Numerical Python : scientific computing and data science applications with Numpy, SciPy and Matplotlib / Robert Johansson.
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, demon...
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| Online Access: |
Full Text (via Skillsoft) |
|---|---|
| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
[Berkeley, CA] :
Apress,
[2019]
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| Edition: | Second edition. |
| Subjects: |
MARC
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| 100 | 1 | |a Johansson, Robert, |e author. | |
| 245 | 1 | 0 | |a Numerical Python : |b scientific computing and data science applications with Numpy, SciPy and Matplotlib / |c Robert Johansson. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a [Berkeley, CA] : |b Apress, |c [2019] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 588 | 0 | |a Online resource; title from digital title page (viewed on May 06, 2019). | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Computer programming. | |
| 650 | 7 | |a Computer programming |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Johansson, Robert. |t Numerical Python : Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib. |d Berkeley, CA : Apress L.P., ©2018 |z 9781484242452 |
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