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nostalgebraist · 10 months

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clarification re: ChatGPT, " a a a a", and data leakage

In August, I posted:

For a good time, try sending chatGPT the string ` a` repeated 1000 times. Like " a a a" (etc). Make sure the spaces are in there. Trust me.

People are talking about this trick again, thanks to a recent paper by Nasr et al that investigates how often LLMs regurgitate exact quotes from their training data.

The paper is an impressive technical achievement, and the results are very interesting.

Unfortunately, the online hive-mind consensus about this paper is something like:

When you do this "attack" to ChatGPT -- where you send it the letter 'a' many times, or make it write 'poem' over and over, or the like -- it prints out a bunch of its own training data. Previously, people had noted that the stuff it prints out after the attack looks like training data. Now, we know why: because it really is training data.

It's unfortunate that people believe this, because it's false. Or at best, a mixture of "false" and "confused and misleadingly incomplete."

The paper

So, what does the paper show?

The authors do a lot of stuff, building on a lot of previous work, and I won't try to summarize it all here.

But in brief, they try to estimate how easy it is to "extract" training data from LLMs, moving successively through 3 categories of LLMs that are progressively harder to analyze:

"Base model" LLMs with publicly released weights and publicly released training data.

"Base model" LLMs with publicly released weights, but undisclosed training data.

LLMs that are totally private, and are also finetuned for instruction-following or for chat, rather than being base models. (ChatGPT falls into this category.)

Category #1: open weights, open data

In their experiment on category #1, they prompt the models with hundreds of millions of brief phrases chosen randomly from Wikipedia. Then they check what fraction of the generated outputs constitute verbatim quotations from the training data.

Because category #1 has open weights, they can afford to do this hundreds of millions of times (there are no API costs to pay). And because the training data is open, they can directly check whether or not any given output appears in that data.

In category #1, the fraction of outputs that are exact copies of training data ranges from ~0.1% to ~1.5%, depending on the model.

Category #2: open weights, private data

In category #2, the training data is unavailable. The authors solve this problem by constructing "AuxDataset," a giant Frankenstein assemblage of all the major public training datasets, and then searching for outputs in AuxDataset.

This approach can have false negatives, since the model might be regurgitating private training data that isn't in AuxDataset. But it shouldn't have many false positives: if the model spits out some long string of text that appears in AuxDataset, then it's probably the case that the same string appeared in the model's training data, as opposed to the model spontaneously "reinventing" it.

So, the AuxDataset approach gives you lower bounds. Unsurprisingly, the fractions in this experiment are a bit lower, compared to the Category #1 experiment. But not that much lower, ranging from ~0.05% to ~1%.

Category #3: private everything + chat tuning

Finally, they do an experiment with ChatGPT. (Well, ChatGPT and gpt-3.5-turbo-instruct, but I'm ignoring the latter for space here.)

ChatGPT presents several new challenges.

First, the model is only accessible through an API, and it would cost too much money to call the API hundreds of millions of times. So, they have to make do with a much smaller sample size.

A more substantial challenge has to do with the model's chat tuning.

All the other models evaluated in this paper were base models: they were trained to imitate a wide range of text data, and that was that. If you give them some text, like a random short phrase from Wikipedia, they will try to write the next part, in a manner that sounds like the data they were trained on.

However, if you give ChatGPT a random short phrase from Wikipedia, it will not try to complete it. It will, instead, say something like "Sorry, I don't know what that means" or "Is there something specific I can do for you?"

So their random-short-phrase-from-Wikipedia method, which worked for base models, is not going to work for ChatGPT.

Fortuitously, there happens to be a weird bug in ChatGPT that makes it behave like a base model!

Namely, the "trick" where you ask it to repeat a token, or just send it a bunch of pre-prepared repetitions.

Using this trick is still different from prompting a base model. You can't specify a "prompt," like a random-short-phrase-from-Wikipedia, for the model to complete. You just start the repetition ball rolling, and then at some point, it starts generating some arbitrarily chosen type of document in a base-model-like way.

Still, this is good enough: we can do the trick, and then check the output against AuxDataset. If the generated text appears in AuxDataset, then ChatGPT was probably trained on that text at some point.

If you do this, you get a fraction of 3%.

This is somewhat higher than all the other numbers we saw above, especially the other ones obtained using AuxDataset.

On the other hand, the numbers varied a lot between models, and ChatGPT is probably an outlier in various ways when you're comparing it to a bunch of open models.

So, this result seems consistent with the interpretation that the attack just makes ChatGPT behave like a base model. Base models -- it turns out -- tend to regurgitate their training data occasionally, under conditions like these ones; if you make ChatGPT behave like a base model, then it does too.

Language model behaves like language model, news at 11

Since this paper came out, a number of people have pinged me on twitter or whatever, telling me about how this attack "makes ChatGPT leak data," like this is some scandalous new finding about the attack specifically.

(I made some posts saying I didn't think the attack was "leaking data" -- by which I meant ChatGPT user data, which was a weirdly common theory at the time -- so of course, now some people are telling me that I was wrong on this score.)

This interpretation seems totally misguided to me.

Every result in the paper is consistent with the banal interpretation that the attack just makes ChatGPT behave like a base model.

That is, it makes it behave the way all LLMs used to behave, up until very recently.

I guess there are a lot of people around now who have never used an LLM that wasn't tuned for chat; who don't know that the "post-attack content" we see from ChatGPT is not some weird new behavior in need of a new, probably alarming explanation; who don't know that it is actually a very familiar thing, which any base model will give you immediately if you ask. But it is. It's base model behavior, nothing more.

Behaving like a base model implies regurgitation of training data some small fraction of the time, because base models do that. And only because base models do, in fact, do that. Not for any extra reason that's special to this attack.

(Or at least, if there is some extra reason, the paper gives us no evidence of its existence.)

The paper itself is less clear than I would like about this. In a footnote, it cites my tweet on the original attack (which I appreciate!), but it does so in a way that draws a confusing link between the attack and data regurgitation:

In fact, in early August, a month after we initial discovered this attack, multiple independent researchers discovered the underlying exploit used in our paper, but, like us initially, they did not realize that the model was regenerating training data, e.g., https://twitter.com/nostalgebraist/status/1686576041803096065.

Did I "not realize that the model was regenerating training data"? I mean . . . sort of? But then again, not really?

I knew from earlier papers (and personal experience, like the "Hedonist Sovereign" thing here) that base models occasionally produce exact quotations from their training data. And my reaction to the attack was, "it looks like it's behaving like a base model."

It would be surprising if, after the attack, ChatGPT never produced an exact quotation from training data. That would be a difference between ChatGPT's underlying base model and all other known LLM base models.

And the new paper shows that -- unsurprisingly -- there is no such difference. They all do this at some rate, and ChatGPT's rate is 3%, plus or minus something or other.

3% is not zero, but it's not very large, either.

If you do the attack to ChatGPT, and then think "wow, this output looks like what I imagine training data probably looks like," it is nonetheless probably not training data. It is probably, instead, a skilled mimicry of training data. (Remember that "skilled mimicry of training data" is what LLMs are trained to do.)

And remember, too, that base models used to be OpenAI's entire product offering. Indeed, their API still offers some base models! If you want to extract training data from a private OpenAI model, you can just interact with these guys normally, and they'll spit out their training data some small % of the time.

The only value added by the attack, here, is its ability to make ChatGPT specifically behave in the way that davinci-002 already does, naturally, without any tricks.

#ai tag

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ariapmdeol · 6 months

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How to play RPGMaker Games in foreign languages with Machine Translation

This is in part a rewrite of a friend's tutorial, and in part a more streamlined version of it based on what steps I found important, to make it a bit easier to understand!

Please note that as with any Machine Translation, there will errors and issues. You will not get the same experience as someone fluent in the language, and you will not get the same experience as with playing a translation done by a real person.

If anyone has questions, please feel free to ask!

1. Ensure that you are downloading your game in the right region Locale.

In your region (administrative) settings, change 'Language for non-unicode programs' to the language of your choice. This will ensure that the file names are extracted to the right language. MAKE SURE you download AND extract the game with the right settings! DO NOT CHECK THE 'use utf-8 encoding' BOX. This ONLY needs to be done during the initial download and extraction; once everything is downloaded+extracted, you can and should set your region back to your previous settings. helpful tutorials are here and here!

