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{"slug": "krzjoa--awesome-python-data-science", "title": "Awesome Python Data Science", "description": "Probably the best curated list of data science software in Python.", "github_url": "https://github.com/krzjoa/awesome-python-data-science", "stars": "3.4K", "tag": "Programming Languages", "entry_count": 412, "subcategory_count": 22, "subcategories": [{"name": "General", "parent": "", "entries": [{"name": "Contents", "url": "#contents", "description": ""}, {"name": "Machine Learning", "url": "#machine-learning", "description": ""}, {"name": "Deep Learning", "url": "#deep-learning", "description": ""}, {"name": "Automated Machine Learning", "url": "#automated-machine-learning", "description": ""}, {"name": "Natural Language Processing", "url": "#natural-language-processing", "description": ""}, {"name": "Computer Audition", "url": "#computer-audition", "description": ""}, {"name": "Computer Vision", "url": "#computer-vision", "description": ""}, {"name": "Time Series", "url": "#time-series", "description": ""}, {"name": "Reinforcement Learning", "url": "#reinforcement-learning", "description": ""}, {"name": "Graph Machine Learning", "url": "#graph-machine-learning", "description": ""}, {"name": "Graph Manipulation", "url": "#graph-manipulation", "description": ""}, {"name": "Learning-to-Rank & Recommender Systems", "url": "#learning-to-rank-&-recommender-systems", "description": ""}, {"name": "Probabilistic Graphical Models", "url": "#probabilistic-graphical-models", "description": ""}, {"name": "Probabilistic Methods", "url": "#probabilistic-methods", "description": ""}, {"name": "Model Explanation", "url": "#model-explanation", "description": ""}, {"name": "Optimization", "url": "#optimization", "description": ""}, {"name": "Genetic Programming", "url": "#genetic-programming", "description": ""}, {"name": "Feature Engineering", "url": "#feature-engineering", "description": ""}, {"name": "Visualization", "url": "#visualization", "description": ""}, {"name": "Data Manipulation", "url": "#data-manipulation", "description": ""}, {"name": "Deployment", "url": "#deployment", "description": ""}, {"name": "Statistics", "url": "#statistics", "description": ""}, {"name": "Distributed Computing", "url": "#distributed-computing", "description": ""}, {"name": "Experimentation", "url": "#experimentation", "description": ""}, {"name": "Data Validation", "url": "#data-validation", "description": ""}, {"name": "Evaluation", "url": "#evaluation", "description": ""}, {"name": "Computations", "url": "#computations", "description": ""}, {"name": "Web Scraping", "url": "#web-scraping", "description": ""}, {"name": "Spatial Analysis", "url": "#spatial-analysis", "description": ""}, {"name": "Quantum Computing", "url": "#quantum-computing", "description": ""}, {"name": "Conversion", "url": "#conversion", "description": ""}, {"name": "Contributing", "url": "#contributing", "description": ""}, {"name": "License", "url": "#license", "description": ""}]}, 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src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/spark_big.png\" alt=\"Apache Spark based\">", "stars": "1.1k"}, {"name": "mlpack", "url": "https://github.com/mlpack/mlpack", "description": "A scalable C++ machine learning library (Python bindings).", "stars": "5.6k"}, {"name": "dlib", "url": "https://github.com/davisking/dlib", "description": "Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).", "stars": "14k"}, {"name": "MLxtend", "url": "https://github.com/rasbt/mlxtend", "description": "Extension and helper modules for Python's data analysis and machine learning libraries. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "5.1k"}, {"name": "hyperlearn", "url": "https://github.com/danielhanchen/hyperlearn", "description": "50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels. <img 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Python. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "705"}, {"name": "pystruct", "url": "https://github.com/pystruct/pystruct", "description": "Simple structured learning framework for Python. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "670"}, {"name": "sklearn-expertsys", "url": "https://github.com/tmadl/sklearn-expertsys", "description": "Highly interpretable classifiers for scikit learn. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "489"}, {"name": "RuleFit", "url": "https://github.com/christophM/rulefit", "description": "Implementation of the rulefit. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "443"}, {"name": "metric-learn", "url": "https://github.com/all-umass/metric-learn", "description": "Metric learning algorithms in Python. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "1.4k"}, {"name": "pyGAM", "url": "https://github.com/dswah/pyGAM", "description": "Generalized Additive Models in Python.", "stars": "991"}, {"name": "causalml", "url": "https://github.com/uber/causalml", "description": "Uplift modeling and causal inference with machine learning algorithms. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "5.8k"}]}, {"name": "Gradient Boosting", "parent": "Machine Learning", "entries": [{"name": "XGBoost", "url": "https://github.com/dmlc/xgboost", "description": "Scalable, Portable, and Distributed Gradient Boosting. <img height=\"20\" 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src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/gpu_big.png\" alt=\"GPU accelerated\">", "stars": "8.9k"}, {"name": "ThunderGBM", "url": "https://github.com/Xtra-Computing/thundergbm", "description": "Fast GBDTs and Random Forests on GPUs. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\"> <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/gpu_big.png\" alt=\"GPU accelerated\">", "stars": "712"}, {"name": "NGBoost", "url": "https://github.com/stanfordmlgroup/ngboost", "description": "Natural Gradient Boosting for Probabilistic Prediction.", "stars": "1.9k"}, {"name": "TensorFlow Decision Forests", "url": "https://github.com/tensorflow/decision-forests", "description": "A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/keras_big.png\" alt=\"keras\"> <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/tf_big2.png\" alt=\"TensorFlow\">", "stars": "695"}]}, {"name": "Ensemble Methods", "parent": "Machine Learning", "entries": [{"name": "ML-Ensemble", "url": "http://ml-ensemble.com/", "description": "High performance ensemble learning. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">"}, {"name": "Stacking", "url": "https://github.com/ikki407/stacking", "description": "Simple and useful stacking library written in Python. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "230"}, {"name": "stacked\\_generalization", "url": "https://github.com/fukatani/stacked_generalization", "description": "Library for machine learning stacking generalization. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "119"}, {"name": "vecstack", "url": "https://github.com/vecxoz/vecstack", "description": "Python package for stacking (machine learning technique). <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "700"}]}, {"name": "Imbalanced Datasets", "parent": "Machine Learning", "entries": [{"name": "imbalanced-learn", "url": "https://github.com/scikit-learn-contrib/imbalanced-learn", "description": "Module to perform under-sampling and over-sampling with various techniques. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "7.1k"}, {"name": "imbalanced-algorithms", "url": "https://github.com/dialnd/imbalanced-algorithms", "description": "Python-based implementations of algorithms for learning on imbalanced data. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\"> <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/tf_big2.png\" alt=\"sklearn\">", "stars": "241"}]}, {"name": "Kernel Methods", "parent": "Machine Learning", "entries": [{"name": "pyFM", "url": "https://github.com/coreylynch/pyFM", "description": "Factorization machines in python. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "926"}, {"name": "fastFM", "url": "https://github.com/ibayer/fastFM", "description": "A library for Factorization Machines. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/sklearn_big.png\" alt=\"sklearn\">", "stars": "1.1k"}, {"name": "tffm", "url": "https://github.com/geffy/tffm", 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alt=\"sklearn\">", "stars": "1.8k"}, {"name": "TensorLight", "url": "https://github.com/bsautermeister/tensorlight", "description": "A high-level framework for TensorFlow. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/tf_big2.png\" alt=\"sklearn\">", "stars": "11"}, {"name": "Mesh TensorFlow", "url": "https://github.com/tensorflow/mesh", "description": "Model Parallelism Made Easier. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/tf_big2.png\" alt=\"sklearn\">", "stars": "1.6k"}, {"name": "Ludwig", "url": "https://github.com/uber/ludwig", "description": "A toolbox that allows one to train and test deep learning models without the need to write code. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/tf_big2.png\" alt=\"sklearn\">", "stars": "12k"}]}, {"name": "JAX", "parent": "Deep Learning", "entries": [{"name": "JAX", "url": 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src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/keras_big.png\" alt=\"Keras compatible\">", "stars": "1.6k"}, {"name": "Hyperas", "url": "https://github.com/maxpumperla/hyperas", "description": "Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/keras_big.png\" alt=\"Keras compatible\">", "stars": "2.2k"}, {"name": "Elephas", "url": "https://github.com/maxpumperla/elephas", "description": "Distributed Deep learning with Keras & Spark. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/keras_big.png\" alt=\"Keras compatible\">", "stars": "1.6k"}, {"name": "qkeras", "url": "https://github.com/google/qkeras", "description": "A quantization deep learning library. <img height=\"20\" src=\"https://github.com/krzjoa/awesome-python-data-science/raw/master/img/keras_big.png\" alt=\"Keras compatible\">", 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