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Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning 2021 ed. [Kõva köide]

  • Formaat: Hardback, 229 pages, kõrgus x laius: 235x155 mm, kaal: 541 g, 104 Illustrations, color; 8 Illustrations, black and white; XV, 229 p. 112 illus., 104 illus. in color., 1 Hardback
  • Sari: Springer Textbooks in Earth Sciences, Geography and Environment
  • Ilmumisaeg: 17-Sep-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030780546
  • ISBN-13: 9783030780548
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Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning 2021 ed.Suurem pilt
  • Formaat: Hardback, 229 pages, kõrgus x laius: 235x155 mm, kaal: 541 g, 104 Illustrations, color; 8 Illustrations, black and white; XV, 229 p. 112 illus., 104 illus. in color., 1 Hardback
  • Sari: Springer Textbooks in Earth Sciences, Geography and Environment
  • Ilmumisaeg: 17-Sep-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030780546
  • ISBN-13: 9783030780548
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030780548.html
Märksõnad:
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
Part I Python for Geologists: A Kickoff
1 Setting Up Your Python Environment, Easily
3(8)
1.1 The Python Programming Language
3(1)
1.2 Programming Paradigms
4(1)
1.3 A Local Python Environment for Scientific Computing
5(2)
1.4 Remote Python Environments
7(1)
1.5 Python Packages for Scientific Applications
8(1)
1.6 Python Packages Specifically Developed for Geologists
9(2)
2 Python Essentials for a Geologist
11(14)
2.1 Start Working with IPython Console
11(2)
2.2 Naming and Style Conventions
13(1)
2.3 Working with Python Scripts
14(3)
2.4 Conditional Statements, Indentation, Loops, and Functions
17(4)
2.5 Importing External Libraries
21(1)
2.6 Basic Operations and Mathematical Functions
21(4)
3 Solving Geology Problems Using Python: An Introduction
25(18)
3.1 My First Binary Diagram Using Python
25(7)
3.2 Making Our First Models in Earth Science
32(4)
3.3 Quick Intro to Spatial Data Representation
36(7)
Part II Describing Geological Data
4 Graphical Visualization of a Geological Data Set
43(24)
4.1 Statistical Description of a Data Set: Key Concepts
43(1)
4.2 Visualizing Univariate Sample Distributions
44(3)
4.3 Preparing Publication-Ready Binary Diagrams
47(19)
4.4 Visualization of Multivariate Data: A First Attempt
66(1)
5 Descriptive Statistics 1: Univariate Analysis
67(16)
5.1 Basics of Descriptive Statistics
67(1)
5.2 Location
67(5)
5.3 Dispersion or Scale
72(5)
5.4 Skewness
77(2)
5.5 Descriptive Statistics in Pandas
79(1)
5.6 Box Plots
80(3)
6 Descriptive Statistics 2: Bivariate Analysis
83(16)
6.1 Covariance and Correlation
83(4)
6.2 Simple Linear Regression
87(3)
6.3 Polynomial Regression
90(1)
6.4 Nonlinear Regression
91(8)
Part III Integrals and Differential Equations in Geology
7 Numerical Integration
99(18)
7.1 Definite Integrals
99(1)
7.2 Basic Properties of Integrals
99(2)
7.3 Analytical and Numerical Solutions of Definite Integrals
101(1)
7.4 Fundamental Theorem of Calculus and Analytical Solutions----
101(2)
7.5 Numerical Solutions of Definite Integrals
103(6)
7.6 Computing the Volume of Geological Structures
109(1)
7.7 Computing the Lithostatic Pressure
110(7)
8 Differential Equations
117(20)
8.1 Introduction
117(1)
8.2 Ordinary Differential Equations
118(4)
8.3 Numerical Solutions of First-Order Ordinary Differential Equations
122(4)
8.4 Fick's Law of Diffusion--A Widely Used Partial Differential Equation
126(11)
Part IV Probability Density Functions and Error Analysis
9 Probability Density Functions and Their Use in Geology
137(18)
9.1 Probability Distribution and Density Functions
137(1)
9.2 The Normal Distribution
138(4)
9.3 The Log-Normal Distribution
142(2)
9.4 Other Useful PDFs for Geological Applications
144(1)
9.5 Density Estimation
144(8)
9.6 The Central Limit Theorem and Normal Distributed Means ----
152(3)
10 Error Analysis
155(26)
10.1 Dealing with Errors in Geological Measurements
155(7)
10.2 Reporting Uncertainties in Binary Diagrams
162(6)
10.3 Linearized Approach to Error Propagation
168(4)
10.4 The Mote Carlo Approach to Error Propagation
172(9)
Part V Robust Statistics and Machine Learning
11 Introduction to Robust Statistics
181(14)
11.1 Classical and Robust Approaches to Statistics
181(1)
11.2 Normality Tests
182(4)
11.3 Robust Estimators for Location and Scale
186(6)
11.4 Robust Statistics in Geochemistry
192(3)
12 Machine Learning
195(14)
12.1 Introduction to Machine Learning in Geology
195(2)
12.2 Machine Learning in Python
197(1)
12.3 A Case Study of Machine Learning in Geology
197(12)
Appendix A Python Packages and Resources for Geologists 209(2)
Appendix B Introduction to Object Oriented Programming 211(4)
Appendix C The Matplotlib Object Oriented API 215(4)
Appendix D Working with Pandas 219(4)
Further Readings 223
Maurizio Petrelli works as a researcher in petrology and volcanology at the Department of Physics and Geology, University of Perugia. In 2001, he graduated in Geology and obtained his PhD in February 2006 at the University of Perugia.His current studies are focused on the petrological, volcanological and geochemical characterization of magmatic systems with particular emphasis on time-scales estimates of magmatic processes. He combines the use of numerical simulations, experimental petrology and the study of natural samples. Since 2016, he has developed a new line of research at the Department of Physics and Geology, University of Perugia focused on the application of Machine Learning techniques to petrological and volcanological studies.