This article revolves around scrapping weather prediction d data using python and bs4 library. Let’s checkout components used in the script –
BeautifulSoup– It is a powerful Python library for pulling out data from HTML/XML files. It creates a parse tree for parsed pages that can be used to extract data from HTML/XML files.
Requests – It is a Python HTTP library. It makes HTTP requests simpler. we just need to add the URL as an argument and the get() gets all the information from it.
Step 1 – Run the following command to get the stored content from the URL into the response object(file):
import requests # to get data from website |
Step 2 – Parse HTML content:
# import Beautifulsoup for scraping the data from bs4 import BeautifulSoup soup = BeautifulSoup(file.content, "html.parser") |
Step 3 – Scraping the data from weather site run the following code:
# 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) |
find_all: It is used to pick up all the HTML elements of tag passed in as an argument and its descendants.
find:It will search for the elements of the tag passed.
list.append(dict): This will append all the data to the list of type list.
Step 4 – Convert the list file into CSV file to view the organized weather forecast data.
Use the following code to convert the list into CSV file and store it into output.csv file:
import pandas as pd convert = pd.DataFrame(list) convert.to_csv("output.csv") |
.
Syntax: pandas.DataFrame(data=None, index: Optional[Collection] = None, columns: Optional[Collection] = None, dtype: Union[str, numpy.dtype, ExtensionDtype, None] = None, copy: bool = False)
Parameters:
data: Dict can contain Series, arrays, constants, or list-like objects.
index : It is used for resulting frame. Will default to RangeIndex if no indexing information part of input data and no index provided.
columns: column labels to use for resulting frame. Will default to RangeIndex (0, 1, 2, …, n) if no column labels are provided.
dtype: It is used to set the Default value.
copy: It copy the data from input. default value is false.
# read csv file using pandas a = pd.read_csv("output.csv") print(a) |
Output :
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