csv to html Converter. csv to datatables Converter.

• Save your time. This simple conversion algorithm will convert csv to html!
• The next set of algorthims will convert it for datatables viewing!

import pandas as pd
import re

# Read the CSV file without headers and assign column names
df = pd.read_csv('posts.csv', header=None, names=['title', 'slug'])

# Function to extract number and file format from the title
def extract_info(title):
    # Match patterns like "Task XXX: ...", "Task XXX - ...", "Test XXX: ..."
    match = re.match(r'(Task|Test)\s+(\d+)[ :\-.]+(.*)', title.strip())
    if match:
        return match.group(2), match.group(3).strip()
    else:
        return '', title.strip()

# Apply the extraction function to create new columns
df[['number', 'file_format']] = df['title'].apply(lambda x: pd.Series(extract_info(x)))

# Create the HTML link column
df['html_link'] = df.apply(lambda row: f'<a href="{row["slug"]}">{row["file_format"]}</a>', axis=1)

# Select the relevant columns for the table
table_df = df[['number', 'file_format', 'html_link']]

# Generate the base HTML table
html_table = table_df.to_html(escape=False, index=False, header=['Number', 'File Format', 'HTML Link'])

# Add style to center the first column
# We replace the default <th> tags with styled ones
styled_html = html_table.replace(
    '<thead>\n<tr>',
    '<thead>\n<tr style="text-align: center;">'
)

# Optional: also center the number cells in the body (for consistency)
# This will center all <td> in the first column
styled_html = styled_html.replace(
    '<td>',
    '<td style="text-align: center;">',
    1  # Only replace the first occurrence (header) — then we handle body separately
)

# Better approach: add a class or inline style only to the first column
# We'll insert style for the first <td> in each row
# A simple way is to use CSS (recommended) or inline styles

# Alternative: wrap the table in a div with CSS
final_html = f"""
<style>
    table {{ border-collapse: collapse; width: 100%; }}
    th, td {{ padding: 8px; border: 1px solid #ddd; }}
    th:nth-child(1), td:nth-child(1) {{ text-align: center; }}
</style>
{final_html}
"""

print(final_html)

If you need more powerful types of data viewing: Consider datatables.

csv to datatables converter

• The entire thing was extremely simple. Wow.

import pandas as pd

def csv_to_datatables_html(csv_file_path, table_id='example', classes='display'):
    """
    Converts a CSV file to an HTML snippet suitable for insertion into a webpage,
    formatted for use with DataTables jQuery plugin. Assumes the webpage includes
    necessary DataTables libraries (CSS and JS).

    Parameters:
    - csv_file_path (str): Path to the input CSV file.
    - table_id (str): ID for the HTML table element (default: 'example').
    - classes (str): CSS classes for the table (default: 'display' for DataTables).

    Returns:
    - str: HTML string containing the table and DataTables initialization script.
    """
    # Read the CSV file into a DataFrame
    df = pd.read_csv(csv_file_path)
    
    # Generate the HTML table from the DataFrame
    html_table = df.to_html(table_id=table_id, classes=classes, index=False, escape=False)
    
    # DataTables initialization script
    init_script = f"""
    <script>
        document.addEventListener('DOMContentLoaded', function() {{
            new DataTable('#{table_id}');
        }});
    </script>
    """
    
    # Combine and return the HTML
    return html_table + init_script

As a class format it will come out as:

import pandas as pd

class CSVToDataTablesHTML:
    """
    A class to convert a CSV file to an HTML snippet formatted for use with the DataTables jQuery plugin.
    It generates the HTML table and initialization script, and provides a method to save the output to a file.
    Assumes the webpage includes necessary DataTables libraries (CSS and JS).

    Attributes:
    - csv_file_path (str): Path to the input CSV file.
    - table_id (str): ID for the HTML table element (default: 'example').
    - classes (str): CSS classes for the table (default: 'display' for DataTables).
    """

    def __init__(self, csv_file_path, table_id='example', classes='display'):
        self.csv_file_path = csv_file_path
        self.table_id = table_id
        self.classes = classes

    def generate_html(self):
        """
        Generates the HTML string containing the table and DataTables initialization script.

        Returns:
        - str: The generated HTML string.
        """
        # Read the CSV file into a DataFrame
        df = pd.read_csv(self.csv_file_path)
        
        # Generate the HTML table from the DataFrame
        html_table = df.to_html(table_id=self.table_id, classes=self.classes, index=False, escape=False)
        
        # DataTables initialization script
        init_script = f"""
        <script>
            document.addEventListener('DOMContentLoaded', function() {{
                new DataTable('#{self.table_id}');
            }});
        </script>
        """
        
        # Combine and return the HTML
        return html_table + init_script

    def save_to_file(self, output_path):
        """
        Generates the HTML and saves it to the specified file path.

        Parameters:
        - output_path (str): The path where the HTML file will be saved.
        """
        html = self.generate_html()
        with open(output_path, 'w', encoding='utf-8') as file:
            file.write(html)