000			02508nam a22002057a 4500
005			20240207114709.0
008			240207b |||||||| |||| 00| 0 eng d
020			_a9783030689544
082			_a005.133 _bUNP
100			_aUnpingco, Jose _914003
245			_aPython programming for data analysis
260			_bSpringer _aSwitzerland _c2021
300			_axii, 263 p.
365			_aEURO _b54.99
520			_aThis 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.
650			_aData mining _913331
650			_aProgramming languages - Electronic computers _915232
650			_aPython - Computer program language _913655
942			_cBK _2ddc
999			_c5736 _d5736