Understanding the Issue with Extracting Industry/Sector from Yahoo Finance Package

The question you see before you is related to extracting industry and sector information from stocks listed on the Yahoo finance package. The user in this case is trying to extract these fields from a list of stocks, but they are encountering an error.

Background Information

Yahoo finance provides APIs that allow users to access financial data for various companies. One such API is yfinance, which uses Yahoo finance data. This data includes fields like industry and sector for each stock.

However, not all stocks have this information available in their API responses. The user here has encountered an issue when they tried to extract the “industry” field from a certain stock.

Code Explanation

The code snippet provided is a simple Python script that uses yfinance to fetch data about multiple stocks and then stores it in a pandas DataFrame:

import yfinance as yf
import pandas as pd

# Define the list of stocks
stocks = ['ADSK', 'DDD', 'DM', 'FARO', 'MTLS', 'SSYS', 'XONE', 'AAPL', 'NXTG', 'QCOM']

df = pd.DataFrame()
for stock in stocks:
    info = yf.Ticker(stock).info
    industry = info['industry']
    beta = info['beta']
    sector = info['sector']
    df = df.append({'Stock':stock,'Industry':industry,'Beta':beta,'Sector':sector}, ignore_index=True)

df

This code is straightforward. It loops through each stock in the list, fetches its information from yfinance, and then appends this information to a DataFrame.

However, there’s an error introduced by NXTG stock which doesn’t have “industry”, nor “beta” fields available in its API response.

Problem Analysis

To understand why the code is failing, let’s break it down. When we iterate over each stock in the list, we fetch its information from yfinance using yf.Ticker(stock).info.

The error occurs when NXTG’s info doesn’t contain a “industry” field. This causes an exception, since we’re trying to access an attribute of None.

Solution Explanation

To solve this problem, you can use Python’s built-in dictionary method .get(), which allows us to specify a default value if the specified key does not exist in the dictionary:

import yfinance as yf
import pandas as pd

# Define the list of stocks
stocks = ['ADSK', 'DDD', 'DM', 'FARO', 'MTLS', 'SSYS', 'XONE', 'AAPL', 'NXTG', 'QCOM']

df = pd.DataFrame()
for stock in stocks:
  info = yf.Ticker(stock).info
  industry = info.get('industry')
  beta = info.get('beta')
  sector = info.get('sector')
  df = df._append({'Stock':stock,'Industry':industry,'Beta':beta,'Sector':sector}, ignore_index=True)

df

In this modified code, if the “industry”, “beta”, or “sector” keys do not exist in info, .get() will return None instead of raising an exception.

Practical Applications

While working with APIs and data extraction can be complex, understanding how to handle missing values is crucial for effective data analysis. In a practical scenario, you might encounter situations where some stocks have available information while others don’t. Being able to handle such inconsistencies is vital for accurate insights.

Additionally, the use of .get() helps avoid exceptions, making your code more robust and easier to maintain. By using this approach, you can write cleaner, more reliable code that’s less prone to errors.

Best Practices

When working with APIs or data extraction in general, it’s good practice to include error handling mechanisms. This ensures that your code doesn’t crash unexpectedly due to missing values or other inconsistencies in the data. In our modified example above, we’ve incorporated .get() for this very purpose.

Last modified on 2024-08-04