{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "**👨🏻‍🏫 Week 05 lab – Web Scraping practice** \n", "\n", "DS105W – Data for Data Science\n", "\n", "**AUTHORS:** Dr. [Jon Cardoso-Silva](https://jonjoncardoso.github.io)\n", "\n", "**DEPARTMENT:** [LSE Data Science Institute](https://lse.ac.uk/dsi)\n", "\n", "**OBJECTIVE**: Continue from where we left off in W04 lecture + a few more things\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "# Part 0: Export your chat logs (~ 3 min)\n", "\n", "As part of the ![](/figures/icons/GENIAL_favicon.png){width=1em} GENIAL project, we ask that you fill out the following form as soon as you come to the lab:\n", "\n", "🎯 **ACTION POINTS**\n", "\n", "1. 🔗 [**CLICK HERE**](https://forms.office.com/e/689MersZzV) to export your chat log.\n", "\n", " Thanks for being GENIAL! You are now one step closer to earning some prizes! 🎟️\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Part I: ⚙️ The setup\n", "\n", "You will need to install the requests and Scrapy packages in order to complete this lab. I will assume you have configured the virtual environment for this course as follows. \n", "\n", "\n", "\n", "Open the terminal (directly from within VS Code will be easier) and run each of the following commands:\n", "\n", "\n", "```bash\n", "pip install pandas requests scrapy\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import requests # This is how we access the web\n", "import pandas as pd # This is how we work with data frames\n", "\n", "from pprint import pprint # Print things in a pretty way\n", "from scrapy import Selector # This is how we parse HTML" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part I: An important recap (20 min)\n", "\n", "**🧑‍🏫 TEACHING MOMENT:** Your class teacher will recap the essentials of HTML as well as `requests` and `scrapy` with you.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

1. HTML in brief

\n", "\n", "- You learned that **HTML files are structured into [tags](https://www.w3schools.com/TAGs/) (or elements)**. Each tag carries a specific meaning, allowing browsers to display the information accurately. \n", "\n", " - For example, a `

` tells the browser, 'This is a paragraph,' \n", " \n", " - whereas a `
` tag tells the browser, 'this is a box of elements'.\n", "\n", "- HTML tags can have **attributes**.\n", "\n", " - For example, whenever we add a link (``), we need to specify the location where this link is pointing to (`href`):\n", "\n", " ```html\n", " DS105 main page\n", " ```\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

2. Styling (CSS) in brief

\n", "\n", "You also learned that one can apply **styles** to a tag using a language called CSS.\n", "\n", "- Styles can appear **inline**, as specified by the `style` attribute: \n", "\n", " ```html\n", "

Some text

\n", " ```\n", "\n", "- But styles can also be specified separately via a `.css` file. In that file, one uses **CSS Selectors** to identify which tags should be styled and how. For example, if I want _all_ my `

` tags to have the same style, I'd write:\n", "\n", " ```css\n", " p {\n", " margin-bottom:10px;\n", " background-color:red;\n", " color:white\n", " }\n", " ```\n", "\n", " When I load this CSS file into my HTML, the styling above will apply to all `

`s.\n", "\n", " For the above to work, I'd have to add the following to my HTML document:\n", "\n", " ```html\n", " \n", " \n", " \n", " \n", "\n", " \n", " ...\n", " \n", " \n", " ```\n", "\n", "

\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

3. The class attribute

\n", "\n", "A class can be applied to style multiple elements at once. \n", "\n", "```html\n", "

\n", "```\n", "\n", "The way to specify the style of a class using **CSS selectors** is with a dot (`.`). \n", "\n", "For example, the class above can be specified in my CSS file as:\n", "\n", "```css\n", "p.coloured{\n", " ......\n", "}\n", "```\n", "\n", "or simply:\n", "\n", "```css\n", ".coloured{\n", " ......\n", "}\n", "```\n", "\n", "\n", "
\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

4. The id attribute

\n", "\n", "An `id` is a unique identifier or an element. It should only appear once in a page. We specify ids with the 'hashtag' symbol (`#`).\n", "\n", "Therefore, if I have a \n", "\n", "```html\n", "

\n", "```\n", "\n", "I could specify the **CSS selector** as:\n", "\n", "```css\n", "p#uniquely-huge {\n", " ......\n", "}\n", "``` \n", "\n", "or simply:\n", "\n", "```css\n", "#uniquely-huge {\n", " ......\n", "}\n", "```\n", "\n", "(we don't even need to specify the tag)\n", "\n", "

\n", "\n" ] }, { "attachments": { "Screenshot 2024-02-11 130802.png": { "image/png": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "

5. A full example with classes and id

\n", "\n", "Take, for example, the following HTML document:\n", "\n", "```html\n", "\n", " \n", " \n", " \n", "\n", " \n", "

Some text

\n", "\n", "

Some text with coloured background

\n", "\n", "

Some text with coloured background

\n", "\n", "

\n", " \n", "\n", "```\n", "\n", "Suppose we also have a `my_styles.css` file as below:\n", "\n", "```css\n", "p {\n", " margin-bottom:10px;\n", "}\n", "\n", "p.coloured {\n", " background-color:red;\n", " color:white\n", "}\n", "\n", "#uniquely-huge {\n", " font-size: 2em;\n", "}\n", "```\n", "\n", "This will render as:\n", "\n", "![Screenshot 2024-02-11 130802.png](){style=\"width:30%\"}\n", "\n", "
\n", "\n", "\n", "\n", " \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