2. Download and extract the Locale Emulator

Linked here!

3. Download your desired game and, if necessary, relevant RTP

The tools MUST be downloaded and extracted in the game's language. For japanese, they are here. ensure that you are still in the right locale for non-unicode programs!

4. Download and set up your Textractor!

Textractor tutorial and using it with DeepL. The Firefox tools for DeepL were broken, last I checked, but the Chrome tools work fine! If the tools don't work on chrome, you should be able to either use the inbuilt extensions, or copy-paste the text in yourself.

5. Run through the Locale Emulator

YES, this is a necessary step, EVEN IF YOUR REGION SETTINGS ARE CORRECT. Some games will not display the correct text unless you also RUN it in the right locale. You should be able to right click the game and see the Locale Emulator as an option like this. Run in Japanese (or whatever language is needed). You don't need to run as Admin if you don't want to, it should work either way.

6. Attach the Textractor and follow previously linked tutorials on how to set up the tools and the MTL.

Other notes:

There are also inbuilt Machine Translation Extensions, but those have a usage limit due to restrictions on the API. The Chrome/Firefox add-ons in the walkthrough in step 4 get around this by using the website itself, which doesn't have the same restrictions as the API does.

This will work best for RPGMaker games. For VNs, the textractor can have difficulties hooking in to extract the text, and may take some finagling.

#rpgmaker #tutorial #rpgmaker games #aria rambles #been meaning to make a proper version of this for a while #i have another version of this but it's specifically about coe #it was time to make a more generalized version

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reviewgatorsusa · 5 months

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How Web Scraping TripAdvisor Reviews Data Boosts Your Business Growth

Are you one of the 94% of buyers who rely on online reviews to make the final decision? This means that most people today explore reviews before taking action, whether booking hotels, visiting a place, buying a book, or something else.

We understand the stress of booking the right place, especially when visiting somewhere new. Finding the balance between a perfect spot, services, and budget is challenging. Many of you consider TripAdvisor reviews a go-to solution for closely getting to know the place.

Here comes the accurate game-changing method—scrape TripAdvisor reviews data. But wait, is it legal and ethical? Yes, as long as you respect the website's terms of service, don't overload its servers, and use the data for personal or non-commercial purposes. What? How? Why?

Do not stress. We will help you understand why many hotel, restaurant, and attraction place owners invest in web scraping TripAdvisor reviews or other platform information. This powerful tool empowers you to understand your performance and competitors' strategies, enabling you to make informed business changes. What next?

Let's dive in and give you a complete tour of the process of web scraping TripAdvisor review data!

What Is Scraping TripAdvisor Reviews Data?

Extracting customer reviews and other relevant information from the TripAdvisor platform through different web scraping methods. This process works by accessing publicly available website data and storing it in a structured format to analyze or monitor.

Various methods and tools available in the market have unique features that allow you to extract TripAdvisor hotel review data hassle-free. Here are the different types of data you can scrape from a TripAdvisor review scraper:

Hotels

Ratings

Awards

Location

Pricing

Number of reviews

Review date

Reviewer's Name

Restaurants

Images

You may want other information per your business plan, which can be easily added to your requirements.

What Are The Ways To Scrape TripAdvisor Reviews Data?

TripAdvisor uses different web scraping methods to review data, depending on available resources and expertise. Let us look at them:

Scrape TripAdvisor Reviews Data Using Web Scraping API

An API helps to connect various programs to gather data without revealing the code used to execute the process. The scrape TripAdvisor Reviews is a standard JSON format that does not require technical knowledge, CAPTCHAs, or maintenance.

Now let us look at the complete process:

First, check if you need to install the software on your device or if it's browser-based and does not need anything. Then, download and install the desired software you will be using for restaurant, location, or hotel review scraping. The process is straightforward and user-friendly, ensuring your confidence in using these tools.

Now redirect to the web page you want to scrape data from and copy the URL to paste it into the program.

Make updates in the HTML output per your requirements and the information you want to scrape from TripAdvisor reviews.

Most tools start by extracting different HTML elements, especially the text. You can then select the categories that need to be extracted, such as Inner HTML, href attribute, class attribute, and more.

Export the data in SPSS, Graphpad, or XLSTAT format per your requirements for further analysis.

Scrape TripAdvisor Reviews Using Python

TripAdvisor review information is analyzed to understand the experience of hotels, locations, or restaurants. Now let us help you to scrape TripAdvisor reviews using Python:

Continue reading https://www.reviewgators.com/how-web-scraping-tripadvisor-reviews-data-boosts-your-business-growth.php

#review scraping #Scraping TripAdvisor Reviews #web scraping TripAdvisor reviews #TripAdvisor review scraper

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tsreviews · 7 months

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AvatoAI Review: Unleashing the Power of AI in One Dashboard

Here's what Avato Ai can do for you

Data Analysis:

Analyze CV, Excel, or JSON files using Python and libraries like pandas or matplotlib.

Clean data, calculate statistical information and visualize data through charts or plots.

Document Processing:

Extract and manipulate text from text files or PDFs.

Perform tasks such as searching for specific strings, replacing content, and converting text to different formats.

Image Processing:

Upload image files for manipulation using libraries like OpenCV.

Perform operations like converting images to grayscale, resizing, and detecting shapes or

Machine Learning:

Utilize Python's machine learning libraries for predictions, clustering, natural language processing, and image recognition by uploading

Versatile & Broad Use Cases:

An incredibly diverse range of applications. From creating inspirational art to modeling scientific scenarios, to designing novel game elements, and more.

User-Friendly API Interface:

Access and control the power of this advanced Al technology through a user-friendly API.

Even if you're not a machine learning expert, using the API is easy and quick.

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Lets you create custom visual content by inputting a simple text prompt.

The Al will generate an image based on your provided description, enhancing the creativity and efficiency of your work.

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Enrich Your Image Generation to Unprecedented Heights.

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Multi-Lingual Support:

Generate captivating visuals based on prompts in multiple languages.

Set the panorama parameter to 'yes' and watch as our API stitches together images to create breathtaking wide-angle views.

Variation for Creative Freedom:

Embrace creative diversity with the Variation parameter. Introduce controlled randomness to your generated images, allowing for a spectrum of unique outputs.

Efficient Image Analysis:

Save time and resources with automated image analysis. The feature allows the Al to sift through bulk volumes of images and sort out vital details or tags that are valuable to your context.

Advance Recognition:

The Vision API integration recognizes prominent elements in images - objects, faces, text, and even emotions or actions.

Interactive "Image within Chat' Feature:

Say goodbye to going back and forth between screens and focus only on productive tasks.

Here's what you can do with it:

Visualize Data:

Create colorful, informative, and accessible graphs and charts from your data right within the chat.

Interpret complex data with visual aids, making data analysis a breeze!

Manipulate Images:

Want to demonstrate the raw power of image manipulation? Upload an image, and watch as our Al performs transformations, like resizing, filtering, rotating, and much more, live in the chat.

Generate Visual Content:

Creating and viewing visual content has never been easier. Generate images, simple or complex, right within your conversation

Preview Data Transformation:

If you're working with image data, you can demonstrate live how certain transformations or operations will change your images.

This can be particularly useful for fields like data augmentation in machine learning or image editing in digital graphics.

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Say goodbye to static text as our innovative technology crafts natural-sounding voices. Choose from a variety of male and female voice types to tailor the auditory experience, adding a dynamic layer to your content and making communication more effortless and enjoyable.

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Break barriers and reach a wider audience. Our Text-to-Speech feature enhances accessibility by converting written content into audio, ensuring inclusivity and understanding for all users.

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#digital marketing #Avato AI Review #Avato AI #AvatoAI #ChatGPT #Bing AI #AI Video Creation #Make Money Online #Affiliate Marketing

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mindyourtopics44 · 8 months

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25 Python Projects to Supercharge Your Job Search in 2024

Introduction: In the competitive world of technology, a strong portfolio of practical projects can make all the difference in landing your dream job. As a Python enthusiast, building a diverse range of projects not only showcases your skills but also demonstrates your ability to tackle real-world challenges. In this blog post, we'll explore 25 Python projects that can help you stand out and secure that coveted position in 2024.

