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Learning IPython for Interactive Computing and Data Visualization - Second Edition

More Information
Learn
  • Install Anaconda and code in Python in the Jupyter Notebook
  • Load and explore datasets interactively
  • Perform complex data manipulations effectively with pandas
  • Create engaging data visualizations with matplotlib and seaborn
  • Simulate mathematical models with NumPy
  • Visualize and process images interactively in the Jupyter Notebook with scikit-image
  • Accelerate your code with Numba, Cython, and IPython.parallel
  • Extend the Notebook interface with HTML, JavaScript, and D3
About

Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.

This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.

Features
  • Learn the basics of Python in the Jupyter Notebook
  • Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn
  • Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel
Page Count 200
Course Length 6 hours 0 minutes
ISBN9781783986989
Date Of Publication 20 Oct 2015

Authors

Cyrille Rossant

Cyrille Rossant, PhD, is a neuroscience researcher and software engineer at University College London. He is a graduate of École Normale Supérieure, Paris, where he studied mathematics and computer science. He has also worked at Princeton University and Collège de France. While working on data science and software engineering projects, he gained experience in numerical computing, parallel computing, and high-performance data visualization.

He is the author of Learning IPython for Interactive Computing and Data Visualization, Second Edition, Packt Publishing.