naturaldsl 0.3.0

A human-first natural language programming language for data science

NaturalDSL: Documentation

Overview

NaturalDSL is a human-first, natural programming language designed for data science. It abstracts away the complexity of traditional programming languages while providing powerful tools for data manipulation, visualization, and analysis. With seamless integration of Pandas, Numpy, Seaborn, Matplotlib, and Dask, NaturalDSL offers intuitive commands for handling common data analysis tasks.

Core Features

• Human-Readable Syntax: Use natural language constructs for common data science tasks.
• Data Manipulation: Load, clean, transform, and aggregate data.
• Data Visualization: Simple, intuitive plotting commands using Seaborn and Matplotlib.
• Parallel Computing: Handle large datasets with Dask.
• Extensibility: Easily extendable through plugins.
• AI Integration: Code autocompletion for frequent data science tasks.

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Getting Started

System Requirements

• Python 3.7 or higher
• Pip for managing packages

Installation

To install NaturalDSL, use the following steps:

1. Clone the repository (if you haven't done so yet):

git clone https://github.com/pradumana/naturaldsl.git
cd naturaldsl

2. Install the required dependencies:

pip install -r requirements.txt

3. (Optional) Install globally from PyPI:

pip install naturaldsl

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CLI Usage

Basic Command Syntax

[COMMAND] [OPTIONS]

Here’s a summary of the most common commands for data analysis using NaturalDSL:

1. Loading Data

Load your data from CSV or Parquet files:

load data.csv

This command loads the data into NaturalDSL for further processing.

2. Data Cleaning

• Drop rows with missing values in a specific column:

drop_missing age

• Fill missing values in a specific column with a constant value:

fill_missing age 0

3. Data Transformation

• Rename a column:

rename old_name new_name

• Sort data by a column:

sort age descending

• Convert a column to a specific data type (e.g., datetime):

convert date_column datetime

4. Grouping and Aggregation

Group data by a specific column and apply an aggregation (e.g., sum, average):

group_by age sum

5. Data Visualization

• Generate a bar plot between two columns:

plot_bar age salary

• Generate a scatter plot between two columns:

plot_scatter age salary

6. Save Data

Save the processed data to a new file:

save cleaned_data.csv

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Example CLI Session

Here’s an example session showing common data analysis steps:

load data.csv
drop_missing age
rename old_column new_column
sort age descending
group_by age mean
plot_scatter age salary
save cleaned_data.csv

This series of commands loads a dataset, cleans it, performs some transformations, generates a plot, and saves the cleaned data to a new file.

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Advanced Features

Multiple Command Chaining

You can chain multiple commands in a single line to improve your workflow:

load data.csv && drop_missing age && plot_scatter age salary

Plot Customization

Customize the appearance of your plots, such as colors, labels, and titles, directly from the CLI. You could, for example, modify the color of a scatter plot:

plot_scatter age salary --color blue

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Python API Usage

You can also use NaturalDSL in Python scripts for more control and flexibility.

Basic API Workflow

from natural_dsl_interpreter import NaturalDSLInterpreter

# Initialize the interpreter
interpreter = NaturalDSLInterpreter()

# Load data
interpreter.load("data.csv")

# Clean data
interpreter.drop_missing("age")
interpreter.fill_missing("age", 0)

# Transform data
interpreter.rename("old_column", "new_column")
interpreter.sort("age", ascending=False)

# Group and aggregate
interpreter.group_by("age", "sum")

# Visualize data
interpreter.plot_bar("age", "salary")

# Save data
interpreter.save("cleaned_data.csv")

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Extending NaturalDSL

Creating Custom Functions

You can extend NaturalDSL by adding custom functions for specific use cases. Here’s an example:

1. Define a custom function:

def custom_mean(dataframe, column):
    return dataframe[column].mean()

2. Register the custom function with the interpreter:

interpreter.register_function("custom_mean", custom_mean)

Using Plugins

You can extend NaturalDSL by creating plugins for additional features like new plotting methods, data sources, or algorithms. Once a plugin is created, you can load it using the CLI or Python API.

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Contributing

We encourage contributions from the community! To contribute:

1. Fork the repository on GitHub.
2. Clone your fork and create a new branch:

git checkout -b feature-branch

3. Make your changes, commit, and push them:

git commit -m "Added new feature"
git push origin feature-branch

4. Open a pull request to merge your changes into the main repository.

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License

NaturalDSL is licensed under the MIT License. See the LICENSE file for more details.

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Contact

For more information or questions, please contact me at:

Email: [e-mail]