1. Personal Portfolio Website

Create a dynamic portfolio website that highlights your skills, projects, and resume. Showcase your creativity and design skills to make a lasting impression.

2. Blog with User Authentication

Build a fully functional blog with features like user authentication and comments. This project demonstrates your understanding of web development and security.

3. E-Commerce Site

Develop a simple online store with product listings, shopping cart functionality, and a secure checkout process. Showcase your skills in building robust web applications.

4. Predictive Modeling

Create a predictive model for a relevant field, such as stock prices, weather forecasts, or sales predictions. Showcase your data science and machine learning prowess.

5. Natural Language Processing (NLP)

Build a sentiment analysis tool or a text summarizer using NLP techniques. Highlight your skills in processing and understanding human language.

6. Image Recognition

Develop an image recognition system capable of classifying objects. Demonstrate your proficiency in computer vision and deep learning.

7. Automation Scripts

Write scripts to automate repetitive tasks, such as file organization, data cleaning, or downloading files from the internet. Showcase your ability to improve efficiency through automation.

8. Web Scraping

Create a web scraper to extract data from websites. This project highlights your skills in data extraction and manipulation.

9. Pygame-based Game

Develop a simple game using Pygame or any other Python game library. Showcase your creativity and game development skills.

10. Text-based Adventure Game

Build a text-based adventure game or a quiz application. This project demonstrates your ability to create engaging user experiences.

11. RESTful API

Create a RESTful API for a service or application using Flask or Django. Highlight your skills in API development and integration.

12. Integration with External APIs

Develop a project that interacts with external APIs, such as social media platforms or weather services. Showcase your ability to integrate diverse systems.

13. Home Automation System

Build a home automation system using IoT concepts. Demonstrate your understanding of connecting devices and creating smart environments.

14. Weather Station

Create a weather station that collects and displays data from various sensors. Showcase your skills in data acquisition and analysis.

15. Distributed Chat Application

Build a distributed chat application using a messaging protocol like MQTT. Highlight your skills in distributed systems.

16. Blockchain or Cryptocurrency Tracker

Develop a simple blockchain or a cryptocurrency tracker. Showcase your understanding of blockchain technology.

17. Open Source Contributions

Contribute to open source projects on platforms like GitHub. Demonstrate your collaboration and teamwork skills.

18. Network or Vulnerability Scanner

Build a network or vulnerability scanner to showcase your skills in cybersecurity.

19. Decentralized Application (DApp)

Create a decentralized application using a blockchain platform like Ethereum. Showcase your skills in developing applications on decentralized networks.

20. Machine Learning Model Deployment

Deploy a machine learning model as a web service using frameworks like Flask or FastAPI. Demonstrate your skills in model deployment and integration.

21. Financial Calculator

Build a financial calculator that incorporates relevant mathematical and financial concepts. Showcase your ability to create practical tools.

22. Command-Line Tools

Develop command-line tools for tasks like file manipulation, data processing, or system monitoring. Highlight your skills in creating efficient and user-friendly command-line applications.

23. IoT-Based Health Monitoring System

Create an IoT-based health monitoring system that collects and analyzes health-related data. Showcase your ability to work on projects with social impact.

24. Facial Recognition System

Build a facial recognition system using Python and computer vision libraries. Showcase your skills in biometric technology.

25. Social Media Dashboard

Develop a social media dashboard that aggregates and displays data from various platforms. Highlight your skills in data visualization and integration.

Conclusion: As you embark on your job search in 2024, remember that a well-rounded portfolio is key to showcasing your skills and standing out from the crowd. These 25 Python projects cover a diverse range of domains, allowing you to tailor your portfolio to match your interests and the specific requirements of your dream job.

If you want to know more, Click here:https://analyticsjobs.in/question/what-are-the-best-python-projects-to-land-a-great-job-in-2024/

#python projects #top python projects #best python projects #analytics jobs #python #coding #programming #machine learning

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kurgal-nibru-ki · 1 year

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Some grammar analyzer

带翻译，但不是句法分析

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pythonfan-blog · 2 years

Link

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fileformatcom-blog · 11 days

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Transform Images into Text using Leading Open Source Java OCR Libraries

Optical Character Recognition (OCR) technology has revolutionized the way we interact with physical documents, converting printed or handwritten text into machine-readable digital formats. Open source OCR Java APIs provide an accessible and flexible solution for developers to integrate OCR functionality into their applications without relying on expensive, proprietary software. These APIs use algorithms to process images, scanned documents, or PDFs and extract the text content within them. Being open source, these libraries offer several advantages, including transparency in development, community support, and the freedom to modify the code to suit specific needs. It supports multiple languages, making it possible to extract text in different languages from documents. Developers can use OCR APIs to build tools that convert massive amounts of paper documents into structured data that can be stored, analyzed, and retrieved electronically. Open source Java OCR libraries are highly customizable. Software Developers have full access to the source code, allowing them to tweak and modify the algorithms to meet their specific project requirements. These OCR APIs are cross-platform, meaning they can be integrated into a wide range of applications, from desktop software to web applications and mobile apps. By utilizing open source OCR libraries, developers can streamline their workflows, improve accuracy in text extraction, and automate tedious manual processes. With access to powerful OCR engines like Tesseract, Asprise, and GOCR, the possibilities for integrating OCR into software development projects are virtually limitless.

#Open Source Java OCR #Optical Character Recognition API #Free Java OCR API #OCR SDK #Image Text Recognition #Text Extraction Java API #Machine Vision API #Text Recognition Software #Java OCR Engine #Text Recognition API #Free OCR Software

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virtualexpertsolutionsinc · 2 years

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Electro AI Writer - Bulk AI Blog Generator

Generate fully SEO Optimized Blogs with an Introduction, Multiple Headings, AI Images, FAQs and More.

Electro Bulk AI Blogs Generator – Generate fully SEO Optimised Blogs with an Introduction, Multiple Headings, AI Images, FAQs and a Conclusion in one Click

Introducing the world’s most UNIQUE AI writing tool with a fully automated bulk articles generator in one attempt.

Electro can generate multiple full blogs with just one click. A bulk AI Blogs Generator is never introduced, even at this price.

It is now easier than ever to create professional content quickly and effectively by creating full AI articles and blog posts by generating informative and engaging introduction AI headings with their content, AI Art images, AI questions and answers, and a detailed AI conclusion.

ELECTRO is a powerful AI Powered Article Generator that revolutionizes the world of professional audience content creation and makes it fast, easy and enjoyable.

All content is generated with a creative and engaging tone of voice, even though you can select audience, tone of voice and many more controls over bulk article generation.

Features of Electro

Keywords Generators

Find Short keywords

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Full Blog Article

HTML Full Blog Article

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Generate Images

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Blog Post Outline

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Blog Post Intro Paragraph

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FAQs Generator

Listicle

Paragraph Generator

Custom Commands

Text Helpers

Content Improver

Text Summarizer

Tone Detector

SEO Helpers

SEO – Title and Meta Descriptions

Marketing Frameworks

AIDA Framework

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Amazon Product Description Paragraph

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What’s the potential of this tool?

It is now easier than ever to create content by creating full AI articles and blog posts by generating informative and engaging introduction AI headings with their content, AI Art images, AI questions and answers, and a detailed AI conclusion.

You can build a whole site with just one click. Only feed Electro with all topics.

Requirements to run the software.

It would be best if you had a working OpenAI API key to use in the software.

It is a desktop-based software that works with Windows 10 only.

After purchasing, contact me. I will create your license and provide details, including downloadable files.

Once downloading and extracting the zip file, you must add the main extracted folder into windows defender or anti-virus exclusions. Otherwise, you will get virus alerts, which are false positives. Watch the video to see how to add software to windows defender exclusions to avoid false positives.

Refund Policy This software allows you to generate Unlimited AI content, which is instant results. So I can offer a 15-day money-back guarantee.

For more info contact us via Facebook today.

