Playing With Jupyter Notebooks And Python_ipynb_2_
# Extracting data
# pip install newspaper3k lxml_html_clean
from newspaper import Article #Make sure to run pip install lxml_html_clean
from IPython.display import display, Markdown
urls = ["http://cnn.com/2023/03/29/entertainment/the-mandalorian-episode-5-recap/index.html",
"https://www.cnn.com/2023/06/09/entertainment/jurassic-park-anniversary/index.html"]
for url in urls:
article = Article(url)
article.download()
article.parse()
# Jupyter Notebook Display
# print(article.title)
display(Markdown(article.title)) # Jupyter display only
display(Markdown(article.text)) # Jupyter display only
print("\n")
‘The Mandalorian’ finally comes into focus, while giving out a ‘Rebels’ yell
Editor’s Note: The following contains spoilers about the fifth episode of “The Mandalorian,” Season 3, “The Pirate.”
CNN —
After what can at best be described as a somewhat disjointed third season thus far, the fifth episode of “The Mandalorian” began to bring those pieces together and into focus, while continuing to draw upon the “Star Wars” animated series that preceded it, including another cameo by a character from the rightfully lauded “Rebels.”
Subtitled “The Pirate,” the episode presented further evidence of the dysfunctional nature of the New Republic, unable or unwilling to defend a faraway planet from an invading band of pirates. (Lucasfilm being a unit of Disney, the marauders had a certain “Yo ho, yo ho” vibe to them.)
The siege also played into Mandalorian politics, and the efforts of Bo-Katan (Katee Sackhoff) to reclaim her heritage and potentially reunite her people’s various tribes, after leading them, along with Din Djarin (voiced by Pedro Pascal), to the rescue of his pal Greef Karga (Carl Weathers) and the planet’s residents.
Still, the most pleasing moment for longtime “Star Wars” fans was likely what amounted to a throwaway scene, introducing a live-action version of the hulking alien Zeb, a character from the animated “Star Wars Rebels,” which concluded in 2018. “The Mandalorian” has drawn heavily from those properties, which were overseen by one of its executive producers, Dave Filoni. (In another nice touch, Steve Blum again provided the voice of the character, and Zeb looked a whole lot better than the pirate leader.)
Finally, the episode closed with evidence that the evil Moff Gideon (played by Giancarlo Esposito previously) had seemingly been freed from the prison ship that was transporting him to stand trial, reviving that potential threat.
Having resolved the fate of Grogu, a.k.a. Baby Yoda, during the first two seasons, “The Mandalorian” has thus moved on to fill in narrative gaps about an under-explored chapter in “Star Wars” history – namely, the factors that resulted in the fall of the New Republic and the rise of the First Order, the plot line featured in the most recent trilogy that began with “The Force Awakens.”
“This isn’t a rebellion anymore,” a bureaucrat (played by Tim Meadows) says about what happens outside of the New Republic’s jurisdiction, conveying an indifference to the fate of the planet Nevarro overtly articulated later when it was observed that the governing body in Coruscant “doesn’t care.”
Executive producers Jon Favreau and Filoni have taken their time in reaching this point, juggling these various issues in somewhat ungainly fashion through the first half of the season. That perhaps reflects the transition of the show to an emphasis on the macro instead of the micro, while still finding time to detour for the occasional “Rebels” yell.
‘Jurassic Park’ still has bite at 30 years old, and here’s why
CNN —
It’s been 30 years since Steven Spielberg’s dinosaurs stampeded across the screen in the first “Jurassic Park,” but it feels more recent.
I was 12 in June of 1993 and vividly remember watching with glee when the Tyrannosaurus Rex, with its teeny arms and perpetual scowl, blew the walls of the bathroom down like a big bad wolf and promptly ate the lawyer character (played to hilarious effect by Martin Ferrero). Part of this, surely, had to do with the fact that I was a mouthy pre-teen, and many adults in my sphere at the time opined that I “would make a great lawyer” just like my father – a fate I abhorred.
Admittedly, I was the exact target audience for this creature feature, and even though I was already somewhat of a self-taught critic (note the aforementioned mouthiness), I was awed by what I saw that summer three decades ago, and my impressions of “Jurassic Park” remain to this day.
