♢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.

Deep Learning Libraries Within Python

♢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.

NLP Libraries Within Python

- **NumPy**: Used for array operations and scientific/data computing.
- **Pandas**: Used for data manipulation, cleaning, and analysis via dataframes.
- **Matplotlib**: A core plotting library offering multiple data visualization options.
- **Seaborn**: Built on Matplotlib, used for creating attractive statistical graphics like heatmaps.
- **BeautifulSoup**: Used for web crawling and scraping data from HTML/XML files.
- **SciPy**: An open-source scientific computing library covering statistics, integration, optimization, and image processing.
- **StatsModels**: Provides classes and functions for estimating and testing various statistical models.

University of California, Santa Cruz

University of Michigan - Ann Arbor

Google

Python is a powerful language that allows data scientists to perform data manipulation, analysis, and visualizations through its vast number of libraries such as numpy and pandas.

Data Science with Python

Python's data science libraries can be broken down into three different categories:
- Data Mining
- Data Processing and Modeling
- Data Visualization

Categorizing Python Libraries for DS

Several Python libraries are commonly downloaded and used for the processing, visualization, and analysis of data. These include:

- **scikit-learn**: A machine learning library.
- **NumPy**: A library for scientific computing.
- **pandas**: A library for data manipulation and analysis.
- **Matplotlib**: A plotting library.

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