Metadata-Version: 2.1
Name: dsbundle
Version: 1.0
Summary: Streamline your data science setup with dsbundle in one effortless install.
Home-page: https://github.com/shubhamjangra009
Author: Shubham Jangra
Author-email: shubhamjangra.ds@gmail.com
License: MIT
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: License File.ylf
Requires-Dist: ipython
Requires-Dist: pep8
Requires-Dist: gitpython
Requires-Dist: jupyter
Requires-Dist: jupyterlab
Requires-Dist: notebook
Requires-Dist: nbconvert
Requires-Dist: ipykernel
Requires-Dist: ipywidgets
Requires-Dist: markdown
Requires-Dist: pipreqs
Requires-Dist: pyparsing
Requires-Dist: pandoc
Requires-Dist: regex
Requires-Dist: latex
Requires-Dist: pylatex
Requires-Dist: tabulate
Requires-Dist: prettytable
Requires-Dist: great-tables
Requires-Dist: pendulum
Requires-Dist: urllib3
Requires-Dist: beautifulsoup4
Requires-Dist: scrapy
Requires-Dist: mysql-connector-python
Requires-Dist: pymysql
Requires-Dist: sqlalchemy
Requires-Dist: psycopg2
Requires-Dist: openpyxl
Requires-Dist: pylightxl
Requires-Dist: xlsxwriter
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: polars
Requires-Dist: xarray
Requires-Dist: pyarrow
Requires-Dist: h5py
Requires-Dist: pymc
Requires-Dist: matplotlib
Requires-Dist: palettable
Requires-Dist: seaborn
Requires-Dist: plotly
Requires-Dist: bokeh
Requires-Dist: ipyvizzu
Requires-Dist: pygal
Requires-Dist: altair
Requires-Dist: plotnine[all]
Requires-Dist: lifelines
Requires-Dist: surpyval
Requires-Dist: statsmodels
Requires-Dist: sympy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: scikit-optimize
Requires-Dist: scikit-image
Requires-Dist: sklearn-crfsuite
Requires-Dist: imbalanced-learn
Requires-Dist: mlxtend
Requires-Dist: xgboost
Requires-Dist: lightgbm
Requires-Dist: catboost
Requires-Dist: hyperopt
Requires-Dist: optuna
Requires-Dist: pycaret
Requires-Dist: textblob
Requires-Dist: nltk
Requires-Dist: spacy
Requires-Dist: vadersentiment
Requires-Dist: sktime
Requires-Dist: prophet
Requires-Dist: darts
Requires-Dist: librosa
Requires-Dist: audioflux
Requires-Dist: dask
Requires-Dist: pyspark
Requires-Dist: llama-index
Requires-Dist: langchain
Requires-Dist: langsmith
Requires-Dist: langgraph
Requires-Dist: ollama
Requires-Dist: haystack-ai
Requires-Dist: pinecone-client
Requires-Dist: pillow
Requires-Dist: opencv-contrib-python
Requires-Dist: simplecv
Requires-Dist: mahotas
Requires-Dist: tensorflow
Requires-Dist: jax
Requires-Dist: keras
Requires-Dist: keras-nlp
Requires-Dist: keras-cv
Requires-Dist: torch
Requires-Dist: torchvision
Requires-Dist: torchaudio
Requires-Dist: lightning
Requires-Dist: dm-sonnet
Requires-Dist: eli5
Requires-Dist: fastai
Requires-Dist: huggingface_hub
Requires-Dist: tokenizers
Requires-Dist: accelerate
Requires-Dist: evaluate
Requires-Dist: transformers[torch]
Requires-Dist: diffusers[torch]
Requires-Dist: mlflow
Requires-Dist: geopandas
Requires-Dist: cartopy
Requires-Dist: folium
Requires-Dist: quandl
Requires-Dist: quantlib
Requires-Dist: pyfolio-reloaded
Requires-Dist: dash
Requires-Dist: streamlit
Requires-Dist: taipy
Requires-Dist: gradio
Requires-Dist: fastapi
Requires-Dist: ydata-profiling
Requires-Dist: ruff
Requires-Dist: autoflake
Requires-Dist: pytest
Requires-Dist: mkdocs
Requires-Dist: pyforest
Requires-Dist: numpy-stl
Requires-Dist: medpy
Requires-Dist: pydicom
Requires-Dist: simpleitk
Requires-Dist: nilearn
Requires-Dist: nibabel
Requires-Dist: vtk
Requires-Dist: monai
Requires-Dist: dicom2nifti

# dsbundle

Enhance your Python data science workflow with `dsbundle`, an all-in-one package that consolidates essential libraries and tools to empower users in data manipulation, visualization, statistical analysis, machine learning, and beyond. This comprehensive bundle simplifies the setup process, ensuring that crucial dependencies are readily available for immediate use.

