From 0cae8252c7f6c08a025304f8f16bc04f6fb7170a Mon Sep 17 00:00:00 2001 From: Paul Sin Date: Thu, 12 Nov 2020 15:22:19 -0500 Subject: [PATCH] Scrape Hacktoberfest Events 2020 fixes#16 --- scripts/35_scrape_hacktoberfest_events.py | 55 +++++++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 scripts/35_scrape_hacktoberfest_events.py diff --git a/scripts/35_scrape_hacktoberfest_events.py b/scripts/35_scrape_hacktoberfest_events.py new file mode 100644 index 0000000..00bbfcd --- /dev/null +++ b/scripts/35_scrape_hacktoberfest_events.py @@ -0,0 +1,55 @@ +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') + + pFad - Phonifier reborn

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