Randas

A dataframe library in rust

This is a dataframe library in rust, i am converting one by one all the functions which i have been using in python panadas to rust. Lack of creativity lead to the name randas, as in pandas in rust.

Description

At its core, the library features the DataFrame struct, which organizes data into rows and columns. It supports a wide range of data types, such as :

• Integer(i64),
• Float(f64),
• Boolean(bool),
• String(String),

Features

1. Dataframe functions: get, get_index, sum, mean, max, variance, std_dev, min, max, shape, loc, iloc etc.
2. read functions: read_csv, read_excel, read_json, read_sql
3. write functions: to_csv, to_excel, to_json, to_sql

Installation

Step 1: Install Rust on Windows

1. Download Rust Installer:

   • Visit the official Rust website at rust-lang.org and download the rustup-init.exe installer for Windows.

2. Run the Installer:

   • Open the downloaded rustup-init.exe file and follow the on-screen instructions to install Rust. Make sure to allow rustup to add Rust to your system PATH.

3. Verify the Installation:

   • Open a new command prompt or a terminal in vscode and run rustc --version to check if Rust has been installed correctly.

Step 2: Clone the Repository

1. Open the Git bash and execute: git clone
2. Navigate into the project directory: cd your-repo-name
3. Build the Project:
   • Compile the project with cargo build
4. Run the Project (Optional):
   • Execute the application (if applicable) with cargo run

Example Usage

To use randas after cloning the git repo, follow this example:

// main.rs
use randas::io::{read_excel, to_excel};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Path to your Excel file
    let input_file_path = "path_to_your_excel.xlsx";
    
    // Read the Excel file into a DataFrame
    let df = read_excel(input_file_path)?;

    // Display the DataFrame to the console
    println!("DataFrame read from Excel:");
    println!("{}", df);

    // Modify the DataFrame or use it as demonstrated
    // For example, add a new column or filter rows

    // Path for the output Excel file
    let output_file_path = "output_data.xlsx";

    // Write the DataFrame back to another Excel file
    to_excel(&df, output_file_path)?;

    Ok(())
}


to create a sample dataframe using hashmap and randas
/// ID	    Name          Score
/// 1	      Alice         3.5
/// 2	      Bob           4.0
/// 3	      Charlie       2.5
fn setup_test_dataframe() -> DataFrame {
    // Define the column names
    let columns = vec!["ID".to_string(), "Name".to_string(), "Score".to_string()];

    // Organize data by columns
    let mut data: HashMap<String, Vec<Option<DataFrameValue>>> = HashMap::new();
    data.insert(
        "ID".to_string(),
        vec![
            Some(DataFrameValue::Integer(1)),
            Some(DataFrameValue::Integer(2)),
            Some(DataFrameValue::Integer(3)),
        ],
    );
    data.insert(
        "Name".to_string(),
        vec![
            Some(DataFrameValue::String("Alice".to_string())),
            Some(DataFrameValue::String("Bob".to_string())),
            Some(DataFrameValue::String("Charlie".to_string())),
        ],
    );
    data.insert(
        "Score".to_string(),
        vec![
            Some(DataFrameValue::Float(3.5)),
            Some(DataFrameValue::Float(4.0)),
            Some(DataFrameValue::Float(2.5)),
        ],
    );

    DataFrame::new(data, columns).expect("Failed to create DataFrame")
  }