ahhammoud

from statsmodels.tsa.statespace.sarimax import SARIMAX

period = 14
df_log = np.log(df[:-period])

model = SARIMAX(df_log, 
              order=(1, 1, 2),
              seasonal_order=(1, 1, 2, 7),
              enforce_stationarity=False,
              enforce_invertibility=False)

ahhammoud

# here we will be forecasting 21 days into future using the full dataset
from statsmodels.tsa.statespace.sarimax import SARIMAX

df_log = np.log(df)

model = SARIMAX(df_log, 
              order=(1, 1, 2),
              seasonal_order=(1, 1, 2, 7),
              enforce_stationarity=False,
              enforce_invertibility=False)

ahhammoud

# set the country you are interested in looking at
country = "uk"

# download the page that we're going to be using
page = requests.get("https://www.worldometers.info/coronavirus/country/"+ country + "/")

# create the soup from the contents on the page
soup = BeautifulSoup(page.content, "html.parser")

#%%

ahhammoud

# we first import the libraries we are going to be using
import requests
import pandas as pd
from bs4 import BeautifulSoup
import random
import time
import numpy as np

#%%
# this is the address of the first page in our search query

ahhammoud

number_class = "property-header__list-count property-header__list-count--new ge_resultsnumber text--size2 text--color1 text--normal"

number_listings = int(soup.find(class_ = number_class).get_text().replace(" results",""))
number_cards = len(cards)
number_pages = number_listings // number_cards + 1

ahhammoud

# import pandas and the optionally os
import pandas as pd
import os

# optional: set the path to save out your csv file
os.chdir("path of the folder you want to save the file")

# construct the dataframe and pass details_list
data = pd.DataFrame(properties)
# assign the names of the columns

ahhammoud

# initialize an empty list to pass property details into
details_list = []

# define the name of the function and the variables going into it
def scrape_page(soup,details_list):
    
    cards = soup.find_all(class_="card__content")
    
    for card in cards:
        

ahhammoud

# import the necessary libraries
import requests
from bs4 import BeautifulSoup

# download the page that will be scraped
page = requests.get("address of your page")
# parse the webpage using BeautifulSoup
soup = BeautifulSoup(page.content, "html.parser")

# collect all the listing cards on the page

ahhammoud

# with the data ready we now add it to a dataframe and save it out for further analysis
data = pd.DataFrame(properties)
data.columns = ["description", "building", "price", "property type", "bedrooms", "bathrooms", "area"]
data.to_csv("BusinessBay_data.csv")

ahhammoud

# we then use the page scraper to scrape the pages that we will be digging data out of
details_list = []
pages = "the number of pages in your search"

for page_number in range(1,pages,1):
    url = "https://www.propertyfinder.ae/en/search?c=1&l=36&ob=pd&page="+str(page_number)+"&t=1"
    page = requests.get(url)
    soup = BeautifulSoup(page.content, "html.parser")
    scrape_page(soup, details_list)
    print("finished page: %i" %page_number)