Nitin Kumar
Nitin Kumar

Reputation: 69

How to scrape hidden table content using BeautifulSoup in python?

I am trying to scrape data from a stock website, but the problem is that the contents of the table are hidden. The website is http://www.moneycontrol.com/stocks/histstock.php

1.Select Index
2.Select S&P BSE MIDCAP
3.Filter data from Jan 2019 to Jan 2020 to get to the final page 
4.I want to scrape the table contents of this page

This is what I have tried using soup

import requests
from bs4 import BeautifulSoup
link='http://www.moneycontrol.com/stocks/hist_index_result.php?indian_indices=25'
html=requests.get(link)
html.status_code #200
raw=html.content
soup=BeautifulSoup(raw,'html.parser') #have tried with xml and html5lib
soup.find_all('table',{'class':'tblchart'})
#output
[<table border="0" cellpadding="0" cellspacing="0" class="tblchart">
                    </table>]

I have tried using selenium as well but the result is the same.

I am having difficulty trying to get the information.

Any suggestions, answers or a nudge in the right direction will be appreciated.

Upvotes: 1

Views: 811

Answers (2)

Andrej Kesely
Andrej Kesely

Reputation: 195418

A solution just with BeautifulSoup. The data is loaded dynamically via Ajax, but you can simulate the requests just with requests module:

import requests
from bs4 import BeautifulSoup


data = {
    'mth_frm_mth':'01',
    'mth_frm_yr':'2019',
    'mth_to_mth':'01',
    'mth_to_yr':'2020',
    'hdn':'monthly'
}

url = 'https://www.moneycontrol.com/stocks/hist_index_result.php?indian_indices=26'
soup = BeautifulSoup(requests.post(url, data=data).content, 'html.parser')

all_data = []
for tr in soup.select('.tblchart tr:has(td)'):
    tds = [td.get_text(strip=True) for td in tr.select('td')]
    all_data.append(tds)

# print on screen
print('{:<15}{:<15}{:<15}{:<15}{:<15}'.format('Date', 'Open', 'High', 'Low', 'Close'))
for row in all_data:
    print('{:<15}{:<15}{:<15}{:<15}{:<15}'.format(*row))

Prints:

Date           Open           High           Low            Close          
Jan 2020       13720.24       14946.21       13686.28       14667.96       
Dec 2019       13584.07       13716.74       13103.54       13699.37       
Nov 2019       13598.71       13729.32       13310.46       13560.57       
Oct 2019       13190.78       13583.13       12669.63       13558.05       
Sep 2019       12536.96       13648.30       12321.25       13170.76       
Aug 2019       12698.94       12755.07       11950.86       12534.70       
July 2019      14275.76       14375.47       12492.30       12692.18       
June 2019      14882.18       15022.09       13803.07       14239.33       
May 2019       14653.64       15039.53       13693.41       14867.04       
Apr 2019       15069.13       15229.85       14585.92       14624.56       
Mar 2019       13719.93       15034.53       13719.80       15027.36       
Feb 2019       13961.93       14064.51       13099.46       13689.84       
Jan 2019       14724.03       14790.99       13652.03       13926.22       

Upvotes: 1

Nitin Kumar
Nitin Kumar

Reputation: 69

Okay guys I actually solved it using selenium, I had to update my selenium package and it worked like a charm.

Here is how I did it:

  import pandas as pd
  from selenium import webdriver

  link='http://www.moneycontrol.com/stocks/histstock.php'

  driver=webdriver.Chrome()
  driver.get(link)

  #selecting the index in Step 1
  driver.find_element_by_xpath('//*[@id="wutabs2"]').click()

  #Selecting from the dropdown Index options in step 2
  drop=driver.find_element_by_xpath('//*[@id="indian_indices"]')
  drop.click()
  drop.send_keys('S&P BSE MIDCAP')      

  #select the month in step 3

  month=driver.find_element_by_xpath('/html/body/div[3]/div[3]/div/div[7]/div[2]/div[6]/table/tbody/tr/td[3]/form/div[2]/select[2]')
  month.click()
  month.send_keys('2019')

  #click on search 
  driver.find_element_by_xpath('/html/body/div[3]/div[3]/div/div[7]/div[2]/div[6]/table/tbody/tr/td[3]/form/div[4]/input[1]').click()

  #getting the contents
  for i in driver.find_elements_by_css_selector('table.tblchart'):
       a=i.text

  a=a.split('\n')

  #storing it as a data frame
  df=pd.DataFrame(a)

  #removing the first column as it contained table headers
  df.drop(df.iloc[0:1,:],inplace=True)

  #splitting the columns using space and storing them seperately
  df['Month']=df[0].str.split(' ', expand=True)[0]
  df['Year']=df[0].str.split(' ', expand=True)[1]
  df['Open']=df[0].str.split(' ', expand=True)[2]
  df['High']=df[0].str.split(' ', expand=True)[3]
  df['Low']=df[0].str.split(' ', expand=True)[4]
  df['Close']=df[0].str.split(' ', expand=True)[5]

Upvotes: 0

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