#ai writer #software #automation

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seven23ai · 12 days

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Transform Speech into Meaningful Insights with AssemblyAI

AssemblyAI offers advanced Speech AI technology that allows developers to build powerful products with high accuracy in speech-to-text transcription, sentiment analysis, speaker detection, and more. Ideal for companies needing to process voice data, AssemblyAI provides an all-in-one API that delivers unmatched accuracy and comprehensive speech understanding.

Main Content:

Core Functionality: AssemblyAI converts speech into text and extracts valuable insights from audio data with industry-leading accuracy.

Key Features:

Speech-to-Text: Accurate transcription with speaker diarization and language detection.

Speech Understanding: Extract insights like sentiment and key phrases from audio data.

Streaming Capabilities: Real-time transcription for live audio.

Benefits:

High Accuracy: Superior performance in speech recognition and understanding.

Scalability: Easily integrate into products with scalable API solutions.

Advanced Insights: Beyond transcription, gain deeper understanding from voice data.

Call to Action: Start transforming your voice data with AssemblyAI.

Visit https://aiwikiweb.com/product/assembly-ai/

#SpeechAI #VoiceData #AssemblyAI #Transcription #AI #MachineLearning

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korshubudemycoursesblog · 17 days

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Ultimate YAML Course: YAML JSON JSONPath Zero

In the world of data processing, YAML, JSON, and JSONPath play crucial roles in simplifying data structuring and access. If you're looking to master these essential technologies, the Ultimate YAML Course: YAML JSON JSONPath Zero is the perfect place to begin your journey.

In this blog, we will take an in-depth look at these technologies, their significance in modern development, and why this course can help you gain a competitive edge. This article is crafted with top-performing keywords from SERP, Google Trends, and Google Keyword Planner to enhance its SEO performance.

What is YAML?

YAML (YAML Ain't Markup Language) is a human-readable data serialization standard. It is widely used for configuration files and data interchange between different applications. YAML is popular because of its simplicity and ease of reading, which makes it ideal for applications like Kubernetes, Ansible, and other configuration management tools.

Why YAML is Important?

YAML is preferred over JSON and XML for configuration files due to its clean and minimalistic structure. As an aspiring developer or DevOps engineer, understanding YAML is essential because it’s used in popular technologies such as Docker, Kubernetes, and CI/CD pipelines.

Key features of YAML:

Human-readable format

Supports complex data structures (lists, maps)

Minimal syntax with indentation-based structure

To master these skills, the Ultimate YAML Course: YAML JSON JSONPath Zero can be the stepping stone towards a proficient understanding of YAML.

What is JSON?

JSON (JavaScript Object Notation) is a lightweight data format that is easy to read and write for humans and simple to parse and generate for machines. It’s an essential skill for developers, especially those working with web applications and APIs.

Why JSON is Important?

JSON is omnipresent in the tech ecosystem. It is the standard format for data exchange between web clients and servers, especially in REST APIs. Whether you’re building front-end web applications, back-end services, or working with APIs, you’ll come across JSON.

Key features of JSON:

Text format that is language-independent

Easily readable and writable

Frequently used in APIs for data transfer

In the Ultimate YAML Course: YAML JSON JSONPath Zero, you’ll gain a solid understanding of JSON’s fundamentals, allowing you to work effectively with various data sources.

What is JSONPath?

JSONPath is a query language for JSON, much like XPath is for XML. It allows you to extract specific data from JSON structures using path-like syntax. JSONPath is powerful because it simplifies the process of navigating through complex JSON data.

Why JSONPath is Important?

In scenarios where JSON is the primary format for data exchange, JSONPath simplifies the process of retrieving and manipulating data. For example, in APIs, databases, and cloud services that use JSON, JSONPath can streamline data extraction.

Key features of JSONPath:

Querying nested JSON data

Supports conditional expressions

Allows for easy data extraction from JSON

The Ultimate YAML Course: YAML JSON JSONPath Zero covers JSONPath in detail, ensuring you gain the skills necessary to manipulate JSON data with precision.

Who Should Take the Ultimate YAML Course?

The Ultimate YAML Course: YAML JSON JSONPath Zero is perfect for:

Developers looking to expand their knowledge of data serialization.

DevOps engineers working with tools like Kubernetes and Ansible.

Data engineers who need to process JSON data in ETL pipelines.

API developers who want to master JSON for seamless client-server communication.

System administrators who frequently deal with configuration files written in YAML.

What You Will Learn in the Ultimate YAML Course

This course provides a comprehensive overview of YAML, JSON, and JSONPath, starting from the basics and advancing to more complex topics. Below are the key concepts covered in the Ultimate YAML Course: YAML JSON JSONPath Zero:

1. YAML Fundamentals

Introduction to YAML syntax

Writing YAML configuration files

YAML best practices

Converting data between YAML, JSON, and XML

2. JSON Mastery

Understanding JSON syntax

Parsing and generating JSON data in various programming languages

Working with REST APIs and handling JSON responses

Converting YAML to JSON and vice versa

3. Mastering JSONPath

Introduction to JSONPath expressions

Writing queries to extract data from complex JSON

Real-world applications of JSONPath in data analysis and API development

Integrating JSONPath with JavaScript, Python, and other languages

Top Use Cases of YAML, JSON, and JSONPath

Kubernetes Configuration Management YAML is the de facto standard for writing configuration files in Kubernetes. Understanding YAML is crucial for deploying applications, managing clusters, and configuring services in Kubernetes.

Web APIs with JSON JSON is widely used in web APIs for exchanging data between the client and server. Mastering JSON enables developers to build efficient API endpoints that can process and return structured data.

Automating Tasks with JSONPath JSONPath simplifies the process of querying JSON data, making it essential for developers working with complex datasets in APIs, databases, or data pipelines.

Why Choose the Ultimate YAML Course?

With the growing importance of YAML, JSON, and JSONPath in modern development workflows, mastering these tools can significantly enhance your career prospects. Whether you’re an aspiring DevOps engineer, a backend developer, or a system administrator, understanding how to work with structured data is crucial.

In the Ultimate YAML Course: YAML JSON JSONPath Zero, you’ll gain practical, hands-on experience with real-world use cases that you can apply immediately in your job. The course is designed for beginners, so no prior experience is required.

Why This Course Stands Out?

Comprehensive Coverage: From YAML syntax to JSONPath queries, the course provides an all-inclusive guide.

Practical Applications: Learn how YAML, JSON, and JSONPath are used in tools like Kubernetes, APIs, and automation scripts.

Up-to-date Content: The course includes modern use cases and integrates the latest trends in DevOps, API development, and cloud computing.

Final Thoughts: Why You Should Enroll in the Ultimate YAML Course

Whether you're just starting your development journey or are looking to enhance your DevOps skills, the Ultimate YAML Course: YAML JSON JSONPath Zero will equip you with the tools to handle structured data efficiently. You'll walk away from this course with a thorough understanding of YAML, JSON, and JSONPath, as well as practical skills that can be applied immediately in your job.

Take the next step in your career by mastering these essential technologies with the Ultimate YAML Course: YAML JSON JSONPath Zero. The demand for these skills is only increasing, and by enrolling today, you’ll be ahead of the curve.

Start learning today and transform your ability to manage and manipulate data!

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abiinnovate · 19 days

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What is data science?

Data science is an interdisciplinary field that involves using scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of statistics, computer science, domain expertise, and data engineering to analyze large volumes of data and derive actionable insights.

Key Components of Data Science:

Data Collection

Definition: Gathering data from various sources, which can include databases, APIs, web scraping, sensors, and more.

Types of Data:

Structured Data: Organized in tables (e.g., databases).

Unstructured Data: Includes text, images, videos, etc.

Data Cleaning and Preparation

Definition: Processing and transforming raw data into a clean format suitable for analysis. This step involves handling missing values, removing duplicates, and correcting errors.

Importance: Clean data is crucial for accurate analysis and model building.

Exploratory Data Analysis (EDA)

Definition: Analyzing the data to discover patterns, trends, and relationships. This involves statistical analysis, data visualization, and summary statistics.

Tools: Common tools for EDA include Python (with libraries like Pandas and Matplotlib), R, and Tableau.

Data Modeling

Definition: Building mathematical models to represent the underlying patterns in the data. This includes statistical models, machine learning models, and algorithms.

