July 2019
Beginner to intermediate
740 pages
16h 52m
English
Content preview from Hands-On Data Analysis with Pandas
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Jupyter Notebooks
Each chapter of this book includes Jupyter Notebooks for following along. Jupyter Notebooks are omnipresent in Python data science because they make it very easy to write and test code in more of a discovery environment compared to writing a program. We can execute one block of code at a time and have the results printed to the notebook, right beneath the code that generated it. In addition, we can use Markdown to add text explanations to our work. Jupyter Notebooks can be easily packaged up and shared: they can be pushed to GitHub (where they display as we saw them on our computer), converted into HTML or PDF, sent to someone else, or presented.
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