Joseph Mazzello in “Jurassic Park.” Amblin/Universal/Kobal/Shutterstock
Part of that lasting impact, of course, has to do with the still-groundbreaking effects in the movie, which surprisingly hold up, and on a fairly hi-tech 72-inch TV screen to boot. While the first dino money shot – of the plant-eating brachiosaurus – might look just a tad soupy in 2023, it still looks considerably better than more contemporary fare, and the ensuing imagery of the more predatory beasts (like T-rex and especially those raptors) remains top-notch. The computer-generated imagery in the movie is essentially credited with marking the end of stop-motion animation as the go-to effects option for films such as these, notably used in everything that came before, from 1933’s “King Kong” to 1981’s “Clash of the Titans.” The animatronics are something to behold as well, particularly the ailing triceratops responsible for that “one big pile of s—,” one of many priceless quips uttered by Ian Malcolm (Jeff Goldblum).
The appeal of “Jurassic,” based on Michael Crichton’s acclaimed novel, is also largely due to the film’s suspenseful and pared-down pacing, which of course can be linked to Spielberg, who learned a thing or two about keeping his cards close to his chest with “Jaws” – the great white mother of all creature features that famously showed startlingly little of the big fish before the climax.
Another “Jaws” connection is prolific film composer John Williams, the Spielberg collaborator who created a majestic score for “Jurassic Park” that is still synonymous with an air of discovery, one that can easily be hummed when looking upon any great view or upon entering a new and uncharted space.
Laura Dern, Sam Neill and Joseph Mazzello in “Jurassic Park.” Amblin/Universal/Kobal/Shutterstock
And then there’s the casting, an element that sometimes takes a number of years to truly appreciate. Aside from the always-dependable Goldblum, there’s Laura Dern, who carved out her own Sigourney Weaver-shaped notch in the movie thanks to that one terrifying sequence in the control shed. Plus, her reaction shot to that first dinosaur reveal – along with that of Sam Neill – could be viewed as a textbook for green-screen acting, which has become the standard ever since, in Marvel movies and beyond. Add to that the amazing and meme-worthy smaller performances from Samuel L. Jackson (“Hold onto your butts!”), Wayne Knight (“Ah ah ah! You didn’t say the magic word!”) and Bob Peck (“Clever girl”), and you’ve got a crowd-pleaser that is equal parts adventure, comedy and chomp-chomp thriller.
While the rest of the entries in the “Jurassic” franchise have not exactly been up to par (aside from 2015’s not-terrible first reboot “Jurassic World”), the original flick still “rules” – and is definitely worth a rewatch on the occasion of its 30th birthday.
# Extracting Data II
# pip install wikipedia
import wikipedia
from IPython.display import display, Markdown #add for Jupyter
terms = ["C++", "Assembly (programming)", "C"]
for term in terms:
# Search for a page
result = wikipedia.search(term)
# Get the summary of the first result
summary = wikipedia.summary(result[0])
print(term)
#print(summary) # console display
display(Markdown(summary))
C++
C++ (, pronounced “C plus plus” and sometimes abbreviated as CPP) is a high-level, general-purpose programming language created by Danish computer scientist Bjarne Stroustrup. First released in 1985 as an extension of the C programming language, it has since expanded significantly over time; as of 1997, C++ has object-oriented, generic, and functional features, in addition to facilities for low-level memory manipulation for systems like microcomputers or to make operating systems like Linux or Windows. It is usually implemented as a compiled language, and many vendors provide C++ compilers, including the Free Software Foundation, LLVM, Microsoft, Intel, Embarcadero, Oracle, and IBM. C++ was designed with systems programming and embedded, resource-constrained software and large systems in mind, with performance, efficiency, and flexibility of use as its design highlights. C++ has also been found useful in many other contexts, with key strengths being software infrastructure and resource-constrained applications, including desktop applications, video games, servers (e.g., e-commerce, web search, or databases), and performance-critical applications (e.g., telephone switches or space probes). C++ is standardized by the International Organization for Standardization (ISO), with the latest standard version ratified and published by ISO in December 2020 as ISO/IEC 14882:2020 (informally known as C++20). The C++ programming language was initially standardized in 1998 as ISO/IEC 14882:1998, which was then amended by the C++03, C++11, C++14, and C++17 standards. The current C++20 standard supersedes these with new features and an enlarged standard library. Before the initial standardization in 1998, C++ was developed by Stroustrup at Bell Labs since 1979 as an extension of the C language; he wanted an efficient and flexible language similar to C that also provided high-level features for program organization. Since 2012, C++ has been on a three-year release schedule with C++23 as the next planned standard.
