Paperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Condizione: Acceptable. Readable, but has significant damage / tears. Has a remainder mark. paperback Used - Acceptable 2018.
Da: Big River Books, Powder Springs, GA, U.S.A.
Condizione: good. This book is in good condition. The cover has minor creases or bends. The binding is tight and pages are intact. Some pages may have writing or highlighting.
Da: medimops, Berlin, Germania
EUR 41,88
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
Paperback. Condizione: new. New Copy. Customer Service Guaranteed.
Da: Untje.com, Roeselare, Belgio
EUR 16,23
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Aggiungi al carrelloPaperback. Condizione: Fine. 2. English Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, SymPy, FEniCS, Matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates computation problem solving and techniques that have applications in such diverse fields as scientific research, engineering, finance, and data analytics. Numerical Python, Second Edition, presents many case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. You Will: Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Related Titles Python Data Analytics Advanced Data Analytics Using Python Practical Machine Learning with Python Python Descriptors. Intro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Introduction -- Chapter 1: Introduction to Computing with Python -- Environments for Computing with Python -- Python -- Interpreter -- IPython Console -- Input and Output Ca.