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Python 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.
Interactive Computing with Jupyter Notebook, 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. This course covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming.
In short, you will master relatively advanced methods in interactive numerical computing, high-performance computing, and data visualization.
The code bundle for the video course is available at –
Style and Approach
This practical, hands-on course will teach you how to analyze and visualize all kinds of data in Jupyter Notebook.
Released: Thursday, June 21, 2018
A Tour of Interactive Computing with Jupyter and IPython
The Course Overview
Introducing IPython and the Jupyter Notebook
Getting Started with Exploratory Data Analysis in the Jupyter Notebook
Introducing the Multidimensional Array in NumPy for Fast Array Computations
Creating an IPython Extension with Custom Magic Commands
Mastering the Jupyter Notebook
Architecture of the Jupyter Notebook
Converting a Jupyter Notebook to Other Formats with nbconvert
Mastering Widgets in the Jupyter Notebook
Creating Custom Jupyter Notebook Widgets in Python, HTML, and JavaScript
Configuring the Jupyter Notebook
Profiling and Optimizing
Evaluating the Time Taken by a Command in IPython
Profiling Your Code Easily with cProfile and IPython
Profiling Your Code Line-by-Line with line_profiler
Profiling the Memory Usage of Your Code with memory_profiler
Understanding the Internals of NumPy to Avoid Unnecessary Array Copying
Processing Large NumPy Arrays with Memory Mapping
High Performance Computing
Using Python to Write Faster Code
Accelerating Pure Python Code with Numba and Just-In-Time Compilation
Accelerating Array Computations with NumExpr
Accelerating Python Code with Cython
Releasing the GIL to Take Advantage of Multi-Core Processors
Writing Massively Parallel Code for NVIDIA Graphics Cards (GPUs)
Distributing Python Code Across Multiple Cores with IPython
Interacting with Asynchronous Parallel Tasks in IPython
Performing Out-of-Core Computations on Large Arrays with Dask
Data Visualization
Using Matplotlib Styles
Creating Statistical Plots Easily with Seaborn
Creating Interactive Web Visualizations with Bokeh and HoloViews
Creating Plots with Altair and the Vega-Lite Specification



AuthorCyrille Rossant
level Intermediate
Duration2 hours 17 minutes
File siza361 MB



Packtpub Interactive Computing with Jupyter Notebook