!pip install beautifulsoup4Requirement already satisfied: beautifulsoup4 in /Users/enfageorge/miniconda/lib/python3.10/site-packages (4.12.2)
Requirement already satisfied: soupsieve>1.2 in /Users/enfageorge/miniconda/lib/python3.10/site-packages (from beautifulsoup4) (2.4.1)import requestsURL = "https://csc380.beingenfa.com/Syllabus/Key_Info.html"r = requests.get(URL)r.status_code200r.text'<!DOCTYPE html>\n<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>\n\n<meta charset="utf-8">\n<meta name="generator" content="quarto-1.3.361">\n\n<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">\n\n\n<title>CSC 380 – key_info</title>\n<style>\ncode{white-space: pre-wrap;}\nspan.smallcaps{font-variant: small-caps;}\ndiv.columns{display: flex; gap: min(4vw, 1.5em);}\ndiv.column{flex: auto; overflow-x: auto;}\ndiv.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}\nul.task-list{list-style: none;}\nul.task-list li input[type="checkbox"] {\n width: 0.8em;\n margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ \n 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class="sidebar-item-text sidebar-link">\n <span class="menu-text">W2: Political Content</span></a>\n </div>\n</li>\n <li class="sidebar-item">\n <div class="sidebar-item-container"> \n <a href="../Ethics/Week_3.html" class="sidebar-item-text sidebar-link">\n <span class="menu-text">W3: Creative Work</span></a>\n </div>\n</li>\n <li class="sidebar-item">\n <div class="sidebar-item-container"> \n <a href="../Ethics/Week_4.html" class="sidebar-item-text sidebar-link">\n <span class="menu-text">W4: Mental Health Support</span></a>\n </div>\n</li>\n </ul>\n </li>\n <li class="sidebar-item sidebar-item-section">\n <div class="sidebar-item-container"> \n <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="true">\n <span class="menu-text">Syllabus</span></a>\n <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="true" aria-label="Toggle 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id="toc-syllabus-key-info" class="nav-link active" data-scroll-target="#syllabus-key-info">Syllabus (Key Info)</a>\n <ul class="collapse">\n <li><a href="#description" id="toc-description" class="nav-link" data-scroll-target="#description">Description</a></li>\n <li><a href="#course-objective" id="toc-course-objective" class="nav-link" data-scroll-target="#course-objective">Course Objective</a></li>\n <li><a href="#expected-learning-outcomes" id="toc-expected-learning-outcomes" class="nav-link" data-scroll-target="#expected-learning-outcomes">Expected Learning Outcomes</a></li>\n </ul></li>\n </ul>\n</nav>\n </div>\n<!-- main -->\n<main class="content" id="quarto-document-content">\n\n\n\n<section id="syllabus-key-info" class="level1">\n<h1>Syllabus (Key Info)</h1>\n<section id="description" class="level2">\n<h2 class="anchored" data-anchor-id="description">Description</h2>\n<p>The course introduces students to the principles of data science, which are essential for computer scientists to make effective decisions in their professional careers. In today’s data-driven world, a wide range of computer science sub-disciplines heavily rely on data collection, analysis, and interpretation. With the pervasive presence of artificial intelligence (AI) in our lives, understanding the basics of how these systems work is becoming increasingly important. Additionally, it covers the basics of artificial intelligence (AI) systems and examines practical use cases, current news, and ethical considerations through readings and discussions.</p>\n</section>\n<section id="course-objective" class="level2">\n<h2 class="anchored" data-anchor-id="course-objective">Course Objective</h2>\n<p>Course Objectives</p>\n<p>This course aims to introduce students to the principles and techniques of data science, enabling them to make effective decisions in their computer science careers. During this course, the student will,</p>\n<ul class="task-list">\n<li><p><input type="checkbox">Understand the fundamental concepts and principles of data science, including data collection, preprocessing, analysis, and interpretation.