Cover
Contents
Preface
I: Data Analysis Essentials
Chapter 1. Introduction
1.1 Quantitative Data Analysis and the Humanities
1.2 Overview of the Book
1.3 Related Books
1.4 How to Use This Book
1.4.1 What you should know
1.4.2 Packages and data
1.4.3 Exercises
1.5 An Exploratory Data Analysis of the United Statesโ
Culinary History
1.6 Cooking with Tabular Data
1.7 Taste Trends in Culinary US History
1.8 Americaโs Culinary Melting Pot
1.9 Further Reading
Chapter 2. Parsing and Manipulating Structured Data
2.1 Introduction
2.2 Plain Text
2.3 CSV
2.4 PDF
2.5 JSON
2.6 XML
2.6.1 Parsing XML
2.6.2 Creating XML
2.6.3 TEI
2.7 HTML
2.7.1 Retrieving HTML from the web
2.8 Extracting Character Interaction Networks
2.9 Conclusion and Further Reading
Chapter 3. Exploring Texts Using the Vector Space Model
3.1 Introduction
3.2 From Texts to Vectors
3.2.1 Text preprocessing
3.3 Mapping Genres
3.3.1 Computing distances between documents
3.3.2 Nearest neighbors
3.4 Further Reading
3.5 Appendix: Vectorizing Texts with NumPy
3.5.1 Constructing arrays
3.5.2 Indexing and slicing arrays
3.5.3 Aggregating functions
3.5.4 Array broadcasting
Chapter 4. Processing Tabular Data
4.1 Loading, Inspecting, and Summarizing Tabular Data
4.1.1 Reading tabular data with Pandas
4.2 Mapping Cultural Change
4.2.1 Turnover in naming practices
4.2.2 Visualizing turnovers
4.3 Changing Naming Practices
4.3.1 Increasing name diversity
4.3.2 A bias for names ending in n?
4.3.3 Unisex names in the United States
4.4 Conclusions and Further Reading
II: Advanced Data Analysis
Chapter 5. Statistics Essentials: Who Reads Novels?
5.1 Introduction
5.2 Statistics
5.3 Summarizing Location and Dispersion
5.3.1 Data: Novel reading in the United States
5.4 Location
5.5 Dispersion
5.5.1 Variation in categorical values
5.6 Measuring Association
5.6.1 Measuring association between numbers
5.6.2 Measuring association between categories
5.6.3 Mutual information
5.7 Conclusion
5.8 Further Reading
Chapter 6. Introduction to Probability
6.1 Uncertainty and Thomas Pynchon
6.2 Probability
6.2.1 Probability and degree of belief
6.3 Example: Bayesโs Rule and Authorship Attribution
6.3.1 Random variables and probability distributions
6.4 Further Reading
6.5 Appendix
6.5.1 Bayesโs rule
6.5.2 Fitting a negative binomial distribution
Chapter 7. Narrating with Maps
7.1 Introduction
7.2 Data Preparations
7.3 Projections and Basemaps
7.4 Plotting Battles
7.5 Mapping the Development of the War
7.6 Further Reading
Chapter 8. Stylometry and the Voice of Hildegard
8.1 Introduction
8.2 Authorship Attribution
8.2.1 Burrowsโs Delta
8.2.2 Function words
8.2.3 Computing document distances with Delta
8.2.4 Authorship attribution evaluation
8.3 Hierarchical Agglomerative Clustering
8.4.1 Applying PCA
8.4.2 The intuition behind PCA
8.4.3 Loadings
8.4 Principal Component Analysis
8.5 Conclusions
8.6 Further Reading
Chapter 9. A Topic Model of United States Supreme Court Opinions, 1900โ2000
9.1 Introduction
9.2 Mixture Models: Artwork Dimensions
in the Tate Galleries
9.3 Mixed-Membership Model of Texts
9.3.1 Parameter estimation
9.3.2 Checking an unsupervised model
9.3.3 Modeling different word senses
9.3.4 Exploring trends over time in the Supreme Court
9.4 Conclusion
9.5 Further Reading
9.6 Appendix: Mapping Between Our Topic Model and Lauderdale and Clark (2014)
Epilogue: Good Enough Practices
Bibliography
Index