Showing results for Skill Background PNG
GitHub Repo
https://github.com/abdulmanan-main/BGVanisher
abdulmanan-main/BGVanisher
Remove image backgrounds instantly with AI magic! Perfect for e-commerce, content creators & social media. Upload, click, download transparent PNGs. No skills needed - professional results in seconds. Works on all devices. Powered by Abdul Manan | Made with ❤️. Free, fast & flawless background removal! ✨ #BGVanisher #AI #BackgroundRemover
GitHub Repo
https://github.com/AI-TEENU/royal_teenu
AI-TEENU/royal_teenu
Hi 👋 My name is TEENU ====================== Full-Stack Developer -------------------- * 🌍 I'm based in india * 🖥️ See my portfolio at [My website](http://instagram.com/doge_3d?utm_source=qr) * ✉️ You can contact me at [royalteenu2018@gmail.com](mailto:royalteenu2018@gmail.com) * 🚀 I'm currently working on [crypto](http://t.me/doge3d) * 🧠 I'm learning a new framework * 🤝 I'm open to collaborating on WEB3 * ⚡ USUALLY I AM SILENT BUT ONE DAY MY SUCCESS WILL SHAKE THE WORLD <a href="https://www.twitter.com/ai_teenu" target="_blank" rel="noreferrer"><img src="https://img.shields.io/twitter/follow/ai_teenu?logo=twitter&style=for-the-badge&color=0891b2&labelColor=1c1917" /></a><a href="https://www.github.com/AI-TEENU" target="_blank" rel="noreferrer"><img src="https://img.shields.io/github/followers/AI-TEENU?logo=github&style=for-the-badge&color=0891b2&labelColor=1c1917" /></a> ### Skills <p align="left"> <a href="https://docs.microsoft.com/en-us/cpp/?view=msvc-170" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/skills/c-colored.svg" width="36" height="36" alt="C" /></a> <a href="https://docs.microsoft.com/en-us/cpp/?view=msvc-170" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/skills/cplusplus-colored.svg" width="36" height="36" alt="C++" /></a> <a href="https://www.perl.org/" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/skills/perl-colored.svg" width="36" height="36" alt="Perl" /></a> <a href="https://www.python.org/" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/skills/python-colored.svg" width="36" height="36" alt="Python" /></a> <a href="https://www.oracle.com/java/" target="_blank" rel="noreferrer"><img 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rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/skills/ethereum-colored.svg" width="36" height="36" alt="Ethereum" /></a> <a href="https://solana.com/" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/skills/solana-colored.svg" width="36" height="36" alt="Solana" /></a> <a href="https://polygon.technology/" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/skills/polygon-colored.svg" width="36" height="36" alt="Polygon" /></a> </p> ### Socials <p align="left"> <a href="https://www.dev.to/@teenu" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/socials/devdotto-dark.svg" width="32" height="32" /></a> <a href="https://www.github.com/AI-TEENU" target="_blank" rel="noreferrer"><img 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width="32" height="32" /></a> <a href="https://www.stackoverflow.com/users/Teenu" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/socials/stackoverflow.svg" width="32" height="32" /></a> <a href="https://www.twitter.com/ai_teenu" target="_blank" rel="noreferrer"><img src="https://raw.githubusercontent.com/danielcranney/readme-generator/main/public/icons/socials/twitter.svg" width="32" height="32" /></a></p> ### Badges <b>My GitHub Stats</b> <a href="http://www.github.com/AI-TEENU"><img src="https://github-readme-stats.vercel.app/api?username=AI-TEENU&show_icons=true&hide=&count_private=true&title_color=0891b2&text_color=ffffff&icon_color=0891b2&bg_color=1c1917&hide_border=true&show_icons=true" alt="AI-TEENU's GitHub stats" /></a> <a href="http://www.github.com/AI-TEENU"><img 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GitHub Repo
https://github.com/smith-jj/09-SQL_Homework
smith-jj/09-SQL_Homework
# Employee Database: A Mystery in Two Parts  ## Background It is a beautiful spring day, and it is two weeks since you have been hired as a new data engineer at Pewlett Hackard. Your first major task is a research project on employees of the corporation from the 1980s and 1990s. All that remain of the database of employees from that period are six CSV files. In this assignment, you will design the tables to hold data in the CSVs, import the CSVs into a SQL database, and answer questions about the data. In other words, you will perform: 1. Data Modeling 2. Data Engineering 3. Data Analysis ## Instructions #### Data Modeling Inspect the CSVs and sketch out an ERD of the tables. Feel free to use a tool like [http://www.quickdatabasediagrams.com](http://www.quickdatabasediagrams.com). #### Data Engineering * Use the information you have to create a table schema for each of the six CSV files. Remember to specify data types, primary keys, foreign keys, and other constraints. * Import each CSV file into the corresponding SQL table. #### Data Analysis Once you have a complete database, do the following: 1. List the following details of each employee: employee number, last name, first name, gender, and salary. 2. List employees who were hired in 1986. 3. List the manager of each department with the following information: department number, department name, the manager's employee number, last name, first name, and start and end employment dates. 4. List the department of each employee with the following information: employee number, last name, first name, and department name. 5. List all employees whose first name is "Hercules" and last names begin with "B." 6. List all employees in the Sales department, including their employee number, last name, first name, and department name. 7. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name. 8. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name. ## Bonus (Optional) As you examine the data, you are overcome with a creeping suspicion that the dataset is fake. You surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee. To confirm your hunch, you decide to take the following steps to generate a visualization of the data, with which you will confront your boss: 1. Import the SQL database into Pandas. (Yes, you could read the CSVs directly in Pandas, but you are, after all, trying to prove your technical mettle.) This step may require some research. Feel free to use the code below to get started. Be sure to make any necessary modifications for your username, password, host, port, and database name: ```sql from sqlalchemy import create_engine engine = create_engine('postgresql://localhost:5432/<your_db_name>') connection = engine.connect() ``` * Consult [SQLAlchemy documentation](https://docs.sqlalchemy.org/en/latest/core/engines.html#postgresql) for more information. * If using a password, do not upload your password to your GitHub repository. See [https://www.youtube.com/watch?v=2uaTPmNvH0I](https://www.youtube.com/watch?v=2uaTPmNvH0I) and [https://martin-thoma.com/configuration-files-in-python/](https://martin-thoma.com/configuration-files-in-python/) for more information. 2. Create a bar chart of average salary by title. 3. You may also include a technical report in markdown format, in which you outline the data engineering steps taken in the homework assignment. ## Epilogue Evidence in hand, you march into your boss's office and present the visualization. With a sly grin, your boss thanks you for your work. On your way out of the office, you hear the words, "Search your ID number." You look down at your badge to see that your employee ID number is 499942. ## Submission * Create an image file of your ERD. * Create a `.sql` file of your table schemata. * Create a `.sql` file of your queries. * (Optional) Create a Jupyter Notebook of the bonus analysis. * Create and upload a repository with the above files to GitHub and post a link on BootCamp Spot.
GitHub Repo
https://github.com/furic/extract-alpha
furic/extract-alpha
Claude Code skill for removing backgrounds from AI-generated images using two-pass difference matting (white + black background pairs → transparent PNG)
GitHub Repo
https://github.com/pnguenda/pandas-challenge
pnguenda/pandas-challenge
# Pandas Homework - Pandas, Pandas, Pandas ## Background The data dive continues! Now, it's time to take what you've learned about Python Pandas and apply it to new situations. For this assignment, you'll need to complete **one of two** (not both) Data Challenges. Once again, which challenge you take on is your choice. Just be sure to give it your all -- as the skills you hone will become powerful tools in your data analytics tool belt. ### Before You Begin 1. Create a new repository for this project called `pandas-challenge`. **Do not add this homework to an existing repository**. 2. Clone the new repository to your computer. 3. Inside your local git repository, create a directory for the Pandas Challenge you choose. Use folder names corresponding to the challenges: **HeroesOfPymoli** or **PyCitySchools**. 4. Add your Jupyter notebook to this folder. This will be the main script to run for analysis. 5. Push the above changes to GitHub or GitLab. ## Option 1: Heroes of Pymoli  Congratulations! After a lot of hard work in the data munging mines, you've landed a job as Lead Analyst for an independent gaming company. You've been assigned the task of analyzing the data for their most recent fantasy game Heroes of Pymoli. Like many others in its genre, the game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. As a first task, the company would like you to generate a report that breaks down the game's purchasing data into meaningful insights. Your final report should include each of the following: ### Player Count * Total Number of Players ### Purchasing Analysis (Total) * Number of Unique Items * Average Purchase Price * Total Number of Purchases * Total Revenue ### Gender Demographics * Percentage and Count of Male Players * Percentage and Count of Female Players * Percentage and Count of Other / Non-Disclosed ### Purchasing Analysis (Gender) * The below each broken by gender * Purchase Count * Average Purchase Price * Total Purchase Value * Average Purchase Total per Person by Gender ### Age Demographics * The below each broken into bins of 4 years (i.e. <10, 10-14, 15-19, etc.) * Purchase Count * Average Purchase Price * Total Purchase Value * Average Purchase Total per Person by Age Group ### Top Spenders * Identify the the top 5 spenders in the game by total purchase value, then list (in a table): * SN * Purchase Count * Average Purchase Price * Total Purchase Value ### Most Popular Items * Identify the 5 most popular items by purchase count, then list (in a table): * Item ID * Item Name * Purchase Count * Item Price * Total Purchase Value ### Most Profitable Items * Identify the 5 most profitable items by total purchase value, then list (in a table): * Item ID * Item Name * Purchase Count * Item Price * Total Purchase Value As final considerations: * You must use the Pandas Library and the Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of three observable trends based on the data. * See [Example Solution](HeroesOfPymoli/HeroesOfPymoli_starter.ipynb) for a reference on expected format. ## Option 2: PyCitySchools  Well done! Having spent years analyzing financial records for big banks, you've finally scratched your idealistic itch and joined the education sector. In your latest role, you've become the Chief Data Scientist for your city's school district. In this capacity, you'll be helping the school board and mayor make strategic decisions regarding future school budgets and priorities. As a first task, you've been asked to analyze the district-wide standardized test results. You'll be given access to every student's math and reading scores, as well as various information on the schools they attend. Your responsibility is to aggregate the data to and showcase obvious trends in school performance. Your final report should include each of the following: ### District Summary * Create a high level snapshot (in table form) of the district's key metrics, including: * Total Schools * Total Students * Total Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### School Summary * Create an overview table that summarizes key metrics about each school, including: * School Name * School Type * Total Students * Total School Budget * Per Student Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Top Performing Schools (By % Overall Passing) * Create a table that highlights the top 5 performing schools based on % Overall Passing. Include: * School Name * School Type * Total Students * Total School Budget * Per Student Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Bottom Performing Schools (By % Overall Passing) * Create a table that highlights the bottom 5 performing schools based on % Overall Passing. Include all of the same metrics as above. ### Math Scores by Grade\*\* * Create a table that lists the average Math Score for students of each grade level (9th, 10th, 11th, 12th) at each school. ### Reading Scores by Grade * Create a table that lists the average Reading Score for students of each grade level (9th, 10th, 11th, 12th) at each school. ### Scores by School Spending * Create a table that breaks down school performances based on average Spending Ranges (Per Student). Use 4 reasonable bins to group school spending. Include in the table each of the following: * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Scores by School Size * Repeat the above breakdown, but this time group schools based on a reasonable approximation of school size (Small, Medium, Large). ### Scores by School Type * Repeat the above breakdown, but this time group schools based on school type (Charter vs. District). As final considerations: * Use the pandas library and Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of at least two observable trends based on the data. * See [Example Solution](PyCitySchools/PyCitySchools_starter.ipynb) for a reference on the expected format. ## Hints and Considerations * These are challenging activities for a number of reasons. For one, these activities will require you to analyze thousands of records. Hacking through the data to look for obvious trends in Excel is just not a feasible option. The size of the data may seem daunting, but pandas will allow you to efficiently parse through it. * Second, these activities will also challenge you by requiring you to learn on your feet. Don't fool yourself into thinking: "I need to study pandas more closely before diving in." Get the basic gist of the library and then _immediately_ get to work. When facing a daunting task, it's easy to think: "I'm just not ready to tackle it yet." But that's the surest way to never succeed. Learning to program requires one to constantly tinker, experiment, and learn on the fly. You are doing exactly the _right_ thing, if you find yourself constantly practicing Google-Fu and diving into documentation. There is just no way (or reason) to try and memorize it all. Online references are available for you to use when you need them. So use them! * Take each of these tasks one at a time. Begin your work, answering the basic questions: "How do I import the data?" "How do I convert the data into a DataFrame?" "How do I build the first table?" Don't get intimidated by the number of asks. Many of them are repetitive in nature with just a few tweaks. Be persistent and creative! * Expect these exercises to take time! Don't get discouraged if you find yourself spending hours initially with little progress. Force yourself to deal with the discomfort of not knowing and forge ahead. Consider these hours an investment in your future! * As always, feel encouraged to work in groups and get help from your TAs and Instructor. Just remember, true success comes from mastery and _not_ a completed homework assignment. So challenge yourself to truly succeed! ### Copyright Trilogy Education Services © 2019. All Rights Reserved.
GitHub Repo
https://github.com/adiraju-madhav/Objective-towards-clone
adiraju-madhav/Objective-towards-clone
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Guide to Web Scraping\n", "\n", "Let's get you started with web scraping and Python. Before we begin, here are some important rules to follow and understand:\n", "\n", "1. Always be respectful and try to get premission to scrape, do not bombard a website with scraping requests, otherwise your IP address may be blocked!\n", "2. Be aware that websites change often, meaning your code could go from working to totally broken from one day to the next.\n", "3. Pretty much every web scraping project of interest is a unique and custom job, so try your best to generalize the skills learned here.\n", "\n", "OK, let's get started with the basics!\n", "\n", "## Basic components of a WebSite\n", "\n", "### HTML\n", "HTML stands for Hypertext Markup Language and every website on the internet uses it to display information. Even the jupyter notebook system uses it to display this information in your browser. If you right click on a website and select \"View Page Source\" you can see the raw HTML of a web page. This is the information that Python will be looking at to grab information from. Let's take a look at a simple webpage's HTML:\n", "\n", " <!DOCTYPE html> \n", " <html> \n", " <head>\n", " <title>Title on Browser Tab</title>\n", " </head>\n", " <body>\n", " <h1> Website Header </h1>\n", " <p> Some Paragraph </p>\n", " <body>\n", " </html>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's breakdown these components.\n", "\n", "Every <tag> indicates a specific block type on the webpage:\n", "\n", " 1.<DOCTYPE html> HTML documents will always start with this type declaration, letting the browser know its an HTML file.\n", " 2. The component blocks of the HTML document are placed between <html> and </html>.\n", " 3. Meta data and script connections (like a link to a CSS file or a JS file) are often placed in the <head> block.\n", " 4. The <title> tag block defines the title of the webpage (its what shows up in the tab of a website you're visiting).\n", " 5. Is between <body> and </body> tags are the blocks that will be visible to the site visitor.\n", " 6. Headings are defined by the <h1> through <h6> tags, where the number represents the size of the heading.\n", " 7. Paragraphs are defined by the <p> tag, this is essentially just normal text on the website.\n", "\n", " There are many more tags than just these, such as <a> for hyperlinks, <table> for tables, <tr> for table rows, and <td> for table columns, and more!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CSS\n", "\n", "CSS stands for Cascading Style Sheets, this is what gives \"style\" to a website, including colors and fonts, and even some animations! CSS uses tags such as **id** or **class** to connect an HTML element to a CSS feature, such as a particular color. **id** is a unique id for an HTML tag and must be unique within the HTML document, basically a single use connection. **class** defines a general style that can then be linked to multiple HTML tags. Basically if you only want a single html tag to be red, you would use an id tag, if you wanted several HTML tags/blocks to be red, you would create a class in your CSS doc and then link it to the rest of these blocks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scraping Guidelines\n", "\n", "Keep in mind you should always have permission for the website you are scraping! Check a websites terms and conditions for more info. Also keep in mind that a computer can send requests to a website very fast, so a website may block your computer's ip address if you send too many requests too quickly. Lastly, websites change all the time! You will most likely need to update your code often for long term web-scraping jobs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Web Scraping with Python\n", "\n", "There are a few libraries you will need, you can go to your command line and install them with conda install (if you are using anaconda distribution), or pip install for other python distributions.\n", "\n", " conda install requests\n", " conda install lxml\n", " conda install bs4\n", " \n", "if you are not using the Anaconda Installation, you can use **pip install** instead of **conda install**, for example:\n", "\n", " pip install requests\n", " pip install lxml\n", " pip install bs4\n", " \n", "Now let's see what we can do with these libraries.\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example Task 0 - Grabbing the title of a page\n", "\n", "Let's start very simple, we will grab the title of a page. Remember that this is the HTML block with the **title** tag. For this task we will use **www.example.com** which is a website specifically made to serve as an example domain. Let's go through the main steps:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Step 1: Use the requests library to grab the page\n", "# Note, this may fail if you have a firewall blocking Python/Jupyter \n", "# Note sometimes you need to run this twice if it fails the first time\n", "res = requests.get(\"http://www.example.com\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This object is a requests.models.Response object and it actually contains the information from the website, for example:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "requests.models.Response" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(res)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'<!doctype html>\\n<html>\\n<head>\\n <title>Example Domain</title>\\n\\n <meta charset=\"utf-8\" />\\n <meta http-equiv=\"Content-type\" content=\"text/html; charset=utf-8\" />\\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\\n <style type=\"text/css\">\\n body {\\n background-color: #f0f0f2;\\n margin: 0;\\n padding: 0;\\n font-family: -apple-system, system-ui, BlinkMacSystemFont, \"Segoe UI\", \"Open Sans\", \"Helvetica Neue\", Helvetica, Arial, sans-serif;\\n \\n }\\n div {\\n width: 600px;\\n margin: 5em auto;\\n padding: 2em;\\n background-color: #fdfdff;\\n border-radius: 0.5em;\\n box-shadow: 2px 3px 7px 2px rgba(0,0,0,0.02);\\n }\\n a:link, a:visited {\\n color: #38488f;\\n text-decoration: none;\\n }\\n @media (max-width: 700px) {\\n div {\\n margin: 0 auto;\\n width: auto;\\n }\\n }\\n </style> \\n</head>\\n\\n<body>\\n<div>\\n <h1>Example Domain</h1>\\n <p>This domain is for use in illustrative examples in documents. You may use this\\n domain in literature without prior coordination or asking for permission.</p>\\n <p><a href=\"https://www.iana.org/domains/example\">More information...</a></p>\\n</div>\\n</body>\\n</html>\\n'" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "____\n", "Now we use BeautifulSoup to analyze the extracted page. Technically we could use our own custom script to loook for items in the string of **res.text** but the BeautifulSoup library already has lots of built-in tools and methods to grab information from a string of this nature (basically an HTML file). Using BeautifulSoup we can create a \"soup\" object that contains all the \"ingredients\" of the webpage. Don't ask me about the weird library names, I didn't choose them! :)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import bs4" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "soup = bs4.BeautifulSoup(res.text,\"lxml\")" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<!DOCTYPE html>\n", "<html>\n", "<head>\n", "<title>Example Domain</title>\n", "<meta charset=\"utf-8\"/>\n", "<meta content=\"text/html; charset=utf-8\" http-equiv=\"Content-type\"/>\n", "<meta content=\"width=device-width, initial-scale=1\" name=\"viewport\"/>\n", "<style type=\"text/css\">\n", " body {\n", " background-color: #f0f0f2;\n", " margin: 0;\n", " padding: 0;\n", " font-family: -apple-system, system-ui, BlinkMacSystemFont, \"Segoe UI\", \"Open Sans\", \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n", " \n", " }\n", " div {\n", " width: 600px;\n", " margin: 5em auto;\n", " padding: 2em;\n", " background-color: #fdfdff;\n", " border-radius: 0.5em;\n", " box-shadow: 2px 3px 7px 2px rgba(0,0,0,0.02);\n", " }\n", " a:link, a:visited {\n", " color: #38488f;\n", " text-decoration: none;\n", " }\n", " @media (max-width: 700px) {\n", " div {\n", " margin: 0 auto;\n", " width: auto;\n", " }\n", " }\n", " </style>\n", "</head>\n", "<body>\n", "<div>\n", "<h1>Example Domain</h1>\n", "<p>This domain is for use in illustrative examples in documents. You may use this\n", " domain in literature without prior coordination or asking for permission.</p>\n", "<p><a href=\"https://www.iana.org/domains/example\">More information...</a></p>\n", "</div>\n", "</body>\n", "</html>" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "soup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's use the **.select()** method to grab elements. We are looking for the 'title' tag, so we will pass in 'title'\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<title>Example Domain</title>]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "soup.select('title')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice what is returned here, its actually a list containing all the title elements (along with their tags). You can use indexing or even looping to grab the elements from the list. Since this object it still a specialized tag, we cna use method calls to grab just the text." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "title_tag = soup.select('title')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<title>Example Domain</title>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "title_tag[0]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "bs4.element.Tag" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(title_tag[0])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Example Domain'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "title_tag[0].getText()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example Task 1 - Grabbing all elements of a class\n", "\n", "Let's try to grab all the section headings of the Wikipedia Article on Grace Hopper from this URL: https://en.wikipedia.org/wiki/Grace_Hopper" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First get the request\n", "res = requests.get('https://en.wikipedia.org/wiki/Grace_Hopper')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a soup from request\n", "soup = bs4.BeautifulSoup(res.text,\"lxml\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now its time to figure out what we are actually looking for. Inspect the element on the page to see that the section headers have the class \"mw-headline\". Because this is a class and not a straight tag, we need to adhere to some syntax for CSS. In this case" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<table>\n", "\n", "<thead >\n", "<tr>\n", "<th>\n", "<p>Syntax to pass to the .select() method</p>\n", "</th>\n", "<th>\n", "<p>Match Results</p>\n", "</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>\n", "<p><code>soup.select('div')</code></p>\n", "</td>\n", "<td>\n", "<p>All elements with the <code><div></code> tag</p>\n", "</td>\n", "</tr>\n", "<tr>\n", "<td>\n", "<p><code>soup.select('#some_id')</code></p>\n", "</td>\n", "<td>\n", "<p>The HTML element containing the <code>id</code> attribute of <code>some_id</code></p>\n", "</td>\n", "</tr>\n", "<tr>\n", "<td>\n", "<p><code>soup.select('.notice')</code></p>\n", "</td>\n", "<td>\n", "<p>All the HTML elements with the CSS <code>class</code> named <code>notice</code></p>\n", "</td>\n", "</tr>\n", "<tr>\n", "<td>\n", "<p><code>soup.select('div span')</code></p>\n", "</td>\n", "<td>\n", "<p>Any elements named <code><span></code> that are within an element named <code><div></code></p>\n", "</td>\n", "</tr>\n", "<tr>\n", "<td>\n", "<p><code>soup.select('div > span')</code></p>\n", "</td>\n", "<td>\n", "<p>Any elements named <code class=\"literal2\"><span></code> that are <span><em >directly</em></span> within an element named <code class=\"literal2\"><div></code>, with no other element in between</p>\n", "</td>\n", "</tr>\n", "<tr>\n", "\n", "</tr>\n", "</tbody>\n", "</table>" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<span class=\"mw-headline\" id=\"Early_life_and_education\">Early life and education</span>,\n", " <span class=\"mw-headline\" id=\"Career\">Career</span>,\n", " <span class=\"mw-headline\" id=\"World_War_II\">World War II</span>,\n", " <span class=\"mw-headline\" id=\"UNIVAC\">UNIVAC</span>,\n", " <span class=\"mw-headline\" id=\"COBOL\">COBOL</span>,\n", " <span class=\"mw-headline\" id=\"Standards\">Standards</span>,\n", " <span class=\"mw-headline\" id=\"Retirement\">Retirement</span>,\n", " <span class=\"mw-headline\" id=\"Post-retirement\">Post-retirement</span>,\n", " <span class=\"mw-headline\" id=\"Anecdotes\">Anecdotes</span>,\n", " <span class=\"mw-headline\" id=\"Death\">Death</span>,\n", " <span class=\"mw-headline\" id=\"Dates_of_rank\">Dates of rank</span>,\n", " <span class=\"mw-headline\" id=\"Awards_and_honors\">Awards and honors</span>,\n", " <span class=\"mw-headline\" id=\"Military_awards\">Military awards</span>,\n", " <span class=\"mw-headline\" id=\"Other_awards\">Other awards</span>,\n", " <span class=\"mw-headline\" id=\"Legacy\">Legacy</span>,\n", " <span class=\"mw-headline\" id=\"Places\">Places</span>,\n", " <span class=\"mw-headline\" id=\"Programs\">Programs</span>,\n", " <span class=\"mw-headline\" id=\"In_popular_culture\">In popular culture</span>,\n", " <span class=\"mw-headline\" id=\"Grace_Hopper_Celebration_of_Women_in_Computing\">Grace Hopper Celebration of Women in Computing</span>,\n", " <span class=\"mw-headline\" id=\"Notes\">Notes</span>,\n", " <span class=\"mw-headline\" id=\"Obituary_notices\">Obituary notices</span>,\n", " <span class=\"mw-headline\" id=\"See_also\">See also</span>,\n", " <span class=\"mw-headline\" id=\"References\">References</span>,\n", " <span class=\"mw-headline\" id=\"Further_reading\">Further reading</span>,\n", " <span class=\"mw-headline\" id=\"External_links\">External links</span>]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# note depending on your IP Address, \n", "# this class may be called something different\n", "soup.select(\".toctext\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Early life and education\n", "Career\n", "World War II\n", "UNIVAC\n", "COBOL\n", "Standards\n", "Retirement\n", "Post-retirement\n", "Anecdotes\n", "Death\n", "Dates of rank\n", "Awards and honors\n", "Military awards\n", "Other awards\n", "Legacy\n", "Places\n", "Programs\n", "In popular culture\n", "Grace Hopper Celebration of Women in Computing\n", "Notes\n", "Obituary notices\n", "See also\n", "References\n", "Further reading\n", "External links\n" ] } ], "source": [ "for item in soup.select(\".toctext\"):\n", " print(item.text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example Task 3 - Getting an Image from a Website\n", "\n", "Let's attempt to grab the image of the Deep Blue Computer from this wikipedia article: https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res = requests.get(\"https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "soup = bs4.BeautifulSoup(res.text,'lxml')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "image_info = soup.select('.thumbimage')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<img alt=\"\" class=\"thumbimage\" data-file-height=\"601\" data-file-width=\"400\" decoding=\"async\" height=\"331\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/b/be/Deep_Blue.jpg/220px-Deep_Blue.jpg\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/b/be/Deep_Blue.jpg/330px-Deep_Blue.jpg 1.5x, //upload.wikimedia.org/wikipedia/commons/b/be/Deep_Blue.jpg 2x\" width=\"220\"/>,\n", " <img alt=\"\" class=\"thumbimage\" data-file-height=\"600\" data-file-width=\"800\" decoding=\"async\" height=\"165\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/6/6f/Kasparov_Magath_1985_Hamburg-2.png/220px-Kasparov_Magath_1985_Hamburg-2.png\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/6/6f/Kasparov_Magath_1985_Hamburg-2.png/330px-Kasparov_Magath_1985_Hamburg-2.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/6/6f/Kasparov_Magath_1985_Hamburg-2.png/440px-Kasparov_Magath_1985_Hamburg-2.png 2x\" width=\"220\"/>]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_info" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(image_info)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "computer = image_info[0]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "bs4.element.Tag" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ],
GitHub Repo
https://github.com/Dovineowuor/command_line_for_the_win
Dovineowuor/command_line_for_the_win
Background Context CMD CHALLENGE is a pretty cool game challenging you on Bash skills. Everything is done via the command line and the questions are becoming increasingly complicated. It’s a good training to improve your command line skills! This project is NOT mandatory at all. It is 100% optional. Doing any part of this project will add a project grade of over 100% to your average. Your score won’t get hurt if you don’t do it, but if your current average is greater than your score on this project, your average might go down. Have fun! Requirements General A README.md file, at the root of the folder of the project, is mandatory This project will be manually reviewed. As each task is completed, the name of that task will turn green Create a screenshot, showing that you completed the required levels Push this screenshot with the right name to GitHub, in either the PNG or JPEG format
GitHub Repo
https://github.com/sannal123/MyPortfolio
sannal123/MyPortfolio
<!DOCTYPE> <html> <head> <title> Snl.exe_</title> <style> html { margin: 0; padding: 0; } body { font-family: 'Handlee', cursive; font-size: 13pt; background-color: #efefef; padding: 10px; margin: 0; } h1 { font-size: 15pt; color: #20bcd5; text-align: center; padding: 18px 0 18px 0; margin: 0 0 10px 0; } h1 span { border: 4px dashed #20bcd5; padding: 10px; } p { padding: 0; margin: 0; } .img-circle { border: 3px solid white; border-radius: 50%; } .section { background-color: #fff; padding: 15px; margin-bottom: 10px; border-radius: 10px; } #header { background-image: url("https://www.sololearn.com/Uploads/header.jpg"); background-size: cover; } #header img { display: block; width: 80px; height: 80px; margin: auto; } #header p { font-size: 25pt; color: #3b464c; padding-top: 5px; margin: 0; font-weight: bold; text-align: center; } .quote { font-size: 12pt; text-align: right; margin-top: 10px; } table { width: 100%; } table, th, td { border: 2px solid #cecece; border-collapse: collapse; text-align: center; table-layout: fixed; } .selected { background-color: #f36f48; font-weight: bold; color: white; } li { margin-bottom: 15px; font-weight: bold; } progress { width: 70%; height: 20px; color: #3fb6b2; background: #efefef; } progress::-webkit-progress-bar { background: #efefef; } progress::-webkit-progress-value { background: #3fb6b2; } progress::-moz-progress-bar { color: #3fb6b2; background: #efefef; } iframe, audio { display: block; margin: 0 auto; border: 3px solid #3fb6b2; } hr { border: 0; height: 1px; background: #f36f48; } form { text-align: center; margin-top: 0; } .submit { background-color: #3fb6b2; padding: 12px 45px; border-radius: 5px; cursor: pointer; color: #ffffff; border: none; outline: none; margin: 0; font-weight: bold; } .submit:hover { background-color: #43a09d; } textarea { height: 100px; } input, textarea { margin-bottom: 10px; font-size: 11pt; padding: 15px 10px 10px; border: 1px solid #cecece; background-color: #efefef; color: #787575; border-radius: 5px; width: 70%; outline: none; } .face { transform: scale(0.4); margin: 0 auto; display: block; margin-top: -35px; margin-bottom: -25px; } #contacts img { height: 50px; width: 50px; margin-left: 7px; margin-right: 7px; } #contacts a { text-decoration: none; } #contacts img:hover { opacity: 0.8; } #contacts { text-align: center; } .copyright { font-size: 8pt; text-align: right; padding-bottom: 10px; color: grey; } </style> </head> <body> <!--Heder start--> <div id="header" class="section"> <img alt="" class="img-circle" src="IMG_20210101_105359.jpg"> <p>Sannal Yadav</p> </div> <!--About me section--> <div class="section"> <h1><span>About Me</span></h1> <p> Hey! I am <strong> Sannal </strong> Coding has changed my world. it is mot just abut apps. Learning code give me <i> problem-solving skills</i> and a way to communicate with others on a technical level. I can also develop websites and use my coding skills to get a better job. And I learned it all at <strong>IIMT COLLEGE OF ENGINEERING</strong> where they build your self-esteem and keep you motivated. Join me in this rewarding journey. You will have fun, get help. and learn along the way! </p> <p class="quote">"Declare variable,not war"</p> </div> <!--Schedule section start--><h1><span>My Coding Schedule</span></h1> <table> <tr> <th>Day</th> <th>Mon</th> <th>Tue</th> <th>Web</th> <th>thu</th> <th>Fri</th> </tr> <tr> <td>8-8:30</td> <td class="selected">Learn</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>9-10</td> <td ></td> <td class="selected">Practice</td> <td></td> <td></td> <td></td> </tr> <tr> <td>1-1:30</td> <td></td> <td></td> <td class="selected">Play</td> <td></td> <td></td> </tr> <tr> <td>3:45-5</td> <td ></td> <td></td> <td></td> <td class="selected">Code</td> <td></td> </tr> <tr> <td>6-6:15</td> <td ></td> <td></td> <td></td> <td></td> <td class="selected">Discuss</td> </tr> </table> <!--My Skills Section--> <div class="section"> <h1><span>My Skills</span></h1> <ul> <li>Java<br/> <progress min="0" max="100" value="90"></progress> </li> <li>DSA<br/> <progress min="0" max="100" value="60"></progress> </li> <li>HTML<br/> <progress min="0" max="100" value="80"></progress> </li> <li>CSS<br/> <progress min="0" max="100" value="80"></progress> </li> <li>Javascript<br/> <progress min="0" max="100" value="50"></progress> </li> <li>Python<br/> <progress min="0" max="100" value="50"></progress> </li> </ul> </div> <!-- Media section start --> <div class="section"> <h1><span>My Media</span></h1> <iframe height="150" width="300" src="https://www.youtube.com/embed/Q6_5InVJZ88 " allowfullscreen frameborder="0"></iframe> </div> <!-- Media section end --> <!-- Form section start --> <div class="section"> <h1><span>Contact Me</span></h1> <svg class="face" height="100" width="100"> <circle cx="50" cy="50" r="50" fill="#FDD835"/> <circle cx="30" cy="30" r="10" fill="#FFFFFF"/> <circle cx="70" cy="30" r="10" fill="#FFFFFF"/> <circle cx="30" cy="30" r="5" fill="#000000"/> <circle cx="70" cy="30" r="5" fill="#000000"/> <path d="M 30 70 q 20 20 40 0" stroke="#FFFFFF" stroke-width="5" fill="none" /> </svg> <form> <input name="name" placeholder="Name" type="text" required /><br/> <input name="email" placeholder="Email" type="email" required /><br/> <textarea name="message" placeholder="Message" required ></textarea> <input type="submit" value="SEND" class="submit" /> </form> </div> <!-- Form section end --> <!-- Contacts section start --> <div class="section" id="contacts"> <h1><span>Follow Me</span></h1> <div> <a href="https://www.sololearn.com/" target="_blank"> <img alt="SoloLearn" src="https://www.sololearn.com/Uploads/icons/sololearn.png" /> </a> <a href="#"> <img alt="Facebook" src="https://www.sololearn.com/Uploads/icons/facebook.png"/> </a> <a href="#"> <img alt="Twitter" src="https://www.sololearn.com/Uploads/icons/twitter.png" /> </a> </div> </div> <!-- Contacts section end --> <div class="copyright"> © Snl.exe_ All rights reserved. </div> </body> </html>
GitHub Repo
https://github.com/claudius-ars/bg-removal-skill
claudius-ars/bg-removal-skill
Claude skill: remove backgrounds from images and produce transparent PNGs with anti-aliased edge blending
GitHub Repo
https://github.com/Sauravlipun/Monadicons