</p></li>\n<li><p><input type="checkbox">Apply data analysis and visualization techniques to derive insights from diverse datasets.</p></li>\n<li><p><input type="checkbox">Gain familiarity with machine learning algorithms and their practical applications.</p></li>\n<li><p><input type="checkbox">Develop proficiency in using data science tools and programming languages.</p></li>\n<li><p><input type="checkbox">Engage in critical thinking and problem-solving through project-based assignments.</p></li>\n<li><p><input type="checkbox">Explore the ethical considerations associated with data-driven decision-making.</p></li>\n<li><p><input type="checkbox">Stay informed about current trends and developments in data science and artificial intelligence.</p></li>\n</ul>\n</section>\n<section id="expected-learning-outcomes" class="level2">\n<h2 class="anchored" data-anchor-id="expected-learning-outcomes">Expected Learning Outcomes</h2>\n<p>A student who successfully completes this course will be able to:</p>\n<ul class="task-list">\n<li><p><input type="checkbox">Explain the difference between different measures of centrality and variability (means vs. medians, variance vs. interquartile range, etc.)</p></li>\n<li><p><input type="checkbox">Convert a raw data source into a version appropriate for downstream analysis using Python.</p></li>\n<li><p><input type="checkbox">Write appropriate visualizations for different sources and types of data.</p></li>\n<li><p><input type="checkbox">Explain why we seek to build machine learning models that generalize rather than memorize their input.</p></li>\n<li><p><input type="checkbox">Explain the different uses for training, validation, and testing datasets</p></li>\n<li><p><input type="checkbox">Select the appropriate evaluation measure for the dataset and task being solved</p></li>\n<li><p><input type="checkbox">Articulate the difference between supervised and unsupervised machine learning, as well as select the appropriate methodology for a given problem</p></li>\n<li><p><input type="checkbox">Demonstrate awareness of bias and ethics in data science.</p></li>\n</ul>\n\n\n</section>\n</section>\n\n</main> <!-- /main -->\n<script id="quarto-html-after-body" type="application/javascript">\nwindow.document.addEventListener("DOMContentLoaded", function (event) {\n const toggleBodyColorMode = (bsSheetEl) => {\n const mode = bsSheetEl.getAttribute("data-mode");\n const bodyEl = window.document.querySelector("body");\n if (mode === "dark") {\n bodyEl.classList.add("quarto-dark");\n bodyEl.classList.remove("quarto-light");\n } else {\n bodyEl.classList.add("quarto-light");\n bodyEl.classList.remove("quarto-dark");\n }\n }\n const toggleBodyColorPrimary = () => {\n const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");\n if (bsSheetEl) {\n toggleBodyColorMode(bsSheetEl);\n }\n }\n toggleBodyColorPrimary(); 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i<bibliorefs.length; i++) {\n const ref = bibliorefs[i];\n const citeInfo = findCites(ref);\n if (citeInfo) {\n tippyHover(citeInfo.el, function() {\n var popup = window.document.createElement(\'div\');\n citeInfo.cites.forEach(function(cite) {\n var citeDiv = window.document.createElement(\'div\');\n citeDiv.classList.add(\'hanging-indent\');\n citeDiv.classList.add(\'csl-entry\');\n var biblioDiv = window.document.getElementById(\'ref-\' + cite);\n if (biblioDiv) {\n citeDiv.innerHTML = biblioDiv.innerHTML;\n }\n popup.appendChild(citeDiv);\n });\n return popup.innerHTML;\n });\n }\n }\n});\n</script>\n</div> <!-- /content -->\n\n\n\n</body></html>'from bs4 import BeautifulSoupsoup = BeautifulSoup(r.text, 'html.parser')print(soup.prettify()) #Output cleared for webprint(soup.get_text()) #Output cleared for web# creating a list of all common heading tags
heading_tags = ["h1", "h2", "h3"]
for tags in soup.find_all(heading_tags):
print(tags.text.strip())On this page
Syllabus (Key Info)
Description
Course Objective
Expected Learning OutcomesSupport for large, multi-dimensional arrays and matrices, and a large collection of high-level mathematical functions to operate on these arrays.
import numpy as npndarray object: an n-dimensional array of homogeneous data types, with many operations being performed in compiled code for performance
numpy.dtype - intc (same as a C integer) and intp (used for indexing) - int8, int16, int32, int64 - uint8, uint16, uint32, uint64 - float16, float32, float64 - complex64, complex128
Create a numpy array
np.array([2,3,1,0])array([2, 3, 1, 0])np.zeros((5, 5)) #np.zeros(shape)array([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]])np.ones((6, 2))#np.ones(shape)array([[1., 1.],
[1., 1.],
[1., 1.],
[1., 1.],
[1., 1.],
[1., 1.]])np.arange(15,5,-1) #Like range function in pythonarray([15, 14, 13, 12, 11, 10, 9, 8, 7, 6])#Return evenly spaced numbers over a specified interval.
np.linspace(0, 100, 5) # numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)array([ 0., 25., 50., 75., 100.])np.random.random() #Keeps changing0.7026417115321625random_obj = np.random.default_rng(seed=None) #default_rng is the recommended constructor for the random number class
random_obj.random() #changes if you do not give a seed0.21864992820211548random_obj = np.random.default_rng(seed=42) #default_rng is the recommended constructor for the random number class
random_obj.random()0.7739560485559633print("Original:\n",np.arange(9))
print("After using reshape:\n",np.arange(9).reshape(3,3))Original:
[0 1 2 3 4 5 6 7 8]
After using reshape:
[[0 1 2]
[3 4 5]
[6 7 8]]x = np.arange(2,10)
print(x)
x[-1][2 3 4 5 6 7 8 9]9x.shape = (1,3)
print(x)
x[-1] # next slideValueError: cannot reshape array of size 8 into shape (1,3)x.shape = (2,4)
print("Array:\n",x,"\n")
print("x[-1]: ",x[0])
print("x[1,3]: ", x[0,3])Array:
[[2 3 4 5]
[6 7 8 9]]
x[-1]: [2 3 4 5]
x[1,3]: 5a = np.arange(1,11)
b = np.arange(12,22)
a+barray([13, 15, 17, 19, 21, 23, 25, 27, 29, 31])a = np.arange(1,11).reshape(2,5)
b = np.arange(12,22).reshape(5,2)
result = np.dot(a,b) # To multiply two arrays
resultarray([[260, 275],
[660, 700]])result.transpose()array([[260, 660],
[275, 700]])np.linalg.inv(result) # and finallyarray([[ 1.4 , -0.55],
[-1.32, 0.52]])ex: - Fourier Transforms (scipy.fftpack) - Multidimensional image processing (scipy.ndimage) - Spatial data structures and algorithms (scipy.spatial) ..
import pandas as pdWORLD_DATA_PATH = "spotify-top-50/data/spotify-streaming-top-50-usa.csv"world_df = pd.read_csv(WORLD_DATA_PATH)world_df.sample()| date | position | song | artist | popularity | duration_ms | album_type | total_tracks | release_date | is_explicit | album_cover_url | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 269 | 2023-05-23 | 20 | Wasted On You | Morgan Wallen | 86 | 178520 | album | 30 | 2021-01-08 | False | https://i.scdn.co/image/ab67616d0000b2737d6813... |
Q: The time range of the dataset?
world_df['date'].dtypedtype('O')type(world_df['date'][0])str# Convert column date to date datatype
world_df['date'] = pd.to_datetime(world_df['date'])# Q : What is the time range in which this dataset is recording top 50?
# Assume that it records everyday
world_df['date'].max(), world_df['date'].min()(Timestamp('2023-06-27 00:00:00'), Timestamp('2023-05-18 00:00:00'))