csv-field-extractor 1.0.1

A simple utility to extract specific fields from CSV files

CSV Field Extractor

A simple Python utility to extract specific fields/columns from CSV files.

Features

• Extract any field by name from a CSV file
• Optional alphabetical sorting of results
• Automatic filtering of empty/null values
• Field validation with helpful error messages
• Clean, stripped values (removes whitespace)

Requirements

• Python 3.x
• pandas library

Install pandas if you don't have it:

pip install pandas

Usage

Command Line Interface (Recommended for local use)

# Basic usage - extract and print field values line by line
python csv_field_extractor.py Results.csv Symbol

# Sort alphabetically
python csv_field_extractor.py Results.csv Symbol --sort

# Different output formats
python csv_field_extractor.py Results.csv Symbol --sort --output comma
python csv_field_extractor.py Results.csv Description --output space

# Get help
python csv_field_extractor.py --help

As a Python Library

from csv_field_extractor import extract_field_from_csv

# Extract symbols from a CSV file
symbols = extract_field_from_csv('Results.csv', 'Symbol')
print(symbols)

Library Usage Examples

# Extract and sort alphabetically
symbols = extract_field_from_csv('Results.csv', 'Symbol', sort_alphabetically=True)
for symbol in symbols:
    print(symbol)

# Extract descriptions
descriptions = extract_field_from_csv('Results.csv', 'Description')

# Extract expense ratios
expense_ratios = extract_field_from_csv('Results.csv', 'Net Expense Ratio')

# Extract any field
morningstar_ratings = extract_field_from_csv('Results.csv', 'Morningstar Overall')

Command Line Options

• csv_file: Path to your CSV file (required)
• field_name: Name of the column/field to extract (required)
• --sort, -s: Sort results alphabetically
• --output, -o: Output format - lines (default), comma, or space

Examples

# Extract symbols, sorted, comma-separated (perfect for copy-paste)
python csv_field_extractor.py Results.csv Symbol --sort --output comma

# Extract descriptions as separate lines
python csv_field_extractor.py Results.csv Description

# Quick field validation (see available fields if you mistype)
python csv_field_extractor.py Results.csv WrongFieldName

Function Parameters

• csv_file_path (str): Path to your CSV file
• field_name (str): Name of the column/field to extract
• sort_alphabetically (bool, optional): Sort results alphabetically (default: False)

Return Value

Returns a list of strings containing the field values, with empty/null values filtered out.

Error Handling

If the specified field name doesn't exist in the CSV, the function will raise a ValueError with a list of available field names to help you identify the correct field name.

Example Output

symbols = extract_field_from_csv('Results.csv', 'Symbol', sort_alphabetically=True)
# Returns: ['ARKF', 'ARKG', 'ARKK', 'ARKQ', 'ARKW', 'ARKX', 'IZRL', 'PRNT']

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/yourusername/csv-field-extractor.git
cd csv-field-extractor

# Create virtual environment and install dependencies
make setup

# Or manually:
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

Running Tests

# Run tests using make
make test

# Or run directly
python -m unittest test_csv_field_extractor.py -v

# Run specific test class
python -m unittest test_csv_field_extractor.TestCSVFieldExtractor -v

Installing as Package

# Install in development mode (changes reflected immediately)
pip install -e .

# Now you can use the command anywhere
csv-extract /path/to/data.csv Symbol --sort

Other Make Commands

make setup    # Set up virtual environment
make test     # Run tests
make clean    # Remove build artifacts and cache files
make lint     # Check code style (requires: pip install flake8)
make build    # Build distribution packages
make help     # Show all available commands

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature-name)
3. Make your changes and add tests
4. Run tests (make test)
5. Commit your changes (git commit -am 'Add feature')
6. Push to the branch (git push origin feature-name)
7. Create a Pull Request