Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben

2021 | Buch

Setting Up Your Python Environment, Easily Kapitel lesen Erstes Kapitel lesen

Introduction to Python in Earth Science Data Analysis

From Descriptive Statistics to Machine Learning

verfasst von: Ph.D. Maurizio Petrelli

Verlag: Springer International Publishing

Buchreihe : Springer Textbooks in Earth Sciences, Geography and Environment

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten
insite
SUCHEN

Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Python for Geologists: A Kickoff

Frontmatter
Chapter 1. Setting Up Your Python Environment, Easily
Abstract
This chapter introduces the Python programming language to geologists. To do so, it first describes the main features of Python, and subsequently defines the programming paradigms supported in Python. Finally, it provides a step-by-step guide to prepare a Python environment for scientific computing.
Maurizio Petrelli
Chapter 2. Python Essentials for a Geologist
Abstract
Chapter 2 provides the basics to start working with Python. It introduces the IPython console, Python variable types, and style conventions, and then describes how to start working with Python scripts in the Spyder Integrated Development Environment (IDE). It also explains how to perform conditional statements and loops, and how to define a function and perform basic mathematical operations.
Maurizio Petrelli
Chapter 3. Solving Geology Problems Using Python: An Introduction
Abstract
Chapter 3 allows the reader to start solving geology problems using Python. As a first step, it demonstrates how to import a geological data set and visualize relevant information in binary diagrams. It guides the reader through the design of a simple Earth Science model, and provides a quick overview of spatial data representation.
Maurizio Petrelli

Describing Geological Data

Frontmatter
Chapter 4. Graphical Visualization of a Geological Data Set
Abstract
Chapter 4 deals with the visualization of a geological data set using Python. It introduces the reader to the visualization of univariate data using histograms and cumulative distribution functions. Then, it begins showing how to prepare a publication-ready diagram. The chapter ends with a first attempt at visualizing multivariate data.
Maurizio Petrelli
Chapter 5. Descriptive Statistics 1: Univariate Analysis
Abstract
Mastering descriptive statistics is mandatory for a geologist. Chapter 5 shows how to describe a geological data set using Python programming, starting with basic metrics such as the location, dispersion, and degree of symmetry of a univariate data set. It then shows how to perform descriptive statistics in pandas and introduces box plot diagrams.
Maurizio Petrelli
Chapter 6. Descriptive Statistics 2: Bivariate Analysis
Abstract
Chapter 6 deals with the descriptive statistics of bivariate data and regression analysis. It presents the concepts of covariance and correlation, and their implementation in Python. Then, it shows how to perform linear and nonlinear regression. Finally, it ends with an example of nonlinear regression in earth science: the application of the crystal-lattice-strain model to interpret experimental data in petrology.
Maurizio Petrelli

Integrals and Differential Equations in Geology

Frontmatter
Chapter 7. Numerical Integration
Abstract
Chapter 7 is about numerical integration and its applications in geology. It defines definite integrals and the fundamental theorem of calculus. Subsequently, it shows the simplest methods to approximate, numerically, the solution of a definite integral. Finally, it introduces two geological problems that can be solved successfully, through numerical integration: estimating the volume of geological structures and computing the lithostatic pressure.
Maurizio Petrelli
Chapter 8. Differential Equations
Abstract
Differential equations govern numerous problems in physics, engineering, biology, and earth sciences. Chapter 8 starts providing the basics to work with differential equations and introduces common visualization tools to study first-order ordinary differential equations. Chapter 8 continues with the introduction of numerical solutions to ordinary and partial differential equations. To do so, it provides examples in geology, like the radioactive decay and Fick’s second law of diffusion, used to date accessory phases such as zircons, and to estimate pre-eruptive timescales, respectively.
Maurizio Petrelli

Probability Density Functions and Error Analysis

Frontmatter
Chapter 9. Probability Density Functions and Their Use in Geology
Abstract
Chapter 9 deals with probability density functions and their application to geology problems. It defines probability distributions and probability density functions, and introduces meaningful probability density functions in geology. Examples are the normal and log-normal distributions. Chapter 9 ends by showing how to perform a probability density estimation.
Maurizio Petrelli
Chapter 10. Error Analysis
Abstract
Chapter 10 is about errors and error propagation. It defines precision, accuracy, standard error, and confidence intervals. Then it demonstrates how to report uncertainties in binary diagrams. Finally, it shows two approaches to propagate the uncertainties: the linearized and Monte Carlo methods.
Maurizio Petrelli

Robust Statistics and Machine Learning

Frontmatter
Chapter 11. Introduction to Robust Statistics
Abstract
Chapter 11 introduces robust statistics. It presents an approach to determine whether or not a sample follows a normal distribution. Chapter 11 continues defining robust approaches for the estimation of the location and the scale of a sample. It concludes by discussing the role of robust statistics in geochemistry.
Maurizio Petrelli
Chapter 12. Machine Learning
Abstract
Chapter 12 introduces the reader to the application of machine learning techniques in geology. It provides some basic concepts of machine learning and their implementation in Python, and guides the reader through a geological case study that utilizes machine learning.
Maurizio Petrelli
Backmatter
Metadaten
Titel
Introduction to Python in Earth Science Data Analysis
verfasst von
Ph.D. Maurizio Petrelli
Copyright-Jahr
2021
Electronic ISBN
978-3-030-78055-5
Print ISBN
978-3-030-78054-8
DOI
https://doi.org/10.1007/978-3-030-78055-5