numerical python scientific computing data de johansson robert (18 resultados)

Paperback. Condición: 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!

Condición: Acceptable. Readable, but has significant damage / tears. Has a remainder mark. paperback Used - Acceptable 2018.

Condición: 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.

Condición: New.

Condición: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!

Condición: New.

Condición: As New. Unread book in perfect condition.

paperback. Condición: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.

paperback. Condición: Fine.

paperback. Condición: Very Good.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. New Copy. Customer Service Guaranteed.

paperback. Condición: Sehr gut. 723 Seiten; 9781484242452.2 Gewicht in Gramm: 2.

Paperback. Condición: 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.

Condición: New. In.

Condición: new. Questo è un articolo print on demand.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and 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.What You'll LearnWork with vectors and matrices using NumPyReview Symbolic computing with SymPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyUnderstand statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.