#web scraping using google colab

[zynetoglobaltechnologies]

Top Custom Web App Development Company Near You

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In the digital age, having a well-designed, high-performing website or web application is crucial to a business’s success. Zyneto Technologies stands out as a trusted web app development company, providing top-tier custom web development services tailored to meet the specific goals of your business. Whether you’re searching for “website developers near me” or partnering with global experts, Zyneto offers scalable, responsive, and feature-rich solutions that are designed to help your business grow.

Why Zyneto Technologies is the Top Custom Web Development Company Near You

Zyneto Technologies is a highly regarded name in the world of web development, with a reputation for delivering custom web solutions that perfectly align with your business objectives. Whether you're a startup looking for a personalized web solution or an established enterprise aiming for a digital overhaul, Zyneto offers custom web development services that deliver lasting value. With a focus on modern web technologies and frameworks, their development team crafts innovative and robust web applications and websites that drive business growth.

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visit - https://zyneto.com/

[jamingbenn]

year in review - hockey rpf on ao3

hello!! the annual ao3 year in review had some friends and i thinking - wouldn't it be cool if we had a hockey rpf specific version of that. so i went ahead and collated the data below!!

i start with a broad overview, then dive deeper into the 3 most popular ships this year (with one bonus!)

if any images appear blurry, click on them to expand and they should become clear!

₊˚⊹♡ . ݁₊ ⊹ . ݁˖ . ݁𐙚 ‧₊˚ ⋅. ݁

before we jump in, some key things to highlight: - CREDIT TO: the webscraping part of my code heavily utilized the ao3 wrapped google colab code, as lovingly created by @kyucultures on twitter, as the main skeleton. i tweaked a couple of things but having it as a reference saved me a LOT of time and effort as a first time web scraper!!! thank you stranger <3 - please do NOT, under ANY circumstances, share any part of this collation on any other website. please do not screenshot or repost to twitter, tiktok, or any other public social platform. thank u!!! T_T - but do feel free to send requests to my inbox! if you want more info on a specific ship, tag, or you have a cool idea or wanna see a correlation between two variables, reach out and i should be able to take a look. if you want to take a deeper dive into a specific trope not mentioned here/chapter count/word counts/fic tags/ship tags/ratings/etc, shoot me an ask!

˚　　. 　 ˚　.　　　　　 . ✦　　　 　˚　　　　 . ★⋆. ࿐࿔

with that all said and done... let's dive into hockey_rpf_2024_wrapped_insanity.ipynb

BIG PICTURE OVERVIEW

i scraped a total of 4266 fanfics that dated themselves as published or finished in the year 2024. of these 4000 odd fanfics, the most popular ships were:

Note: "Minor or Background Relationship(s)" clocked in at #9 with 91 fics, but I removed it as it was always a secondary tag and added no information to the chart. I did not discern between primary ship and secondary ship(s) either!

breaking down the 5 most popular ships over the course of the year, we see:

super interesting to see that HUGE jump for mattdrai in june/july for the stanley cup final. the general lull in the offseason is cool to see as well.

as for the most popular tags in all 2024 hockey rpf fic...

weee like our fluff. and our established relationships. and a little H/C never hurt no one.

i got curious here about which AUs were the most popular, so i filtered down for that. note that i only regex'd for tags that specifically start with "Alternate Universe - ", so A/B/O and some other stuff won't appear here!

idk it was cool to me.

also, here's a quick breakdown of the ratings % for works this year:

and as for the word counts, i pulled up a box plot of the top 20 most popular ships to see how the fic length distribution differed amongst ships:

mattdrai-ers you have some DEDICATION omg. respect

now for the ship by ship break down!!

₊ . ݁ ݁ . ⊹ ࣪ ˖͙͘͡★ ⊹ .

#1 MATTDRAI

most popular ship this year. peaked in june/july with the scf. so what do u people like to write about?

fun fun fun. i love that the scf is tagged there like yes actually she is also a main character

₊ . ݁ ݁ . ⊹ ࣪ ˖͙͘͡★ ⊹ .

#2 SIDGENO

(my babies) top tags for this ship are:

folks, we are a/b/o fiends and we cannot lie. thank you to all the selfless authors for feeding us good a/b/o fic this year. i hope to join your ranks soon.

(also: MPREG. omega sidney crosby. alpha geno. listen, the people have spoken, and like, i am listening.)

₊ . ݁ ݁ . ⊹ ࣪ ˖͙͘͡★ ⊹ .

#3 NICOJACK

top tags!!

it seems nice and cozy over there... room for one more?

₊ . ݁ ݁ . ⊹ ࣪ ˖͙͘͡★ ⊹ .

BONUS: JDTZ.

i wasnt gonna plot this but @marcandreyuri asked me if i could take a look and the results are so compelling i must include it. are yall ok. do u need a hug

top tags being h/c, angst, angst, TRADES, pining, open endings... T_T katie said its a "torture vortex" and i must concurr

₊ . ݁ ݁ . ⊹ ࣪ ˖͙͘͡★ ⊹ .

BONUS BONUS: ALPHA/BETA/OMEGA

as an a/b/o enthusiast myself i got curious as to what the most popular ships were within that tag. if you want me to take a look about this for any other tag lmk, but for a/b/o, as expected, SID GENO ON TOP BABY!:

thats all for now!!! if you have anything else you are interested in seeing the data for, send me an ask and i'll see if i can get it to ya!

[d2071art]

NO AI

TL;DR: almost all social platforms are stealing your art and use it to train generative AI (or sell your content to AI developers); please beware and do something. Or don’t, if you’re okay with this.

Which platforms are NOT safe to use for sharing you art:

Facebook, Instagram and all Meta products and platforms (although if you live in the EU, you can forbid Meta to use your content for AI training)

Reddit (sold out all its content to OpenAI)

Twitter

Bluesky (it has no protection from AI scraping and you can’t opt out from 3rd party data / content collection yet)

DeviantArt, Flikr and literally every stock image platform (some didn’t bother to protect their content from scraping, some sold it out to AI developers)

Here’s WHAT YOU CAN DO:

1. Just say no:

Block all 3rd party data collection: you can do this here on Tumblr (here’s how); all other platforms are merely taking suggestions, tbh

Use Cara (they can’t stop illegal scraping yet, but they are currently working with Glaze to built in ‘AI poisoning’, so… fingers crossed)

2. Use art style masking tools:

Glaze: you can a) download the app and run it locally or b) use Glaze’s free web service, all you need to do is register. This one is a fav of mine, ‘cause, unlike all the other tools, it doesn’t require any coding skills (also it is 100% non-commercial and was developed by a bunch of enthusiasts at the University of Chicago)

Anti-DreamBooth: free code; it was originally developed to protect personal photos from being used for forging deepfakes, but it works for art to

Mist: free code for Windows; if you use MacOS or don’t have powerful enough GPU, you can run Mist on Google’s Colab Notebook

(art style masking tools change some pixels in digital images so that AI models can’t process them properly; the changes are almost invisible, so it doesn’t affect your audiences perception)

3. Use ‘AI poisoning’ tools

Nightshade: free code for Windows 10/11 and MacOS; you’ll need GPU/CPU and a bunch of machine learning libraries to use it though.

4. Stay safe and fuck all this corporate shit.

[smellslikebot]

"how do I keep my art from being scraped for AI from now on?"

if you post images online, there's no 100% guaranteed way to prevent this, and you can probably assume that there's no need to remove/edit existing content. you might contest this as a matter of data privacy and workers' rights, but you might also be looking for smaller, more immediate actions to take.

...so I made this list! I can't vouch for the effectiveness of all of these, but I wanted to compile as many options as possible so you can decide what's best for you.

Discouraging data scraping and "opting out"

robots.txt - This is a file placed in a website's home directory to "ask" web crawlers not to access certain parts of a site. If you have your own website, you can edit this yourself, or you can check which crawlers a site disallows by adding /robots.txt at the end of the URL. This article has instructions for blocking some bots that scrape data for AI.

HTML metadata - DeviantArt (i know) has proposed the "noai" and "noimageai" meta tags for opting images out of machine learning datasets, while Mojeek proposed "noml". To use all three, you'd put the following in your webpages' headers:

<meta name="robots" content="noai, noimageai, noml">

Have I Been Trained? - A tool by Spawning to search for images in the LAION-5B and LAION-400M datasets and opt your images and web domain out of future model training. Spawning claims that Stability AI and Hugging Face have agreed to respect these opt-outs. Try searching for usernames!

Kudurru - A tool by Spawning (currently a Wordpress plugin) in closed beta that purportedly blocks/redirects AI scrapers from your website. I don't know much about how this one works.

ai.txt - Similar to robots.txt. A new type of permissions file for AI training proposed by Spawning.

ArtShield Watermarker - Web-based tool to add Stable Diffusion's "invisible watermark" to images, which may cause an image to be recognized as AI-generated and excluded from data scraping and/or model training. Source available on GitHub. Doesn't seem to have updated/posted on social media since last year.

Image processing... things

these are popular now, but there seems to be some confusion regarding the goal of these tools; these aren't meant to "kill" AI art, and they won't affect existing models. they won't magically guarantee full protection, so you probably shouldn't loudly announce that you're using them to try to bait AI users into responding

Glaze - UChicago's tool to add "adversarial noise" to art to disrupt style mimicry. Devs recommend glazing pictures last. Runs on Windows and Mac (Nvidia GPU required)

WebGlaze - Free browser-based Glaze service for those who can't run Glaze locally. Request an invite by following their instructions.

Mist - Another adversarial noise tool, by Psyker Group. Runs on Windows and Linux (Nvidia GPU required) or on web with a Google Colab Notebook.

Nightshade - UChicago's tool to distort AI's recognition of features and "poison" datasets, with the goal of making it inconvenient to use images scraped without consent. The guide recommends that you do not disclose whether your art is nightshaded. Nightshade chooses a tag that's relevant to your image. You should use this word in the image's caption/alt text when you post the image online. This means the alt text will accurately describe what's in the image-- there is no reason to ever write false/mismatched alt text!!! Runs on Windows and Mac (Nvidia GPU required)

Sanative AI - Web-based "anti-AI watermark"-- maybe comparable to Glaze and Mist. I can't find much about this one except that they won a "Responsible AI Challenge" hosted by Mozilla last year.

Just Add A Regular Watermark - It doesn't take a lot of processing power to add a watermark, so why not? Try adding complexities like warping, changes in color/opacity, and blurring to make it more annoying for an AI (or human) to remove. You could even try testing your watermark against an AI watermark remover. (the privacy policy claims that they don't keep or otherwise use your images, but use your own judgment)

given that energy consumption was the focus of some AI art criticism, I'm not sure if the benefits of these GPU-intensive tools outweigh the cost, and I'd like to know more about that. in any case, I thought that people writing alt text/image descriptions more often would've been a neat side effect of Nightshade being used, so I hope to see more of that in the future, at least!

[aidevelopmenttrends]

How to Develop AI: A Step-by-Step Guide for Beginners

Artificial Intelligence (AI) is reshaping industries, automating tasks, and driving innovation across the globe. If you're wondering how to develop AI or want to build intelligent solutions, you're not alone. Whether you're a student, developer, or tech enthusiast, this step-by-step guide provides a simplified roadmap to help you get started with AI development.

What Is AI and Why Learn to Develop It?

Artificial Intelligence refers to machines that mimic human cognitive functions like learning, problem-solving, and decision-making. From Siri to self-driving cars, AI is everywhere.

According to The Business Research Company, the no-code development platform market will reach $35.86 billion by 2025, showing the rising importance of AI-powered app development.

Types of AI You Should Know Before Development

Narrow AI (Weak AI): Specialized in specific tasks (e.g., ChatGPT, Siri).

General AI (Strong AI): Hypothetical AI that matches human intelligence.

Superintelligent AI: AI that surpasses human capabilities (still theoretical).

Step-by-Step Guide to Develop AI

Step 1: Understand the Fundamentals

Before you begin, learn basic AI concepts:

AI: Machines mimicking intelligent behavior

Machine Learning (ML): Systems that learn from data

Deep Learning: Uses neural networks for complex tasks

Natural Language Processing (NLP): Understanding human language

Computer Vision: Interpreting visual data

💡 Pro Tip: Use real-world examples and beginner-friendly resources to build your foundational knowledge.

Step 2: Learn Key Skills and Tools

To develop AI systems, you need to master:

Programming Languages

Python: The go-to language for AI, thanks to its simplicity and libraries

R: Great for statistical analysis

Java/C++: Useful in robotics and game development

Popular AI Libraries & Frameworks

TensorFlow / Keras: For deep learning

Scikit-learn: For traditional ML algorithms

PyTorch: Ideal for research and prototyping

Tools to Know

Jupyter Notebook / Google Colab: For writing and running code

GitHub: Version control and collaboration

APIs / Web Scrapers: For collecting data

Step 3: Define a Clear Problem Statement

AI projects always start with a problem:

Do you want to predict, classify, detect, or recommend something?

Can you gather the data required?

Examples:

Predict house prices

Detect spam emails

Recommend movies

Recognize handwritten digits

Step 4: Collect and Prepare Data

Data is the fuel of any AI system. It must be clean, relevant, and well-labeled.

Gathering Data

Use public datasets (Kaggle, UCI, etc.)

Scrape websites or APIs for data

Preparing Data

Clean missing or duplicate values

Normalize or scale numerical data

Label data for supervised learning

Step 5: Choose the Right AI Model

Select an AI model based on your problem type: TaskModelPredicting numbersLinear RegressionBinary classificationLogistic RegressionImage/Voice RecognitionNeural NetworksComplex decision makingRandom Forests, Decision Trees

Step 6: Train the AI Model

Training is where your model learns patterns from the dataset.

Steps include:

Split data into training and testing sets

Feed training data to the model

Optimize parameters using algorithms like Adam or SGD

Monitor epochs, which are full passes over the training data

Step 7: Test and Evaluate the Model

After training, evaluate how well the model performs using:

Accuracy: Percentage of correct results

Precision & Recall: For classification problems

Confusion Matrix: Shows predicted vs actual results

If accuracy is low, consider retraining or selecting a better model.

Step 8: Deploy the AI Solution

A trained model must be deployed to be useful. Choose a platform:

Web Apps: Use Flask or FastAPI

Mobile Apps: Use TensorFlow Lite for Android/iOS

Cloud Platforms: AWS SageMaker, Google AI, or Azure ML

Ensure your deployment is scalable, secure, and easy to maintain.

Step 9: Monitor and Improve the Model

Your AI model isn’t done once it’s live. Continuously monitor and update it:

Collect user feedback and interaction logs

Re-train periodically with fresh data

Use tools like MLflow, Prometheus, or TensorBoard

AI vs. Traditional Programming: What’s Different?

FactorTraditional ProgrammingAI DevelopmentProblem-solvingRule-basedData-drivenLearning abilityNoneLearns from dataFlexibilityLowHighPredictabilityHighVariableCode ComplexityHigh with more conditionsData-focusedScalabilityLimitedHighMaintenanceManualSemi-automatedUse CasesAccounting, CalculatorsChatbots, Vision, NLP

AI adapts and learns over time, unlike traditional software, which requires hard-coded instructions.

Ready to Build Your First AI App?

Working with an AI development company in the USA or India like Rytsense Technologies can help accelerate your journey. From building your first model to deploying on cloud or mobile, our team ensures secure, scalable AI solutions tailored to your needs.

Final Thoughts: Start Small, Think Big

Understanding how to develop AI is your first step toward a future-driven career. The learning curve may seem steep, but with consistent effort, access to the right tools, and expert guidance, you can build intelligent solutions that truly make an impact.

Whether you aim to become an AI engineer, automate tasks in your business, or create the next breakthrough app, the journey starts here.

[apotac]

Python Certification Course in 2025

Your Future-Ready Career Begins Here – Powered by APOTAC

As we step into an increasingly tech-driven future, Python stands tall as one of the most versatile, beginner-friendly, and high-demand programming languages in the world. In 2025, mastering Python is more than a skill—it's a gateway to unlocking career opportunities across data science, web development, automation, AI, and more. That’s where APOTAC’s Python Certification Course comes in—designed to equip you with real-world skills, hands-on projects, and the confidence to land your first (or next) job in tech.

✅ Why Learn Python in 2025?

Python’s popularity continues to rise in 2025 due to its simplicity and power. It is used extensively by companies like Google, Netflix, and Spotify for building scalable applications and smart algorithms.

Top Reasons to Learn Python This Year:

🚀 Essential for AI & Data Science

💼 Demand in Every Industry (Finance, Health, E-commerce, EdTech)

🛠️ Ideal for Automation and Productivity Tools

🌐 Backbone of Web Development & APIs

🧠 Simple Syntax – Perfect for Beginners

Python is no longer “optional.” It’s the language of innovation—and now is the time to learn it.

 About APOTAC’s Python Certification Program

APOTAC's 100% Job-Ready Python Certification Course is built to take you from absolute beginner to confident developer. Whether you want to become a Python developer, data analyst, or automation engineer, this course gives you the knowledge, tools, and mentorship to succeed.

🌟 Course Highlights:

🧠 Beginner-Friendly Learning Path

💻 Hands-On Projects & Real-Time Coding

📊 Data Analysis Using Pandas, NumPy, Matplotlib

🔄 Automation Scripts & Web Scraping

🔌 Working with APIs and JSON

📜 Industry-Recognized Certification

🎥 Lifetime Access to Sessions

🤝 1-on-1 Mentor Support & Career Guidance

🛠️ What You'll Learn

The course is structured into easy-to-follow modules with a strong emphasis on practical implementation:

Python Fundamentals (Variables, Loops, Functions)

Data Structures & Object-Oriented Programming

File Handling & Exception Management

Data Analysis with Pandas & NumPy

Visualization with Matplotlib & Seaborn

Web Scraping with BeautifulSoup & Selenium

API Integration & Basic Automation Projects

Capstone Project for Portfolio Building

👩‍🎓 Who Should Join?

This course is ideal for:

Students entering the tech space

Freshers looking for a first job in IT

Working professionals upskilling or switching careers

Entrepreneurs automating business workflows

Freelancers who want to offer coding/data services

No prior coding experience? No problem! We start from scratch and guide you all the way to job readiness.

💼 Career Opportunities After Certification

Once you complete APOTAC’s Python Certification, you can confidently apply for:

Python Developer

Data Analyst

Automation Engineer

Backend Developer

Junior Data Scientist

SDET / QA Tester

AI & ML Internships (with further learning)

This certification opens the door to in-demand, high-paying, and future-proof roles in the IT and data ecosystem.

🌐 Tools & Platforms You'll Work With

Python 3.x

Jupyter Notebook / Google Colab

VS Code

Git & GitHub

Pandas, NumPy, Matplotlib

APIs & JSON

BeautifulSoup / Selenium

🏅 Certification That Matters

On completion, you’ll receive an industry-recognized certificate from APOTAC—perfect to showcase on your resume, LinkedIn, and GitHub portfolio. You'll also get interview prep, resume building, and job guidance as part of the program.

🚀 Why APOTAC?

We don’t just teach – we mentor, guide, and place. At APOTAC, we focus on outcome-based learning with a strong support system.

✅ 100% Job-Oriented Curriculum

✅ Expert Mentors from the Industry

✅ Real-Time Projects & Portfolio Building

✅ Career Guidance & Interview Preparation

✅ Peer Learning Community & Alumni Network

✅ Lifetime Access & Updates

📢 What Our Students Say

“This course gave me confidence to switch from BPO to tech. I landed a Python Developer role within 3 months of completing it!” — Prateek M., Hyderabad

“I knew nothing about coding. The mentors at APOTAC made everything simple, and now I’m working as a Data Analyst!” — Sneha R., Mumbai

📅 Enroll Today – Limited Seats!

Take the first step toward your tech career in 2025. Join APOTAC Python Certification Course and transform your future with hands-on learning, expert guidance, and job-ready skills.

Become a certified Python professional. Your journey starts now.

Home 

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[industry212]

10 Must-Have AI Chrome Extensions for Data Scientists in 2024

Empowering data scientists with Top 10 AI Chrome Extensions

The field of data science demands a toolkit that evolves with the industry's advancements. As we enter 2024, the significance of AI Chrome extensions for data scientists cannot be overstated. This article discusses the top 10 extensions that enable data scientists to enhance productivity and streamline workflows.

Codeium:

Codeium, a versatile tool for programmers, streamlines code efficiency in over 20 languages. Through analysis and optimization, it significantly accelerates program execution, minimizing resource consumption. Whether you're a seasoned coder or a beginner, Codeium proves invaluable in enhancing code performance for quicker results and improved resource management.

EquatIO:

EquatIO transforms mathematical expression creation into a seamless digital experience. Whether typing, handwriting, or using voice dictation, it effortlessly translates thoughts into precise formulas. Compatible with Google Docs, Forms, Slides, Sheets, and Drawings, EquatIO fosters an engaging learning environment, offering advanced features like interactive quizzes and chemistry formula prediction.

Instant Data Scraper:

Instant Data Scraper is a powerful and free browser extension that uses AI for seamless data extraction from any website. No scripting needed; it analyzes HTML structures for relevant data, providing customization options for precision. Ideal for lead generation, SEO, and more, with secure data handling. No spyware, just efficient web scraping.

Challenge Hunt:

Challenge Hunt is your go-to app for staying updated on global programming competitions and hackathons. It covers coding challenges, hackathons, data science competitions, and hiring challenges. Set reminders for upcoming events and personalize your experience by selecting preferred online platforms. Never miss a coding opportunity with this all-in-one competition tracker.

CatalyzeX:

CatalyzeX is a browser extension that revolutionizes how researchers and developers access machine learning implementations. Seamlessly integrated into your web browser, it adds intuitive "[CODE] buttons" to research papers across Google, ArXiv, Scholar, Twitter, and Github. Instantly navigate to open source code, powered by the esteemed CatalyzeX.com repository, unlocking a world of cutting-edge machine learning advancements.

Sider:

Sider is a versatile text processing tool designed to streamline tasks in data science. Whether clarifying complex concepts, translating foreign text, summarizing articles, or rephrasing documents, Sider adapts seamlessly. Its versatility proves invaluable to students, writers, and professionals across academia, business, and technology.

Originality.AI:

Originality.AI is a vital data science tool addressing the challenge of discerning between human and AI-generated text. It accurately identifies authorship, distinguishing content created by humans from that generated by neural networks as AI advances in text creation.

Fireflies:

Fireflies, powered by GPT-4, is an invaluable assistant for data scientists. It excels in navigating and summarizing diverse content types like articles, YouTube videos, emails, and documents. In the era of information overload, Fireflies efficiently sorts and summarizes content from various sources, offering a vital solution for data professionals.

AIPRM:

AIPRM facilitates optimal use of Generative Pretrained Transformers by offering a diverse catalog of well-structured prompts designed for data scientists and IT professionals. With scenarios covering a range of use cases, users can customize GPT model responses to precise requirements, enhancing overall model effectiveness in diverse applications.

Code Squire.AI:

Code Squire.AI is a dedicated code assistant for data science, excelling in Pandas and supporting JupyterLab and Colab. It streamlines coding, reduces errors, and boosts efficiency in data science tasks.

[foodspark-scraper]

Guide To Scrape Food Data Using Python & Google Colab

Are you tired of manually collecting food data for your recipe app or meal planning service? Look no further! With the power of web scraping and automation, you can easily gather all the necessary information for your food database. In this guide, we will show you how to scrape food data using Python and Google Colab.

What is Web Scraping?

Web scraping is the process of extracting data from websites. It involves using a program or script to automatically navigate through web pages and gather information. This data can then be saved in a structured format, such as a CSV or JSON file, for further analysis or use.

Why Use Python and Google Colab?

Python is a popular programming language for web scraping due to its ease of use and powerful libraries such as BeautifulSoup and Requests. Google Colab, on the other hand, is a free online platform for writing and running Python code. It also offers the ability to store and access data on Google Drive, making it a convenient choice for web scraping projects.

Setting Up Google Colab

Before we begin, make sure you have a Google account and are signed in to Google Drive. Then, go to Google Colab and create a new notebook. You can also upload an existing notebook if you have one.

Installing Libraries

To scrape data from websites, we will need to install two libraries: BeautifulSoup and Requests. In the first cell of your notebook, type the following code and run it:!pip install beautifulsoup4 !pip install requests

Scraping Food Data

Now, we are ready to start scraping food data. For this example, we will scrape data from a popular recipe website, Allrecipes.com. We will extract the recipe name, ingredients, and instructions for each recipe. First, we need to import the necessary libraries and specify the URL we want to scrape:

from bs4 import BeautifulSoup import requests

url = "https://www.allrecipes.com/recipes/84/healthy-recipes/"Next, we will use the Requests library to get the HTML content of the webpage and then use BeautifulSoup to parse it:

page = requests.get(url) soup = BeautifulSoup(page.content, 'html.parser')

Now, we can use BeautifulSoup to find the specific elements we want to scrape. In this case, we will use the "article" tag to find each recipe and then extract the recipe name, ingredients, and instructions:

recipes = soup.find_all('article')

for recipe in recipes: name = recipe.find('h3').text ingredients = recipe.find('ul', class_='ingredients-section').text 

instructions = recipe.find('ol', class_='instructions-section').text

print(name) print(ingredients) print(instructions)

Finally, we can save the scraped data in a CSV file for further use:

import csv

with open('recipes.csv', 'w', newline='') as file: 

writer = csv.writer(file) writer.writerow("Name", "Ingredients", "Instructions")

for recipe in recipes: name = recipe.find('h3').text ingredients = recipe.find('ul', class_='ingredients-section').text instructions = recipe.find('ol', class_='instructions-section').text writer.writerow([name, ingredients, instructions])

Conclusion

With just a few lines of code, we were able to scrape food data from a website and save it in a structured format. This process can be automated and repeated for multiple websites to gather a large amount of data for your food database. Happy scraping! 

[any-other-fandom-blog]

For those who are not as familiar with Python and Google Colab - it's basically a shared notebook that lets you run my code, so you don't need any additional software or anything. I've hopefully commented everything so it should be fairly intuitive to run if you are not as familiar with how this sort of thing works. I've also enabled private outputs, meaning anything you do will not be saved - none of your ao3 data, your graphs, anything will be saved once you exit or even reload the page.

I know there will still be concerns about privacy and entering your data into a 3rd party app - I had concerns too, which is part of why I wanted to code it myself! If you want to know more about what I've done to hopefully maximise security - see below:

The way web scraping (how I get the ao3 data so I can run the stats) works is the code will connect to a web page and retrieve the HTML of the site - so it can get the information/text from the site.

Your Ao3 history page is password-protected however. That means, to access it, it does need a username and password. However, as mentioned, I'm using private outputs so that anything you enter into it cannot be saved or shared in any way. Any information you enter when running the code is deleted after the session is finished! If you are really concerned, you can save the notebook to your own Google Drive to run the code there, or even download the file to your own computer (but if you do that you need an application that can run Python code, such as Anaconda or Visual Studio Code).

A lot of the webscraping code is originally from klipklapper, (originally found here), an extension of teresachenec's wrapped from 2021, updated for the most recent ao3 API (as of 01/01/2024). I have also updated the function that retrieves your Ao3 history page so that the password is not saved after it accesses the page - it's deleted from memory! You can test that yourself by creating a new cell and typing the variable "password" (which is where your password is temporarily stored) or checking with the variable checker. It should come up as "variable not found" meaning it hasn't been stored!

If you really don't want to do it this way, I've also included a way to scrape data from your browser yourself using an extension - no password necessary! As long as you're already logged in (which you can do before you get the extension) you're good to go!

I know my code isn't perfect - and the way Ao3 saves your history (eg. each fic is only stored in your history once) means that the further back you go, the less accurate your stats will be if you've reread fics, and may make comparisons over time less accurate. It also assumes you've read every single work in your history all the way through only once - so word count may be off (I'm hoping the number of works you've not read fully cancels out the rereads in terms of word count).

However, if anyone who is more experienced with Python has any suggestion to optimise the code or improve security, or even ideas for further stats they'd be interested in I'd love to hear it! I can't guarantee that I'll work on it a lot, but any suggestions will be taken into consideration.

Whenever Spotify Wrapped releases, it reminds me of this project I've been thinking about for a while.

And now, The StoryGraph released its version of wrapped for the year (at the actual end of the year, as god intended) and I always think I read a lot less than I used to when I was younger.

Except...I still read on ao3. And I am, at heart, a stats nerd, (see me: nerding out about TSG graphs) so I've always wanted to analyse my ao3 reading history. I've seen people make their own version of ao3 wrapped, but I like having comprehensive stats, so instead of doing any of my actual coding work, I made my own version (with maybe too many stats and graphs according to my horrified non-coding friends I showed this to).

And I figured that there would be some folks out there who are as nerdy about this as I am and would appreciate it as well. So I made the code shareable and hopefully something that anyone can run.

So without further ado, here is my ao3 wrapped:

https://colab.research.google.com/drive/1DikTD0T9YjwPAL-Z3DTD8bdkM5fk_TS9?usp=sharing#scrollTo=cdaded40

Some things that my code can do:

scrape your data from Ao3 and organise it in a nice neat table you can download and keep

allow you to pick a time period

sample a random fic you read from that time period

look at the total number of fics you read, and what percentage of those have been deleted

look at how many works are on your 'Marked for Later' list and see information (top fandom and top characters) about them

show you graphs of the percentage of fics you read with each type of warning, rating, and ship type, as well as your ratio of completed works read

show you the last time you viewed a fic compared with the last time it was updated

the number of fics read over time (sorted by years, months or days depending on whether you are looking at all time, a certain year, or certain month respectively)

analyse the number of words you read and words read over time (similar to fics read over time)

identify your top 20 authors, fandoms, relationships, characters and tags

dynamically choose and plot the changes to your top 10 authors, fandoms, relationships, characters and tags over the time period (by years, months or days depending on the initial overview, similar to , read over time)

see how the rankings of your top 5 have changed from the previous time peroiod, and check the ranking of a specified author/fandom/ship/character/tag in that previous time period

So anyways, have fun!

[webscreenscraping]

How To Extract Restaurant Data Using Google Maps Data Scraping?

Do you need a comprehensive listing of restaurants having their addresses as well as ratings when you go for some holidays? Certainly, yes because it makes your path much easier and the coolest way to do that is using web scraping.

Data scraping or web scraping extracts data from the website to a local machine. The results are in spreadsheet form so you can have the whole listing of restaurants accessible around me getting their address and ratings in easy spreadsheets!

Here at Web Screen Scraping, we utilize Python 3 scripts for scraping food and restaurant data as well as installing Python might be extremely useful. For script proofreading, we have used Google Colab to run a script because it assists us in running Python scripts using the cloud.

As our purpose is to get a complete list of different places, extracting Google Maps data is the answer! With Google Maps scraping, it’s easy to scrape a place name, kind of place, coordinates, address, phone number, ratings, and other vital data. For starting, we can utilize a Place Scraping API. Using a Place Scraping API, it’s very easy to scrape Places data.

1st Step: Which data is needed?

Here, we would search for the “restaurants around me” phrase in Sanur, Bali in a radius of 1 km. So, the parameters could be ‘restaurants’, ‘Sanur Beach’, and ‘1 km’.

Let’s translate that into Python:

coordinates = ['-8.705833, 115.261377'] keywords = ['restaurant'] radius = '1000' api_key = 'acbhsjbfeur2y8r' #insert your API key here

All the ‘keywords’ will help us get places that are listed as results or restaurants having ‘restaurants’ in them. It’s superior than utilize the ‘types’ or ‘names’ of the places because we can get a complete list of different places that the name and type, has ‘restaurant’. For example, we could use restaurant names like Sushi Tei & Se’i Sapi. In case, we utilize ‘names’, then we’ll have places whose names are having a ‘restaurant’ word in that. In case, we utilize ‘type’, then we’ll have places where any type is a ‘restaurant’. Though, the drawback of utilizing ‘keywords’ is, this will need extra time to clean data.

2nd Step: Create some required libraries, like:

import pandas as pd, numpy as np import requests import json import time from google.colab import files

Have you observed “from imported files of google.colab”? Yes, the usage of the Google Colab requires us to use google.colab library to open or save data files.

3rd Step: Write the code that produces data relying on the given parameters in 1st Step.

for coordinate in coordinates: for keyword in keywords:url = 'https://maps.googleapis.com/maps/api/place/nearbysearch/json?location='+coordinate+'&radius='+str(radius)+'&keyword='+str(keyword)+'&key='+str(api_key)while True: print(url) respon = requests.get(url) jj = json.loads(respon.text) results = jj['results'] for result in results: name = result['name'] place_id = result ['place_id'] lat = result['geometry']['location']['lat'] lng = result['geometry']['location']['lng'] rating = result['rating'] types = result['types'] vicinity = result['vicinity']data = [name, place_id, lat, lng, rating, types, vicinity] final_data.append(data)time.sleep(5)if 'next_page_token' not in jj: break else:next_page_token = jj['next_page_token']url = 'https://maps.googleapis.com/maps/api/place/nearbysearch/json?key='+str(api_key)+'&pagetoken='+str(next_page_token)labels = ['Place Name','Place ID', 'Latitude', 'Longitude', 'Types', 'Vicinity']

The code will help us find a place’s name, ids, ratings, latitude-longitude, kinds, and areas for all keywords as well as their coordinates. Because Google displays merely 20 entries on each page, we had to add ‘next_page_token’ to scrape the data of the next page. Let’s accept we are having 40 restaurants close to Sanur, then Google will display results on two pages. For 65 results, there will be four pages.

The utmost data points, which we extract are only 60 places. It is a rule of Google. For example, 140 restaurants are available around Sanur within a radius of 1 km from where we had started. It means that only 60 of the total 140 restaurants will get produced. So, to avoid inconsistencies, we have to control the radius and also coordinate proficiently. Please make certain that the radius doesn’t become very wide, which results in “only 60 points are made whereas there are several of them”. Moreover, just ensure that the radius isn’t extremely small, which results in listing different coordinates. Both of them could not become well-organized, so we need to understand the context of the location previously.

4th Step: Save this data into a local machine

export_dataframe_1_medium = pd.DataFrame.from_records(final_data, columns=labels) export_dataframe_1_medium.to_csv('export_dataframe_1_medium.csv')

Last Step: Associate all these steps with the complete code:

import pandas as pd, numpy as np import requests import json import time final_data = []# Parameters coordinates = ['-8.705833, 115.261377'] keywords = ['restaurant'] radius = '1000' api_key = 'acbhsjbfeur2y8r' #insert your Places APIfor coordinate in coordinates: for keyword in keywords:url = 'https://maps.googleapis.com/maps/api/place/nearbysearch/json?location='+coordinate+'&radius='+str(radius)+'&keyword='+str(keyword)+'&key='+str(api_key)while True: print(url) respon = requests.get(url) jj = json.loads(respon.text) results = jj['results'] for result in results: name = result['name'] place_id = result ['place_id'] lat = result['geometry']['location']['lat'] lng = result['geometry']['location']['lng'] rating = result['rating'] types = result['types'] vicinity = result['vicinity']data = [name, place_id, lat, lng, rating, types, vicinity] final_data.append(data)time.sleep(5)if 'next_page_token' not in jj: break else:next_page_token = jj['next_page_token']url = 'https://maps.googleapis.com/maps/api/place/nearbysearch/json?key='+str(api_key)+'&pagetoken='+str(next_page_token)labels = ['Place Name','Place ID', 'Latitude', 'Longitude', 'Types', 'Vicinity']export_dataframe_1_medium = pd.DataFrame.from_records(final_data, columns=labels) export_dataframe_1_medium.to_csv('export_dataframe_1_medium.csv')

Now, it’s easy to download data from various Google Colab files. You just need to click on an arrow button provided on the left-side pane as well as click ‘Files’ to download data!

Your extracted data would be saved in CSV format as well as it might be imagined with tools that you’re well aware of! It could be R, Python, Tableau, etc. So, we have imagined that using Kepler.gl; a WebGL authorized, data agnostic, as well as high-performance web apps for geospatial analytical visualizations.

This is how the resulted data would look like in a spreadsheet:

And, this is how it looks in a Kepler.gl map:

We can see 59 restaurants from the Sanur beach. Just require to add names and ratings in the map as well as we’re prepared to search foods around the area!

Still not sure about how to scrape food data with Google Maps Data Scraping? Contact Web Screen Scraping for more details!

[zynetoglobaltechnologies]

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Zyneto Technologies is known for delivering high-quality mobile app development solutions that are tailored to your business needs. With a team of highly skilled developers, they specialize in building responsive, scalable, and feature

website- zyneto.com

[locationscloudusa]

How To Extract Restaurant Data Using Google Maps Data Scraping?

Do you need a comprehensive listing of restaurants having their addresses as well as ratings when you go for some holidays? Certainly, yes because it makes your path much easier and the coolest way to do that is using web scraping.

Data scraping or web scraping extracts data from the website to a local machine. The results are in spreadsheet form so you can have the whole listing of restaurants accessible around me getting their address and ratings in easy spreadsheets!

Here at Web Screen Scraping, we utilize Python 3 scripts for scraping food and restaurant data as well as installing Python might be extremely useful. For script proofreading, we have used Google Colab to run a script because it assists us in running Python scripts using the cloud.

As our purpose is to get a complete list of different places, extracting Google Maps data is the answer! With Google Maps scraping, it’s easy to scrape a place name, kind of place, coordinates, address, phone number, ratings, and other vital data. For starting, we can utilize a Place Scraping API. Using a Place Scraping API, it’s very easy to scrape Places data.

1st Step: Which data is needed?

Here, we would search for the “restaurants around me” phrase in Sanur, Bali in a radius of 1 km. So, the parameters could be ‘restaurants’, ‘Sanur Beach’, and ‘1 km’.

Let’s translate that into Python:

coordinates = ['-8.705833, 115.261377'] keywords = ['restaurant'] radius = '1000' api_key = 'acbhsjbfeur2y8r' #insert your API key here

All the ‘keywords’ will help us get places that are listed as results or restaurants having ‘restaurants’ in them. It’s superior than utilize the ‘types’ or ‘names’ of the places because we can get a complete list of different places that the name and type, has ‘restaurant’. For example, we could use restaurant names like Sushi Tei & Se’i Sapi. In case, we utilize ‘names’, then we’ll have places whose names are having a ‘restaurant’ word in that. In case, we utilize ‘type’, then we’ll have places where any type is a ‘restaurant’. Though, the drawback of utilizing ‘keywords’ is, this will need extra time to clean data.

2nd Step: Create some required libraries, like:

import pandas as pd, numpy as np import requests import json import time from google.colab import files

Have you observed “from imported files of google.colab”? Yes, the usage of the Google Colab requires us to use google.colab library to open or save data files.

3rd Step: Write the code that produces data relying on the given parameters in 1st Step.

for coordinate in coordinates: for keyword in keywords:url = 'https://maps.googleapis.com/maps/api/place/nearbysearch/json?location='+coordinate+'&radius='+str(radius)+'&keyword='+str(keyword)+'&key='+str(api_key)while True: print(url) respon = requests.get(url) jj = json.loads(respon.text) results = jj['results'] for result in results: name = result['name'] place_id = result ['place_id'] lat = result['geometry']['location']['lat'] lng = result['geometry']['location']['lng'] rating = result['rating'] types = result['types'] vicinity = result['vicinity']data = [name, place_id, lat, lng, rating, types, vicinity] final_data.append(data)time.sleep(5)if 'next_page_token' not in jj: break else:next_page_token = jj['next_page_token']url = 'https://maps.googleapis.com/maps/api/place/nearbysearch/json?key='+str(api_key)+'&pagetoken='+str(next_page_token)labels = ['Place Name','Place ID', 'Latitude', 'Longitude', 'Types', 'Vicinity']

The code will help us find a place’s name, ids, ratings, latitude-longitude, kinds, and areas for all keywords as well as their coordinates. Because Google displays merely 20 entries on each page, we had to add ‘next_page_token’ to scrape the data of the next page. Let’s accept we are having 40 restaurants close to Sanur, then Google will display results on two pages. For 65 results, there will be four pages.

The utmost data points, which we extract are only 60 places. It is a rule of Google. For example, 140 restaurants are available around Sanur within a radius of 1 km from where we had started. It means that only 60 of the total 140 restaurants will get produced. So, to avoid inconsistencies, we have to control the radius and also coordinate proficiently. Please make certain that the radius doesn’t become very wide, which results in “only 60 points are made whereas there are several of them”. Moreover, just ensure that the radius isn’t extremely small, which results in listing different coordinates. Both of them could not become well-organized, so we need to understand the context of the location previously.

4th Step: Save this data into a local machine

export_dataframe_1_medium = pd.DataFrame.from_records(final_data, columns=labels) export_dataframe_1_medium.to_csv('export_dataframe_1_medium.csv')

Last Step: Associate all these steps with the complete code:

import pandas as pd, numpy as np import requests import json import time final_data = []# Parameters coordinates = ['-8.705833, 115.261377'] keywords = ['restaurant'] radius = '1000' api_key = 'acbhsjbfeur2y8r' #insert your Places APIfor coordinate in coordinates: for keyword in keywords:url = 'https://maps.googleapis.com/maps/api/place/nearbysearch/json?location='+coordinate+'&radius='+str(radius)+'&keyword='+str(keyword)+'&key='+str(api_key)while True: print(url) respon = requests.get(url) jj = json.loads(respon.text) results = jj['results'] for result in results: name = result['name'] place_id = result ['place_id'] lat = result['geometry']['location']['lat'] lng = result['geometry']['location']['lng'] rating = result['rating'] types = result['types'] vicinity = result['vicinity']data = [name, place_id, lat, lng, rating, types, vicinity] final_data.append(data)time.sleep(5)if 'next_page_token' not in jj: break else:next_page_token = jj['next_page_token']url = 'https://maps.googleapis.com/maps/api/place/nearbysearch/json?key='+str(api_key)+'&pagetoken='+str(next_page_token)labels = ['Place Name','Place ID', 'Latitude', 'Longitude', 'Types', 'Vicinity']export_dataframe_1_medium = pd.DataFrame.from_records(final_data, columns=labels) export_dataframe_1_medium.to_csv('export_dataframe_1_medium.csv')

Now, it’s easy to download data from various Google Colab files. You just need to click on an arrow button provided on the left-side pane as well as click ‘Files’ to download data!

Your extracted data would be saved in CSV format as well as it might be imagined with tools that you’re well aware of! It could be R, Python, Tableau, etc. So, we have imagined that using Kepler.gl; a WebGL authorized, data agnostic, as well as high-performance web apps for geospatial analytical visualizations.

This is how the resulted data would look like in a spreadsheet:

And, this is how it looks in a Kepler.gl map:

We can see 59 restaurants from the Sanur beach. Just require to add names and ratings in the map as well as we’re prepared to search foods around the area!

Still not sure about how to scrape food data with Google Maps Data Scraping? Contact Web Screen Scraping for more details!

[fooddatascrape]

How To Extract Food Data From Google Maps With Google Colab & Python?

Do you want a comprehensive list of restaurants with reviews and locations every time you visit a new place or go on vacation? Sure you do, because it makes your life so much easier. Data scraping is the most convenient method.

Web scraping, also known as data scraping, is the process of transferring information from a website to a local network. The result is in the form of spreadsheets. So you can get a whole list of restaurants in your area with addresses and ratings in one simple spreadsheet! In this blog, you will learn how to use Python and Google Colab to Extract food data From Google Maps.

WWe are scraping restaurant and food data using Python 3 scripts since installing Python can be pretty handy. We use Google Colab to run the proofreading script since it allows us to run Python scripts on the server.

As our objective is to get a detailed listing of locations, extracting Google Maps data is an ideal solution. Using Google Maps data scraping, you can scrape data like name, area, location, place types, ratings, phone numbers, and other applicable information. For startups, we can utilize a places data scraping API. A places Scraping API makes that very easy to scrape location data.

Step 1: What information would you need?

For example, here we are searching for "restaurants near me" in Sanur, Bali, within 1 kilometer. So the criteria could be "restaurants," "Sanur Beach," and "1 mile."Let us convert this into Python:

These "keywords" help us find places categorized as restaurants OR results that contain the term "restaurant." A comprehensive list of sites whose names and types both have the word "restaurant" is better than using "type" or "name" of places.

For example, we can make reservations at Se'i Sapi and Sushi Tei at the same time. If we use the term "name," we will only see places whose names contain the word "restaurant." If we use the word "type," we get areas whose type is "restaurant." However, using "keywords" has the disadvantage that data cleaning takes longer.

Step 2: Create some necessary libraries, like:

Create some necessary modules, such as:

The "files imported from google. colab" did you notice? Yes, to open or save data in Google Colab, we need to use google. colab library.

Step 3: Create a piece of code that generates data based on the first Step's variables.

With this code, we get the location's name, longitude, latitude, IDs, ratings, and area for each keyword and coordinate. Suppose there are 40 locales near Sanur; Google will output the results on two pages. If there are 55 results, there are three pages. Since Google only shows 20 entries per page, we need to specify the 'next page token' to retrieve the following page data.

The maximum number of data points we retrieve is 60, which is Google's policy. For example, within one kilometer of our starting point, there are 140 restaurants. This means that only 60 of the 140 restaurants will be created.

So, to avoid inconsistencies, we need to get both the radius and the coordinates right. Ensure that the diameter is not too large so that "only 60 points are created, although there are many of them". Also, ensure the radius is manageable, as this would result in a long list of coordinates. Neither can be efficient, so we need to capture the context of a location earlier.

Continue reading the blog to learn more how to extract data from Google Maps using Python.

Step 4: Store information on the user's computer

Final Step: To integrate all these procedures into a complete code:

You can now quickly download data from various Google Colab files. To download data, select "Files" after clicking the arrow button in the left pane!

Your data will be scraped and exported in CSV format, ready for visualization with all the tools you know! This can be Tableau, Python, R, etc. Here we used Kepler.gl for visualization, a powerful WebGL-enabled web tool for geographic diagnostic visualizations.

The data is displayed in the spreadsheet as follows:

In the Kepler.gl map, it is shown as follows:

From our location, lounging on Sanur beach, there are 59 nearby eateries. Now we can explore our neighborhood cuisine by adding names and reviews to a map!

Conclusion:

Food data extraction using Google Maps, Python, and Google Colab can be an efficient and cost-effective way to obtain necessary information for studies, analysis, or business purposes. However, it is important to follow Google Maps' terms of service and use the data ethically and legally. However, you should be aware of limitations and issues, such as managing web-based applications, dealing with CAPTCHA, and avoiding Google blocking.

Are you looking for an expert Food Data Scraping service provider? Contact us today! Visit the Food Data Scrape website and get more information about Food Data Scraping and Mobile Grocery App Scraping. Know more : https://www.fooddatascrape.com/how-to-extract-food-data-from-google-maps-with-google-colab-python.php

[denbanana]

Java download file from url

Java download file from url how to#

Java download file from url pdf#

Java download file from url generator#

Java download file from url code#

Java download file from url zip#

While ((numBytesRead = bufferedInputStream.read(bucket, 0, bucket.

Node.InputStream inputStream = url.openStream() īufferedInputStream bufferedInputStream = new BufferedInputStream(inputStream) įileOutputStream fileOutputStream = new FileOutputStream(outputPath).

Java download file from url pdf#

Vue.js Mozilla PDF.js pdfvuer Module PDF Viewer Example to Display PDF Documents in Browser Using Javascript Full Project For Beginners when the user clicks on the link, it downloads a file from a server url.Python 3 Script to Download PDF Files From URL Using BeautifulSoup4 and Requests Library Full Tutorial For Beginners.Python Tkinter GUI Script to Download PDF Document From URL Desktop App Full Project For Beginners.In this example, we are creating three files: index. Example of downloading file from the server in servlet. But if there is any java file or jsp file etc, you need to create a program to download that file. So there is no need to write the program to download.

Java download file from url zip#

Vue.js PDF.js Library PDF Viewer Example to Display PDF Documents inside Browser Using Javascript Full Project Full Project For Beginners If there is any jar or zip file, you can direct provide a link to that file.

You use the BufferedInputStream class to read the contents of a.

Java download file from url how to#

Python 3 Selenium Web Scraping Script to Take Screenshot of Website URL and Download it as PNG Image File Full Project For Beginners In this tutorial, you learn how to download a file from a URL using the Java IO package.

How to Download File From URL to Google Drive Using Google Colab in Python Full Project For Beginners.

For convenient, script-driven extraction of the downloadable file URLs and.

Java download file from url code#

Javascript PDFMake Library to Export jQuery HTML DataTable to CSV,Excel and PDF Documents in Browser Full Project For Beginners website and especially the download page for source code and.Build a Sapui5 PDF Viewer With Custom Validation Error Messages and Download PDF Button in Browser Using Vanilla Javascript Full Project For Beginners.

Java download file from url generator#

Vue.js Dynamic JSON Data File Generator With Live Preview From Form Field Values and Download as JSON Blob File in Browser Using Javascript Full Project For Beginners.

Python Tkinter Script to Build Download Manger to Download Bulk Multiple Files From URL with Progressbar Animation Using PySmartDL Library Full Project For Beginners.

Using ansferFrom () method class in Java provides several methods for reading, writing, mapping, and manipulating a file. This post provides an overview of some of the available alternatives to accomplish this. Here is Spring boot example to download a file but this code can. It can also be used as the assignment target for a method reference or a lambda expression. StreamingResponseBody is a functional interface. Download File Using StreamingResponseBody.

Python 3 wkhtmltopdf Script to Convert HTML File to PDF or Website URL to PDF Document Using PDFKit Library Full Project For Beginners There are several ways to download a file from a URL in Java. Here we will see following three methods to download a file directly to the client easily: 1.

Sometimes we want to save a web file to our own computer.

Vue.js Mozilla PDF.js vue-pdf-app Library PDF Viewer to Display PDF Documents in Browser Using Javascript Full Tutorial For Beginners The curl tool lets us fetch a given URL from the command-line.

Now sync option will appear at the top right corner click on the sync now option. implementation ‘:android-pdf-viewer:2.8.2’. ReadableByteChannel readChannel Channels.newChannel ( new URL ( ' ).openStream ()) The ReadableByteChannel class creates a stream to read content from the URL.

Node.js Project to Encode Local PDF File or From URL to Base64 String Using pdf-to-base64 Library in Javascript Full Project For Beginners Step 2: Add dependency to adle (Module:app) Navigate to the Gradle Scripts > adle (Module:app) and add the below dependency in the dependencies section. In order to download the contents of a URL, we will use the ReadableByteChannel and the FileChannel classes.

Python 3 Script to Download Image or PDF File From URL Using Requests and Validators Library Full Project For Beginners.

This code will do for downloading a file from path, This code will also work if you have saved your path in database and to download from that path.

jsPDF Tutorial to Export and Print Div HTML Content to PDF Document and Download it as PDF File in Browser Using Javascript Full Project For Beginners In this article we are going to see how to download a file from a folder using Java, In addition to that we are also going to see how to change the format of that file and download it.

jQuery FullCalendar Integration Using PHP MySQL & AJAX.

Open a browser and navigate to the REST services endpoint of the USA map service (URL: http://<.>Image Crop and Upload using JQuery with PHP Ajax Test the SOE in the ArcGIS Server Services Directory.

jQuery NiceForm – Form Validation & AJAX Submit Plugin.

[3idatascraping]

How to Scrape IMDb Top Box Office Movies Data using Python?

Different Libraries for Data Scrapping

We all understand that in Python, you have various libraries for various objectives. We will use the given libraries:

BeautifulSoup: It is utilized for web scraping objectives for pulling data out from XML and HTML files. It makes a parse tree using page source codes, which can be utilized to scrape data in a categorized and clearer manner.

Requests: It allows you to send HTTP/1.1 requests with Python. Using it, it is easy to add content including headers, multipart files, form data, as well as parameters through easy Python libraries. This also helps in accessing response data from Python in a similar way.

Pandas: It is a software library created for Python programming language to do data analysis and manipulation. Particularly, it provides data operations and structures to manipulate numerical tables as well as time series.

For scraping data using data extraction with Python, you have to follow some basic steps:

1: Finding the URL:

Here, we will extract IMDb website data to scrape the movie title, gross, weekly growth, as well as total weeks for the finest box office movies in the US. This URL for a page is https://www.imdb.com/chart/boxoffice/?ref_=nv_ch_cht

2: Reviewing the Page

Do right-click on that element as well as click on the “Inspect” option.

3: Get the Required Data to Scrape

Here, we will go to scrape data including movies title, weekly growth, and name, gross overall, and total weeks are taken for it that is in “div” tag correspondingly.

4: Writing the Code

For doing that, you can utilize Jupiter book or Google Colab. We are utilizing Google Colab here:

Import libraries:

import requests from bs4 import BeautifulSoup import pandas as pd

Make empty arrays and we would utilize them in the future to store data of a particular column.

TitleName=[] Gross=[] Weekend=[] Week=[]

Just open the URL as well as scrape data from a website.

url = "https://www.imdb.com/chart/boxoffice/?ref_=nv_ch_cht" r = requests.get(url).content

With the use of Find as well as Find All techniques in BeautifulSoup, we scrape data as well as store that in a variable.

soup = BeautifulSoup(r, "html.parser") list = soup.find("tbody", {"class":""}).find_all("tr") x = 1 for i in list:     title = i.find("td",{"class":"titleColumn"})     gross = i.find("span",{"class":"secondaryInfo"})     weekend = i.find("td",{"class":"ratingColumn"})     week=i.find("td",{"class":"weeksColumn"}

With the append option, we store all the information in an Array, which we have made before.

TitleName.append(title.text) Gross.append(gross.text) Weekend.append(weekend.text) Week.append(week.text)

5. Storing Data in the Sheet. We Store Data in the CSV Format

df=pd.DataFrame({'Movie Title':TitleName, 'Weekend':Weekend, 'Gross':Gross, 'Week':Week}) df.to_csv('DS-PR1-18IT012.csv', index=False, encoding='utf-8')

6. It’s Time to Run the Entire Code

All the information is saved as IMDbRating.csv within the path of a Python file.

For more information, contact 3i Data Scraping or ask for a free quote about IMDb Top Box Office Movies Data Scraping services.

[foodspark-scraper]

Do you want a complete list of restaurants with their ratings and addresses whenever you visit a place or go for holidays? Off-course yes as it makes your way much easier. The easiest way to do it is using data scraping.

Web scraping or data scraping imports data from a website to the local machine. The result is in the form of spreadsheets so that you can get an entire list of restaurants available around me having its address as well as ratings in the easy spreadsheet!

Here at Foodspark, we use Python 3 scripts to scrape restaurant and food data as installing Python could be very useful. For proofreading the script, we use Google Colab for running the script as it helps us running the Python scripts on the cloud.

As our objective is to find a complete listing of places, scraping Google Maps data is its answer. Using Google Maps scraping, we can extract a place’s name, coordinates, address, kind of place, ratings, phone number, as well as other important data. For a start, we can also use a Places Scraping API. By using the Places Scraping API, it’s easy to extract Places data.