Types of Models:

Supervised Learning: Models that are trained on labeled data (e.g., classification, regression).

Unsupervised Learning: Models that find patterns in unlabeled data (e.g., clustering, dimensionality reduction).

Reinforcement Learning: Models that learn by interacting with an environment to maximize some notion of cumulative reward.

Model Evaluation and Tuning

Definition: Assessing the performance of models using metrics such as accuracy, precision, recall, F1 score, etc. Model tuning involves optimizing the model parameters to improve performance.

Cross-Validation: A technique used to assess how the results of a model will generalize to an independent dataset.

Data Visualization

Definition: Creating visual representations of data and model outputs to communicate insights clearly and effectively.

Tools: Matplotlib, Seaborn, D3.js, Power BI, and Tableau are commonly used for visualization.

Deployment and Monitoring

Definition: Implementing the model in a production environment where it can be used to make real-time decisions. Monitoring involves tracking the model's performance over time to ensure it remains accurate.

Tools: Cloud services like AWS, Azure, and tools like Docker and Kubernetes are used for deployment.

Ethics and Privacy

Consideration: Ensuring that data is used responsibly, respecting privacy, and avoiding biases in models. Data scientists must be aware of ethical considerations in data collection, analysis, and model deployment.

Applications of Data Science:

Business Intelligence: Optimizing operations, customer segmentation, and personalized marketing.

Healthcare: Predicting disease outbreaks, personalized medicine, and drug discovery.

Finance: Fraud detection, risk management, and algorithmic trading.

E-commerce: Recommendation systems, inventory management, and price optimization.

Social Media: Sentiment analysis, trend detection, and user behavior analysis.

Tools and Technologies in Data Science:

Programming Languages: Python, R, SQL.

Machine Learning Libraries: Scikit-learn, TensorFlow, PyTorch.

Big Data Tools: Hadoop, Spark.

Data Visualization: Matplotlib, Seaborn, Tableau, Power BI.

Databases: SQL, NoSQL (MongoDB), and cloud databases like Google BigQuery.

Conclusion

Data science is a powerful field that is transforming industries by enabling data-driven decision-making. With the explosion of data in today's world, the demand for skilled data scientists continues to grow, making it an exciting and impactful career path.

data science course in chennai

data science institute in chennai

data analytics in chennai

data analytics institute in chennai

#data analytics

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govindhtech · 23 days

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Start Using Gemini In BigQuery Newly Released Features

Gemini In BigQuery overview

The Gemini for Google Cloud product suite’s Gemini in BigQuery delivers AI-powered data management assistance. BigQuery ML supports text synthesis and machine translation using Vertex AI models and Cloud AI APIs in addition to Gemini help.

Gemini In BigQuery AI help

Gemini in BigQuery helps you do these with AI:

Explore and comprehend your data with insights. Generally accessible (GA) Data insights uses intelligent queries from your table information to automatically and intuitively find patterns and do statistical analysis. This functionality helps with early data exploration cold-start issues. Use BigQuery to generate data insights.

Data canvas lets BigQuery users find, transform, query, and visualize data. (GA) Use natural language to search, join, and query table assets, visualize results, and communicate effortlessly. Learn more at Analyze with data canvas.

SQL and Python data analysis help. Gemini in BigQuery can generate or recommend SQL or Python code and explain SQL queries. Data analysis might begin with natural language inquiries.

Consider partitioning, clustering, and materialized views to optimize your data infrastructure. BigQuery can track SQL workloads to optimize performance and cut expenses.

Tune and fix serverless Apache Spark workloads. (Preview) Based on best practices and past workload runs, autotuning optimizes Spark operations by applying configuration settings to recurrent Spark workloads. Advanced troubleshooting with Gemini in BigQuery can identify job issues and suggest fixes for sluggish or unsuccessful jobs. Autotuning Spark workloads and Advanced troubleshooting have more information.

Use rules to customize SQL translations. (Preview) The interactive SQL translator lets you tailor SQL translations with Gemini-enhanced translation rules. Use natural language prompts to define SQL translation output modifications or provide SQL patterns to search and replace. See Create a translation rule for details.

Gemini in BigQuery leverages Google-developed LLMs. Billion lines of open source code, security statistics, and Google Cloud documentation and example code fine-tune the LLMs.

Learn when and how Gemini for Google Cloud utilizes your data. As an early-stage technology, Gemini for Google Cloud products may produce convincing but false output. Gemini output for Google Cloud products should be validated before usage. Visit Gemini for Google Cloud and ethical AI for details.

Pricing

All customers can currently use GA features for free. Google will disclose late in 2024 how BigQuery will restrict access to Gemini to these options:

BigQuery Enterprise Plus version: This edition includes all GA Gemini in BigQuery functionalities. Further announcements may allow customers using various BigQuery editions or on-demand computation to employ Gemini in BigQuery features.

SQL code assist, Python code assist, data canvas, data insights, and data preparation will be included in this per-user per-month service. No tips or troubleshooting in this bundle.

84% of enterprises think generative AI would speed up their access to insights, and interestingly, 52% of non-technical users are already using generative AI to extract insightful data, according to Google’s Data and AI Trends Report 2024.

Google Cloud goal with Google’s Data Cloud is to transform data management and analytics by leveraging their decades of research and investments in AI. This will allow businesses to create data agents that are based on their own data and reinvent experiences. Google Cloud unveiled the BigQuery preview of Gemini during Google Cloud Next 2024. Gemini offers AI-powered experiences including data exploration and discovery, data preparation and engineering, analysis and insight generation throughout the data journey, and smart recommendations to maximize user productivity and minimize expenses.

Google Cloud is pleased to announce that a number of Gemini in BigQuery capabilities, including as data canvas, data insights and partitioning, SQL code generation and explanation, Python code generation, and clustering recommendations, are now generally available.

Let’s examine in more detail some of the features that Gemini in BigQuery offers you right now.

What distinguishes Gemini in BigQuery?

Gemini in BigQuery combines cutting-edge models that are tailored to your company’s requirements with the best of Google’s capabilities for AI infrastructure and data management.

Context aware: Interprets your intentions, comprehends your objectives, and actively communicates with you to streamline your processes.

Based on your data: Constantly picks up fresh information and adjusts to your business data to see possibilities and foresee problems

Experience that is integrated: Easily obtainable from within the BigQuery interface, offering a smooth operation across the analytics workflows

How to begin using data insights

Finding the insights you can gain from your data assets and conducting a data discovery process are the initial steps in the data analysis process. Envision possessing an extensive collection of perceptive inquiries, customized to your data – queries you were unaware you ought to ask! Data Insights removes uncertainty by providing instantaneous insights with pre-validated, ready-to-run queries. For example, Data Insights may suggest that you look into the reasons behind churn among particular customer groups if you’re working with a database that contains customer churn data. This is an avenue you may not have considered.

With just one click, BigQuery Studio’s actionable queries may improve your analysis by giving you the insights you need in the appropriate place.

Boost output with help with Python and SQL codes

Gemini for BigQuery uses simple natural language suggestions to help you write and edit SQL or Python code while referencing pertinent schemas and metadata. This makes it easier for users to write sophisticated, precise queries even with little coding knowledge, and it also helps you avoid errors and inconsistencies in your code.

With BigQuery, Gemini understands the relationships and structure of your data, allowing you to get customized code recommendations from a simple natural language query. As an illustration, you may ask it to:

“Generate a SQL query to calculate the total sales for each product in the table.”

“Use pandas to write Python code that correlates the number of customer reviews with product sales.”

Determine the typical journey duration for each type of subscriber.

BigQuery’s Gemini feature may also help you comprehend intricate Python and SQL searches by offering explanations and insights. This makes it simpler for users of all skill levels to comprehend the reasoning behind the code. Those who are unfamiliar with Python and SQL, or who are working with unknown datasets, can particularly benefit from this.

Analytics workflows redesigned using natural language

Data canvas, an inventive natural language-based interface for data curation, wrangling, analysis, and visualization, is part of BigQuery’s Gemini package. With the help of data canvas, you can organize and explore your data trips using a graphical approach, making data exploration and analysis simple and straightforward.

