There are many packages that can be installed using pip, the Python package manager. Some of the commonly used packages that can be installed with pip include:
- TensorFlow: an open-source machine learning framework developed by Google for building and training machine learning models.
- NumPy: a library for scientific computing with Python, providing efficient numerical operations for multi-dimensional arrays and matrices.
- SciPy: a collection of libraries for scientific and technical computing with Python, including tools for optimization, linear algebra, signal processing, and more.
- Pandas: a library for data manipulation and analysis in Python, providing tools for reading, writing, and manipulating tabular data.
- Matplotlib: a library for creating visualizations and plots in Python, providing tools for creating various types of charts and graphs.
- Keras: an open-source neural network library written in Python, designed to enable fast experimentation with deep neural networks.
- SciKit-Learn: a library for machine learning in Python, providing tools for data preprocessing, feature extraction, supervised and unsupervised learning, and model evaluation.
- PyTorch: an open-source machine learning framework developed by Facebook for building and training machine learning models.
- Scrapy: a framework for web scraping and crawling in Python, providing tools for extracting data from websites and APIs.
- BeautifulSoup: a library for parsing HTML and XML documents in Python, providing tools for extracting and manipulating data from web pages.
- LightGBM: a gradient boosting framework that uses tree-based learning algorithms, designed to be efficient and scalable for large-scale machine learning tasks.
- ELI5: a library for explaining and visualizing machine learning models in Python, providing tools for feature importances, model weights, and more.
- Theano: a library for numerical computation in Python, designed to allow developers to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays.
- NuPIC: a machine intelligence platform for building intelligent applications, based on the principles of neuroscience and machine learning.
- Ramp: a library for building predictive models in Python, designed to simplify the process of building and evaluating machine learning models.
- Pipenv: a tool for managing Python dependencies and virtual environments, designed to make it easier to manage packages and versions.
- Bob: a toolbox for machine learning in Python, providing tools for face recognition, speaker recognition, and more.
- PyBrain: a library for building and training neural networks in Python, designed to be modular and flexible for a wide range of tasks.
- Caffe2: a deep learning framework developed by Facebook for building and training neural networks, designed to be efficient and scalable for large-scale tasks.
- Chainer: a Python-based deep learning framework for building and training neural networks, designed to be flexible and scalable for a wide range of tasks.
- Django is a high-level web framework that provides a structured and scalable way to build web applications in Python. It includes built-in tools for handling tasks such as authentication, URL routing, and database schema migrations.
- Flask is a lightweight web framework that provides flexibility and simplicity to developers. It allows you to build web applications and APIs in Python with minimal boilerplate code and provides support for extensions to add functionality.
- Bottle is another lightweight web framework that allows you to build web applications and APIs in Python. It is designed to be simple and easy to use, with minimal dependencies.
- Requests is a package that provides a simple and easy-to-use interface for sending HTTP requests in Python. It supports various HTTP methods such as GET, POST, PUT, DELETE, etc. and also allows you to customize headers, cookies, and other request parameters.
For example, to install the NumPy package, you can use the following command:
pip install numpy
pip install plotly==4.10.0
You can use the following command to check which packages are currently installed in your Python environment using pip:
pip list
This command will display a list of all the packages that have been installed using pip, along with their version numbers. If you want to check the version number of a specific package, you can use the following command:
pip show plotly
pip list | grep plotly