Скачать или смотреть python data science handbook github

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in this tutorial, we will explore the python data science handbook available on github. this handbook serves as an excellent resource for anyone interested in learning about data science using python. the github repository contains code examples, explanations, and tutorials covering various data science topics.
the python data science handbook is a comprehensive guide authored by jake vanderplas. it covers essential tools and techniques for doing data science with python, focusing on the numpy, pandas, matplotlib, and scikit-learn libraries. the handbook is available online for free and also as a published book.
the python data science handbook is hosted on github. you can access it using the following link:
python data science handbook on github
to get started, you'll need to clone the repository to your local machine. you can do this by running the following command in your terminal or command prompt:
once you've cloned the repository, you can explore the contents using your favorite text editor or jupyter notebook. the handbook is organized into different chapters, each covering a specific aspect of data science.
the repository contains jupyter notebooks with code examples for each chapter. you can run these notebooks on your local machine to follow along with the tutorials and experiment with the code.
to run a jupyter notebook, navigate to the directory containing the notebook file (.ipynb) using the terminal or command prompt and run the following command:
this will open jupyter notebook in your web browser, allowing you to select and open the notebook you want to run.
feel free to experiment with the code examples provided in the notebooks. modify the code, try different datasets, and see how the results change. this hands-on approach is crucial for learning and understanding data science concepts effectively.
as an example, let's explore how to use matplotlib, one of the libraries covered in the handbook, for data visualization. we'll create a simple scatter ...

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