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Author: weisscharlesj

_images/chapter_progression.svg

@@ -17,7 +17,7 @@ Learning to use a number of popular Python scientific libraries to solve chemica
 
 ## Organization of Book
 
-This book is organized in order of more fundamental topics first, but not every earlier chapter is a prerequisite for all subsequent chapters. Chapter 0 provides a quick introduction to Jupyter notebooks and chapters 1-2 provide background on the Python programming language. Anyone who already knows Python can skim or skip past these two chapters. Chapter 3 introduces plotting and visualization, and chapter 4 covers the NumPy library. Both of these chapters are used heavily in this book and should not be bypassed. The pandas library is covered in chapter 5 which is used in some subsequent chapters, but not all. This library adds functionality and extra ease-of-use to NumPy. Anyone looking to streamline their schedule could skip this chapter, but be aware that it is heavily utilized in chapters 10 and 12. However, chapters 10 and 12 should be largely readable by someone who is not familiar with pandas or at least has read sections 5.1-5.2. Chapters beyond chapter 5 are mostly applications or cover libraries for very specific applications such as image processing, machine learning, bioinformatics, or optimization. Chapters 6-16 are designed to be modular, so after getting through chapters 0-5, these subsequent chapters can be covered in any order depending up the reader's needs and interests. 
+This book is organized in order of more fundamental topics first, but not every chapter is a prerequisite for all subsequent chapters. Chapter 0 provides a quick introduction to Jupyter notebooks and chapters 1-2 provide background on the Python programming language. Anyone who already knows Python can skim or skip past these two chapters. Chapter 3 introduces plotting and visualization, and chapter 4 covers the NumPy library. Both chapter 3 and 4 are used *heavily* in this book and should not be bypassed. The pandas library is covered in chapter 5 which is used in some subsequent chapters, but not all. This library adds functionality and extra ease-of-use to NumPy. Anyone looking to streamline their schedule could skip this chapter, but be aware that it is heavily utilized in chapters 10, 11, and 13. However, chapters 10 and 13 should be largely readable by someone who is not familiar with pandas or at least has read sections 5.1-5.2. Chapters beyond chapter 5 are mostly applications, advanced topics, or cover libraries for very specific applications such as image processing, machine learning, bioinformatics, or optimization. Chapters 6-17 are designed to be mostly modular, so after getting through chapters 0-5, these subsequent chapters can be covered in any order depending up the reader's needs and interests. This book also has a few appendices that contain interesting topics, such as [controlling your code with widgets](appendix_00) or [visualizing atomic orbitals](appendix_02), that do not fit well into any of the chapters but are still worth checking out.
 
 ![progress through chapters](chapter_progression.svg)
 
@@ -50,7 +50,7 @@ Below is a listing with brief descriptions of the chapters.
 |Appendix 4| Regular Expressions |
 
 
-One of the goals of this book is to provide a streamlined introduction to Python and its scientific libraries in order to allow the reader to start applying these new skills to chemistry as quickly as possible. As a result, not all topics covered in a typical computer science course on Python are included here. Instead, the most relevant topics to chemistry are covered along with a selection of scientific libraries not likely taught in most Python courses. Another difference between this book and a typical computer science course on Python is that many computer science courses would have students write and save code as text files and run them from the command line. In contrast, this book assumes that the reader is running his or her code in a Jupyter notebook, as described in chapter 0, which is an ideal environment for scientific data analysis. The Jupyter notebook provides immediate feedback to the user, convenient graphical outputs, is shareable, and is simpler to use than running Python scripts from the command line. For those students who wish to continue on to run Python scripts from the command line, [chapter 13](13) provides a brief introduction to this process. In an effort to make this text usable in a wide range of courses, there is little in-depth analysis of the data. This book instead focuses more on how to work with the data and leaves the analysis to the individual instructors.
+One of the goals of this book is to provide a streamlined introduction to Python and its scientific libraries in order to allow the reader to start applying these new skills to chemistry as quickly as possible. As a result, not all topics covered in a typical computer science course on Python are included here. Instead, the most relevant topics to chemistry are covered along with a selection of scientific libraries not likely taught in most Python courses. Another difference between this book and a typical computer science course on Python is that many computer science courses would have students write and save code as text files and run them from the command line. In contrast, this book assumes that the reader is running his or her code in a Jupyter notebook, as described in chapter 0, which is an ideal environment for scientific data analysis. The Jupyter notebook provides immediate feedback to the user, convenient graphical outputs, is shareable, and is simpler to use than running Python scripts from the command line. For those students who wish to continue on to run Python scripts from the command line, [chapter 17](17) provides a brief introduction to this process. In an effort to make this text usable in a wide range of courses, there is little in-depth analysis of the data. This book instead focuses more on how to work with the data and leaves the chemical analysis to the individual instructors.
 
 
 ## Chapter and Exercise Data

_sources/notebooks/introduction/intro.md

@@ -423,7 +423,7 @@ <h2>An Introduction to Programming in Python with Chemical Applications<a class=
 </section>
 <section id="organization-of-book">
 <h2>Organization of Book<a class="headerlink" href="#organization-of-book" title="Link to this heading">#</a></h2>
-<p>This book is organized in order of more fundamental topics first, but not every earlier chapter is a prerequisite for all subsequent chapters. Chapter 0 provides a quick introduction to Jupyter notebooks and chapters 1-2 provide background on the Python programming language. Anyone who already knows Python can skim or skip past these two chapters. Chapter 3 introduces plotting and visualization, and chapter 4 covers the NumPy library. Both of these chapters are used heavily in this book and should not be bypassed. The pandas library is covered in chapter 5 which is used in some subsequent chapters, but not all. This library adds functionality and extra ease-of-use to NumPy. Anyone looking to streamline their schedule could skip this chapter, but be aware that it is heavily utilized in chapters 10 and 12. However, chapters 10 and 12 should be largely readable by someone who is not familiar with pandas or at least has read sections 5.1-5.2. Chapters beyond chapter 5 are mostly applications or cover libraries for very specific applications such as image processing, machine learning, bioinformatics, or optimization. Chapters 6-16 are designed to be modular, so after getting through chapters 0-5, these subsequent chapters can be covered in any order depending up the reader’s needs and interests.</p>
+<p>This book is organized in order of more fundamental topics first, but not every chapter is a prerequisite for all subsequent chapters. Chapter 0 provides a quick introduction to Jupyter notebooks and chapters 1-2 provide background on the Python programming language. Anyone who already knows Python can skim or skip past these two chapters. Chapter 3 introduces plotting and visualization, and chapter 4 covers the NumPy library. Both chapter 3 and 4 are used <em>heavily</em> in this book and should not be bypassed. The pandas library is covered in chapter 5 which is used in some subsequent chapters, but not all. This library adds functionality and extra ease-of-use to NumPy. Anyone looking to streamline their schedule could skip this chapter, but be aware that it is heavily utilized in chapters 10, 11, and 13. However, chapters 10 and 13 should be largely readable by someone who is not familiar with pandas or at least has read sections 5.1-5.2. Chapters beyond chapter 5 are mostly applications, advanced topics, or cover libraries for very specific applications such as image processing, machine learning, bioinformatics, or optimization. Chapters 6-17 are designed to be mostly modular, so after getting through chapters 0-5, these subsequent chapters can be covered in any order depending up the reader’s needs and interests. This book also has a few appendices that contain interesting topics, such as <a class="reference internal" href="../appendix_00/appendix_00_notebook.html#appendix-00"><span class="std std-ref">controlling your code with widgets</span></a> or <a class="reference internal" href="../appendix_02/appendix_02_notebook.html#appendix-02"><span class="std std-ref">visualizing atomic orbitals</span></a>, that do not fit well into any of the chapters but are still worth checking out.</p>
 <p><img alt="progress through chapters" src="../../_images/chapter_progression.svg" /></p>
 <p>Below is a listing with brief descriptions of the chapters.</p>
 <div class="pst-scrollable-table-container"><table class="table">
@@ -505,7 +505,7 @@ <h2>Organization of Book<a class="headerlink" href="#organization-of-book" title
 </tbody>
 </table>
 </div>
-<p>One of the goals of this book is to provide a streamlined introduction to Python and its scientific libraries in order to allow the reader to start applying these new skills to chemistry as quickly as possible. As a result, not all topics covered in a typical computer science course on Python are included here. Instead, the most relevant topics to chemistry are covered along with a selection of scientific libraries not likely taught in most Python courses. Another difference between this book and a typical computer science course on Python is that many computer science courses would have students write and save code as text files and run them from the command line. In contrast, this book assumes that the reader is running his or her code in a Jupyter notebook, as described in chapter 0, which is an ideal environment for scientific data analysis. The Jupyter notebook provides immediate feedback to the user, convenient graphical outputs, is shareable, and is simpler to use than running Python scripts from the command line. For those students who wish to continue on to run Python scripts from the command line, <a class="reference internal" href="../chapter_13/chap_13_notebook.html#id1"><span class="std std-ref">chapter 13</span></a> provides a brief introduction to this process. In an effort to make this text usable in a wide range of courses, there is little in-depth analysis of the data. This book instead focuses more on how to work with the data and leaves the analysis to the individual instructors.</p>
+<p>One of the goals of this book is to provide a streamlined introduction to Python and its scientific libraries in order to allow the reader to start applying these new skills to chemistry as quickly as possible. As a result, not all topics covered in a typical computer science course on Python are included here. Instead, the most relevant topics to chemistry are covered along with a selection of scientific libraries not likely taught in most Python courses. Another difference between this book and a typical computer science course on Python is that many computer science courses would have students write and save code as text files and run them from the command line. In contrast, this book assumes that the reader is running his or her code in a Jupyter notebook, as described in chapter 0, which is an ideal environment for scientific data analysis. The Jupyter notebook provides immediate feedback to the user, convenient graphical outputs, is shareable, and is simpler to use than running Python scripts from the command line. For those students who wish to continue on to run Python scripts from the command line, <a class="reference internal" href="../chapter_17/chap_17_notebook.html#id1"><span class="std std-ref">chapter 17</span></a> provides a brief introduction to this process. In an effort to make this text usable in a wide range of courses, there is little in-depth analysis of the data. This book instead focuses more on how to work with the data and leaves the chemical analysis to the individual instructors.</p>
 </section>
 <section id="chapter-and-exercise-data">
 <h2>Chapter and Exercise Data<a class="headerlink" href="#chapter-and-exercise-data" title="Link to this heading">#</a></h2>