Web Scraping for Stock Prices in Python

Web scraping is a data extraction method that collects data only from websites. It is often used for data mining and gathering valuable insights from large websites. Web scraping is also useful for personal use. Python includes a nice library called BeautifulSoup that enables web scraping. In this article, we will extract current stock prices using web scraping and save them in an excel file using Python.

Required Modules

In this article, we’ll look at how to work with the Requests, Beautiful Soup, and Pandas Python packages to consume data from websites. 

• The Requests module allows you to integrate your Python programs with web services.
• The Beautiful Soup module is designed to make screen scraping a snap. Using Python’s interactive console and these two libraries, we’ll walk through how to assemble a web page and work with the textual information available on it.
• The Pandas module is designed to provide high-performance data manipulation in Python. It is used for data analysis that requires lots of processing, such as restructuring, cleaning or merging, etc.

Approach

• Initially, we are going to import our required libraries.
• Then we take the URL stored in our list.
• We will feed the URL to our soup object which will then extract relevant information from the given URL based on the class id we provide it.
• Store all the data in the Pandas Dataframe and save it to a CSV file.

Step 1: Import Libraries

We import the modules for Pandas, Requests, and Beautiful soup. Add a user agent and a header declaration. This makes sure that the target website for web scraping won’t automatically classify the traffic as spam and end up being blacklisted. Many user agents are available at https://developers.whatismybrowser.com/

Step 2: Collect URLs to Scrap

We’ll assign the URL of the required stock web pages, www.investing.com in the list of URLs:

urls = [
    'https://www.investing.com/equities/nike',
    'https://www.investing.com/equities/coca-cola-co',
    'https://www.investing.com/equities/microsoft-corp',
    'https://www.investing.com/equities/3m-co',
    'https://www.investing.com/equities/american-express',
    'https://www.investing.com/equities/amgen-inc',
    'https://www.investing.com/equities/apple-computer-inc',
    'https://www.investing.com/equities/boeing-co',
    'https://www.investing.com/equities/cisco-sys-inc',
    'https://www.investing.com/equities/goldman-sachs-group',
    'https://www.investing.com/equities/ibm',
    'https://www.investing.com/equities/intel-corp',
    'https://www.investing.com/equities/jp-morgan-chase',
    'https://www.investing.com/equities/mcdonalds',
    'https://www.investing.com/equities/salesforce-com',
    'https://www.investing.com/equities/verizon-communications',
    'https://www.investing.com/equities/visa-inc',
    'https://www.investing.com/equities/wal-mart-stores',
    'https://www.investing.com/equities/disney',
    ]

Step 3: Retrieving Element Ids

We identify the element by looking at the rendered web page, but it’s impossible for a script to determine that. To find the target element, get its element ID and enter it into the script. Getting the ID of an item is pretty easy. Let’s say you want the item id for the stock name. All we have to do is go to the URL and see the text in the console. Get the text next to the class=

 

Let’s iterate through the list of stocks we need and use soup.find() to find the tag with the specified id and print the company, current stock price, change in percentage of stocks, and volume of stocks.

company = soup.find(‘h1’, {‘class’: ‘text-2xl font-semibold instrument-header_title__gCaMF mobile:mb-2’}).text

price = soup.find(‘div’, {‘class’: ‘instrument-price_instrument-price__xfgbB flex items-end flex-wrap font-bold’}).find_all(‘span’)[0].text

change = soup.find(‘div’, {‘class’: ‘instrument-price_instrument-price__xfgbB flex items-end flex-wrap font-bold’}).find_all(‘span’)[2].text

volume=soup.find(‘div’,{‘class’: ‘trading-hours_value__5_NnB’}).text

As we can see the price and change has the same Class Id. So let’s fund all the span tags and use the find_all(‘span’)[tag number] and extract the text.

 

Step 4:  Try Data Extraction

Basically, during the extraction of data from a web page, we can expect AttributeError (When we try to access the Tag using BeautifulSoup from a website and that tag is not present on that website then BeautifulSoup always gives an AttributeError). To handle this error let’s use Try and except the concept. Also, you can use the code in Google collab as it has all the updated versions.

How does try() work? 

• First, the try clause is executed i.e. the code between the try and except clause.
• If there is no exception, then only the try clause will run, except the clause is finished.
• If any exception occurs, the try clause will be skipped and except clause will run.
• If any exception occurs, but the except clause within the code doesn’t handle it, it is passed on to the outer try statements. If the exception is left unhandled, then the execution stops.
• A try statement can have more than one except clause.

When the try block is executed we are going to extract data from the individual stock and store the data in the variables 

• Company(name of the stock)
• Price (current price)
• Change(change of stock value +ve increase or -ve decrease)
• Volume(stock volume)

We will use a list and store the company name, price of a stock, change in stock, and volume of each stock and store them in a list that consists of the stock data of all individual stocks.

Step 5: Storing Data to Data Frame 

Let’s declare the column names and using pandas create a Dataframe with columns: Company, Price, Change, and Volume.

Syntax:

Column_names = [list of column names]
dataframe = pd.DataFrame(columns = column_names)

Next, we will iterate through the list and fill each data frame’s each row with the details of each company by using built-in functions loc( ).  The loc() function is label based data selecting method which means that we have to pass the name of the row or column which we want to select. The df.loc[index] = i, assigning the data to that row after that we will update the index in the Data Frame. The reset_index() is used to reset the index of the Data Frame from 0.

 

Step 6: Save it to Excel

To save the data as a CSV file we can use the built-in Function to_excel.

Complete code

Here is the entire code:

Output: