 0.000566951860632936, date device origins pct_eligible_origins_with_good_cwv \\\n", "1 2024-07-01 mobile 312 0.275229 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "1 0.412844 0.744118 \n", "\n", " pct_eligible_origins_with_good_inp \n", "1 0.614458 , date device origins pct_eligible_origins_with_good_cwv \\\n", "2 2024-11-01 mobile 363 0.302083 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "2 0.412987 0.790932 \n", "\n", " pct_eligible_origins_with_good_inp \n", "2 0.663121 , date device origins pct_eligible_origins_with_good_cwv \\\n", "0 2024-07-01 desktop 227 0.37788 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "0 0.53211 0.588496 \n", "\n", " pct_eligible_origins_with_good_inp \n", "0 0.97191 , date device origins pct_eligible_origins_with_good_cwv \\\n", "3 2024-11-01 desktop 266 0.383142 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "3 0.524904 0.62782 \n", "\n", " pct_eligible_origins_with_good_inp \n", "3 0.972477 ], 'cwv_changes_inactive_sites': [0.01742855793811049, 0.012363291471110371, -0.003585737185353821, 0.03574913070510788, -0.00026561188097889987, 0.00628024071174782, -0.006276333951685142, -0.00045329309517982974, date device origins pct_eligible_origins_with_good_cwv \\\n", "1 2024-07-01 mobile 38975 0.390947 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "1 0.536264 0.785201 \n", "\n", " pct_eligible_origins_with_good_inp \n", "1 0.755696 , date device origins pct_eligible_origins_with_good_cwv \\\n", "2 2024-11-01 mobile 41797 0.408376 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "2 0.548627 0.781615 \n", "\n", " pct_eligible_origins_with_good_inp \n", "2 0.791445 , date device origins pct_eligible_origins_with_good_cwv \\\n", "0 2024-07-01 desktop 31126 0.46937 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "0 0.640656 0.693395 \n", "\n", " pct_eligible_origins_with_good_inp \n", "0 0.987947 , date device origins pct_eligible_origins_with_good_cwv \\\n", "3 2024-11-01 desktop 33969 0.469105 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "3 0.646936 0.687118 \n", "\n", " pct_eligible_origins_with_good_inp \n", "3 0.987494 ]}\n" ] } ] }, { "cell_type": "code", "source": [ "latest_dataset = '2024-11-01'" ], "metadata": { "id": "GnSlKJqWZRFF" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Embed Optimizer plus Optimization detective\n" ], "metadata": { "id": "bYE8yA7d_8dv" } }, { "cell_type": "code", "source": [ "# this will use a slightly different approach because we are looking for sites with two features enabled.\n", "before_date = '2024-07-01'\n", "after_date = latest_dataset\n", "generator_tag1 = 'optimization-detective'\n", "generator_tag2 = 'embed-optimizer'\n", "\n", "results_is = query_cwv_compare_feature_to_baseline_by_device_multiple_tags(generator_tag1, generator_tag2, before_date, after_date)\n", "print(results_is)\n", "\n" ], "metadata": { "id": "huI-O2YMAAuO", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "6c55fe80-7933-45a6-c675-0ae583e33250" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '0.20%', 'pct_good_lcp_change_mobile': '1.09%', 'pct_good_cls_change_mobile': '2.08%', 'pct_good_inp_change_mobile': '-0.48%', 'pct_good_cwv_change_desktop': '4.39%', 'pct_good_lcp_change_desktop': '2.12%', 'pct_good_cls_change_desktop': '3.70%', 'pct_good_inp_change_desktop': '-0.22%', 'feature_active_sites': 3523, 'feature_inactive_sites': 50000, 'also_active_at_before': '1481.69%', 'before_date': '2024-07-01', 'after_date': '2024-11-01', 'generator_tag1': 'optimization-detective', 'generator_tag2': 'embed-optimizer', 'cwv_changes_active_sites': [0.022084835266597713, 0.026946373942071167, 0.014579789123231879, 0.03380411086302515, 0.04506952879183501, 0.031181994226598397, 0.032788667906710534, -0.0035211267605633756, date device origins pct_eligible_origins_with_good_cwv \\\n", "0 2024-07-01 mobile 1715 0.260748 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "0 0.363788 0.719718 \n", "\n", " pct_eligible_origins_with_good_inp \n", "0 0.694842 , date device origins pct_eligible_origins_with_good_cwv \\\n", "2 