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for tag in tags:
    match_dictionary = {}
    date = tag.find("span", {"class": "bday dtstart published updated"})
    print(date.get_text())

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for tag in tags:
    match_dictionary = {}
    date = tag.find("span",{"class":"bday dtstart published updated"}

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    for tag in tags:
        match_dictionary = {}
        date = tag.find("span", {"class": "bday dtstart published updated"})
        match_dictionary["Date"] = date.get_text()
        time = table.find("div", {"class": "ftime"})
        match_dictionary["Time"] = time.get_text()
        home_team = table.find("th", {"itemprop": "homeTeam"})
        away_team = table.find("th", {"itemprop": "awayTeam"})
        match_dictionary["Match"] = home_team.get_text() + " vs " + away_team.get_text()
        matches.append(match_dictionary)

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for match in matches:
  print(match)

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$ pip3 install requests, xlsxwriter, bs4

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import requests
import xlsxwriter
from bs4 import BeautifulSoup

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import requests
import xlsxwriter
from bs4 import BeautifulSoup

# Captures Wikipedia article response.
webpage = requests.get("https://en.wikipedia.org/wiki/2022_FIFA_World_Cup")

# Parses Wikipedia article HTML.
soup = BeautifulSoup(webpage.text, "html.parser")
tags = soup.find_all("div", {"class": "footballbox"})

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for index, column in enumerate(COLUMN_NAMES):
    worksheet.write(0, index , column)

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row_index = 1
column_index = 0
for match in matches:
    for value in match.values():
        worksheet.write(row_index, column_index, value)
        column_index += 1
    row_index += 1
    column_index = 0

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import requests
from bs4 import BeautifulSoup
import xlsxwriter

COLUMN_NAMES = ["Date", "Time", "Match"]

# Captures Wikipedia article content.
content = requests.get("https://en.wikipedia.org/wiki/2022_FIFA_World_Cup")

# Parses Wikipedia article HTML.