Tabular_2_Text

Sometimes tabular data is just easier to read as text!

Tabular_2_Text is a Python package that converts tabular data (CSV files or pandas DataFrames) into a formatted text file. Each row from your table is converted into a clearly formatted text entry with labeled fields.

Features

• Convert CSV files directly to formatted text
• Convert pandas DataFrames to formatted text
• Clean, readable output format
• Automatic field labeling
• Simple, user-friendly interface

Installation

pip install tabular-2-text

Usage

Basic Usage

from tabular_2_text import tab2text

# Using a CSV file as input
tab2text("input.csv", "output.txt")

# Using a pandas DataFrame as input
import pandas as pd
df = pd.DataFrame({
    'name': ['John Doe', 'Jane Smith'],
    'age': [30, 25],
    'city': ['New York', 'Los Angeles']
})
tab2text(df, "output.txt")

Example

Input CSV (employees.csv):

name,age,department,salary
John Doe,30,Engineering,75000
Jane Smith,28,Marketing,65000
Bob Johnson,35,Sales,70000

Python code:

from converter import tab2text

# Convert the CSV to text
tab2text("employees.csv", "employees_formatted.txt")

Output (employees_formatted.txt):

name: John Doe
age: 30
department: Engineering
salary: 75000

***********************************************

name: Jane Smith
age: 28
department: Marketing
salary: 65000

***********************************************

name: Bob Johnson
age: 35
department: Sales
salary: 70000

Function Parameters

tab2text(input_data, output_file_path)

• input_data: Either a file path to a CSV file (string) or a pandas DataFrame
• output_file_path: The path where you want to save the output text file (string)

Returns

• Returns the path to the generated text file (string)
• Prints a confirmation message with the file location

Requirements

• Python 3.6 or higher
• pandas

Error Handling

The function will raise a ValueError if:

• The input is neither a string (file path) nor a pandas DataFrame
• The CSV file path is invalid
• The DataFrame is empty

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Common Use Cases

1. Converting spreadsheet data to readable text format
2. Preparing tabular data for document integration
3. Creating human-readable exports from databases
4. Formatting data for report generation
5. Converting CSV exports to readable text

Tips

• Make sure your CSV files are properly formatted
• For large datasets, consider processing in chunks
• Check that your output filepath is writable
• Keep column names simple and readable for best results