2024-11-01 mobile 2066 0.282833 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "2 0.390735 0.734297 \n", "\n", " pct_eligible_origins_with_good_inp \n", "2 0.728647 , date device origins pct_eligible_origins_with_good_cwv \\\n", "1 2024-07-01 desktop 1212 0.323051 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "1 0.479897 0.614108 \n", "\n", " pct_eligible_origins_with_good_inp \n", "1 0.985915 , date device origins pct_eligible_origins_with_good_cwv \\\n", "3 2024-11-01 desktop 1456 0.36812 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "3 0.511079 0.646897 \n", "\n", " pct_eligible_origins_with_good_inp \n", "3 0.982394 ], 'cwv_changes_inactive_sites': [0.020120224450941193, 0.016093714735728737, -0.006181671591478977, 0.038600244501898584, 0.0012012495827020686, 0.009982428443944924, -0.004181185090754269, -0.0013615621774009101, date device origins pct_eligible_origins_with_good_cwv \\\n", "0 2024-07-01 mobile 40668 0.378968 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "0 0.539324 0.77524 \n", "\n", " pct_eligible_origins_with_good_inp \n", "0 0.733711 , date device origins pct_eligible_origins_with_good_cwv \\\n", "3 2024-11-01 mobile 43404 0.399088 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "3 0.555418 0.769058 \n", "\n", " pct_eligible_origins_with_good_inp \n", "3 0.772312 , date device origins pct_eligible_origins_with_good_cwv \\\n", "1 2024-07-01 desktop 33696 0.468656 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "1 0.643538 0.688438 \n", "\n", " pct_eligible_origins_with_good_inp \n", "1 0.986965 , date device origins pct_eligible_origins_with_good_cwv \\\n", "2 2024-11-01 desktop 36528 0.469858 \n", "\n", " pct_eligible_origins_with_good_lcp pct_eligible_origins_with_good_cls \\\n", "2 0.653521 0.684257 \n", "\n", " pct_eligible_origins_with_good_inp \n", "2 0.985604 ]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Graveyard" ], "metadata": { "id": "BoVOOEMO46eL" } }, { "cell_type": "code", "source": [ "from google.cloud import bigquery\n", "import pandas as pd\n", "\n", "def analyse_performance_impact_of_feature(generator_tag, after_date=None):\n", "\n", " client = bigquery.Client()\n", "\n", " # 1. Identify Active and Non-Active Sites\n", " active_sites_query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING)\n", " RETURNS STRING\n", " LANGUAGE js\n", " AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith('$.{{generator}}'));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", " ''';\n", "\n", " SELECT * FROM\n", " (\n", " SELECT\n", " page AS origin,\n", " client AS device,\n", " date,\n", " getFeature(payload) as feature\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", " )\n", " WHERE feature != \"\"\n", " LIMIT 10000\n", " \"\"\"\n", "\n", " non_active_sites_query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING)\n", " RETURNS STRING\n", " LANGUAGE js\n", " AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith('$.{{generator}}'));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", " ''';\n", "\n", " SELECT * FROM\n", " (\n", " SELECT\n", " page AS origin,\n", " client AS device,\n", " date,\n", " getFeature(payload) as feature\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", " )\n", " WHERE feature = \"\"\n", " LIMIT 10000\n", " \"\"\"\n", "\n", " active_sites_df = client.query(active_sites_query).to_dataframe()\n", " non_active_sites_df = client.query(non_active_sites_query).to_dataframe()\n", "\n", "\n", " # 2. Collect CWV Data and Calculate Baselines\n", " def calculate_cwv_stats(df, active=False):\n", " cwv_metrics = ['largest_contentful_paint', 'first_input_delay', 'cumulative_layout_shift']\n", " all_cwv_data = []\n", "\n", " for _, row in df.iterrows():\n", " for month_offset in range(14): # collect data for the past 14 months\n", " query_date = row['parse_date'] - pd.DateOffset(months=month_offset)\n", " query_string = f\"\"\"\n", "\n", " SELECT origin,\n", " IF(fast_lcp_count IS NOT NULL, fast_lcp_count / (fast_lcp_count + avg_lcp_count + slow_lcp_count), NULL) AS lcp_pass_rate,\n", " IF(fast_fid_count IS NOT NULL, fast_fid_count / (fast_fid_count + avg_fid_count + slow_fid_count), NULL) AS fid_pass_rate,\n", " IF(small_cls_count IS NOT NULL, small_cls_count / (small_cls_count + medium_cls_count + large_cls_count), NULL) AS cls_pass_rate,\n", " IF(good_inp_count IS NOT NULL, good_inp_count / (good_inp_count + ok_inp_count + poor_inp_count), NULL) AS inp_pass_rate\n", " FROM `chrome-ux-report.materialized.device_summary`\n", " WHERE origin = '{row[\"url\"]}'\n", " AND DATE_TRUNC(date, MONTH) = DATE_TRUNC(DATE('{query_date}'), MONTH)\n", " \"\"\"\n", "\n", " cwv_data = client.query(query_string).to_dataframe()\n", " if not cwv_data.empty:\n", " cwv_data['month'] = query_date # include the month for each metric\n", " cwv_data['active'] = active\n", " all_cwv_data.append(cwv_data)\n", "\n", " return pd.concat(all_cwv_data)\n", "\n", " # 3. Identify Feature Activation and Calculate Impact\n", " active_cwv_stats = calculate_cwv_stats(active_sites_df, active=True)\n", " non_active_cwv_stats = calculate_cwv_stats(non_active_sites_df)\n", "\n", " def calculate_feature_impact(active_cwv_stats):\n", " impact_data = []\n", " for url, group in active_cwv_stats.groupby('origin'):\n", " activation_month = group[group['active'] == True]['month'].min()\n", " if activation_month is not pd.NaT:\n", " before_activation = group[group['month'] == activation_month - pd.DateOffset(months=1)]\n", " after_activation = group[group['month'] == activation_month + pd.DateOffset(months=1)]\n", "\n", " if not before_activation.empty and not after_activation.empty:\n", " impact = after_activation.mean() - before_activation.mean()\n", " impact['url'] = url\n", " impact_data.append(impact)\n", "\n", " return pd.DataFrame(impact_data)\n", "\n", " feature_impact_df = calculate_feature_impact(active_cwv_stats)\n", "\n", " return feature_impact_df" ], "metadata": { "id": "kD6TC3jpXI2W" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "analyse_performance_impact_of_feature('speculation-rules', '2024_06_01')\n" ], "metadata": { "id": "QrL9fImAtqKX" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "rDn6hWyywriY" } }, { "cell_type": "markdown", "source": [ "## Example using Speculation Rules" ], "metadata": { "id": "CT9vfgXOu0UA" } }, { "cell_type": "code", "source": [ "#@markdown ### Compare speculation rules before/after\n", "results = query_cwv_compare_feature_to_baseline( 'speculation-rules', '2024-03-01', '2024-06-01' )\n", "print(results)" ], "metadata": { "id": "VBtebScA8k-m" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Helper to compare feature before / after" ], "metadata": { "id": "8o_CYabIgMwI" } }, { "cell_type": "code", "source": [ "# Restrict the query to on the from and to dates, and without and with a specific generator tag indicating a feature is active\n", "def query_cwv_compare_dates_by_generator(technology, from_date, to_date, generator):\n", " \"\"\"\n", " Queries Core Web Vitals metrics from HTTP Archive.\n", "\n", " Args:\n", " technology (str): The technology to filter by (e.g., 'WordPress').\n", " from_date (str): The start date in YYYY_MM_DD format.\n", " to_date (str): The end date in YYYY_MM_DD format.\n", " generator (str): The generator tag indicating a feature is installed.\n", " Returns:\n", " A BigQuery query result object containing CWV data.\n", " \"\"\"\n", " # set to_date if not passed\n", " if not to_date:\n", " to_date = latest_dataset\n", "\n", " # set from date if not passed\n", " if not from_date:\n", " from_date = (datetime.strptime(to_date, '%Y_%m_%d').date() - timedelta(days=365)).strftime('%Y_%m_%d')\n", "\n", "\n", " query = f\"\"\"\n", " CREATE TEMP FUNCTION getFeature(payload STRING)\n", "RETURNS STRING\n", "LANGUAGE js\n", "AS '''\n", " try {{\n", " var $ = JSON.parse(payload);\n", " var almanac = JSON.parse($._almanac);\n", " var generators = almanac['meta-nodes'].nodes.find(node => node.name == 'generator' && node.content.startsWith('$.{{generator}}'));\n", " if ( generators.length == 0 ) {{\n", " return \"\";\n", " }}\n", " var content = generators.content;\n", " return content;\n", " }} catch (e) {{\n", " return \"\";\n", " }}\n", "''';\n", "\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", " WITH sites_available_on_from_or_to_date AS (\n", " SELECT\n", " page AS origin,\n", " client AS device,\n", " date,\n", " getFeature(payload) as feature\n", " FROM\n", " `httparchive.all.pages`,\n", " UNNEST(technologies) AS technologies\n", " WHERE\n", " technologies.technology = '{technology}'\n", " AND (\n", " ( date = PARSE_DATE('%Y_%m_%d', '{from_date}') ) OR\n", " ( date = PARSE_DATE('%Y_%m_%d', '{to_date}' ) )\n", " )\n", " AND is_root_page = TRUE\n", "\n", " ),\n", " site_on_to_date_with_feature AS (\n", " SELECT\n", " origin,\n", " device,\n", " date,\n", " feature\n", " FROM sites_available_on_from_or_to_date\n", " WHERE date = PARSE_DATE('%Y_%m_%d', '{to_date}' )\n", " AND feature != \"\"\n", " ),\n", " sites_on_from_date_without_feature AS (\n", " SELECT\n", " origin,\n", " device,\n", " date,\n", " feature\n", " FROM\n", " sites_available_on_from_or_to_date\n", " WHERE\n", " date = PARSE_DATE('%Y_%m_%d', '{from_date}')\n", " AND feature = \"\"\n", " ),\n", " sites_available_for_both_from_and_to AS (\n", " SELECT\n", " sites_available_on_from_or_to_date.origin\n", " FROM\n", " sites_available_on_from_or_to_date\n", " JOIN\n", " sites_on_from_date_without_feature\n", " ON (sites_available_on_from_or_to_date.origin = sites_on_from_date_without_feature.origin)\n", " ),\n", " cwv_metrics_for_origins AS (\n", " SELECT\n", " cwvs.date AS date,\n", " site_on_to_date_with_feature.origin AS origin,\n", " # Device phone or tablet as mobile, otherwise desktop.\n", " IF(cwvs.device = 'phone' OR cwvs.device = 'tablet', 'mobile', cwvs.device) AS device,\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", " JOIN\n", " site_on_to_date_with_feature\n", " ON (\n", " site_on_to_date_with_feature.origin = CONCAT(cwvs.origin, '/') AND\n", " site_on_to_date_with_feature.device = IF(cwvs.device = 'phone' OR cwvs.device = 'tablet', 'mobile', cwvs.device)\n", " )\n", " WHERE\n", " cwvs.date BETWEEN PARSE_DATE('%Y_%m_%d', '{from_date}') AND PARSE_DATE('%Y_%m_%d', '{to_date}' )\n", " AND cwvs.device IN ('desktop', 'tablet', 'phone')\n", " # Restrict to sites that are in both dates\n", " AND CONCAT(cwvs.origin, '/') IN (\n", " SELECT\n", " origin\n", " FROM\n", " sites_available_for_both_from_and_to\n", " )\n", " ),\n", "\n", "\n", " cwvs_by_date AS (\n", " SELECT\n", " date,\n", " device,\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", " FROM\n", " cwv_metrics_for_origins\n", " GROUP BY\n", " date, device\n", " ORDER BY\n", " date, device\n", " )\n", " SELECT * FROM cwvs_by_date\n", " \"\"\"\n", "\n", " query_job = client.query(query)\n", " return query_job.result() # Return the results of the query" ], "metadata": { "id": "uVTvoirMaxIe" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Step By Step testing" ], "metadata": { "id": "5R3EB1yo4Y5A" } }, { "cell_type": "code", "source": [ "generator_tag = 'speculation-rules'\n", "after_date = '2024-06-01'\n", "before_date = '2024-03-01'\n", "\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" ], "metadata": { "id": "TiX_xxi_iv-H" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Count feature active sites that were also feature active at the before date.\n", "print(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" ], "metadata": { "id": "zWiHNoWla0aS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "count_active_sites['origin']" ], "metadata": { "id": "S88ocl0vvgr6" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Calculate the percentage of count_active_at_from_date (which might be 0)\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;" ], "metadata": { "id": "c6_fKLCmvJ9e" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# 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" ], "metadata": { "id": "Y4tpTREfa0SN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "final_results" ], "metadata": { "id": "zj2E6bsda0Gy" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "aRkOWFb247PX" } }, { "cell_type": "markdown", "source": [ "----" ], "metadata": { "id": "yN-yPeZa46y_" } }, { "cell_type": "markdown", "source": [], "metadata": { "id": "z-1YtLdF46qa" } }, { "cell_type": "markdown", "source": [ "# Performance by Feature Queries from 2024-03-01" ], "metadata": { "id": "nuOOxybxdCma" } }, { "cell_type": "markdown", "source": [ "## Speculative Loading" ], "metadata": { "id": "ZhClysvodCmb" } }, { "cell_type": "code", "source": [ "#@markdown ### Compare before/after\n", "#@markdown * `before_date`: a date to analyze where the feature is not enabled\n", "#@markdown * `after_date`: a date to analyze where the feature is enabled\n", "#@markdown * `generator`: the generator tag indicating a feature is installed\n", "\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'speculation-rules'\n", "\n", "results_sl2 = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_sl2)" ], "metadata": { "id": "mpMzlYEMdCmb", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9e7f14b5-99fb-4525-bec3-6561a8880f18" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '1.42%', 'pct_good_lcp_change_mobile': '1.30%', 'pct_good_cls_change_mobile': '2.19%', 'pct_good_inp_change_mobile': '0.56%', 'pct_good_cwv_change_desktop': '2.14%', 'pct_good_lcp_change_desktop': '2.37%', 'pct_good_cls_change_desktop': '1.63%', 'pct_good_inp_change_desktop': '-0.25%', 'feature_active_sites': 9091, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-03-01', 'after_date': '2024-09-01', 'generator_tag': 'speculation-rules', 'cwv_changes_active_sites': [0.0709573638372546, 0.05973671280401477, 0.03339427392262129, 0.09929041920330417, 0.05443899666326818, 0.06367646788742376, 0.02837772363253499, -0.0026272786983606577], 'cwv_changes_inactive_sites': [0.0567628587630879, 0.04675857079386847, 0.011511984996228963, 0.09365062821091052, 0.03303288698159201, 0.0399720547958603, 0.012113975978503277, -0.00013179455489875647]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Image Prioitizer" ], "metadata": { "id": "hkxRu7zLdCmb" } }, { "cell_type": "code", "source": [ "# image-prioritizer\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'image-prioritizer'\n", "\n", "results_ip2 = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_ip2)" ], "metadata": { "id": "TxduuIH2dCmb", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4a6f3cb0-5dcc-4f7f-f4d0-e23fbee60e13" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '0.07%', 'pct_good_lcp_change_mobile': '0.24%', 'pct_good_cls_change_mobile': '1.99%', 'pct_good_inp_change_mobile': '0.58%', 'pct_good_cwv_change_desktop': '2.98%', 'pct_good_lcp_change_desktop': '1.71%', 'pct_good_cls_change_desktop': '2.74%', 'pct_good_inp_change_desktop': '-0.07%', 'feature_active_sites': 3057, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-03-01', 'after_date': '2024-09-01', 'generator_tag': 'image-prioritizer', 'cwv_changes_active_sites': [0.05372470728308859, 0.045826828366386796, 0.031917168837576115, 0.09848436113575865, 0.0579437376094758, 0.05101553940017034, 0.037424997986087116, 0.00018115942028984477], 'cwv_changes_inactive_sites': [0.05306598031955251, 0.04344795680480029, 0.012023500874824844, 0.09271561656330818, 0.028133587541988136, 0.03390036546028041, 0.01006490094309187, 0.0008955579797447211]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Embed Optimizer" ], "metadata": { "id": "pxscfkaxdCmb" } }, { "cell_type": "code", "source": [ "# embed-optimizer\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'embed-optimizer'\n", "\n", "results_eo2 = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_eo2)" ], "metadata": { "id": "jzUIkzusdCmb", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "03316c6d-9b7d-49d1-d7ee-b02c3be80927" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '-6.71%', 'pct_good_lcp_change_mobile': '-6.99%', 'pct_good_cls_change_mobile': '0.25%', 'pct_good_inp_change_mobile': '-0.26%', 'pct_good_cwv_change_desktop': '2.01%', 'pct_good_lcp_change_desktop': '-7.08%', 'pct_good_cls_change_desktop': '5.58%', 'pct_good_inp_change_desktop': '0.01%', 'feature_active_sites': 4908, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-03-01', 'after_date': '2024-09-01', 'generator_tag': 'embed-optimizer', 'cwv_changes_active_sites': [-0.008912738178832036, -0.017832398899997826, 0.013302035749933383, 0.0978406351757094, 0.05156835387437664, -0.030477031089655715, 0.06554045194282965, 0.0013325894195166388], 'cwv_changes_inactive_sites': [0.05813776995746334, 0.05203346729111852, 0.01076988011891955, 0.1004048888519854, 0.03146862294181668, 0.04029451169372489, 0.00971873533213996, 0.0012195856464317645]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Modern Images" ], "metadata": { "id": "XFGBFa5QdCmb" } }, { "cell_type": "code", "source": [ "# webp-uploads\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'webp-uploads'\n", "\n", "results_mi2 = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_mi2)" ], "metadata": { "id": "RSjCFLw9dCmc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9b80c1d2-6511-4578-8bb8-1fc31231a5ab" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '1.53%', 'pct_good_lcp_change_mobile': '0.50%', 'pct_good_cls_change_mobile': '2.53%', 'pct_good_inp_change_mobile': '1.01%', 'pct_good_cwv_change_desktop': '2.67%', 'pct_good_lcp_change_desktop': '2.36%', 'pct_good_cls_change_desktop': '2.13%', 'pct_good_inp_change_desktop': '0.14%', 'feature_active_sites': 16148, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-03-01', 'after_date': '2024-09-01', 'generator_tag': 'webp-uploads', 'cwv_changes_active_sites': [0.06732568282636309, 0.0500484400843042, 0.036909719376889694, 0.1028913557761153, 0.05754036788123096, 0.06235835105115772, 0.03277655490922826, 0.001201716738197467], 'cwv_changes_inactive_sites': [0.05201159105926506, 0.04509394431000929, 0.011659601752061821, 0.09280415428856859, 0.030870589794880943, 0.038753358233235335, 0.011429699662780735, -0.00019254586873129753]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Image Sizes" ], "metadata": { "id": "GVdW1ccMdCmc" } }, { "cell_type": "code", "source": [ "# auto-sizes\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'auto-sizes'\n", "\n", "results_is2 = query_cwv_compare_feature_to_baseline_by_device(generator_tag, before_date, after_date)\n", "print(results_is2)" ], "metadata": { "id": "9EmGXoLLdCmc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ad69451e-c86d-4388-ade8-dd18e0e370c8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'pct_good_cwv_change_mobile': '0.95%', 'pct_good_lcp_change_mobile': '0.17%', 'pct_good_cls_change_mobile': '2.26%', 'pct_good_inp_change_mobile': '2.43%', 'pct_good_cwv_change_desktop': '2.96%', 'pct_good_lcp_change_desktop': '1.45%', 'pct_good_cls_change_desktop': '3.11%', 'pct_good_inp_change_desktop': '0.13%', 'feature_active_sites': 5633, 'feature_inactive_sites': 50000, 'also_active_at_before': '0.00%', 'before_date': '2024-03-01', 'after_date': '2024-09-01', 'generator_tag': 'auto-sizes', 'cwv_changes_active_sites': [0.06442163880919188, 0.048145394148988996, 0.0325715565983854, 0.11997871774408086, 0.06243687002187076, 0.05522724145910157, 0.041617845644969065, 0.001961164045756192], 'cwv_changes_inactive_sites': [0.054874468344218774, 0.04648435338003126, 0.009980232692361701, 0.09568983354224359, 0.032824792702341954, 0.0406943834871375, 0.01054937525763644, 0.0007026135858518856]}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Optimization Detective plus Embed Optimizer" ], "metadata": { "id": "Ow3y6ErZbD6x" } }, { "cell_type": "code", "source": [ "# embed-optimizer\n", "before_date = '2024-03-01'\n", "after_date = latest_dataset\n", "generator_tag = 'embed-optimizer'\n", "\n", "results_eo2 = query_cwv_compare_feature_to_baseline_by_device_multiple(generator_tag, generator_tag2, before_date, after_date)\n", "print(results_eo2)" ], "metadata": { "id": "ic31QLgGbg7u" }, "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "provenance": [], "collapsed_sections": [ "4G2WkwMPzxbT", "BoVOOEMO46eL", "5R3EB1yo4Y5A", "nuOOxybxdCma" ], "name": "WPP Metrics Tracking colab", "toc_visible": true, "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }