chatgpt.txt

You said:
write some python code that will read a website and convert a table from that web site into a CSV file
ChatGPT said:
ChatGPT

To scrape a table from a website and convert it to a CSV file in Python, you can use libraries like requests to fetch the webpage, and BeautifulSoup from bs4 to parse the HTML and extract the table data. The csv module is used to write the extracted data to a CSV file.

Here’s an example Python script that demonstrates this process:
Requirements:

    requests: To fetch the webpage content.
    beautifulsoup4: To parse the HTML and extract the table.
    pandas (optional but useful for easier data handling).

You can install these libraries with pip if you don't have them already:

pip install requests beautifulsoup4 pandas

Python Script:

import requests
from bs4 import BeautifulSoup
import csv

def scrape_table_to_csv(url, csv_filename):
    # Send an HTTP request to the website
    response = requests.get(url)
    
    if response.status_code != 200:
        print("Failed to retrieve the webpage")
        return
    
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.content, 'html.parser')
    
    # Find the first table in the webpage
    table = soup.find('table')
    
    if not table:
        print("No table found on the webpage")
        return
    
    # Extract table headers (if any)
    headers = []
    header_row = table.find('tr')
    if header_row:
        header_cells = header_row.find_all(['th', 'td'])
        headers = [cell.get_text(strip=True) for cell in header_cells]
    
    # Extract table rows
    rows = table.find_all('tr')[1:]  # Skip the header row
    
    table_data = []
    
    for row in rows:
        cells = row.find_all('td')
        row_data = [cell.get_text(strip=True) for cell in cells]
        table_data.append(row_data)
    
    # Write the data to a CSV file
    with open(csv_filename, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        
        # Write headers first (if any)
        if headers:
            writer.writerow(headers)
        
        # Write table rows
        writer.writerows(table_data)
    
    print(f"Data has been written to {csv_filename}")

# Example usage
url = 'https://example.com/your-webpage-with-table'
csv_filename = 'output.csv'
scrape_table_to_csv(url, csv_filename)

How the code works:

    requests.get(url): Sends an HTTP GET request to the website URL.
    BeautifulSoup(response.content, 'html.parser'): Parses the HTML content using BeautifulSoup.
    soup.find('table'): Finds the first <table> element in the HTML.
    Extract headers: If the table has headers (inside <th> elements), they are extracted.
    Extract rows: Iterates over each <tr> row in the table and extracts the cell data (from <td> elements).
    Write to CSV: The headers (if found) and the rows are written to a CSV file using Python's csv module.

Notes:

    You may need to adjust the table parsing if the structure of the table is more complex (e.g., if there are nested tables, spans, etc.).
    If the website is dynamically loaded (using JavaScript), you might need to use something like Selenium or Playwright to render the page before scraping.

Example: How to adjust for dynamic content

If the page requires JavaScript to load the table, requests won't be enough. You can use Selenium to render the page:

from selenium import webdriver
from selenium.webdriver.common.by import By
from bs4 import BeautifulSoup
import csv
import time

def scrape_dynamic_table_to_csv(url, csv_filename):
    # Initialize the WebDriver (make sure you have the correct driver installed)
    driver = webdriver.Chrome(executable_path='/path/to/chromedriver')  # You can also use Firefox, etc.
    
    driver.get(url)
    
    # Wait for the page to load (you may need to adjust this sleep time)
    time.sleep(3)
    
    # Parse the page source
    soup = BeautifulSoup(driver.page_source, 'html.parser')
    
    # Close the WebDriver
    driver.quit()
    
    # Follow the same logic as above to scrape and save the table to CSV
    table = soup.find('table')
    if not table:
        print("No table found on the webpage")
        return

    headers = []
    header_row = table.find('tr')
    if header_row:
        header_cells = header_row.find_all(['th', 'td'])
        headers = [cell.get_text(strip=True) for cell in header_cells]
    
    rows = table.find_all('tr')[1:]
    table_data = []
    
    for row in rows:
        cells = row.find_all('td')
        row_data = [cell.get_text(strip=True) for cell in cells]
        table_data.append(row_data)
    
    # Write the data to CSV
    with open(csv_filename, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        if headers:
            writer.writerow(headers)
        writer.writerows(table_data)
    
    print(f"Data has been written to {csv_filename}")

# Example usage
url = 'https://example.com/dynamic-table-page'
csv_filename = 'dynamic_output.csv'
scrape_dynamic_table_to_csv(url, csv_filename)

This version uses Selenium to load the page and then extracts the table once the page content is fully loaded.

Let me know if you'd like further clarifications or adjustments!
You said:
adjust this to have it read in a list of URLs from stdin and fetch each one. Name the output based on the filename of the html file in the url
ChatGPT said:
ChatGPT

To modify the code so that it can read a list of URLs from standard input (stdin), fetch the content from each URL, and name the output CSV files based on the HTML filename in the URL, you can adjust the script as follows:

    Use sys.stdin.read() to read multiple URLs.
    Parse the filename from the URL to create a CSV filename.
    Loop through the list of URLs, fetch the table, and save each result to a CSV file.

Modified Python Script:

import requests
from bs4 import BeautifulSoup
import csv
import sys
import os
from urllib.parse import urlparse

def scrape_table_to_csv(url):
    # Send an HTTP request to the website
    response = requests.get(url)
    
    if response.status_code != 200:
        print(f"Failed to retrieve the webpage: {url}")
        return
    
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.content, 'html.parser')
    
    # Find the first table in the webpage
    table = soup.find('table')
    
    if not table:
        print(f"No table found on the webpage: {url}")
        return
    
    # Extract table headers (if any)
    headers = []
    header_row = table.find('tr')
    if header_row:
        header_cells = header_row.find_all(['th', 'td'])
        headers = [cell.get_text(strip=True) for cell in header_cells]
    
    # Extract table rows
    rows = table.find_all('tr')[1:]  # Skip the header row
    
    table_data = []
    
    for row in rows:
        cells = row.find_all('td')
        row_data = [cell.get_text(strip=True) for cell in cells]
        table_data.append(row_data)
    
    # Generate a filename based on the URL's HTML file name
    parsed_url = urlparse(url)
    filename = os.path.basename(parsed_url.path)
    
    # If there's no file in the URL (e.g., just a directory), use 'output.csv'
    if not filename or filename == '/':
        filename = 'output.html'

    # Remove any query parameters or fragments from the filename
    filename = filename.split('?')[0].split('#')[0]

    # Change the file extension to CSV
    csv_filename = f"{filename}.csv"

    # Write the data to the CSV file
    with open(csv_filename, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        
        # Write headers first (if any)
        if headers:
            writer.writerow(headers)
        
        # Write table rows
        writer.writerows(table_data)
    
    print(f"Data has been written to {csv_filename} from {url}")

# Read URLs from standard input (stdin)
def read_urls_from_stdin():
    urls = sys.stdin.read().splitlines()
    return urls

# Main function to process each URL
def main():
    urls = read_urls_from_stdin()
    
    if not urls:
        print("No URLs provided.")
        return

    for url in urls:
        scrape_table_to_csv(url)

# Run the script
if __name__ == "__main__":
    main()

Explanation of Changes:

    Reading URLs from stdin:
        The read_urls_from_stdin() function reads all URLs from standard input (sys.stdin.read()), splitting them by newline (splitlines()).
        This allows you to pass multiple URLs to the script at once (e.g., via a file or pipe).

    Generating Output Filename:
        urlparse(url) is used to extract parts of the URL. The os.path.basename(parsed_url.path) method retrieves the last part of the path (i.e., the filename).
        The script handles cases where the URL might not have a filename in its path (e.g., https://example.com/), defaulting to output.html.
        The filename extension is changed to .csv (e.g., example.html becomes example.csv).

    Looping Through URLs:
        The script loops over the list of URLs and processes each URL by calling scrape_table_to_csv(url).

How to Run:

    With Input from a File: You can provide URLs through a file (e.g., urls.txt), and then run the script with:

python scrape_to_csv.py < urls.txt

With URLs Directly from stdin: Alternatively, you can pipe URLs directly to the script:

    echo -e "https://example.com/page1\nhttps://example.com/page2" | python scrape_to_csv.py

Example:

Given the following URL:

https://example.com/table-page1

The output will be saved as table-page1.csv.
Notes:

    Error Handling: If the table is not found on a given page, the script will skip it and print a message.
    File Naming: If the URL has query parameters (e.g., example.com/page?param=1), those are ignored when determining the filename.

This should allow you to handle a list of URLs efficiently and save each table as a corresponding CSV file.
You said:
also make this code remove from the data any text that is a number enclosed in parentheses
ChatGPT said:
ChatGPT

To modify the code so that it removes any text that is a number enclosed in parentheses from the table data, you can use regular expressions to identify and remove such text. We will use Python's re module for this task.
Steps to Modify:

    Use Regular Expressions: We'll define a regex pattern that matches numbers enclosed in parentheses (e.g., (123) or (4.56)).
    Apply this Regex: We'll apply this regex to each cell of the table before writing it to the CSV file.

Here’s how the code can be adjusted to meet your requirement:
Modified Python Script:

import requests
from bs4 import BeautifulSoup
import csv
import sys
import os
from urllib.parse import urlparse
import re

# Function to remove numbers enclosed in parentheses
def remove_numbers_in_parentheses(text):
    # Regular expression to match numbers in parentheses (including decimal numbers)
    return re.sub(r'\(\s*\d+(\.\d+)?\s*\)', '', text)

def scrape_table_to_csv(url):
    # Send an HTTP request to the website
    response = requests.get(url)
    
    if response.status_code != 200:
        print(f"Failed to retrieve the webpage: {url}")
        return
    
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.content, 'html.parser')
    
    # Find the first table in the webpage
    table = soup.find('table')
    
    if not table:
        print(f"No table found on the webpage: {url}")
        return
    
    # Extract table headers (if any)
    headers = []
    header_row = table.find('tr')
    if header_row:
        header_cells = header_row.find_all(['th', 'td'])
        headers = [remove_numbers_in_parentheses(cell.get_text(strip=True)) for cell in header_cells]
    
    # Extract table rows
    rows = table.find_all('tr')[1:]  # Skip the header row
    
    table_data = []
    
    for row in rows:
        cells = row.find_all('td')
        row_data = [remove_numbers_in_parentheses(cell.get_text(strip=True)) for cell in cells]
        table_data.append(row_data)
    
    # Generate a filename based on the URL's HTML file name
    parsed_url = urlparse(url)
    filename = os.path.basename(parsed_url.path)
    
    # If there's no file in the URL (e.g., just a directory), use 'output.csv'
    if not filename or filename == '/':
        filename = 'output.html'

    # Remove any query parameters or fragments from the filename
    filename = filename.split('?')[0].split('#')[0]

    # Change the file extension to CSV
    csv_filename = f"{filename}.csv"

    # Write the data to the CSV file
    with open(csv_filename, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        
        # Write headers first (if any)
        if headers:
            writer.writerow(headers)
        
        # Write table rows
        writer.writerows(table_data)
    
    print(f"Data has been written to {csv_filename} from {url}")

# Read URLs from standard input (stdin)
def read_urls_from_stdin():
    urls = sys.stdin.read().splitlines()
    return urls

# Main function to process each URL
def main():
    urls = read_urls_from_stdin()
    
    if not urls:
        print("No URLs provided.")
        return

    for url in urls:
        scrape_table_to_csv(url)

# Run the script
if __name__ == "__main__":
    main()

Explanation of Changes:

    remove_numbers_in_parentheses function:
        This function uses the regex r'\(\s*\d+(\.\d+)?\s*\)' to match numbers enclosed in parentheses.
            \( and \) match the literal parentheses.
            \d+ matches one or more digits.
            (\.\d+)? optionally matches a decimal point followed by one or more digits.
            \s* allows for optional whitespace inside the parentheses.
        It then uses re.sub() to remove any such matches from the text.

    Applying the function:
        The remove_numbers_in_parentheses function is applied to each cell's text in both the table headers and the rows, removing any numbers enclosed in parentheses.

Example:

Given the following table:
Name	Value
Item 1	$200 (5)
Item 2	$150 (10.5)
Item 3	$300

The output CSV will look like this:
Name	Value
Item 1	$200
Item 2	$150
Item 3	$300

The numbers in parentheses are removed from the "Value" column.
How to Run:

    With Input from a File: You can provide URLs through a file (e.g., urls.txt), and then run the script with:

python scrape_to_csv.py < urls.txt

With URLs Directly from stdin: Alternatively, you can pipe URLs directly to the script:

    echo -e "https://example.com/page1\nhttps://example.com/page2" | python scrape_to_csv.py

Notes:

    The function remove_numbers_in_parentheses ensures that no numbers in parentheses remain in the table data before saving to CSV.
    If there are no tables or the table structure is complex, the script will print a message and skip the page.

This approach will now properly clean the table data by removing numbers in parentheses, as required.
You said:
Also have it search the text of the web page for a date. Take that date and append it as the last field in every line of the csv file
ChatGPT said:
ChatGPT

To enhance the script so that it searches the webpage for a date and appends that date as the last field in every row of the CSV file, you can use regular expressions to find the date in the page content. Once the date is found, it can be added to each row of the table before writing it to the CSV.
Steps to Modify:

    Search for a Date: We'll use a regex pattern to find a date in the text. The date format can vary (e.g., MM/DD/YYYY, YYYY-MM-DD, DD MMM YYYY, etc.), so we'll create a general pattern that captures common date formats.
    Add the Date to Each Row: Once a date is found, append it as the last field in each row before writing to the CSV.

Example Date Regex:

    The regex r'\b(?:\d{1,2}[/-]\d{1,2}[/-]\d{2,4}|\d{4}[/-]\d{1,2}[/-]\d{1,2})\b' captures various date formats such as MM/DD/YYYY, YYYY-MM-DD, DD/MM/YYYY, and others.

Here’s the modified script:
Modified Python Script:

import requests
from bs4 import BeautifulSoup
import csv
import sys
import os
from urllib.parse import urlparse
import re

# Function to remove numbers enclosed in parentheses
def remove_numbers_in_parentheses(text):
    # Regular expression to match numbers in parentheses (including decimal numbers)
    return re.sub(r'\(\s*\d+(\.\d+)?\s*\)', '', text)

# Function to extract the first date found on the webpage
def extract_date_from_page(content):
    # Regular expression pattern for various date formats (e.g., MM/DD/YYYY, YYYY-MM-DD, etc.)
    date_pattern = r'\b(?:\d{1,2}[/-]\d{1,2}[/-]\d{2,4}|\d{4}[/-]\d{1,2}[/-]\d{1,2}|\d{1,2}\s+[A-Za-z]+\s+\d{4})\b'
    match = re.search(date_pattern, content)
    
    if match:
        return match.group(0)
    return None

def scrape_table_to_csv(url):
    # Send an HTTP request to the website
    response = requests.get(url)
    
    if response.status_code != 200:
        print(f"Failed to retrieve the webpage: {url}")
        return
    
    # Extract the page content (HTML) to search for a date
    page_content = response.text
    
    # Extract date from the page content
    page_date = extract_date_from_page(page_content)
    
    if page_date:
        print(f"Found date: {page_date}")
    else:
        print(f"No date found on the page: {url}")
        page_date = "Unknown Date"
    
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.content, 'html.parser')
    
    # Find the first table in the webpage
    table = soup.find('table')
    
    if not table:
        print(f"No table found on the webpage: {url}")
        return
    
    # Extract table headers (if any)
    headers = []
    header_row = table.find('tr')
    if header_row:
        header_cells = header_row.find_all(['th', 'td'])
        headers = [remove_numbers_in_parentheses(cell.get_text(strip=True)) for cell in header_cells]
    
    # Extract table rows
    rows = table.find_all('tr')[1:]  # Skip the header row
    
    table_data = []
    
    for row in rows:
        cells = row.find_all('td')
        row_data = [remove_numbers_in_parentheses(cell.get_text(strip=True)) for cell in cells]
        
        # Append the extracted date to the end of the row data
        row_data.append(page_date)
        
        table_data.append(row_data)
    
    # Generate a filename based on the URL's HTML file name
    parsed_url = urlparse(url)
    filename = os.path.basename(parsed_url.path)
    
    # If there's no file in the URL (e.g., just a directory), use 'output.csv'
    if not filename or filename == '/':
        filename = 'output.html'

    # Remove any query parameters or fragments from the filename
    filename = filename.split('?')[0].split('#')[0]

    # Change the file extension to CSV
    csv_filename = f"{filename}.csv"

    # Write the data to the CSV file
    with open(csv_filename, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        
        # Write headers first (if any)
        if headers:
            headers.append("Date")  # Add 'Date' as the last header
            writer.writerow(headers)
        
        # Write table rows
        writer.writerows(table_data)
    
    print(f"Data has been written to {csv_filename} from {url}")

# Read URLs from standard input (stdin)
def read_urls_from_stdin():
    urls = sys.stdin.read().splitlines()
    return urls

# Main function to process each URL
def main():
    urls = read_urls_from_stdin()
    
    if not urls:
        print("No URLs provided.")
        return

    for url in urls:
        scrape_table_to_csv(url)

# Run the script
if __name__ == "__main__":
    main()

Explanation of Changes:

    extract_date_from_page(content):
        This function uses a regular expression to search for a date in the page's text.
        The regex pattern handles several common date formats, including:
            MM/DD/YYYY
            YYYY-MM-DD
            DD MMM YYYY (e.g., 12 Mar 2024)
        If a date is found, it returns the date as a string; otherwise, it returns "Unknown Date".

    Appending the Date to Each Row:
        After extracting the date from the page, the script appends the date as the last field in each row of the table.
        The headers are also updated to include a column for the date, labeled "Date".

    Filename Generation:
        The filename is still based on the last segment of the URL path, and the file extension is changed to .csv.
        If no table is found or there is an error, the script prints a message and skips to the next URL.

How the Date Extraction Works:

    When the page content is fetched, the function extract_date_from_page searches for a date in the text. If a valid date is found, it is appended to every row in the table as the last field.
    If no date is found, the script appends "Unknown Date" to the rows.

Example:

Given the following table on the webpage:
Name	Value
Item 1	$200 (5)
Item 2	$150 (10.5)
Item 3	$300

And a date found on the page like 2024-11-05, the output CSV will look like this:
Name	Value	Date
Item 1	$200	2024-11-05
Item 2	$150	2024-11-05
Item 3	$300	2024-11-05
How to Run:

    With Input from a File: You can provide URLs through a file (e.g., urls.txt), and then run the script with:

python scrape_to_csv.py < urls.txt

With URLs Directly from stdin: Alternatively, you can pipe URLs directly to the script:

    echo -e "https://example.com/page1\nhttps://example.com/page2" | python scrape_to_csv.py

Notes:

    Date Format: The regex can be adjusted for more specific date formats depending on your needs.
    Multiple Dates: This script captures only the first date it finds. If you need to handle multiple dates or extract them in a more complex way, you can modify the logic accordingly.

This modification ensures that every row in your CSV file will have the corresponding date from the page appended as the last field.
You said:
repeat the last instruction, but when searching for a date, look for a date in the format of a 3 letter month abbreviation, followed by the date number, then a comma, then a 4 digit year. An example would be Apr 23, 2019.
ChatGPT said:
ChatGPT

To adjust the code to specifically search for dates in the format of a three-letter month abbreviation, followed by the day number, a comma, and then a four-digit year (e.g., Apr 23, 2019), we will need to modify the regular expression.
Updated Regular Expression:

The new regex will look for dates in the format of:

    A three-letter month abbreviation (e.g., Jan, Feb, Mar, Apr, etc.).
    A day number (1 or 2 digits).
    A comma, followed by a four-digit year.

Here’s how we can modify the script:
Modified Python Script:

import requests
from bs4 import BeautifulSoup
import csv
import sys
import os
from urllib.parse import urlparse
import re

# Function to remove numbers enclosed in parentheses
def remove_numbers_in_parentheses(text):
    # Regular expression to match numbers in parentheses (including decimal numbers)
    return re.sub(r'\(\s*\d+(\.\d+)?\s*\)', '', text)

# Function to extract the first date found on the webpage (in format: "Mon dd, yyyy")
def extract_date_from_page(content):
    # Regular expression pattern for a date like "Apr 23, 2019"
    date_pattern = r'\b([A-Za-z]{3})\s*(\d{1,2}),\s*(\d{4})\b'
    match = re.search(date_pattern, content)
    
    if match:
        # Return the date in the format "Mon dd, yyyy"
        return match.group(0)
    return None

def scrape_table_to_csv(url):
    # Send an HTTP request to the website
    response = requests.get(url)
    
    if response.status_code != 200:
        print(f"Failed to retrieve the webpage: {url}")
        return
    
    # Extract the page content (HTML) to search for a date
    page_content = response.text
    
    # Extract date from the page content
    page_date = extract_date_from_page(page_content)
    
    if page_date:
        print(f"Found date: {page_date}")
    else:
        print(f"No date found on the page: {url}")
        page_date = "Unknown Date"
    
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.content, 'html.parser')
    
    # Find the first table in the webpage
    table = soup.find('table')
    
    if not table:
        print(f"No table found on the webpage: {url}")
        return
    
    # Extract table headers (if any)
    headers = []
    header_row = table.find('tr')
    if header_row:
        header_cells = header_row.find_all(['th', 'td'])
        headers = [remove_numbers_in_parentheses(cell.get_text(strip=True)) for cell in header_cells]
    
    # Extract table rows
    rows = table.find_all('tr')[1:]  # Skip the header row
    
    table_data = []
    
    for row in rows:
        cells = row.find_all('td')
        row_data = [remove_numbers_in_parentheses(cell.get_text(strip=True)) for cell in cells]
        
        # Append the extracted date to the end of the row data
        row_data.append(page_date)
        
        table_data.append(row_data)
    
    # Generate a filename based on the URL's HTML file name
    parsed_url = urlparse(url)
    filename = os.path.basename(parsed_url.path)
    
    # If there's no file in the URL (e.g., just a directory), use 'output.csv'
    if not filename or filename == '/':
        filename = 'output.html'

    # Remove any query parameters or fragments from the filename
    filename = filename.split('?')[0].split('#')[0]

    # Change the file extension to CSV
    csv_filename = f"{filename}.csv"

    # Write the data to the CSV file
    with open(csv_filename, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        
        # Write headers first (if any)
        if headers:
            headers.append("Date")  # Add 'Date' as the last header
            writer.writerow(headers)
        
        # Write table rows
        writer.writerows(table_data)
    
    print(f"Data has been written to {csv_filename} from {url}")

# Read URLs from standard input (stdin)
def read_urls_from_stdin():
    urls = sys.stdin.read().splitlines()
    return urls

# Main function to process each URL
def main():
    urls = read_urls_from_stdin()
    
    if not urls:
        print("No URLs provided.")
        return

    for url in urls:
        scrape_table_to_csv(url)

# Run the script
if __name__ == "__main__":
    main()

Explanation of Changes:

    extract_date_from_page(content):
        The new regular expression r'\b([A-Za-z]{3})\s*(\d{1,2}),\s*(\d{4})\b' is designed to match dates in the format:
            A three-letter month abbreviation ([A-Za-z]{3}).
            A day number (\d{1,2}).
            A comma and a four-digit year (\d{4}).
        Example match: Apr 23, 2019.

    How it Works:
        The extract_date_from_page function scans the page content for the first occurrence of a date matching this format.
        If found, it returns the date (e.g., Apr 23, 2019); otherwise, it returns "Unknown Date".

    Adding the Date to Each Row:
        After extracting the date from the page, it is appended as the last field to each row of the table data.
        The headers are also updated to include a "Date" column.

Example:

Given the following table on the webpage:
Name	Value
Item 1	$200 (5)
Item 2	$150 (10.5)
Item 3	$300

And a date found on the page like Apr 23, 2019, the output CSV will look like this:
Name	Value	Date
Item 1	$200	Apr 23, 2019
Item 2	$150	Apr 23, 2019
Item 3	$300	Apr 23, 2019
How to Run:

    With Input from a File: You can provide URLs through a file (e.g., urls.txt), and then run the script with:

python scrape_to_csv.py < urls.txt

With URLs Directly from stdin: Alternatively, you can pipe URLs directly to the script:

    echo -e "https://example.com/page1\nhttps://example.com/page2" | python scrape_to_csv.py

Notes:

    Date Formats: This script is designed specifically for dates in the format Mon dd, yyyy (e.g., Apr 23, 2019). If you need to handle other formats, you can adjust the regular expression accordingly.
    Error Handling: If no table or date is found, the script will print a message and continue processing the next URL.

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