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Titlebook: A Python Data Analyst’s Toolkit; Learn Python and Pyt Gayathri Rajagopalan Book 2021 Gayathri Rajagopalan 2021 Python.Data Analytics.Pandas

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31#
發(fā)表于 2025-3-26 23:05:06 | 只看該作者
Recent Developments and Future Outlook,ontainers. You also reviewed more advanced concepts like regular expressions, handling of files, and solving mathematical problems with Python. Our focus now turns to the meat of the book, descriptive data analysis (also called exploratory data analysis).
32#
發(fā)表于 2025-3-27 04:56:40 | 只看該作者
33#
發(fā)表于 2025-3-27 05:43:05 | 只看該作者
https://doi.org/10.1007/b137083his data to derive meaningful inferences can drive decision making, improve productivity, and reduce costs. In the previous chapter, you learned about NumPy?– the library that helps us work with arrays and perform computations, also serving as the backbone for the Pandas library that we discuss in t
34#
發(fā)表于 2025-3-27 12:10:38 | 只看該作者
35#
發(fā)表于 2025-3-27 15:47:35 | 只看該作者
36#
發(fā)表于 2025-3-27 18:39:53 | 只看該作者
978-1-4842-6398-3Gayathri Rajagopalan 2021
37#
發(fā)表于 2025-3-28 01:19:24 | 只看該作者
Recent Developments and Future Outlook,In this chapter, we progress to some other essential concepts in Python - various types of containers, the methods that can be used with each of these containers, object-oriented programming, classes, and objects.
38#
發(fā)表于 2025-3-28 03:08:46 | 只看該作者
39#
發(fā)表于 2025-3-28 06:24:00 | 只看該作者
40#
發(fā)表于 2025-3-28 13:42:45 | 只看該作者
Gayathri RajagopalanExplains important data analytics concepts with real-life applications using Python.Includes multiple-choice and practice questions to bridge the gap between theory and practice.Contains case studies
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