♢nltk

Natural language toolkit, a natural language processing toolkit, is the most commonly used Python Library in the NLP field.

♢Gensim

Python library that automatically extracts semantic topics from documents.

♢JieBa

A powerful thesaurus, especially Chinese word segmentation.

University of Michigan - Ann Arbor

Numpy: A library that can be used for array operations and data computing.

Pandas: A library that allows for the manipulation of data structures with dataframes and clean data presentation.

Matplotlib: A library that offers multiple data visualization options.

Seaborn: A library based on Matplotlib that allows for visualizing statistical models in  the medium of say a heatmap.

BeautifulSoup: A library that can be used for web crawling and data scraping.

SciPy: SciPy is an open source Python scientific computing library, which covers various tools in scientific computing, including statistics, integration, interpolation, optimization, image processing and so on.

StatsModels: A library provides many statistical models for statistical testing, exploration and visualization of statistical data.

Data Science Libraries Within Python

♢Scikit-learn

Scikit-learn is a general machine learning algorithm library, which can realize common machine learning algorithms such as data preprocessing, classification, regression, dimension reduction, model selection and so on.

♢XGBoost / LightGMB

Boosting tree toolkit, in which xgboost is one of the two superstars of kaggle

Machine Learning Libraries Within Python

♢TensorFlow

Developed by Google brain team, the most mainstream deep learning framework has the best ecosystem. However, it is difficult to get started, the API is complex and changeable, and debugging is difficult.

♢PyTorch

Facebook deep learning library is oriented to academia, with simple model construction, easy debugging and excellent ecosystem. But it is not suitable for industrial product level applications.

♢MXNet

The lightweight, portable and flexible deep learning library developed by DMLC has good memory and video memory optimization, and has great potential in the future. Amazon and Baidu support. However, the official documents are not very detailed, and there are confusing parts.

♢Keras

Tensorflow's advanced API can also use theano or cntk as the backend. Focus on user friendliness, can quickly design and build models, and turn ideas into results. It is most suitable for novices. But it is the slowest, highly encapsulated and has poor flexibility.

♢Theano

Theano is the first widely adopted deep learning framework. At present, the development and maintenance of theano project has been terminated.

♢CNTK

Microsoft Research open source deep learning framework.

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