Learn to use IPython and Jupyter Notebook for your data analysis
and visualization work. Key Features Leverage the Jupyter Notebook
for interactive data science and visualization Become an expert in
high-performance computing and visualization for data analysis and
scientific modeling A comprehensive coverage of scientific
computing through many hands-on, example-driven recipes with
detailed, step-by-step explanations Book DescriptionPython is one
of the leading open source platforms for data science and numerical
computing. IPython and the associated Jupyter Notebook offer
efficient interfaces to Python for data analysis and interactive
visualization, and they constitute an ideal gateway to the
platform. IPython Interactive Computing and Visualization Cookbook,
Second Edition contains many ready-to-use, focused recipes for
high-performance scientific computing and data analysis, from the
latest IPython/Jupyter features to the most advanced tricks, to
help you write better and faster code. You will apply these
state-of-the-art methods to various real-world examples,
illustrating topics in applied mathematics, scientific modeling,
and machine learning. The first part of the book covers programming
techniques: code quality and reproducibility, code optimization,
high-performance computing through just-in-time compilation,
parallel computing, and graphics card programming. The second part
tackles data science, statistics, machine learning, signal and
image processing, dynamical systems, and pure and applied
mathematics. What you will learn Master all features of the Jupyter
Notebook Code better: write high-quality, readable, and well-tested
programs; profile and optimize your code; and conduct reproducible
interactive computing experiments Visualize data and create
interactive plots in the Jupyter Notebook Write blazingly fast
Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU
programming (CUDA), parallel IPython, Dask, and more Analyze data
with Bayesian or frequentist statistics (Pandas, PyMC, and R), and
learn from actual data through machine learning (scikit-learn) Gain
valuable insights into signals, images, and sounds with SciPy,
scikit-image, and OpenCV Simulate deterministic and stochastic
dynamical systems in Python Familiarize yourself with math in
Python using SymPy and Sage: algebra, analysis, logic, graphs,
geometry, and probability theory Who this book is forThis book is
intended for anyone interested in numerical computing and data
science: students, researchers, teachers, engineers, analysts, and
hobbyists. A basic knowledge of Python/NumPy is recommended. Some
skills in mathematics will help you understand the theory behind
the computational methods.
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