County_state_fips_data_2023

This repository contains a dataset providing information on the Federal Information Processing Standards (FIPS) codes for U.S. counties and states as of 2023. The dataset is structured to facilitate easy access to the mapping of U.S. counties to their respective state FIPS codes, as well as the state-level FIPS codes themselves.

Table of Contents

Introduction

The Federal Information Processing Standards (FIPS) are used to uniquely identify geographic regions in the U.S. This dataset includes the latest FIPS codes for all U.S. counties, county equivalents, and states. The data is valuable for anyone working with geographic data, including software developers, geographers, or analysts who need to use FIPS codes in their applications.

Data Structure

The data is organized in a CSV format with the following fields:

  • State FIPS Code: The unique 2-digit code for each U.S. state.
  • County FIPS Code: The unique 5-digit code for each county within a state.
  • County Name: The name of the county.
  • State Name: The name of the state.

Example:

State FIPS Code County FIPS Code County Name State Name
06 001 Alameda California
12 003 Broward Florida
36 061 New York County New York

Usage

  1. Clone the repository to your local machine:

    git clone https://github.com/yourusername/County_state_fips_data_2023.git
  2. Navigate to the directory containing the dataset:

    cd County_state_fips_data_2023
  3. The dataset is in a CSV format, and you can load it into your application or perform analysis using tools like Python (Pandas), Excel, or any CSV reader.

    Example usage in Python:

    import pandas as pd
    
    # Load the CSV dataset
    data = pd.read_csv('county_state_fips_data_2023.csv')
    
    # Display the first 5 rows of the dataset
    print(data.head())

Installation

No installation is required, as the dataset is available in CSV format. Simply clone or download the repository and use the dataset according to your needs.

License

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

Contributing

Contributions are welcome! If you would like to improve the dataset or add new features, feel free to fork the repository and submit a pull request. Please follow the standard contribution guidelines for best practices.


Let me know if you need more specific details in the README!