Extracting table Content from html python

I am new to Python.I want to scrape the iso code with the state list of the country from the wiki website. Here's the Link

Required Output:

mapState={'Alabama': 'US-AL', 'Alaska': 'US-AK',.....,'Wyoming':'US-WY}'

Here's the Code i tried:

import requests
from bs4 import BeautifulSoup
def crawl_wiki():
    url = 'https://en.wikipedia.org/wiki/ISO_3166-2:US'
    source_code = requests.get(url)
    plain_text = source_code.text
    print(plain_text)

crawl_wiki()

I have got the text from the site. But i don't know how to get the dict of state with code. Help me with some solutions.

import pandas as pd

df = pd.read_html(
    "https://en.wikipedia.org/wiki/ISO_3166-2:US")[0]
result = df['Subdivision name (en)'], df['Code']
d = pd.DataFrame(result)
d = d.T
newd = d.set_index('Subdivision name (en)', 'Code').to_dict()
print(newd['Code'])

Output:

{'Alabama': 'US-AL', 'Alaska': 'US-AK', 'Arizona': 'US-AZ', 'Arkansas': 'US-AR', 'California': 'US-CA', 'Colorado': 'US-CO', 'Connecticut': 'US-CT', 'Delaware': 'US-DE', 'Florida': 'US-FL', 'Georgia': 'US-GA', 'Hawaii': 'US-HI', 'Idaho': 'US-ID', 'Illinois': 'US-IL', 'Indiana': 'US-IN', 'Iowa': 'US-IA', 'Kansas': 'US-KS', 'Kentucky': 'US-KY', 'Louisiana': 'US-LA', 'Maine': 'US-ME', 'Maryland': 'US-MD', 'Massachusetts': 'US-MA', 'Michigan': 'US-MI', 'Minnesota': 'US-MN', 'Mississippi': 'US-MS', 'Missouri': 'US-MO', 'Montana': 'US-MT', 'Nebraska': 'US-NE', 'Nevada': 'US-NV', 'New Hampshire': 'US-NH', 'New Jersey': 'US-NJ', 'New Mexico': 'US-NM', 'New York': 'US-NY', 'North Carolina': 'US-NC', 'North Dakota': 'US-ND', 'Ohio': 'US-OH', 'Oklahoma': 'US-OK', 'Oregon': 'US-OR', 'Pennsylvania': 'US-PA', 'Rhode Island': 'US-RI', 'South Carolina': 'US-SC', 'South Dakota': 'US-SD', 'Tennessee': 'US-TN', 'Texas': 'US-TX', 'Utah': 'US-UT', 'Vermont': 'US-VT', 'Virginia': 'US-VA', 'Washington': 'US-WA', 'West Virginia': 'US-WV', 'Wisconsin': 'US-WI', 'Wyoming': 'US-WY', 'District of Columbia': 'US-DC', 'American Samoa': 'US-AS', 'Guam': 'US-GU', 'Northern Mariana Islands': 'US-MP', 'Puerto Rico': 'US-PR', 'United States Minor Outlying Islands': 'US-UM', 'Virgin Islands, U.S.': 'US-VI'}

Web Scraping HTML Tables with Python, Finally, we will store the data on a Pandas Dataframe. import requests import lxml .html as lh import pandas as pd. Scrape Table Cells. The code� Files for html-table-extractor, version 1.4.1; Filename, size File type Python version Upload date Hashes; Filename, size html_table_extractor-1.4.1-py2.py3-none-any.whl (4.8 kB) File type Wheel Python version py2.py3 Upload date May 1, 2020

try this:

import bs4
import requests

response = requests.get('https://en.wikipedia.org/wiki/ISO_3166-2:US')
html = response.content.decode('utf-8')

soup = bs4.BeautifulSoup(html, "lxml")
code_list = soup.select("#mw-content-text > div > table:nth-child(11) > tbody > tr > td:nth-child(1) > span")
name_list = soup.select("#mw-content-text > div > table:nth-child(11) > tbody > tr > td:nth-child(2) > a")


mapState = {}
## mapState={'Alabama': 'US-AL', 'Alaska': 'US-AK',.....,'Wyoming':'US-WY}'

for i in range(len(code_list)):
    mapState[code_list[i].string] = name_list[i].string


print(mapState)

Extracting data from HTML table, A Python solution using BeautifulSoup4 (Edit: with proper skipping. Edit3: Using class="details" to select the table ): from bs4 import� #Since out first row is the header, data is stored on the second row onwards for j in range(1,len(tr_elements)): #T is our j'th row T=tr_elements[j] #If row is not of size 10, the //tr data is not from our table if len(T)!=10: break #i is the index of our column i=0 #Iterate through each element of the row for t in T.iterchildren(): data=t.text_content() #Check if row is empty if i>0: #Convert any numerical value to integers try: data=int(data) except: pass #Append the data to the empty list

This is a SimplifiedDoc scheme, similar to BeautifulSoup

import requests
from simplified_scrapy.simplified_doc import SimplifiedDoc 
url = 'https://en.wikipedia.org/wiki/ISO_3166-2:US'
response = requests.get(url)
doc = SimplifiedDoc(response.text,start='Subdivision category',end='</table>')
datas = [tr.tds for tr in doc.trs]
mapState = {}
for tds in datas:
  mapState[tds[1].a.text]=tds[0].text

html-table-extractor � PyPI, A python library for extracting data from html table. HTML Table Extractor is a python library that uses Beautiful Soup to extract data from complicated and� We are now going to pass this variable along with the flag ‘html.parser’ to Beautifulsoup to extract html elements as shown below: from bs4 import BeautifulSoup soup = BeautifulSoup(read_content,'html.parser') From this point on wards, our “soup” Python variable holds all the HTML elements of the webpage.

try pandas read_html -

https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.read_html.html

then extract pandas df to dict

example -

import pandas as pd

df = pd.read_html("https://en.wikipedia.org/wiki/ISO_3166-2:US")[0].to_dict()
print(df)

Parsing HTML Tables in Python with BeautifulSoup and pandas , To parse the table, we'd like to grab a row, take the data from its columns, and then move on to the next row ad nauseam. In the next bit of code, we define a website that is simply the HTML for a table. We load it into BeautifulSoup and parse it, returning a pandas data frame of the contents. I wrote selectolax half a year ago when I was looking for a fast HTML parser in Python. Basically, it is a Cython wrapper to the Modest engine. The engine itself is a very powerful and fast HTML5 parser written in pure C by lexborisov. Selectolax is not limited to only one use case and supports CSS selectors as well as other HTML traversing

Web Scraping html table from Wiki | by Priya Raja, Using Beautiful Soup to extract a climate data from wikipedia. function to extract a Python list of table found by selecting only the text within� Different Ways to Extract Data from Web Page. The following methods are mostly used for extracting data from a web page − Regular Expression. They are highly specialized programming language embedded in Python. We can use it through re module of Python. It is also called RE or regexes or regex patterns.

How to Convert HTML Tables into CSV Files in Python, Extracting HTML tables using requests and beautiful soup and then saving it as the HTML content of `url` passed""" # initialize a session session = requests. Yes, it is possible to extract data from Web and this "jibber-jabber" is called Web Scraping. According to Wikipedia, Web Scraping is: Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites BeautifulSoup is one popular library provided by Python to scrape data from the web.

Python BeautifulSoup Get HTML Table, Learn how to Parse HTML Table data using Python BeautifulSoup Library. Comments are Duration: 4:35 Posted: May 25, 2016 imagine this could not only recognise tables, but also repetitive elements in lists. that would be awesome. 1 Quick Tip: The easiest way to grab data out of a web page in Python