Assembly (programming)
In computer programming, assembly language (alternatively assembler language or symbolic machine code), often referred to simply as assembly and commonly abbreviated as ASM or asm, is any low-level programming language with a very strong correspondence between the instructions in the language and the architecture’s machine code instructions. Assembly language usually has one statement per machine instruction (1:1), but constants, comments, assembler directives, symbolic labels of, e.g., memory locations, registers, and macros are generally also supported. The first assembly code in which a language is used to represent machine code instructions is found in Kathleen and Andrew Donald Booth’s 1947 work, Coding for A.R.C.. Assembly code is converted into executable machine code by a utility program referred to as an assembler. The term “assembler” is generally attributed to Wilkes, Wheeler and Gill in their 1951 book The Preparation of Programs for an Electronic Digital Computer, who, however, used the term to mean “a program that assembles another program consisting of several sections into a single program”. The conversion process is referred to as assembly, as in assembling the source code. The computational step when an assembler is processing a program is called assembly time. Because assembly depends on the machine code instructions, each assembly language is specific to a particular computer architecture. Sometimes there is more than one assembler for the same architecture, and sometimes an assembler is specific to an operating system or to particular operating systems. Most assembly languages do not provide specific syntax for operating system calls, and most assembly languages can be used universally with any operating system, as the language provides access to all the real capabilities of the processor, upon which all system call mechanisms ultimately rest. In contrast to assembly languages, most high-level programming languages are generally portable across multiple architectures but require interpreting or compiling, much more complicated tasks than assembling. In the first decades of computing, it was commonplace for both systems programming and application programming to take place entirely in assembly language. While still irreplaceable for some purposes, the majority of programming is now conducted in higher-level interpreted and compiled languages. In “No Silver Bullet”, Fred Brooks summarised the effects of the switch away from assembly language programming: “Surely the most powerful stroke for software productivity, reliability, and simplicity has been the progressive use of high-level languages for programming. Most observers credit that development with at least a factor of five in productivity, and with concomitant gains in reliability, simplicity, and comprehensibility.” Today, it is typical to use small amounts of assembly language code within larger systems implemented in a higher-level language, for performance reasons or to interact directly with hardware in ways unsupported by the higher-level language. For instance, just under 2% of version 4.9 of the Linux kernel source code is written in assembly; more than 97% is written in C.
C
C, or c, is the third letter of the Latin alphabet, used in the modern English alphabet, the alphabets of other western European languages and others worldwide. Its name in English is cee (pronounced ), plural cees.
# Inspecting a Function
import inspect
from newspaper import Article
# inspect newspaper Article function
print(inspect.getsource(Article))
# C: Prints out the Article function source code
class Article(object):
"""Article objects abstract an online news article page
"""
def __init__(self, url, title='', source_url='', config=None, **kwargs):
"""The **kwargs argument may be filled with config values, which
is added into the config object
"""
if isinstance(title, Configuration) or \
isinstance(source_url, Configuration):
raise ArticleException(
'Configuration object being passed incorrectly as title or '
'source_url! Please verify `Article`s __init__() fn.')
