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55 changes: 55 additions & 0 deletions scripts/35_scrape_hacktoberfest_events.py
Original file line number Diff line number Diff line change
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import requests
import pandas
from bs4 import BeautifulSoup

# creating a soup object with html we got from the response
url = "https://hacktoberfest.digitalocean.com/events"
response = requests.get(url)
html = response.text
soup = BeautifulSoup(html)

# creating array of datas
all_names = []
all_locations = []
all_dates = []
all_time_zones = []
all_urls = []

# iterating on all the "tr" elements.
for tr_element in soup.findAll("tr", attrs={"class": "past"}):

# for each tr element we find the proper value and add it to its proper array
name_element = tr_element.find("td", attrs={"class": "event_name"})
name = name_element.text.strip()
all_names.append(name)

location_element = tr_element.find("td", attrs={"class": "location"})
location = location_element.text.strip()
all_locations.append(location)

date_element = tr_element.find("td", attrs={"data-label": "date"})
date = date_element.text.strip()
all_dates.append(date)

time_zone_element = tr_element.find("td", attrs={"data-label": "zone"})
time_zone = time_zone_element.text.strip()
all_time_zones.append(time_zone)

url_element = tr_element.find("a", attrs={"class": "emphasis"})
url = url_element['href']
all_urls.append(url)

# setting up our Comma Seperated Values
csv_name = "events.csv"
csv_structure = {
"Name": all_names,
"Location": all_locations,
"Date": all_dates,
"Time Zone": all_time_zones,
"URL": all_urls,
}
# Creating a csv
dataFrame = pandas.DataFrame(csv_structure)
dataFrame.to_csv(csv_name, index=False, encoding='utf-8')