Scraping Weather prediction Data using Python and BS4

import requests
# to get data from website
file = requests.get("https://weather.com/en-IN/weather/tenday/l/INKA0344:1:IN")

# import Beautifulsoup for scraping the data 
from bs4 import BeautifulSoup
soup = BeautifulSoup(file.content, "html.parser")

# create empty list
list =[]
all = soup.find("div", {"class":"locations-title ten-day-page-title"}).find("h1").text
 
# find all table with class-"twc-table"
content = soup.find_all("table", {"class":"twc-table"})
for items in content:
    for i in range(len(items.find_all("tr"))-1):
                # create empty dictionary
        dict = {}
        try:   
                        # assign value to given key 

            dict["day"]= items.find_all("span", {"class":"date-time"})[i].text
            dict["date"]= items.find_all("span", {"class":"day-detail"})[i].text            
            dict["desc"]= items.find_all("td", {"class":"description"})[i].text
            dict["temp"]= items.find_all("td", {"class":"temp"})[i].text
            dict["precip"]= items.find_all("td", {"class":"precip"})[i].text
            dict["wind"]= items.find_all("td", {"class":"wind"})[i].text
            dict["humidity"]= items.find_all("td", {"class":"humidity"})[i].text
        except:  
                     # assign None values if no items are there with specified class

            dict["day"]="None"
            dict["date"]="None"
            dict["desc"]="None"
            dict["temp"]="None"
            dict["precip"]="None"
            dict["wind"]="None"
            dict["humidity"]="None"

        # append dictionary values to the list
        list.append(dict)

import pandas as pd
convert = pd.DataFrame(list)
convert.to_csv("output.csv")

# read csv file using pandas
a = pd.read_csv("output.csv")
print(a)