Loading repository data…
Qiskit / repository
Source content for the Qiskit Textbook
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
This repository contains the source files for the Qiskit Textbook. Each page in
the textbook is a Jupyter notebook in the notebooks folder.
[!IMPORTANT] The Qiskit Textbook has been superseded by IBM Quantum Learning. These source files are no longer maintained and may contain errors. We are not accepting any contributions.
@book{qiskitextbook2023,
author = {various authors},
year = {2023},
title = {Qiskit Textbook},
publisher = {Github},
url = {https://github.com/Qiskit/textbook},
}
Selected from shared topics, language and repository description—not editorial ratings.
chxlm27 /
RAG system using Hugging Face models, multiple vector stores (Chroma, Pinecone, FAISS), and CRAG, with sentence transformers and benchmarking tools for optimized retrieval and content generation.
41/100 healthadiraju-madhav /
{ "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" } ],
Python has become one of the most popular programming languages for research in the past decade. Its free, open-source nature and vast online community are some of the reasons behind its success. Countless examples of increased research productivity due to Python can be found across a plethora of domains online, including data science, arti1cial intelligence and scienti1c research. This tutorial’s goal is to help users get started with Python through the installation and setup of the Anaconda software. The goal is to set users on the path toward using the Python language by preparing them to write their 1rst script. This tutorial is divided in the following fashion: a small introduction to Python, how to download the Anaconda software, the different content that comes with the installation, and a simple example related to implementing a Python script.
37/100 healthSejal852 /
This repository contains a Jupyter Notebook for creating a Retrieval-Augmented Generation (RAG) Q&A chatbot. The chatbot is designed to retrieve information from multiple sources, process the data, and generate responses based on the retrieved content.
37/100 healtharistide /
Jupyterlab-code-loader is a self-contained JupyterLab extension that provides a left sidebar panel for browsing, searching, and using code examples (notebooks, Python scripts, R scripts) and reusable code snippets. All content is sourced from a single open Git repository and organized by domain.
59/100 healthUdeshikaDissa /
Predicting the Contraceptive Method Choice of a Woman Based on Demographic and Socio-economic Characteristics - The objective of this study is to to predict the contraceptive methods (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics. A data-set of 1473 married women with their demographic and socio-economic characteristics used in this study. The Source for the data-set is the UCI Machine Learning Repository at, http://http://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice [?]. This study consists of two phases. The objective of Phase I is to preprocess and explore the data-set in order to build the model in Phase II. All the activities have been performed in the Python package in this study and Compiled from Jupyter Notebook This report covers both narratives and the Python pseudocodes for the data preprocessing and exploration performed under phase I. Content of this report is organized as follows. Section 1 describes the data sets and their attributes. Section 2 covers data preprocessing. In Section 3, each attribute and its inter-relationships are explored.
31/100 health