Add exercises 21 - Solved 1
Signed-off-by: Manuel Vergara <manuel@vergaracarmona.es>
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@ -1,6 +1,3 @@
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"""
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01_web_scraping.py
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"""
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import requests
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from bs4 import BeautifulSoup
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import json
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@ -16,14 +13,12 @@ response = requests.get(url)
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soup = BeautifulSoup(response.text, 'html.parser')
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data = {}
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current_section = None
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for section in soup.find_all('section', {'class': 'facts-categories'}):
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for item in soup.find_all('div', {'class': 'facts-wrapper'}):
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section_name = item.find('h5').get_text().strip()
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section_data = {}
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for item in section.find_all('div', {'class': 'facts-wrapper'}):
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section_name = section.find('h5').get_text().strip()
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for li in item.find_all('li'):
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key = li.find('p', {'class': 'text'}).get_text().strip()
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value = li.find('span', {'class': 'value'}).get_text().strip()
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@ -35,75 +30,3 @@ with open('bu_stats.json', 'w') as f:
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json.dump(data, f, indent=2)
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print("Datos guardados en bu_stats.json")
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# 2. Extrae la tabla de esta URL
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# (https://archive.ics.uci.edu/ml/datasets.php)
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# y conviértela en un archivo JSON.
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url = 'https://webcache.googleusercontent.com/search?q=cache:tT4BY9X5RxAJ:https://archive.ics.uci.edu/datasets&cd=8&hl=ca&ct=clnk&gl=es'
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response = requests.get(url)
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soup = BeautifulSoup(response.text, 'html.parser')
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datasets = []
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for div in soup.find_all('div', class_='rounded-box'):
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dataset = {
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'name': div.find('h2').find('a').text.strip(),
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'description': div.find('p').text.strip(),
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}
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metadata_divs = div.find_all('div', class_='col-span-3')
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for metadata_div in metadata_divs:
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icon = metadata_div.find('div').find('svg')['viewBox']
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value = metadata_div.find('span').text.strip()
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dataset[icon] = value
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datasets.append(dataset)
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with open('uci_datasets.json', 'w') as f:
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json.dump(datasets, f, indent=2)
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print("Datos guardados en uci_datasets.json")
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# 3. Realiza un raspado web de la tabla de presidentes
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# y guarda los datos como JSON
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# (https://en.wikipedia.org/wiki/List_of_presidents_of_the_United_States).
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# La tabla no está muy estructurada
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# y el proceso de raspado puede llevar mucho tiempo.
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url = 'https://en.wikipedia.org/wiki/List_of_presidents_of_the_United_States'
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response = requests.get(url)
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soup = BeautifulSoup(response.text, 'html.parser')
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table = soup.find('table', {'class': 'wikitable'})
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headers = [header.get_text().strip() for header in table.find_all('th')]
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rows = []
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for row in table.find_all('tr'):
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cells = row.find_all('td')
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if len(cells) == len(headers):
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rows.append([cell.get_text().strip() for cell in cells])
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data = []
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for row in rows:
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president = {}
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for i, header in enumerate(headers):
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if i < len(row): # Verificar si hay celdas suficientes en la fila
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if header == 'President':
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president['name'] = row[i]
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elif header == 'Party':
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president['party'] = row[i]
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elif header == 'State[a]':
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president['state'] = row[i]
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elif header == 'Took office':
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president['took_office'] = row[i]
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elif header == 'Left office':
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president['left_office'] = row[i]
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data.append(president)
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with open('us_presidents.json', 'w') as f:
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json.dump(data, f, indent=2)
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print("Datos guardados en us_presidents.json")
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@ -6,10 +6,14 @@ Documento original en inglés: [Web Scraping](https://github.com/Asabeneh/30-Day
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1. Realiza un raspado web del siguiente sitio web y guarda los datos en un archivo JSON (URL = 'http://www.bu.edu/president/boston-university-facts-stats/').
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[Solución](01_web_scraping.py)
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2. Extrae la tabla de esta URL (https://archive.ics.uci.edu/ml/datasets.php) y conviértela en un archivo JSON.
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[Solución](02_web_scraping.py)
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3. Realiza un raspado web de la tabla de presidentes y guarda los datos como JSON (https://en.wikipedia.org/wiki/List_of_presidents_of_the_United_States). La tabla no está muy estructurada y el proceso de raspado puede llevar mucho tiempo.
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[Solución](01_web_scraping.py)
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[Solución](03_web_scraping.py)
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[<< Day 21](../21_Clases_y_objetos/README.md) | [Day 23 >>](../23_Entorno_virtual/README.md)
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@ -6,24 +6,35 @@
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"Faculty": "4,309",
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"Nondegree Students": "1,337",
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"Graduate & Professional Students": "18,476",
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"Undergraduate Students": "17,744",
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"Undergraduate Students": "17,744"
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},
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"Campus": {
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"Classrooms": "848",
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"Buildings": "343",
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"Laboratories": "1,481",
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"Libraries": "13",
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"Campus Area (acres)": "140",
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"Campus Area (acres)": "140"
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},
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"Academics": {
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"Study Abroad Programs": "80+",
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"Average Class Size": "30",
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"Faculty": "4,309",
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"Student/Faculty Ratio": "11:1",
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"Schools and Colleges": "17",
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"Programs of Study": "300+",
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"Programs of Study": "300+"
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},
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"Grant & Contract Awards": {
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"Research Expenditures (FY22)": "$630.7M",
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"Research Awards": "$674M",
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"BMC Clinical Research Grants (FY22)": "$82M",
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"BMC Clinical Research Grants (FY22)": "$82M"
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},
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"Undergraduate Financial Aid & Scholarships": {
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"Average Total Need-Based Financial Aid": "$57,237",
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"Average Need-Based Grant/Scholarship": "$53,029",
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"Grants & Scholarships (need-based)": "$388.4M",
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"Grants & Scholarships (non-need-based)": "$26.5M",
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"Grants & Scholarships (non-need-based)": "$26.5M"
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},
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"Student Life": {
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"Community Service Hours": "130,000+",
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"Alternative Service Breaks Participants": "65+",
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"BU on Social": "new accounts daily",
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@ -32,14 +43,20 @@
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"Academic & Professional Organizations": "140+",
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"Art & Performance Organizations": "60+",
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"Student Organizations": "450+",
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"First-Year Student Outreach Project Volunteers": "400+",
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"First-Year Student Outreach Project Volunteers": "400+"
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},
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"Research": {
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"Faculty Publications": "7,000+",
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"Student UROP Participants": "450+",
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"Centers & Institutes": "130+",
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"Centers & Institutes": "130+"
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},
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"International Community": {
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"Global Initiatives": "300+",
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"Cultural Student Groups": "60+",
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"Alumni Countries": "180+",
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"International Students": "10,000+",
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"International Students": "10,000+"
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},
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"Athletics": {
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"Intramural Sports & Tournaments": "12+",
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"Club and Intramural Sports Participants": "7,000+",
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"Club Sports": "36",
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