Troubleshooting Data Import and Analysis with Python, pandas, BeautifulSoup, and requests

Table of Contents

1. Introduction
2. Background and Context
3. Troubleshooting Common Issues
4. Code Review and Suggestions
5. [Example Use Case: Importing Data from a CSV File, Scraping Fundamental Metrics from Finviz.com, and Exporting to a CSV File]
6. Conclusion

Introduction

In today’s fast-paced data-driven world, extracting insights from large datasets is crucial for making informed decisions. One such dataset often involves financial information, which can be obtained from various sources like the stock market or financial websites. In this article, we’ll walk through a step-by-step guide on how to troubleshoot issues with importing data from a CSV file, scraping fundamental metrics from Finviz.com, and exporting the results to another CSV file using Python, pandas, BeautifulSoup, and requests.

Background and Context

Before diving into the code review, it’s essential to understand the basics of each library involved:

• pandas: The pandas library provides data structures and functions designed for efficient storage, manipulation, and analysis of large datasets. It is built on top of the Python NumPy library and offers various tools for data cleaning, filtering, grouping, and transforming.
• BeautifulSoup: BeautifulSoup is a Python library used to parse HTML and XML documents. It creates a parsed representation of the document which can be used to extract specific information from it.
• requests: The requests library allows you to send HTTP requests in Python. It’s an easy-to-use interface for sending various types of requests, such as GETs, POSTs, PUTs, and more.

Troubleshooting Common Issues

When working with these libraries, several common issues may arise:

• Error Handling: In the provided code, error handling is minimal. To troubleshoot issues, it’s crucial to implement proper error handling mechanisms.
• Data Cleaning: Data cleaning is a critical step in data analysis. However, in the given example, there isn’t any data cleaning process.

Code Review and Suggestions

The provided code has some potential improvements:

1. Data Extraction Using BeautifulSoup

def extract_data(soup):
    metric = [
        # 'Inst Own',
        # 'Insider Own',
        'Price',
        'Shs Outstand',
        'Shs Float',
        'Short Float',
        'Short Ratio',
        'Book/sh',
        'Cash/sh',
        'Rel Volume',
        'Earnings',
        'Avg Volume',
        'Volume'
    ]

    data = {}

    for m in metric:
        try:
            value = soup.find(text=m).find_next(class_='snapshot-td2').text
            if not data.get(m, None):
                data[m] = []
            data[m].append(value)
        except AttributeError or Exception as e:
            print(f"Error extracting {m}: {str(e)}")

    return data

2. Data Cleaning

import pandas as pd

def clean_data(data, columns):
    # Drop rows with missing values
    for column in columns:
        if data.get(column, None) is not None and len([x for x in data[column] if isinstance(x, str)]) == 0:
            data[column] = [x.replace(" ", "") for x in data[column]]

    return data

3. Error Handling

try:
    url = 'http://finviz.com/quote.ashx?t=' + symbol.lower()
except Exception as e:
    print(f"Error creating URL: {e}")

Example Use Case: Importing Data from a CSV File, Scraping Fundamental Metrics from Finviz.com, and Exporting to a CSV File

1. Define the CSV file path and symbol list

# Define the CSV file path and symbol list
csv_file_path = 'shortlist.csv'
symbol_list = []

try:
    with open(csv_file_path) as csvDataFile:
        csvReader = csv.reader(csvDataFile)
        for row in csvReader:
            symbol_list.append(row[0])
except FileNotFoundError:
    print(f"The file {csv_file_path} was not found.")

2. Initialize an empty DataFrame

# Initialize an empty DataFrame
df = pd.DataFrame(index=symbol_list, columns=metric)

3. Loop through each symbol and extract fundamental metrics using BeautifulSoup and requests

for symbol in symbol_list:
    try:
        url = 'http://finviz.com/quote.ashx?t=' + symbol.lower()
        
        # Send GET request to the URL
        response = requests.get(url)
        
        if response.status_code == 200:
            soup = BeautifulSoup(response.content, features='html5lib')
            
            data = extract_data(soup)
            
            # Clean and append extracted metrics to the DataFrame
            cleaned_data = clean_data(data, metric)
            
            for column in metric:
                df.loc[symbol, column] = [cleaned_data.get(column, None)]
        else:
            print(f"Failed to retrieve fundamental metrics for {symbol}.")
    except Exception as e:
        print(f"Error processing {symbol}: {e}")

4. Export the DataFrame to a CSV file

# Export the DataFrame to a CSV file
df.to_csv('finviz_' + time.strftime('%Y-%m-%d') + '.csv', index=True)

Conclusion

In this article, we’ve covered how to troubleshoot common issues when working with data from a CSV file using Python, pandas, BeautifulSoup, and requests. By implementing proper error handling mechanisms, extracting fundamental metrics correctly, and cleaning the extracted data, you can efficiently analyze financial information from Finviz.com.

Last modified on 2024-02-17