Ankur Lakhanpal a-lakhanpal
🤓
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| -- this query outputs a data visualisation to explore how many unique products people | |
| -- see on your website, and the conversion rate | |
| -- it uses Big Query SQL on a GA4Dataform events table - check https://github.com/superformlabs/ga4dataform-community | |
| with products_per_user as ( | |
| SELECT | |
| user_pseudo_id, | |
| count( distinct if(event_name = 'view_item', i.item_name, NULL) ) as n_items_viewed, | |
| max( if(event_name = 'purchase', 1, 0)) as purchaser |
dwsmart
/ same_day_previous_year.js
Created
October 10, 2024 08:16
Get the same day of the week in a previous year
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| // get same day of the week in the last x years ago for specific date. | |
| // Default is 1 year ago, and default date is today, takes account of leap year | |
| function getPreviousYearSameDay(dateToStart = false, years = 1) { | |
| const date = dateToStart ? new Date(dateToStart) : new Date(); | |
| const lastYear = new Date(date.getFullYear() - years, date.getMonth(), date.getDate()); | |
| const dayDiff = date.getDay() - lastYear.getDay(); | |
| return new Date(lastYear.setDate(date.getDate() + dayDiff)); | |
| } | |
| console.log(getPreviousYearSameDay('2024-10-10', 1).toISOString()); // 2023-10-11T23:00:00.000Z |
thetafferboy
/ AlsoAsked-UnansweredQuestions.json
Created
May 8, 2024 10:55
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| { | |
| "version": 1, | |
| "snippets": [ | |
| { | |
| "version": 1, | |
| "javascript": "const settings \u003d {\\r\\n chatGpt: {\\r\\n \\/\\/ replace with your ChatGPT API key created at https:\\/\\/platform.openai.com\\/api-keys\\r\\n apiKey: \\\u0027ENTER CHATGPT API KEY\\\u0027,\\r\\n\\r\\n \\/\\/ the OpenAI model to use\\r\\n model: \\\u0027gpt-4-turbo\\\u0027,\\r\\n },\\r\\n alsoAsked: {\\r\\n \\/\\/ replace with your AlsoAsked API key created at https:\\/\\/alsoasked.com\\/developer\\/keys\\r\\n apiKey:\\r\\n \\\u0027ENTER ALSOASKED API KEY\\\u0027,\\r\\n\\r\\n \\/\\/ the language to search in\\r\\n language: \\\u0027en\\\u0027,\\r\\n\\r\\n \\/\\/ the region to search in\\r\\n region: \\\u0027gb\\\u0027,\\r\\n\\r\\n \\/\\/ the depth of the search\\r\\n \\/\\/ 2 is the default and returns the smallest number of questions, and costs 1 credit\\r\\n \\/\\/ 3 is the maximum and returns the largest number of questions, but costs 4 credits\\r\\n depth: 2,\\r\\n\\r\\n |
elisalimli
/ amazon-data-extractor.yaml
Last active
October 29, 2024 18:24
SAML - Amazon Data Extractor
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| # Try out now at https://beta.superagent.sh/workflows | |
| workflows: | |
| - superagent: | |
| llm: gpt-4-turbo-preview | |
| name: JSON Schema generator | |
| prompt: |- | |
| Convert natural language query to JSON schema | |
| Examples: | |
| Question: List all the products launching at https://www.producthunt.com/ today |