For instance, you could use straightforward natural language prompts to collect information from multiple sources, like a point-of-sale (POS) system; integrate it with inventory, customer relationship management (CRM) systems, or external data; find correlations between variables, like revenue, product categories, and store location; or create reports and visualizations for stakeholders, all from within a single user interface, in order to analyze revenue across retail stores.

Optimize analytics for swiftness and efficiency

Data administrators and other analytics experts encounter difficulties in efficiently managing capacity and enhancing query performance as data volumes increase. BigQuery’s Gemini feature provides AI-powered suggestions for partitioning and grouping your tables in order to solve these issues. Without changing your queries, these suggestions try to optimize your tables for quicker returns and less expensive query execution.

Beginning

Phased rollouts of the general availability of Gemini in BigQuery features will begin over the following few months, starting today with suggestions for partitioning and clustering, data canvas, SQL code generation and explanation, and Python code generation.

Currently, all clients can access generally accessible (GA) features at no additional cost. For further details, please refer to the pricing details.

Read more on govindhtech.com

#StartUsingGemini #BigQuery #VertexAI #Datacanvas #NewlyReleasedFeatures #generativeAI #Geminifeatur #BigQuerycapabilities #AIinfrastructure #DataInsights #pricingdetails #Optimizeanalytics #efficiency #Analyticsworkflows #naturallanguage #technology #technews #news #govindhtech

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realdataapi1 · 24 days

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Google Maps Scraper | Google Maps Location Data Collection

RealdataAPI / google-maps-scraper

Scrape the data from hundreds of locations and businesses from Google Maps in a few clicks using Google Maps Scraper. Get data for various fields like location, images, reviews, opening hours, and more. Store all the data with Google Map Scraper in a usable format. Exceed the limits of Google Maps API. Our Google Maps Location Data Collection tool is available in the USA, UK, UAE, Canada, France, Germany, Spain, Singapore, Australia, Mexico, Italy, and other countries.

Customize me! Report an issue Business Marketing Real estate

Readme

API

Input

Related actors

What is Google Maps Data Scraper, and What Does It Scrape?

With this data scraping tool to scrape Google Maps data, you can quickly collect location and business data. Our tool for crawling Google Maps allows you to scrape and compile all the below Google Maps data.

Location, address, exact coordinates, and code

Title, category, URL, subtitle, and place ID

Price and a menu of restaurants

Websites and contact details

Active status of businesses

Popular times based on live occupancy and histogram

Image list

Average ratings, review distribution and count

People also search

Opening hours

Detailed characteristics with additional information

Web results

Hotel booking price, URL, and nearby available hotels

QnA and customer updates

The crawler also helps to collect all the reviews information details as below:

Stars

Published Date

Review text

Review URL and ID

Image list of reviews

Owner response with published date and text

Detailed rating for each service with review context

You must enable personal data scraping of reviewing customers explicitly in the input field.

Name

URL, ID, and image

Review count

Is local guide

Google Maps API also offers below features:

Review sorting and filtering

Translation and language setting

Scraping and browser configuration

Search area zooming with automation to ensure maximum output.

Define location search: it permits you to define the geographical area to extract state, country, city, zip code, etc. It increases search speed with Nomatim Maps API Integration.

How Does Google Maps Data Extractor Work?

Its working process is similar to searching anything on Google Maps and copying the displayed data from resulting pages according to requirements. The scraper opens the official platform of Google Maps, explores the mentioned location, and uses a search bar to find the exact location to scrape. Then it keeps scrolling the page until the final webpage of results. The Google location scraper enqueues each location as an individual page and copies it to paste into a usable data format. The tool shows the same process with much more speed. If you want to learn the process in detail, try it on your device.

What is the cost of using Google Maps Scraper?

Our platform offers a trial plan of 5 USD each month that you can use to extract up to two thousand Google Maps reviews using this scraper. However, platform credit consumption will vary depending on complications and custom requirements for your Google Maps data.

If you want to scrape more data often, you must buy our subscription to the personal plan at 49 USD monthly. In this plan, you will collect up to twenty thousand results from Google Maps.

Why Google Maps Scraper is Better Than Official Google Maps API?

You can scrape up to 30 thousand Maploads monthly worth 200 USD for free using the official API for Google Maps. But it restricts results to sixty only regardless of mentioned location radius. Hence if you like to extract New York-based data for restaurants, you will get output for up to 60 of thousand restaurants in the selected radius.

On the other hand, Google Maps data scrapeR has no restrictions on data results. It is affordable and offers comprehensive outputs with infographics, like histograms, that you can't get using the official Google Maps API.

Can I Scrape Google Maps Data Legally?

You can legally scrape Google Maps data if it is publicly available. But it would help if you didn't try to scrape personal data without any legitimate reason. Personal data include intellectual properties, contact details, images, and users' locations that they don't want to share with the public. Either follow the terms and conditions of Google or consult your lawyer to check the legitimacy of your reason.

How to use Google Maps Data Scraper?

You can follow your stepwise tutorial to learn how to set up and use this scraper. Additionally, you can explore our tutorial to learn how to access geolocation features using Google Maps Scraper.

Where should I use Scraped Google Maps data?

Here are some ways to use collected data from Google Maps:

Generate leads for business

Build a customer base and sales funnel

Search new customers

Track service quality and brand sentiment, and discover fake reviews

Development evergreen and effective marketing strategy

Discover and study competing businesses

Search platforms and locations to buy required products

Study location-based data for engineering or technical work

To get more ideas for utilizing collected data, visit our industry page and learn how industries use web scraping in various fields.

Do you want more scraping options?

Google Maps Reviews Scraper

If you need to scrape only Google Maps reviews, this is one of the best options. The scraper collects each review for all Google Maps places.

You have to submit the location URL to scrape its data and wait for the scraper to get you the data for Google Maps reviews that you can store and download for market research, business analysis, and other activities.

Google Maps & Contact Details

Use contact details to improve Google Maps Data Scraper results. Crawl websites for selected Google Maps locations to get contact information, and add it into a single data file. Gather location, website, email address, mobile number, social media URLs, etc. Setup contact details with Google anals and get more relevant data from the platform comfortably.

Gas Prices Scraper

Discover the lowest prices for gas refills and price update timestamps from local gas stations.

Google Places API Radar Search

It would be best to acquire your Google API key to use this scraper due to the use of the official API for Google. You have an edge in using this tool instead of the official Google API due to its restrictions to scrape selected data.

Input

Input Example of Google Maps Scraper

You can also use the JSON file of equivalent input parameters that you can see in the above image.

{ "searchStringsArray": [ "pet shelter in Prague", "place_id:ChIJ8_JBApXMDUcRDzXcYUPTGUY", ], "maxCrawledPlacesPerSearch": 10, "language": "en", "maxImages": 10, "onlyDataFromSearchPage": false, "deeperCityScrape": true, "maxReviews": 0, "scrapeReviewerName": true, "scrapeReviewerId": true, "scrapeReviewerUrl": true, "scrapeReviewId": true, "scrapeReviewUrl": true, "scrapeResponseFromOwnerText": true, "oneReviewPerRow": false, "reviewsSort": "newest", "allPlacesNoSearchAction": "" }

Output

The scraper will store its output in a usable dataset. Once it finishes its execution successfully, you can export collected data in multiple formats like CSV, XML, JSON, RSS, and more.

Output Example Google Maps Scraper

Or you can see a JSON formatted output example here.

