Web Scraping with python
Founder of makcorps.com, scrapingdog.com & flightapi.io
In this post, we are going to scrape Yahoo Finance using python. This is a great source for stock-market data. We will code a scraper for that. Using that scraper you would be able to scrape stock data of any company from yahoo finance. As you know I like to make things pretty simple, for that, I will also be using a web scraper
which will increase your scraping efficiency.
Why this tool?
This tool will help us to scrape dynamic websites using millions of rotating proxies so that we don’t get blocked. It also provides a captcha clearing facility. It uses headerless chrome to scrape dynamic websites.
Generally, web scraping is divided into two parts:
Fetching data by making an HTTP requestExtracting important data by parsing the HTML DOM
Libraries & Tools
Our setup is pretty simple. Just create a folder and install Beautiful Soup & requests. For creating a folder and installing libraries type below given commands. I am assuming that you have already installed Python 3.x.
pip install beautifulsoup4
pip install requests
Now, create a file inside that folder by any name you like. I am using scraping.py.
Firstly, you have to sign up for the scrapingdog API
. It will provide you with 1000 FREE credits. Then just import Beautiful Soup & requests in your file. like this.
from bs4 import BeautifulSoup
What we are going to scrape
Here is the list of fields we will be extracting:
1. Previous Close
5. Day’s Range
6. 52 Week Range
8. Avg. Volume
9. Market Cap
11. PE Ratio
13. Earnings Rate
14. Forward Dividend & Yield
15. Ex-Dividend & Date
16. 1y target EST
Preparing the Food
Now, since we have all the ingredients to prepare the scraper, we should make a GET request to the target URL
to get the raw HTML data. If you are not familiar with the scraping tool, I would urge you to go through its documentation
. Now we will scrape Yahoo Finance for financial data using requests library as shown below.
r = requests.get("https://api.scrapingdog.com/scrape?api_key=<your-api-key>&url=https://finance.yahoo.com/quote/AMZN?p=AMZN&.tsrc=fin-srch").text
this will provide you with an HTML code of that target URL.
Now, you have to use BeautifulSoup to parse HTML.
soup = BeautifulSoup(r,’html.parser’)
Now, on the entire page, we have four “tbody” tags. We are interested in the first two because we currently don’t need the data available inside the third & fourth “tbody” tags.
First, we will find out all those “tbody” tags using variable “soup”.
alldata = soup.find_all(“tbody”)
As you can notice that the first two “tbody” has 8 “tr” tags and every “tr” tag has two “td” tags.
table1 = alldata.find_all(“tr”)
table2 = alldata.find_all(“tr”)
table2 = None
Now, each “tr” tag has two “td” tags. The first td tag consists of the name of the property and the other one has the value of that property. It’s something like a key-value pair.
At this point, we are going to declare a list and a dictionary before starting a for loop.
For making the code simple I will be running two different “for” loops for each table. First for “table1”
for i in range(0,len(table1)):
table1_td = table1[i].find_all(“td”)
table1_td = None
l[table1_td.text] = table1_td.text
Now, what we have done is we are storing all the td tags in a variable “table1_td”. And then we are storing the value of the first & second td tag in a “dictionary”. Then we are pushing the dictionary into a list. Since we don’t want to store duplicate data we are going to make dictionary empty at the end. Similar steps will be followed for “table2”.
for i in range(0,len(table2)):
table2_td = table2[i].find_all(“td”)
table2_td = None
l[table2_td.text] = table2_td.text
Then at the end when you print the list “u” you get a JSON response.
“Yahoo finance”: [
“Previous Close”: “2,317.80”
“Bid”: “0.00 x 1800”
“Ask”: “2,369.96 x 1100”
“Day’s Range”: “2,320.00–2,357.38”
“52 Week Range”: “1,626.03–2,475.00”
“Avg. Volume”: “6,180,864”
“Market Cap”: “1.173T”
“Beta (5Y Monthly)”: “1.35”
“PE Ratio (TTM)”: “112.31”
“EPS (TTM)”: “20.94”
“Earnings Date”: “Jul 23, 2020 — Jul 27, 2020”
“Forward Dividend & Yield”: “N/A (N/A)”
“Ex-Dividend Date”: “N/A”
“1y Target Est”: “2,645.67”
Isn’t that amazing. We managed to scrape Yahoo finance in just 5 minutes of setup. We have an array of python Object containing the financial data of the company Amazon. In this way, we can scrape the data from any website.
In this article, we understood how we can scrape data using data scraping tool
regardless of the type of website.
Feel free to comment and ask me anything. You can follow me on Twitter
. Thanks for reading and please hit the like button! 👍
And there’s the list! At this point, you should feel comfortable writing your first web scraper to gather data from any website. Here are a few additional resources that you may find helpful during your web scraping journey:
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