Welcome to your first casestudy

• In this case study you have to scrape weather data from the website"http://www.estesparkweather.net/archive_reports.php?date=200901"
• Scrape all the available attributes of weather data for each day from2009-01-01 to 2018-10-28
• Ignore records for missing days
• Represent the scraped data aspandas dataframeobject.

Dataframe specific deatails

• Expected column names (order dose not matter):

 ['Average temperature (F)', 'Average humidity (%)', 'Average dewpoint (F)', 'Average barometer (in)', 'Average windspeed (mph)', 'Average gustspeed (mph)', 'Average direction (deg)', 'Rainfall for month (in)', 'Rainfall for year (in)', 'Maximum rain per minute', 'Maximum temperature (F)', 'Minimum temperature (F)', 'Maximum humidity (%)', 'Minimum humidity (%)', 'Maximum pressure', 'Minimum pressure', 'Maximum windspeed (mph)', 'Maximum gust speed (mph)', 'Maximum heat index (F)'] 

• Each record in the dataframe corresponds to weather deatils of a given day
• Make sure the index column isdate-time format (yyyy-mm-dd)
• Perform necessary data cleaning and type cast each attributes to relevent data type

Saving the dataframe

• Once you are done with you scrapping save your dataframe as pickle file by name 'dataframe.pk'

Sample code to save pickle file

import pickle with open("dataframe.pk", "wb") as file: pickle.dump(<your_dataframe>, file) 

Run the below cell to import necessary packages

• These packages should be sufficient to perform you task
• In case if you are looking are any other packages run!pip3 install --user with in a cell

import bs4

from bs4 import BeautifulSoup

import csv

import requests

import time

import pandas as pd

import urllib

import re

import pickle

#### Start you code here, you are free to add any number of cells