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    Data Science in Python, Volume 2: Data I/O, Jupyter Notebook, GUI, Deployment, Numeric Programming, High Performance Python

    By Alexander Stepanov

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    This volume covers the fundamentals of scientific Python programming, and I assume you are familiar with Python 3. If you need help with obtaining and setting up a scientific Python 3 distribution, make sure to check out volume 1 of this series.

    In this volume I will show how to:
    • Read data from a tab delimited text file, sort, filter, and recover from errors

    • Save data in a tab delimited text or in rich Microsoft Excel format

    • Use an IPython notebook to quickly prototype your program, explore your data interactively, document, and share your research.

    • Give your program a Graphic User Interface (GUI) to make it useful for non-programmers.

    • Package a Python program for deployment on other computers

    • Use Numpy for number crunching

    • Make the Python program run as fast as compiled code

    • Use multiple cores or processors for parallel execution of a Python program


    You might also want to look at volume 3 describing plotting with Matplotlib and using Python together with SQLite database for data analysis.
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