self.config = config or Configuration()
self.config = extend_config(self.config, kwargs)
self.extractor = ContentExtractor(self.config)
if source_url == '':
scheme = urls.get_scheme(url)
if scheme is None:
scheme = 'http'
source_url = scheme + '://' + urls.get_domain(url)
if source_url is None or source_url == '':
raise ArticleException('input url bad format')
# URL to the main page of the news source which owns this article
self.source_url = source_url
self.url = urls.prepare_url(url, self.source_url)
self.title = title
# URL of the "best image" to represent this article
self.top_img = self.top_image = ''
# stores image provided by metadata
self.meta_img = ''
# All image urls in this article
self.imgs = self.images = []
# All videos in this article: youtube, vimeo, etc
self.movies = []
# Body text from this article
self.text = ''
# `keywords` are extracted via nlp() from the body text
self.keywords = []
# `meta_keywords` are extracted via parse() from <meta> tags
self.meta_keywords = []
# `tags` are also extracted via parse() from <meta> tags
self.tags = set()
# List of authors who have published the article, via parse()
self.authors = []
self.publish_date = ''
# Summary generated from the article's body txt
self.summary = ''
# This article's unchanged and raw HTML
self.html = ''
# The HTML of this article's main node (most important part)
self.article_html = ''
# Keep state for downloads and parsing
self.is_parsed = False
self.download_state = ArticleDownloadState.NOT_STARTED
self.download_exception_msg = None
# Meta description field in the HTML source
self.meta_description = ""
# Meta language field in HTML source
self.meta_lang = ""
# Meta favicon field in HTML source
self.meta_favicon = ""
# Meta tags contain a lot of structured data, e.g. OpenGraph
self.meta_data = {}
# The canonical link of this article if found in the meta data
self.canonical_link = ""
# Holds the top element of the DOM that we determine is a candidate
# for the main body of the article
self.top_node = None
# A deepcopied clone of the above object before heavy parsing
# operations, useful for users to query data in the
# "most important part of the page"
self.clean_top_node = None
# lxml DOM object generated from HTML
self.doc = None
# A deepcopied clone of the above object before undergoing heavy
# cleaning operations, serves as an API if users need to query the DOM
self.clean_doc = None
# A property dict for users to store custom data.
self.additional_data = {}
def build(self):
"""Build a lone article from a URL independent of the source (newspaper).
Don't normally call this method b/c it's good to multithread articles
on a source (newspaper) level.
"""
self.download()
self.parse()
self.nlp()
def download(self, input_html=None, title=None, recursion_counter=0):
"""Downloads the link's HTML content, don't use if you are batch async
downloading articles
recursion_counter (currently 1) stops refreshes that are potentially
infinite
"""
if input_html is None:
try:
html = network.get_html_2XX_only(self.url, self.config)
except requests.exceptions.RequestException as e:
self.download_state = ArticleDownloadState.FAILED_RESPONSE
self.download_exception_msg = str(e)
log.debug('Download failed on URL %s because of %s' %
(self.url, self.download_exception_msg))
return
else:
html = input_html
if self.config.follow_meta_refresh:
meta_refresh_url = extract_meta_refresh(html)
if meta_refresh_url and recursion_counter < 1:
return self.download(
input_html=network.get_html(meta_refresh_url),
recursion_counter=recursion_counter + 1)
self.set_html(html)
self.set_title(title)
def parse(self):
self.throw_if_not_downloaded_verbose()
self.doc = self.config.get_parser().fromstring(self.html)
self.clean_doc = copy.deepcopy(self.doc)
if self.doc is None:
# `parse` call failed, return nothing
return
# TODO: Fix this, sync in our fix_url() method
parse_candidate = self.get_parse_candidate()
self.link_hash = parse_candidate.link_hash # MD5
document_cleaner = DocumentCleaner(self.config)
output_formatter = OutputFormatter(self.config)
title = self.extractor.get_title(self.clean_doc)
self.set_title(title)
authors = self.extractor.get_authors(self.clean_doc)
self.set_authors(authors)
meta_lang = self.extractor.get_meta_lang(self.clean_doc)
self.set_meta_language(meta_lang)
if self.config.use_meta_language:
self.extractor.update_language(self.meta_lang)
output_formatter.update_language(self.meta_lang)
meta_favicon = self.extractor.get_favicon(self.clean_doc)
self.set_meta_favicon(meta_favicon)
meta_description = \
self.extractor.get_meta_description(self.clean_doc)
self.set_meta_description(meta_description)
canonical_link = self.extractor.get_canonical_link(
self.url, self.clean_doc)
self.set_canonical_link(canonical_link)
tags = self.extractor.extract_tags(self.clean_doc)
self.set_tags(tags)
meta_keywords = self.extractor.get_meta_keywords(
self.clean_doc)
self.set_meta_keywords(meta_keywords)
meta_data = self.extractor.get_meta_data(self.clean_doc)
self.set_meta_data(meta_data)
self.publish_date = self.extractor.get_publishing_date(
self.url,
self.clean_doc)
# Before any computations on the body, clean DOM object
self.doc = document_cleaner.clean(self.doc)
self.top_node = self.extractor.calculate_best_node(self.doc)
if self.top_node is not None:
video_extractor = VideoExtractor(self.config, self.top_node)
self.set_movies(video_extractor.get_videos())
self.top_node = self.extractor.post_cleanup(self.top_node)
self.clean_top_node = copy.deepcopy(self.top_node)
text, article_html = output_formatter.get_formatted(
self.top_node)
self.set_article_html(article_html)
self.set_text(text)
self.fetch_images()
self.is_parsed = True
self.release_resources()
def fetch_images(self):
if self.clean_doc is not None:
meta_img_url = self.extractor.get_meta_img_url(
self.url, self.clean_doc)
self.set_meta_img(meta_img_url)
imgs = self.extractor.get_img_urls(self.url, self.clean_doc)
if self.meta_img:
imgs.add(self.meta_img)
self.set_imgs(imgs)
if self.clean_top_node is not None and not self.has_top_image():
first_img = self.extractor.get_first_img_url(
self.url, self.clean_top_node)
if self.config.fetch_images:
self.set_top_img(first_img)
else:
self.set_top_img_no_check(first_img)
if not self.has_top_image() and self.config.fetch_images:
self.set_reddit_top_img()
def has_top_image(self):
return self.top_img is not None and self.top_img != ''
def is_valid_url(self):
"""Performs a check on the url of this link to determine if article
is a real news article or not
"""
return urls.valid_url(self.url)
def is_valid_body(self):
"""If the article's body text is long enough to meet
standard article requirements, keep the article
"""
if not self.is_parsed:
raise ArticleException('must parse article before checking \
if it\'s body is valid!')
meta_type = self.extractor.get_meta_type(self.clean_doc)
wordcount = self.text.split(' ')
sentcount = self.text.split('.')
if (meta_type == 'article' and len(wordcount) >
(self.config.MIN_WORD_COUNT)):
log.debug('%s verified for article and wc' % self.url)
return True
if not self.is_media_news() and not self.text:
log.debug('%s caught for no media no text' % self.url)
return False
if self.title is None or len(self.title.split(' ')) < 2:
log.debug('%s caught for bad title' % self.url)
return False
if len(wordcount) < self.config.MIN_WORD_COUNT:
log.debug('%s caught for word cnt' % self.url)
return False
if len(sentcount) < self.config.MIN_SENT_COUNT:
log.debug('%s caught for sent cnt' % self.url)
return False
if self.html is None or self.html == '':
log.debug('%s caught for no html' % self.url)
return False
log.debug('%s verified for default true' % self.url)
return True
def is_media_news(self):
"""If the article is related heavily to media:
gallery, video, big pictures, etc
"""
safe_urls = ['/video', '/slide', '/gallery', '/powerpoint',
'/fashion', '/glamour', '/cloth']
for s in safe_urls:
if s in self.url:
return True
return False
def nlp(self):
"""Keyword extraction wrapper
"""
self.throw_if_not_downloaded_verbose()
self.throw_if_not_parsed_verbose()
nlp.load_stopwords(self.config.get_language())
text_keyws = list(nlp.keywords(self.text).keys())
title_keyws = list(nlp.keywords(self.title).keys())
keyws = list(set(title_keyws + text_keyws))
self.set_keywords(keyws)
max_sents = self.config.MAX_SUMMARY_SENT
summary_sents = nlp.summarize(title=self.title, text=self.text, max_sents=max_sents)
summary = '\n'.join(summary_sents)
self.set_summary(summary)
def get_parse_candidate(self):
"""A parse candidate is a wrapper object holding a link hash of this
article and a final_url of the article
"""
if self.html:
return RawHelper.get_parsing_candidate(self.url, self.html)
return URLHelper.get_parsing_candidate(self.url)
def build_resource_path(self):
"""Must be called after computing HTML/final URL
"""
res_path = self.get_resource_path()
if not os.path.exists(res_path):
os.mkdir(res_path)
def get_resource_path(self):
"""Every article object has a special directory to store data in from
initialization to garbage collection
"""
res_dir_fn = 'article_resources'
resource_directory = os.path.join(settings.TOP_DIRECTORY, res_dir_fn)
if not os.path.exists(resource_directory):
os.mkdir(resource_directory)
dir_path = os.path.join(resource_directory, '%s_' % self.link_hash)
return dir_path
def release_resources(self):
# TODO: implement in entirety
path = self.get_resource_path()
for fname in glob.glob(path):
try:
os.remove(fname)
except OSError:
pass
# os.remove(path)
def set_reddit_top_img(self):
"""Wrapper for setting images. Queries known image attributes
first, then uses Reddit's image algorithm as a fallback.
"""
try:
s = images.Scraper(self)
self.set_top_img(s.largest_image_url())
except TypeError as e:
if "Can't convert 'NoneType' object to str implicitly" in e.args[0]:
log.debug('No pictures found. Top image not set, %s' % e)
elif 'timed out' in e.args[0]:
log.debug('Download of picture timed out. Top image not set, %s' % e)
else:
log.critical('TypeError other than None type error. '
'Cannot set top image using the Reddit '
'algorithm. Possible error with PIL., %s' % e)
except Exception as e:
log.critical('Other error with setting top image using the '
'Reddit algorithm. Possible error with PIL, %s' % e)
def set_title(self, input_title):
if input_title:
self.title = input_title[:self.config.MAX_TITLE]
def set_text(self, text):
text = text[:self.config.MAX_TEXT]
if text:
self.text = text
def set_html(self, html):
"""Encode HTML before setting it
"""
if html:
if isinstance(html, bytes):
html = self.config.get_parser().get_unicode_html(html)
self.html = html
self.download_state = ArticleDownloadState.SUCCESS
def set_article_html(self, article_html):
"""Sets the HTML of just the article's `top_node`
"""
if article_html:
self.article_html = article_html
def set_meta_img(self, src_url):
self.meta_img = src_url
self.set_top_img_no_check(src_url)
def set_top_img(self, src_url):
if src_url is not None:
s = images.Scraper(self)
if s.satisfies_requirements(src_url):
self.set_top_img_no_check(src_url)
def set_top_img_no_check(self, src_url):
"""Provide 2 APIs for images. One at "top_img", "imgs"
and one at "top_image", "images"
"""
self.top_img = src_url
self.top_image = src_url
def set_imgs(self, imgs):
"""The motive for this method is the same as above, provide APIs
for both `article.imgs` and `article.images`
"""
self.images = imgs
self.imgs = imgs
def set_keywords(self, keywords):
"""Keys are stored in list format
"""
if not isinstance(keywords, list):
raise Exception("Keyword input must be list!")
if keywords:
self.keywords = keywords[:self.config.MAX_KEYWORDS]
def set_authors(self, authors):
"""Authors are in ["firstName lastName", "firstName lastName"] format
"""
if not isinstance(authors, list):
raise Exception("authors input must be list!")
if authors:
self.authors = authors[:self.config.MAX_AUTHORS]
def set_summary(self, summary):
"""Summary here refers to a paragraph of text from the
title text and body text
"""
self.summary = summary[:self.config.MAX_SUMMARY]
def set_meta_language(self, meta_lang):
"""Save langauges in their ISO 2-character form
"""
if meta_lang and len(meta_lang) >= 2 and \
meta_lang in get_available_languages():
self.meta_lang = meta_lang[:2]
def set_meta_keywords(self, meta_keywords):
"""Store the keys in list form
"""
self.meta_keywords = [k.strip() for k in meta_keywords.split(',')]
def set_meta_favicon(self, meta_favicon):
self.meta_favicon = meta_favicon
def set_meta_description(self, meta_description):
self.meta_description = meta_description
def set_meta_data(self, meta_data):
self.meta_data = meta_data
def set_canonical_link(self, canonical_link):
self.canonical_link = canonical_link
def set_tags(self, tags):
self.tags = tags
def set_movies(self, movie_objects):
"""Trim video objects into just urls
"""
movie_urls = [o.src for o in movie_objects if o and o.src]
self.movies = movie_urls
def throw_if_not_downloaded_verbose(self):
"""Parse ArticleDownloadState -> log readable status
-> maybe throw ArticleException
"""
if self.download_state == ArticleDownloadState.NOT_STARTED:
raise ArticleException('You must `download()` an article first!')
elif self.download_state == ArticleDownloadState.FAILED_RESPONSE:
raise ArticleException('Article `download()` failed with %s on URL %s' %
(self.download_exception_msg, self.url))
def throw_if_not_parsed_verbose(self):
"""Parse `is_parsed` status -> log readable status
-> maybe throw ArticleException
"""
if not self.is_parsed:
raise ArticleException('You must `parse()` an article first!')
