quick-start.md

Quick Start

Basic Features and Syntax Example

1. Loading Data

   LOAD "data.csv" INTO dataset;
   

   • Explanation: Loads data from a CSV file named data.csv into a variable named dataset.

2. Summarizing Data

   SUMMARIZE dataset;
   

   • Explanation: Provides a summary (statistics) of the dataset stored in the dataset variable.

3. Filtering Data

   FILTER dataset WHERE age > 30 INTO filtered_data;
   

   • Explanation: Filters records in dataset where the age column is greater than 30 and stores the filtered result in filtered_data.

4. Grouping Data

   GROUP dataset BY category INTO grouped_data;
   

   • Explanation: Groups data in dataset by the category column and stores the grouped result in grouped_data.

5. Custom Functions

   DEFINE FUNCTION mean(list) {
       SUM = 0;
       COUNT = 0;
       FOR EACH value IN list {
           SUM = SUM + value;
           COUNT = COUNT + 1;
       }
       RETURN SUM / COUNT;
   }
   

   • Explanation: Defines a custom function mean to calculate the mean of a list of numbers.

6. Plotting Data

   PLOT histogram OF dataset BINS 10;
   

   • Explanation: Generates a histogram plot of the dataset variable with 10 bins.

Implementation Considerations

• Syntax: Keep the syntax simple and intuitive, resembling natural language where possible.
• Data Types: Support a variety of data types such as integers, floats, strings, dates, lists, and dictionaries.
• Operations: Provide built-in functions and operators for common data operations like arithmetic, comparisons, and string manipulations.
• Customization: Allow users to define custom functions and handle complex data manipulations.

Example Usage

LOAD "data.csv" INTO dataset;
SUMMARIZE dataset;
FILTER dataset WHERE age > 30 INTO older_people;
GROUP dataset BY category INTO categories;
DEFINE FUNCTION mean(list) {
    SUM = 0;
    COUNT = 0;
    FOR EACH value IN list {
        SUM = SUM + value;
        COUNT = COUNT + 1;
    }
    RETURN SUM / COUNT;
}
PLOT histogram OF dataset BINS 10;

Notes

• This example provides a basic framework for a data scripting language focused on ease of use and effectiveness.
• Depending on your specific requirements, you may need to expand upon these features, handle more complex data structures, and integrate with external libraries or APIs for advanced functionality.
• Designing such a language involves balancing simplicity with versatility, ensuring that users can perform powerful data manipulations without unnecessary complexity.