Python is a high-level programming language that has become one of the most popular programming languages in recent years due to its simplicity, ease of learning, and flexibility. Python has a large number of libraries and frameworks, making it extremely flexible and applicable, and is widely used in web development, data analysis, artificial intelligence and other fields.
1. Web framework
1、Django
Django is a full-featured web application framework that consists of many components, including ORM (Object Relational Mapping), template engine, routing system, form processing, and more. With complete documentation and strong community support, Django has become one of the most popular web frameworks in Python. Using Django you can quickly build a fully functional web application.
2、Flask
Flask is a micro web application framework designed with the philosophy of "a minimal framework for web development using Python". Flask does not include components such as an ORM or template engine, but it can easily integrate third-party libraries. Flask is flexible and simple to use, making it an ideal choice, especially for small web applications or API projects.
3、Pyramid
Pyramid is a small web framework that supports both out-of-the-box and higher-level use cases. It has the characteristics of strong scalability, high security, and excellent performance, and is suitable for medium-sized to large-scale web applications.
4、Tornado
Tornado is a high-performance, non-blocking web framework that can handle a large number of concurrent requests and is suitable for high-traffic web applications. Tornado can also be used in real-time web applications, chat applications, game servers and other scenarios.
5、Bottle
Bottle is a small, fast web framework that is characterized by concise code, easy to use, and no configuration required. Bottle is suitable for small web applications and API projects.
2. Data analysis library
1、NumPy
NumPy is a Python-based scientific computing library that focuses on efficient numerical calculations on arrays and matrices. NumPy provides a variety of data types and array operation methods, and is an important tool for scientific computing and data analysis.
2、Pandas
Pandas is a data analysis library based on NumPy. It provides data structure and data manipulation tools, including data cleaning, processing, conversion and other operations. Pandas can read data in multiple data formats, such as CSV, Excel, SQL, etc., and supports data analysis and data visualization.
3、Matplotlib
Matplotlib is a Python-based data visualization library that provides a variety of graphic drawing functions and tools, including line charts, histograms, scatter plots, contour plots, and more. Matplotlib also supports dynamic interactive plotting.
4、SciPy
SciPy is a scientific computing library based on NumPy, which provides many advanced algorithms and functions, including linear algebra, optimization, interpolation, signal processing, image processing, statistical analysis, and more. SciPy can be used to solve various scientific computing problems and is one of the important tools for scientific computing and data analysis.
5、Scikit-learn
Scikit-learn is a Python-based machine learning library that provides many commonly used machine learning algorithms and tools, including classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also provides tools such as model selection, feature selection, and model evaluation, which can be used to build and evaluate machine learning models.
3. Artificial Intelligence Library
1、TensorFlow
TensorFlow is a Python-based deep learning framework developed by Google that supports GPU acceleration and distributed computing. TensorFlow provides many commonly used deep learning models and tools, including convolutional neural networks, recurrent neural networks, natural language processing, and more. TensorFlow can also be used to build custom models.
2、PyTorch
PyTorch is a Python-based deep learning framework developed by Facebook that supports GPU acceleration and dynamic calculation graphs. PyTorch provides many commonly used deep learning models and tools, including convolutional neural networks, recurrent neural networks, natural language processing, and more. PyTorch can also be used to build custom models.
3、Hard
Keras is a Python-based deep learning library that provides a high-level deep learning API that can easily build and train deep learning models. Keras supports a variety of deep learning frameworks, including TensorFlow, Theano, etc.
4、OpenCV
OpenCV is a Python-based computer vision library that provides many commonly used computer vision algorithms and tools, including image processing, target detection, face recognition, motion estimation, and more. OpenCV also supports multiple platforms and programming languages and is one of the important tools for computer vision development.
5、NLTK
NLTK is a Python-based natural language processing library that provides many commonly used natural language processing algorithms and tools, including text classification, language model, part-of-speech tagging, named entity recognition, etc. NLTK also provides a variety of corpora and data sets that can be used for training.
6、Gensim
Gensim is a Python-based natural language processing library that provides many commonly used natural language processing algorithms and tools, including document similarity, topic models, word vectors, and more. Gensim also supports a variety of corpora and data sets that can be used to train and evaluate natural language processing models.
4. Image processing library
1、OpenCV
OpenCV is a Python-based computer vision library that provides commonly used image processing and computer vision algorithms, including image reading, image processing, target detection, face recognition, etc. OpenCV also supports a variety of computer vision applications and development platforms and can be used to build high-performance computer vision systems.
2、Pillow
Pillow is an image processing library based on Python, which provides commonly used image processing and conversion tools, including image reading, image saving, image adjustment, image filtering, etc. Pillow also supports a variety of image formats and data types, which can be used for image processing and conversion.
3、Matplotlib
Matplotlib is a Python-based drawing library that provides commonly used data visualization tools, including line graphs, scatter plots, bar charts, pie charts, and more. Matplotlib also supports a variety of data formats and styles, which can be used for data visualization and analysis.
4. Seaborn
Seaborn is a Python-based data visualization library that provides commonly used statistical charts and data visualization tools, including scatter plots, box plots, heat maps, density plots, and more. Seaborn also supports a variety of data formats and styles, which can be used for data visualization and analysis.
5、Pygame
Pygame is a Python-based game development library that provides commonly used game development tools and modules, including image processing, audio processing, event processing, etc. Pygame also supports a variety of game development platforms and plug-ins, which can be used to build game applications and systems.