عنوان

Numerical Python :

(Number (ISBN

1484242467

(Number (ISBN

1484242475

(Number (ISBN

1484246489

(Number (ISBN

9781484242469

(Number (ISBN

9781484242476

(Number (ISBN

9781484246481

Erroneous ISBN

1484242459

Erroneous ISBN

9781484242452

Title Proper

Numerical Python :

General Material Designation

[Book]

Other Title Information

scientific computing and data science applications with Numpy, SciPy and Matplotlib /

First Statement of Responsibility

Robert Johansson.

Edition Statement

Second edition.

Place of Publication, Distribution, etc.

[Berkeley, CA] :

Name of Publisher, Distributor, etc.

Apress,

Date of Publication, Distribution, etc.

[2019]

Specific Material Designation and Extent of Item

1 online resource

Text of Note

Includes bibliographical references and index.

Text of Note

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 Caching; Autocompletion and Object Introspection; Documentation; Interaction with the System Shell; IPython Extensions; File System Navigation; Running Scripts from the IPython Console; Debugger; Reset; Timing and Profiling Code; Interpreter and Text Editor as Development Environment; Jupyter; The Jupyter QtConsole; The Jupyter Notebook; Jupyter Lab; Cell Types

Text of Note

Axis Ticks, Tick Labels, and GridsLog Plots; Twin Axes; Spines; Advanced Axes Layouts; Insets; Subplots; Subplot2grid; GridSpec; Colormap Plots; 3D Plots; Summary; Further Reading; References; Chapter 5: Equation Solving; Importing Modules; Linear Equation Systems; Square Systems; Rectangular Systems; Eigenvalue Problems; Nonlinear Equations; Univariate Equations; Systems of Nonlinear Equations; Summary; Further Reading; References; Chapter 6: Optimization; Importing Modules; Classification of Optimization Problems; Univariate Optimization; Unconstrained Multivariate Optimization

Text of Note

Constants and Special SymbolsFunctions; Expressions; Manipulating Expressions; Simplification; Expand; Factor, Collect, and Combine; Apart, Together, and Cancel; Substitutions; Numerical Evaluation; Calculus; Derivatives; Integrals; Series; Limits; Sums and Products; Equations; Linear Algebra; Summary; Further Reading; Reference; Chapter 4: Plotting and Visualization; Importing Modules; Getting Started; Interactive and Noninteractive Modes; Figure; Axes; Plot Types; Line Properties; Legends; Text Formatting and Annotations; Axis Properties; Axis Labels and Titles; Axis Range

Text of Note

Editing CellsMarkdown Cells; Rich Output Display; nbconvert; HTML; PDF; Python; Spyder: An Integrated Development Environment; Source Code Editor; Consoles in Spyder; Object Inspector; Summary; Further Reading; References; Chapter 2: Vectors, Matrices, and Multidimensional Arrays; Importing the Modules; The NumPy Array Object; Data Types; Real and Imaginary Parts; Order of Array Data in Memory; Creating Arrays; Arrays Created from Lists and Other Array-Like Objects; Arrays Filled with Constant Values; Arrays Filled with Incremental Sequences; Arrays Filled with Logarithmic Sequences

Text of Note

Meshgrid ArraysCreating Uninitialized Arrays; Creating Arrays with Properties of Other Arrays; Creating Matrix Arrays; Indexing and Slicing; One-Dimensional Arrays; Multidimensional Arrays; Views; Fancy Indexing and Boolean-Valued Indexing; Reshaping and Resizing; Vectorized Expressions; Arithmetic Operations; Elementwise Functions; Aggregate Functions; Boolean Arrays and Conditional Expressions; Set Operations; Operations on Arrays; Matrix and Vector Operations; Summary; Further Reading; References; Chapter 3: Symbolic Computing; Importing SymPy; Symbols; Numbers; Integer; Float; Rational

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Text of Note

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new 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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9781484242469

Title

Numerical Python : Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib.

International Standard Book Number

9781484242452

Computer programming.

Python (Computer program language)

Computer programming.

Python (Computer program language)

COM051360

UMX

UMX

Number

Edition

Class number

Book number

Johansson, Robert

Date of Transaction

20200823032639.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y