{ "title": "Sdružení na ochranu zvířat v krajní nouzi", "description": "Naše zraněné přírůstky po výstavě – poškozené ouško u 6letého Pouziho, vykloubená nožka 5měsíčního bílomouratého Míši, 8měsíční černý dlouhosrstý Mikulka se zraněnou nožkou, holýma zadníma nožkama a holým ocáskem, Cyril sražený autem, neovládá zadní nožky.", "subTitle": null, "price": null, "menu": null, "categoryName": "Pet adoption service", "address": "Na Pláni 2006, 150 00 Praha 5, Czechia", "locatedIn": null, "neighborhood": "Na Pláni 2006", "street": "Na Pláni 2006", "city": "Prague 5", "postalCode": "150 00", "state": null, "countryCode": "CZ", "plusCode": "397W+49 Prague 5, Czechia", "website": "https://www.kocici-utulek.cz/", "phone": "+420 603 225 948", "temporarilyClosed": false, "claimThisBusiness": true, "location": { "lat": 50.062872, "lng": 14.3958755 }, "permanentlyClosed": false, "totalScore": 4.3, "isAdvertisement": false, "rank": 9, "placeId": "ChIJuxlGAU6UC0cRI_jkTCUIboA", "categories": [ "Pet adoption service" ], "cid": "9254343240589834275", "url": "https://www.google.com/maps/place/Sdru%C5%BEen%C3%AD+na+ochranu+zv%C3%AD%C5%99at+v+krajn%C3%AD+nouzi/@50.062872,14.3958755,17z/data=!3m1!4b1!4m5!3m4!1s0x470b944e014619bb:0x806e08254ce4f823!8m2!3d50.0628787!4d14.3958708?hl=en", "popularTimesLiveText": "Not too busy", "popularTimesLivePercent": 38, "popularTimesHistogram": {}, // See section below "openingHours": [], // See section below "additionalInfo": {}, // See section below "searchPageUrl": "https://www.google.com/maps/search/pet+shelter+in+Prague/@37.6,-95.665,4z?hl=en", "searchPageLoadedUrl": "https://www.google.com/maps/search/pet+shelter+in+Prague/@37.6,-95.665,4z?hl=en", "searchString": "pet shelter in Prague", "scrapedAt": "2022-07-28T12:42:10.969Z", "reviewsCount": 40, "reviewsDistribution": { "oneStar": 6, "twoStar": 0, "threeStar": 1, "fourStar": 2, "fiveStar": 31 }, "imageUrls": [ "https://lh5.googleusercontent.com/p/AF1QipOeEWgXD8Jjmj3DpIa7U9VeJ3E83xaRpefxbYZh=w1920-h1080-k-no", "https://lh5.googleusercontent.com/p/AF1QipN2XNEQQrjtpMIHLe0WlJHYWd4nhniifUiy9BYq=w1920-h1080-k-no", "https://lh5.googleusercontent.com/p/AF1QipOShH8UZgA-gtJlc83n2uBLhgkd5HRacPIOx_V6=w1920-h1080-k-no", "https://lh5.googleusercontent.com/p/AF1QipPrcwuF0i7y32PUwX-ff-jdkRovD7XQ6fmBWHmr=w1920-h1080-k-no" ], "imagesCount": 7, "reviews": [], "reviewsTags": [ { "title": "facebook", "count": 10 }, ], "placesTags": [ { "title": "Parking", "count": 142 } ], "orderBy": [], "peopleAlsoSearch": [ { "category": "Czech restaurants", "title": "Restaurant Mlýnec", "reviewsCount": 2561, "totalScore": 4.7 } ], "googleFoodUrl": "https://food.google.com/chooseprovider?restaurantId=/g/11hfvkwdgg&g2lbs=ADZRdks7YEfR3so8mfaIvxzxIBfhj30edVasvqVLbfhKcBJvKOet7xVoQdb2e4Fec3KE_TIOmL_bbKvopcN_U9WD4tjF-q0EeS7Pne3vmHfe3kT5b3gcraE5TVEddjqfP5a0mVdhsCri&hl=en-US&gl=us&fo_m=MfohQo559jFvMWvkddHzV46uMT_RFPQ05bfKMQVr5-7IofUJMU_hT8vrWuwRMUv3d8yCO4PgMUOzJVpjPL1YMfZ3bInYwBDuMfaXTPp5KXh-&utm_source=tactile&gei=kw8sZP6wLsibsAefq5b4DA&ei=kw8sZP6wLsibsAefq5b4DA&fo_s=OA,AH,SOE", "webResults": [] // See section below, }

You can also scrape some additional data fields for particular conditions.

Popular Times

"popularTimesHistogram": { "Su": [ { "hour": 6, "occupancyPercent": 0 }, // etc... ], "Mo": [ { "hour": 6, "occupancyPercent": 0 }, // etc... ], // etc... },

Opening hours

"openingHours": [ { "day": "Monday", "hours": "10:30 AM to 11 PM" }, { "day": "Tuesday", "hours": "10:30 AM to 11 PM" }, { "day": "Wednesday", "hours": "10:30 AM to 11 PM" }, { "day": "Thursday", "hours": "10:30 AM to 11 PM" }, { "day": "Friday", "hours": "10:30 AM to 12 AM" }, { "day": "Saturday", "hours": "11 AM to 12 AM" }, { "day": "Sunday", "hours": "11 AM to 10 PM" } ]

Additional info

"additionalInfo": { "Service options": [ { "Outdoor seating": true }, { "Delivery": true }, { "Takeout": true }, { "Dine-in": true } ], "Accessibility": [ { "Wheelchair accessible entrance": true }, { "Wheelchair accessible parking lot": true }, { "Wheelchair accessible restroom": true }, { "Wheelchair accessible seating": true } ], "Offerings": [ { "Alcohol": true }, { "Beer": true }, { "Coffee": true }, { "Happy hour food": true }, { "Hard liquor": true }, { "Wine": true } ], "Dining options": [ { "Lunch": true }, { "Dinner": true }, { "Dessert": true }, { "Seating": true } ], "Amenities": [ { "Bar onsite": true }, { "Good for kids": true }, { "Restroom": true }, { "Free Wi-Fi": true } ], "Atmosphere": [ { "Casual": true }, { "Cozy": true } ], "Crowd": [ { "Groups": true } ], "Planning": [ { "Accepts reservations": true } ], "Payments": [ { "Debit cards": true }, { "NFC mobile payments": true }, { "Credit cards": true } ] }

Reviews

"reviews": [ { "name": "What’s in your pantry?", "text": "One of the best pizza place in Pardubice. Great selection of pizzas for good value of money.", "textTranslated": null, "publishAt": "4 years ago", "publishedAtDate": "2018-08-26T15:21:22.259Z", "likesCount": 0, "reviewId": "ChdDSUhNMG9nS0VJQ0FnSUN3aU5mS3Z3RRAB", "reviewUrl": "https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChdDSUhNMG9nS0VJQ0FnSUN3aU5mS3Z3RRAB!2m1!1s0x0:0x79ba84b5e3261fea!3m1!1s2@1:CIHM0ogKEICAgICwiNfKvwE%7CCgsI8oqL3AUQ-KHBew%7C?hl=en-US", "reviewerId": "117322307728195096581", "reviewerUrl": "https://www.google.com/maps/contrib/117322307728195096581?hl=en-US", "reviewerPhotoUrl": "https://lh3.googleusercontent.com/a-/AD5-WCmhximZjAUEJaQ3T-z36al5yqCsyk_VhpBZ5zv3pqs=s120-c-c0x00000000-cc-rp-mo-ba3-br100", "reviewerNumberOfReviews": 45, "isLocalGuide": true, "stars": 4, "rating": null, "responseFromOwnerDate": null, "responseFromOwnerText": null, "reviewImageUrls": [ "https://lh5.googleusercontent.com/p/AF1QipMn71E_8-sqjiLKYJbbI5gqrYhVn3n3ueHpGknt=w150-h150-k-no-p", "https://lh5.googleusercontent.com/p/AF1QipOHWWSXkDgNn_d8kbpPxMmmb7PaHQKMULJggIMe=w150-h150-k-no-p", "https://lh5.googleusercontent.com/p/AF1QipMpm926guA2_yc6hamEVQpp0GWkePyv0e6OXwgU=w150-h150-k-no-p" ], "reviewContext": { "Service": "Dine in", "Meal type": "Lunch" }, "reviewDetailedRating": { "Food": 5, "Service": 3, "Atmosphere": 4 } } ]

Hotels

"moreHotelsOptions": [ { "url": "https://www.booking.com/hotel/us/pittsburgh-3454-forbes.html?&checkin=2023-03-10&checkout=2023-03-11&group_adults=2&req_adults=2&show_room=33940104_356536769_2_2_0&lang=en&selected_currency=USD&exrt=1.00000000&ext_price_total=345.21&ext_price_tax=42.39&xfc=USD&group_children=0&req_children=0&&exfcam=_2&ts=1673860104&no_rooms=1&utm_source=metagha&utm_medium=mapresults&utm_campaign=US&utm_term=hotel-339401&utm_content=dev-desktop_los-1_bw-53_dow-Friday_defdate-1_room-0_gstadt-2_rateid-public_aud-0_gacid-_mcid-10_ppa-0_clrid-0_ad-0_gstkid-0_checkin-20230310_&aid=2127489&label=metagha-link-MRUS-hotel-339401_dev-desktop_los-1_bw-53_dow-Friday_defdate-1_room-0_gstadt-2_rateid-public_aud-0_gacid-_mcid-10_ppa-0_clrid-0_ad-0_gstkid-0_checkin-20230310_", "title": "Booking.com", "price": "$303" }, { "url": "https://www.hotels.com/Hotel-Search?selected=118998&startDate=2023-03-10&endDate=2023-03-11&", "title": "Hotels.com", "price": "$303" }, ], "similarHotelsNearby": [ { "name": "Residence U Malvaz", "rating": 4.3, "reviews": 406, "description": "Cozy hotel offering a restaurant", "price": "$70" }, { "name": "Hotel U Zlatého Stromu", "rating": 3.6, "reviews": 656, "description": "Refined rooms, plus breakfast & dining", "price": "$64" }, ]

Updates From Customers

"updatesFromCustomers": { "text": "Disneyland California Adventure small area with large park all inclusive celebrations. This is a glimpse into Los Reyes parade. I'm a true fan. Thanks", "language": "en", "postDate": "a week ago", "postedBy": { "name": "Kayla Arredondo", "url": "https://www.google.com/maps/contrib/102968882116587973980?hl=en-US", "title": "Local Guide", "totalReviews": 225 }, "media": [ { "link": "https://lh3.googleusercontent.com/ggms/AF1QipNNaoT0NSbcWOPSduvZNqJ0kSqUs-dod32FeBtr=m18", "postTime": "a week ago" } ] }

Questions & Answers

"questionsAndAnswers": { "question": "Which is the best easier way to drop off a family to Disneyland Park", "answer": "best way for drop off family is at down town Disney. Drop them off then you can take a short walk to the park. ", "askDate": "5 years ago", "askedBy": { "name": "Cecilia Salcedo", "url": "https://www.google.com/maps/contrib/109041536347893604294" }, "answerDate": "5 years ago", "answeredBy": { "name": "Gabby Lujan", "url": "https://www.google.com/maps/contrib/105966144333216697667" } }

Web Results

"webResults": [ { "title": "Hotel & restaurant SIGNAL, Pardubice – Updated 2023 Prices", "displayedUrl": "https://www.booking.com › hotel › signal", "description": "Located 1.2 mi from Pardubice city center, this hotel offers a ... Josefa Janáčka 708, Pardubice, 53012, Czech Republic – Great location - show map." }, { "title": "Hotel & restaurant SIGNAL, Pardubice - Booking.com", "displayedUrl": "https://www.booking.com › signal.en-gb.html", "description": "Located 2 km from Pardubice city centre, this hotel offers a traditional ... Josefa Janáčka 708, Pardubice, 53012, Czech Republic – Great location - show ..." }, { "title": "Hotel & restaurant SIGNAL, Pardubice - 2023 Reviews ...", "displayedUrl": "https://www.agoda.com › ... › Pardubice Hotels", "description": "Hotel & restaurant SIGNAL. Josefa Janáčka 708, Pardubice III, Pardubice, Czech Republic, 53012 - See map." } ],

Tips and Tricks for Search Location Definition

It is a stepwise tutorial to show you methods to customize features with geo locations and cross the limit of a hundred and twenty outputs.

Location, Postal Code, City, State, and Country

Visit the Open Street Map webpage to check the selected location that matches your input for geolocation. Google Maps Scraper uses this API for geographical location.

The text locationQuery sets up a location for you. However, if it doesn't work, you can combine city, state, country, etc.

Zooming With Automation

The Google Maps Scraper uses automation to effectively zoom the map to scrape data. It uses a higher zoom option for smaller geolocation than bigger ones. The options with higher zoom can discover more locations, but it is costly and time-consuming. Typically, if you increase zoom, it will give you diminishing outputs.

Here are a few sample values for the zoom option for some locations:

United States- 10,371,139 km2 with 10 zooms

London - 1,595 km2 with 15 zooms

Germany - 380,878 km2 with 12 zooms

Soho - 0.35 km2 with 17 zooms

Manhattan - 87.5 km2 with 16 zooms

There is an option to override zoom input between 1 to 21 values for the world and a few homes.

Custom Search Area

If you can't find your location or want to change it according to your requirements, you can use the Custom Search Area function to create starting URLs. For example, try the Nominatim API and check the geoJSON field with Cambridge in Britain as an example. You can access many search location geometry categories. Each geometry follows the official GeoJSON-based RFC.

Remember that latitude and longitude have a reverse order in GeoJSON, than Google Map platform. You must use longitude as the first field and latitude as the second field.

GeoJSON is a superb tool that you can use to develop any customGeolocation with the proper data format.

The tool is compatible with all GeoJSON types, but Circle, Polygon, and MultiPolygon are the most valuable.

Polygon

The most common GeoJSON type of location is a polygon. Its point sets define locations. Consider closing the polygon using the exact coordinates for the first and last points. The customGeolocation has the following example format.

This sample covers the top portion of the city in the UK, like London.

{ "type": "Polygon", "coordinates": [ [ [ // Must be the same as last one -0.322813, // Longitude 51.597165 // Latitude ], [ -0.314990, 51.388023 ], [ 0.060493, 51.389199 ], [ 0.051936, 51.600360 ], [ // Must be the same as first one -0.322813, 51.597165 ] // ... ] ] }

MultiPolygon

It can combine multiple discontinuous polygons

{ "type": "MultiPolygon", "coordinates": [ [ // first polygon [ [ 12.0905752, // Longitude 50.2524063 // Latitude ], [ 12.1269337, 50.2324336 ], // ... ] ], [ // second polygon // ... ] ] }

Circle

You can use the point category for a circle with radiusKm as a custom parameter. Remember to vary the circle radius according to your requirements. Here is an example of Basel City in Switzerland.

{ "type": "Point", "coordinates": ["7.5503", "47.5590"], "radiusKm": 8 }

Advanced Configuration

Single Review in Every Row

Typically, every output item comes with single-place data. You will see the tabular format for every item as a single row. Studying tabular data format is challenging due to the high volume and complex data available in the dataset. Luckily, you can overcome these challenges.

You can click the input toggle oneReviewPerRow to get the result as a single review in each row. Read more if you want to adjust the already existing dataset format.

For instance, if you want to study reviews, you can set up data based download of your requirement and adjust the data format. Here is the process to get the reviews list using a location title with single reviews in each data row:

Please select the required format and copy the download JRL to paste it into another tab.

Add &unwind=reviews&fields=reviews,title at the URL end.

Download it by pressing Enter key. You'll see reviews in its row with unwind=reviews.

The fields=reviews,title option, will download the title and reviews by skipping other data fields. Otherwise, there will be a large output. However, if you dint use any field, there won't be any issue.

Every result location will have five thousand reviews if you dislike using oneReviewPerRow. In case of more reviews for a single location, you'll see the exact locations in the following five thousand reviews. For example, if there are fifty thousand reviews, the output dataset will have ten results for the exact location.

Changelog

Google Maps Scraper is under development. Our team is constantly working on adding new scraper features and resolving technical issues. If you want any new feature to the scraper or want us to resolve any bug, please create an issue from your console account.

Google Maps Data Scraper with Integrations

Lastly, it is possible to integrate the Google Maps location data collection tool with any web application or cloud service using Integrations available on our platform. Further, you can connect the scraper with Airbyte, Zapier, GitHub, Google Sheets, Slack, Make, Google Sheets, and other platforms. In case of an event occurrence, you can use webhooks to take any action. For example, you can receive an alert once the scraper completes the execution successfully.

Using Google Maps Data Scraper Real Data API Actor

The actor gives you programmatic permission to access our platform. We have organized the actor around RESTful HTTP endpoints to allow you to schedule, manage, and execute scrapers. It also allows you to retrieve results, track performance, access datasets, update and develop scraper versions, and more.

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