Language Name: DataLang

High-Level Description

DataLang is a language designed specifically for data-oriented tasks and optimized for performance and ease of use in data science applications. It combines the best features of Python, R, and SQL, along with unique features designed to streamline data science workflows.

Key Differentiating Features

Native data structures: DataLang will have built-in data structures like data frames and tensors, which are commonly used in data science tasks. These structures will have integrated functionality for handling missing values, schema enforcement, and data type coercion.

Lazy evaluation: To improve performance, DataLang will use lazy evaluation for data manipulation tasks, where operations are only executed when necessary. This can save memory and computation time, especially when working with large datasets.

Querying capabilities: Inspired by SQL, DataLang will have built-in querying capabilities that allow users to filter, sort, join, and aggregate data without needing an external library.

Integrated visualization: DataLang will have a built-in visualization library that supports various chart types and is easily customizable. The visualization capabilities will integrate seamlessly with the native data structures.

Functional and parallel programming: DataLang will encourage functional programming practices and support parallel processing for better performance on large-scale data tasks.

Extensive libraries: DataLang will have extensive standard libraries focused on statistical analysis, machine learning, deep learning, natural language processing, and time-series analysis.

Interoperability: DataLang will have built-in support for interoperability with other popular languages like Python and R, allowing users to leverage existing libraries and codebases.

Possible Issues

Adoption: As a new language, DataLang may face challenges in attracting users and building a strong community around it.

Compatibility: Ensuring compatibility with existing tools, libraries, and platforms will be crucial for the success of DataLang.

Performance optimization: Balancing ease of use and performance optimization will be an ongoing challenge.

The decision for the functional paradigm: Functional programming is chosen because it promotes immutability, statelessness, and the use of pure functions, which are advantageous in data science workflows. These properties make it easier to reason about the code, test, and parallelize operations, which are essential when working with large datasets or computationally intensive tasks like machine learning.

Machine learning and statistical modeling libraries in DataLang would follow a modular and consistent API, with separate modules for different tasks like linear regression, classification, clustering, and deep learning. The design would be inspired by popular libraries like scikit-learn, TensorFlow, and PyTorch, but with optimizations and simplifications for data science workflows.

DataLang would support the following data structures natively:

DataFrame: A tabular data structure with labeled columns, similar to a spreadsheet or SQL table.
Series: A one-dimensional array-like data structure, akin to a single column of a DataFrame.
Tensor: A multidimensional array-like data structure, used for more complex data manipulation, especially in deep learning.
Graph: A data structure to represent relationships between entities in the form of nodes and edges.
TimeSeries: A specialized DataFrame for handling time-stamped data.
DataLang would allow the creation of new data structures by providing an extensible and customizable class system. Users can define their data structures by subclassing existing ones or creating entirely new classes, utilizing built-in data structures and methods for their specific use cases.

The language might run a risk of trying to be too many things for too many people, but this risk can be mitigated by focusing on providing essential and widely-used functionality in the standard libraries while maintaining a modular design. This would allow users to choose and utilize only the modules they need, minimizing the overhead and complexity.

# Mutable variable declaration
let mutable_var = 5

# Immutable variable declaration
const immutable_var = 10

# Attempting to change the value of an immutable variable
immutable_var = 20  # This would raise a compilation error or runtime exception

Yes, allowing type hinting or type setting in DataLang would be beneficial. Type hinting helps improve code readability and maintainability by making the variable types explicit. It also allows for better error checking, either at compile-time or during runtime, reducing the likelihood of type-related bugs.

In DataLang, we can use a similar syntax to TypeScript or Python's type hinting with a colon and the type.

By supporting type hinting in DataLang, we can help developers write more robust and self-documenting code, improving the overall development experience for data science tasks.

Certainly, we can replace the def __init__ syntax with a more explicit keyword like constructor to define the constructor for a class. This will make the code more readable and easier to understand.

With the constructor keyword, the code becomes more explicit and easier to understand, enhancing the developer experience when working with custom classes and data structures in DataLang.

You are correct that having a uniform approach for assigning variables would be beneficial. To maintain consistency with the let and const keywords, we can declare instance variables using these keywords inside the constructor. For updating object variables, we can use setter methods.

# Import the built-in DataFrame
import datalang.datastructures as dds

# Create the custom data structure
class CustomDataFrame(dds.DataFrame):
    constructor(self, data: dict):
        super().constructor(data)

    def custom_summary(self) -> str:
        let num_columns: int = len(self.columns)
        let num_rows: int = len(self)
        return f"CustomDataFrame with {num_columns} columns and {num_rows} rows."