## Comprehensive Library Coverage

### Data Manipulation and Analysis

- **numpy**: Efficient numerical computing with powerful array operations and linear algebra capabilities.
- **pandas**: Data structures and tools for data manipulation and analysis, ideal for handling structured data.
- **polars**: A fast DataFrame library in Rust, focusing on performance and ease of use for data manipulation tasks.
- **xarray**: N-D labeled arrays and datasets, extending pandas to support multidimensional data.
- **pyarrow**: Columnar data format for efficient storage and processing of large datasets.
- **h5py**: Interface to HDF5, a versatile file format and data model for scientific computing.
- **openpyxl**: Read/write Excel files in Python, useful for integrating with spreadsheet data.

### Visualization and Plotting

- **matplotlib**: Comprehensive 2D plotting library for creating static, animated, and interactive visualizations.
- **seaborn**: Statistical data visualization based on matplotlib, providing a high-level interface for drawing informative statistical graphics.
- **plotly**: Interactive plotting library for creating web-based charts and dashboards.
- **bokeh**: Interactive visualization library that targets modern web browsers for presentation.
- **altair**: Declarative statistical visualization library for creating interactive visualizations in a concise syntax.
- **plotnine**: Implementation of the grammar of graphics in Python, based on ggplot2.

### Machine Learning and Deep Learning

- **scikit-learn**: Simple and efficient tools for data mining and data analysis, including classification, regression, and clustering algorithms.
- **tensorflow**: End-to-end open-source platform for machine learning, with extensive support for deep learning.
- **pytorch**: Deep learning framework that facilitates research and production deployment with flexibility and speed.
- **keras**: High-level neural networks API, capable of running on top of TensorFlow, Theano, or CNTK.
- **fastai**: Simplified deep learning library built on top of PyTorch, focusing on usability and best practices.

### Natural Language Processing

- **nltk**: Natural Language Toolkit for symbolic and statistical natural language processing.
- **spacy**: Industrial-strength natural language processing library with pre-trained models and support for over 50 languages.
- **gensim**: Topic modeling for human-readable data, providing efficient implementations of common algorithms like Word2Vec. NOTE: You have to install `Gensim` manually with **tar.gz** file, click [here](https://pypi.org/project/gensim/#files) to download & install.

### Big Data and Distributed Computing

- **dask**: Parallel computing library for scaling out Python computations across multiple cores and clusters.
- **pyspark**: Apache Spark Python API, enabling large-scale data processing with distributed computing.
- **ray**: Distributed computing framework that supports both task and actor models for scalable and efficient execution.

### Additional Tools and Utilities

- **jupyter**: Interactive computing environment for creating notebooks that integrate code execution, rich text, mathematics, plots, and media.
- **pytest**: Framework for building simple and scalable test cases in Python.
- **mkdocs**: Static site generator for creating beautiful project documentation.
- **streamlit**: Framework for turning data scripts into shareable web apps.
- **dash**: Framework for building analytical web applications in Python.
- **gradio**: GUI platform for sharing machine learning models as web apps.

## Installation and Usage

To install `dsbundle` and gain access to this extensive suite of libraries and tools, simply execute the following command using pip:

```bash
Copy code
pip install dsbundle
```

This command automates the installation process, ensuring all included libraries are installed and ready for use in your Python environment.

## License

This package is licensed under the MIT License, granting users the freedom to use, modify, and distribute the software. For detailed license terms, please refer to the LICENSE file included in the repository.

------

This detailed description provides an extensive overview of `dsbundle`, highlighting its comprehensive coverage of essential libraries and tools for data science, machine learning, visualization, and beyond. It emphasizes ease of installation, robust functionality, and community-driven development, making it an invaluable resource for data scientists, researchers, and developers seeking a unified solution for Python-based data analysis and machine learning projects.