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| javascript:(function()%7Bjavascript%3A!function()%7Bfunction%20e(e)%7Blet%20l%3De.querySelectorAll(%22tr%22)%2Cn%3Dl%5B1%5D%3Bif(!(n%26%26(n.querySelector(%22%3Anth-child(4)%20%3E%20span%20%3E%20span%20%3E%20span%22)%7C%7Cn.querySelector(%22%3Anth-child(7)%20%3E%20span%20%3E%20span%20%3E%20span%22))))for(let%20a%3D1%3Ba%3Cl.length%3Ba%2B%2B)%7Blet%20r%3Dl%5Ba%5D%3Br.cells%5B1%5D.getAttribute(%22data-numeric-value%22)%3Blet%20i%3Dr.cells%5B2%5D.getAttribute(%22data-numeric-value%22)%2Cc%3Dr.cells%5B3%5D.getAttribute(%22data-numeric-value%22)%3Br.cells%5B4%5D.getAttribute(%22data-numeric-value%22)%3Blet%20s%3Dr.cells%5B5%5D.getAttribute(%22data-numeric-value%22)%2Co%3Dr.cells%5B6%5D.getAttribute(%22data-numeric-value%22)%2Cd%3Di%2Cu%3Ds%2Cp%3Dt(c%2Cd)%2Ch%3Dt(o%2Cu)%2Cf%3Dr.querySelector(%22%3Anth-child(4)%20%3E%20span%20%3E%20span%22)%2Cm%3Dr.querySelector(%22%3Anth-child(7)%20%3E%20span%20%3E%20span%22)%2Cg%3Ddocument.createElement(%22span%22)%2C%24%3Ddocument.createElement(%22span%22)%3Bg.innerText%3D%60%20( |
neubig
/ dispatch_openai_requests.py
Last active
February 19, 2024 17:55
A simple script to get results from the OpenAI Asynchronous API
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| # NOTE: | |
| # You can find an updated, more robust and feature-rich implementation | |
| # in Zeno Build | |
| # - Zeno Build: https://github.com/zeno-ml/zeno-build/ | |
| # - Implementation: https://github.com/zeno-ml/zeno-build/blob/main/zeno_build/models/providers/openai_utils.py | |
| import openai | |
| import asyncio | |
| from typing import Any |
eliasdabbas
/ brands_knowledge_graph.py
Created
September 20, 2022 11:30
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| import advertools as adv | |
| import pandas as pd | |
| key = 'YOUR_GOOGLE_KEY' | |
| brands = [ | |
| 'nike', | |
| 'adidas', | |
| 'puma', | |
| 'asics', |
eliasdabbas
/ serp_heatmap.py
Last active
February 2, 2024 22:58
Create a heatmap of SERPs, using a table with columns: "keyword", "rank", and "domain"
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| import plotly.graph_objects as go | |
| import pandas as pd | |
| def serp_heatmap(df, num_domains=10, select_domain=None): | |
| df = df.rename(columns={'domain': 'displayLink', | |
| 'searchTerms': 'keyword'}) | |
| top_domains = df['displayLink'].value_counts()[:num_domains].index.tolist() | |
| top_domains = df['displayLink'].value_counts()[:num_domains].index.tolist() | |
| top_df = df[df['displayLink'].isin(top_domains) & df['displayLink'].ne('')] |
eliasdabbas
/ bert_pipeline_unmasker.py
Last active
August 9, 2022 04:59
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| # !pip install --upgrade transformers plotly pandas | |
| import plotly.graph_objects as go | |
| import pandas as pd | |
| pd.options.display.max_columns = None | |
| from transformers import pipeline | |
| unmasker = pipeline('fill-mask', model='bert-base-uncased') | |
| results = [] | |
| cars = ['mercedes', 'audi', 'bmw', 'volkswagen', 'ford', 'toyota', |
jonathanmooredigital
/ screaming-frog-lazy-loading-custom-extraction-config.txt
Last active
December 8, 2024 10:57
Screaming Frog Lazy-Loading Custom Extraction Config
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| # TIPS | |
| 1) Remove the count function and paste into dev tools to test further | |
| 2) To spot additonal oportunities swap the img selector to video, iframe or wildcards | |
| 3) Render JS in SF to discover all images and spot differences | |
| 4) Spot check source to find different lazy loading libraries e.g. Lazysizes, JQuery Lazy, yall.js etc | |
| # CAVEATS | |
| 1) Don't forget to check background CSS images | |
| 2) Other implimentaiton techniques may exist, adapt this approach to suit your needs e.g yall.js - lazy-bg-loaded |
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