# Instantiate the custom data structure
let data_dict: dict = {"A": [1, 2, 3], "B": [4, 5, 6]}
let custom_df: CustomDataFrame = CustomDataFrame(data_dict)

# Use the custom method
print(custom_df.custom_summary())

import datalang.io as dio

class DataIngestor:
    # Supported file types
    const SUPPORTED_FILE_TYPES: list[str] = ["csv", "json"]

    def read_data(self, file_path: str) -> dds.DataFrame:
        # Extract file extension
        file_ext = file_path.split(".")[-1].lower()

        if file_ext not in self.SUPPORTED_FILE_TYPES:
            raise ValueError(f"Unsupported file type: {file_ext}")

        if file_ext == "csv":
            return dio.read_csv(file_path)
        elif file_ext == "json":
            return dio.read_json(file_path)

# Instantiate the DataIngestor class
data_ingestor = DataIngestor()

# Read CSV and JSON files
csv_data = data_ingestor.read_data("data.csv")
json_data = data_ingestor.read_data("data.json")

import datalang.io
import datalang.data
import datalang.ml
import datalang.viz

# Read the dataset
let dataset: DataFrame = io.read_csv("dataset.csv")

# Split the dataset into train and test sets
let (train_data, test_data): (DataFrame, DataFrame) = data.train_test_split(dataset, ratio=0.8)

# Train a Naive Bayes model
let model: NaiveBayes = ml.NaiveBayes()
model.train(train_data)

# Test the model on the test data
let predictions: Array = model.predict(test_data)

# Calculate accuracy
let accuracy: Float = ml.accuracy(test_data.labels, predictions)
print("Accuracy: ", accuracy)

# Plot results
viz.plot_confusion_matrix(test_data.labels, predictions)

Designing a logo for DataLang requires a combination of creativity and symbolism that represents the essence of the programming language. Here's a suggestion for a logo concept:

Base: Utilize a stylized "D" and "L" for DataLang, which could be combined or overlapped in a visually appealing and unique way.

Data representation: Integrate a graphical element that represents data, such as a bar chart, line chart, or scatter plot. This can be subtle, but it should be recognizable.

Code aspect: Include a code-like element in the design, such as curly braces or angle brackets, to emphasize the programming language aspect of DataLang.

Color scheme: Use a combination of colors that are associated with technology and innovation, such as shades of blue and green, or a more sophisticated color palette with greyscale and a pop of color for emphasis.

Keep in mind that this is just a suggestion, and a professional designer or design team can further develop the idea and create a final, polished logo that captures the spirit and essence of DataLang.

Install the SLY library: First, make sure you have the SLY library installed. You can install it using pip:

pip install sly

Define the lexer: Create a Python file named datalang_lexer.py and import the Lexer class from the sly module. Define a class named DataLangLexer that inherits from sly.Lexer. Inside this class, define your lexer rules as class attributes using regular expressions. For each token, also define a method that will be called when the lexer matches the token in the input.

Define the parser: Create another Python file named datalang_parser.py and import the Parser class from the sly module. Define a class named DataLangParser that inherits from sly.Parser. Inside this class, define your parser rules as methods. These methods will have names derived from your non-terminal symbols and will contain the appropriate production rules.

Define the abstract syntax tree (AST): Create another Python file named datalang_ast.py. In this file, define classes for each AST node type, such as expressions, statements, declarations, etc. These classes will be used to build the AST when parsing the DataLang code.

Modify the parser to build the AST: Update the parser methods in datalang_parser.py to build and return AST nodes using the classes you defined in datalang_ast.py.

Create an interpreter or code generator: Depending on your goal (interpreting the code or generating code for another language), create a Python file named datalang_interpreter.py or datalang_codegen.py. Implement a visitor pattern or another suitable pattern to walk the AST and either interpret the code or generate target code.

Create a driver program: Create a Python file named datalang_compiler.py that ties everything together. In this file, instantiate the lexer and parser classes and use them to lex and parse the DataLang source code. Then, use the interpreter or code generator to process the AST.

Run the compiler: Finally, run the datalang_compiler.py script with a DataLang source file as input, and observe the output or the generated code.

These are the high-level steps to build a compiler using the Python SLY library. Each step requires a deep understanding of DataLang's syntax and semantics, as well as how to leverage the SLY library effectively.