6. Using CSS selectors for web scraping

\n", "\n", "In brief, when collecting data from a public webpage, this is a skeleton what you need:\n", "\n", "```python\n", "response = requests.get('')\n", "sel = Selector(response.text)\n", "sel.css('')\n", "```\n", "You can also refer to your W04 lecture notebook to remember the full syntax. The key for the rest of this lab is identifying what must be written in the ``. \n", "\n", "- You learned that you can include the names of specific tags directly. For example, `sel.css('h3').extract_all()` will return a list of all H3 in the entire page\n", "- You also learned that you can find the closest **container** (say, `div.card-box`) and then scrape the contents of this box later." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

7. CSS selectors cheatsheet ⭐

\n", "\n", "
\n", "\n", "| Selector | Example | Use Case Scenario |\n", "|-----------------------|--------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|\n", "| * | * | This selector picks all elements within a page. It’s not that different from a page. Not much use for it but still good to know |\n", "| .class | .card-title | The simplest CSS selector is targeting the class attribute. If only your target element is using it, then it might be sufficient. |\n", "| .class1.class2 | .card-heading.card-title | There are elements with a class like class=“card-heading card-title”. When we see a space, it is because the element is using several classes. However, there’s no one fixed way of selecting the element. Try keeping the space, if that doesn’t work, then replace the space with a dot. |\n", "| #id | #card-description | What if the class is used in too many elements or if the element doesn’t have a class? Picking the ID can be the next best thing. The only problem is that IDs are unique per element. So won’t cut to scrape several elements at once. |\n", "| element | h4 | To pick an element, all we need to add to our parser is the HTML tag name. |\n", "| element.class | h4.card-title | This is the most common we’ll be using in our projects. |\n", "| parentElement > childElement | div > h4 | We can tell our scraper to extract an element inside another. In this example, we want it to find the h4 element whose parent element is a div. |\n", "| parentElement.class > childElement | div.card-body > h4 | We can combine the previous logic to specify a parent element and extract a specific CSS child element. This is super useful when the data we want doesn’t have any class or ID but is inside a parent element with a unique class/ID. |\n", "| [attribute] | [href] | Another great way to target an element with no clear class to choose from. Your scraper will extract all elements containing the specific attribute. In this case, it will take all tags which are the most common element to contain an href attribute. |\n", "| [attribute=value] | [target=_blank] | We can tell our scraper to extract only the elements with a specific value inside its attribute. |\n", "| element[attribute=value] | a[rel=next] | This is the selector we used to add a crawling feature to our Scrapy script: next_page = response.css(‘a[rel=next]’).attrib[‘href’] The target website was using the same class for all its pagination links so we had to come up with a different solution. |\n", "| [attribute~=value] | [title~=rating] | This selector will pick all the elements containing the word ‘rating’ inside its title attribute. |\n", "\n", "
\n", "\n", "Source: [The Only CSS Selectors Cheat Sheet You Need for Web Scraping](https://www.scraperapi.com/blog/css-selectors-cheat-sheet/#CSS-Selectors-Cheat-Sheet)\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 PRO-TIP: Did you notice that we're using a mix of markdown + HTML in this Jupyter notebook?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part II: Time to put all of this into practice (60-70 min)\n", "\n", "Now go over the action points below in pairs:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "🎯 **ACTION POINTS**\n", "\n", "1. Go to the [Data Science Seminar series](https://socialdatascience.network/index.html#schedule) website and inspect the page (mouse right-click + Inspect) and find the way to the name of the first event on the page. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Write down the **full** \"directions\" inside the HTML file to reach the event title. For example, maybe you will find that:\n", "\n", " > _The first event title is inside a \\ ➡️ \\ ➡️ \\ ➡️ \\ tag_.\n", "\n", " Write it in the markdown cell below:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Delete this line and write your answer here_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Write the required Python code to scrape the CSS selector you identified above. \n", "\n", " - Don't use the notion of containers just yet - we will practice that later in the W05 lecture. \n", " - For now, just write the full CSS selector you identified above\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Delete this line and replace it with your code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. **Let's simplify.** Let's capture the title of the **first event** again, but instead of writing the entire full absolute path, like above, identify a more direct way to capture it. \n", "\n", " - Note: Either use scrapy's `.extract_first()` or use `extract()` and later filter the list using regular Python" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Delete this line and replace it with your code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5. **Collect all the titles**. OK, now let's practice getting all event titles from the entire page. Save the titles into a list.\n", "\n", " **NOTE:** Again, collect all the information from the webpage at once. Don't use the notion of containers just yet. We will practice it in the W05 lecture." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Delete this line and replace it with your code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6. Do the same with the dates of the events and speaker names and save them to separate lists. \n", "\n", " **NOTE:** Again, collect all the information from the webpage at once. Don't use the notion of containers just yet. We will practice it in the W05 lecture.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Delete this line and replace it with your code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7. 🥇 **Challenge:** Combine all these lists you captured above into a single pandas data frame and save it to a CSV file. \n", "\n", " Tip 1: Say you have lists called `dates`, `titles`, `speakers`, you can create a data frame (a table) like this:\n", " \n", " ```python\n", " df = pd.DataFrame({'date': dates,\n", " 'title': titles,\n", " 'speakers': speakers})\n", " ``` \n", " \n", " Tip 2: What if an event does not have a date or speaker name? Set that particular event's date or speaker to `None`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Delete this line and replace it with your code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8. Double-check that the CSV file was created correctly by opening it using pandas. Then convert the columns to appropriate data types." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Delete this line and replace it with your code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "If there is some time left, use it to work on your 📝 [W06 Summative](https://lse-dsi.github.io/DS105/2023/winter-term/assessments/w06-summative.html)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "66a23b877595d3e158647673320c5aac91a1fe2874d6334c4fd4c069dffc5915" } } }, "nbformat": 4, "nbformat_minor": 2 }