# Python Data Types
import sys
from typing import Union
# Define types for mean functions, trying to analyze input possibilities
Number = Union[int, float] # Number can be either int or float type
Numbers = list[Number] # Numbers is a list of Number types
Scores = Union[Number, Numbers] # Scores can be single or multiple
def mean(scores: Scores, method: int = 1) -> float:
"""
Calculate the mean of a list of scores.
Average and Average2 are hidden functions performing mean algorithm
If a single score is provided in scores, it is returned as the mean.
If a list of scores is provided, the average is calculated and returned.
"""
def average(scores):
"""Calculate the average of a list of scores using a Python for loop with rounding."""
sum = 0
len = 0
for score in scores:
if isinstance(score, Number):
sum += score
len += 1
else:
print("Bad data: " + str(score) + " in " + str(scores))
sys.exit()
return sum / len
def average2(scores):
"""Calculate the average of a list of scores using the built-in sum() function with rounding."""
return sum(scores) / len(scores)
# test to see if scores is a list of numbers
if isinstance(scores, list):
if method == 1:
# long method
result = average(scores)
else:
# built in method
result = average2(scores)
return round(result + 0.005, 2)
return scores # case where scores is a single valu
# try with one number
singleScore = 100
print("Print test data: " + str(singleScore)) # concat data for single line
print("Mean of single number: " + str(mean(singleScore)))
print()
# define a list of numbers
testScores = [90.5, 100, 85.4, 88]
print("Print test data: " + str(testScores))
print("Average score, loop method: " + str(mean(testScores)))
print("Average score, function method: " + str(mean(testScores, 2)))
print()
"""
badData = [100, "NaN", 90]
print("Print test data: " + str(badData))
print("Mean with bad data: " + str(mean(badData)))
"""
Print test data: 100
Mean of single number: 100
Print test data: [90.5, 100, 85.4, 88]
Average score, loop method: 90.98
Average score, function method: 90.98
'\nbadData = [100, "NaN", 90]\nprint("Print test data: " + str(badData))\nprint("Mean with bad data: " + str(mean(badData)))\n'
Hacks
Summary:
- Formatting messages with emojis
- Exploring data with newspaper and wikipedia libraries
- Finding code on how the library we used was made
- Learning about data types while writing an algorithm for mean
# Calculations for various geometric shapes
from math import pi
# Pythagorean Theorem
def pythagorean(a: Number, b: Number) -> float:
# Calculate the hypotenuse of a right triangle
c = (a ** 2 + b ** 2) ** 0.5 # Take the square root of the sum of the squares of the side lengths
return c
def circle(r: Number) -> float:
# Calculate the area of a circle
return pi * r ** 2
def rectangle(l: Number, w: Number) -> float:
# Calculate the area of a rectangle
return l * w
# Input section
print("Options:")
print("1. Pythagorean Theorem")
print("2. Circle")
print("3. Rectangle")
option = input("Enter the option number: ")
if option == '1':
# Pythagorean Theorem
a = float(input("Enter the length of side a: "))
b = float(input("Enter the length of side b: "))
result = pythagorean(a, b)
print("The hypotenuse is:", result)
elif option == '2':
# Circle
r = float(input("Enter the radius of the circle: "))
result = circle(r)
print("The area of the circle is:", result)
elif option == '3':
# Rectangle
l = float(input("Enter the length of the rectangle: "))
w = float(input("Enter the width of the rectangle: "))
result = rectangle(l, w)
print("The area of the rectangle is:", result)
else:
print("Invalid option")
Options:
1. Pythagorean Theorem
2. Circle
3. Rectangle
The area of the circle is: 314.1592653589793