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awesome-python-tools

Awesome Python Tools
https://github.com/dr-saad-la/awesome-python-tools

Last synced: 3 days ago
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  • Data Science

    • Data Manipulations

      • Dask
      • cuDF - accelerated DataFrame library based on Pandas, part of the RAPIDS ecosystem for faster large-scale data processing.
      • cuDF - accelerated DataFrame library based on Pandas, part of the RAPIDS ecosystem for faster large-scale data processing.
      • Bcolz - memory analytics, provides columnar and compressed data containers for faster manipulation of large datasets.
      • PySpark
      • Koalas - like API built on Apache Spark, allowing distributed DataFrame operations while maintaining a familiar syntax.
      • Bcolz - memory analytics, provides columnar and compressed data containers for faster manipulation of large datasets.
      • PySpark
      • Koalas - like API built on Apache Spark, allowing distributed DataFrame operations while maintaining a familiar syntax.
      • Mars - scale data computation using multi-dimensional arrays and DataFrames in distributed settings.
      • Mars - scale data computation using multi-dimensional arrays and DataFrames in distributed settings.
      • Awkward Array - like data and scientific research.
      • SQLAlchemy
      • PyArrow - scale data manipulation, commonly used with Parquet and Feather file formats.
      • Pandas
      • Pandas
      • PyJanitor
      • SQLite - in Python library for handling structured data stored in SQLite databases.
      • SQLite - in Python library for handling structured data stored in SQLite databases.
      • Dask
      • Vaex - of-core DataFrame library, optimized for working with datasets larger than memory.

  • Data Visualizations

    • Data Manipulations

      • Matplotlib - quality figures.
      • Seaborn
      • Plotly - ready plots. It supports a wide range of chart types, including 3D plots, geographic maps, and dashboards.
      • Bokeh - time streaming data and interactive dashboards.
      • Altair
      • ggplot - based approach to data visualization.
      • Holoviews
      • Datashader
      • hvPlot - level plotting API that simplifies the creation of interactive visualizations for Pandas, Dask, and Xarray data structures, integrating with Holoviews and Bokeh.
      • Streamlit
      • Flask-Dashboard - based visualizations on top of Flask.
      • Folium
      • Cartopy
      • Kepler.gl - scale geographic data visualizations with beautiful, interactive maps.
      • PyGraphviz
      • mplfinance
      • PyMC3
      • Manim - quality mathematical animations and presentations, widely used for educational videos.
      • Mayavi
      • PyVista
      • Vispy - performance interactive 2D/3D data visualization library that leverages the power of OpenGL.
      • Matplotlib 3D - in 3D plotting capabilities in Matplotlib, ideal for basic 3D visualizations and surface plots.
      • Dash - driven apps without requiring frontend knowledge.
      • Vaex - in fast visualization of large datasets, including scatter plots and histograms.

  • Machine Learning

    • General Machine Learning Libraries

      • Scikit-learn
      • PyCaret - source, low-code machine learning library that automates many aspects of machine learning pipelines, including preprocessing, model selection, and tuning.

    • Gradient Boosting Libraries

      • XGBoost - world applications. It is particularly effective for structured/tabular data.
      • CatBoost

    • Neural Networks and Deep Learning Integration

      • TensorFlow - source deep learning framework widely used for neural network modeling, machine learning, and artificial intelligence applications. It supports both high-level and low-level APIs for building, training, and deploying machine learning models.
      • Keras - level neural networks API, built on top of TensorFlow, that simplifies the process of building and training neural networks. Keras is user-friendly and modular, making it ideal for rapid experimentation.
      • PyTorch

    • Model Explainability and Interpretability

      • LIME - agnostic Explanations (LIME) is a library that explains the predictions of any machine learning classifier or regressor by approximating it locally with interpretable models.

    • Hyperparameter Tuning and Optimization

    • Automated Machine Learning (AutoML)

      • Auto-sklearn - learn, automating model selection, hyperparameter optimization, and data preprocessing.
      • TPOT - based AutoML library that automates the selection of models and hyperparameters.

    • Model Deployment and Monitoring

      • MLflow - source platform for managing the machine learning lifecycle. MLflow tracks experiments, packages code into reproducible runs, and manages model deployment.
      • DVC - source tool for managing datasets and machine learning models. It helps version data, manage pipelines, and reproduce results.

    • Dimensionality Reduction

      • UMAP - dimensional data.
      • t-SNE - Distributed Stochastic Neighbor Embedding is a non-linear dimensionality reduction technique useful for visualizing high-dimensional data.
      • PCA

    • Specialized Machine Learning Libraries

      • FastAI - level library built on top of PyTorch, designed to simplify the implementation of deep learning models with minimal code.
      • OpenCV

  • Text Analysis

  • Speech Recognition

  • Time Series Analysis

  • Statistical Analysis

  • Web Development

    • Specialized Libraries

      • Django - level Python web framework that encourages rapid development and clean, pragmatic design. Includes ORM, admin interface, and authentication.
      • Flask
      • FastAPI - performance) web framework for building APIs with Python, based on standard Python type hints. Known for its speed and ease of use.
      • Pyramid

  • Programming

Programming Languages
Python 30 C++ 3 Roff 2 Jupyter Notebook 2 JavaScript 1
Categories
Programming 88 Data Visualizations 24 Data Science 21 Time Series Analysis 21 Machine Learning 20 Statistical Analysis 16 Text Analysis 4 Web Development 4 Speech Recognition 2
Sub Categories
Data Manipulations 45 Specialized Machine Learning Libraries 41 Specialized Libraries 8 Hyperparameter Tuning and Optimization 3 Dimensionality Reduction 3 Neural Networks and Deep Learning Integration 3 Model Deployment and Monitoring 2 Gradient Boosting Libraries 2 Automated Machine Learning (AutoML) 2 General Machine Learning Libraries 2 Model Explainability and Interpretability 1
Keywords
python 17 time-series 9 data-science 8 machine-learning 7 forecasting 5 deep-learning 4 statistics 3 scikit-learn 3 automl 3 feature-engineering 3 panel-data 2 time-series-regression 2 time-series-classification 2 anomaly-detection 2 neural-networks 2 rapids 2 pydata 2 pandas 2 gpu 2 dataframe 2 data-analysis 2 dask 2 cudf 2 cuda 2 cpp 2 arrow 2 autopep8 2 code 2 gofmt 2 codeformatter 2 formatter 2 pre-commit-hook 2 yapf 2 shell 2 cli 2 pip 2 pypi 2 venv 2 tag-production 2 database 2 python3 2 selenium 2 testcontainers 2 testing 2 automated-machine-learning 1 automation 1 gradient-boosting 1 hyperparameter-optimization 1 model-selection 1 nia 1