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Python Programming for Data Analysis (Paperback, 1st ed. 2021)
Loot Price: R1,324
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Python Programming for Data Analysis (Paperback, 1st ed. 2021)
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This textbook grew out of notes for the ECE143 Programming for Data
Analysis class that the author has been teaching at University of
California, San Diego, which is a requirement for both graduate and
undergraduate degrees in Machine Learning and Data Science. This
book is ideal for readers with some Python programming experience.
The book covers key language concepts that must be understood to
program effectively, especially for data analysis applications.
Certain low-level language features are discussed in detail,
especially Python memory management and data structures. Using
Python effectively means taking advantage of its vast ecosystem.
The book discusses Python package management and how to use
third-party modules as well as how to structure your own Python
modules. The section on object-oriented programming explains
features of the language that facilitate common programming
patterns. After developing the key Python language features, the
book moves on to third-party modules that are foundational for
effective data analysis, starting with Numpy. The book develops key
Numpy concepts and discusses internal Numpy array data structures
and memory usage. Then, the author moves onto Pandas and details
its many features for data processing and alignment. Because strong
visualizations are important for communicating data analysis, key
modules such as Matplotlib are developed in detail, along with
web-based options such as Bokeh, Holoviews, Altair, and Plotly. The
text is sprinkled with many tricks-of-the-trade that help avoid
common pitfalls. The author explains the internal logic embodied in
the Python language so that readers can get into the Python mindset
and make better design choices in their codes, which is especially
helpful for newcomers to both Python and data analysis. To get the
most out of this book, open a Python interpreter and type along
with the many code samples.
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