Python for Social Science

by

Jean Mark Gawron

Python for Social Science by Jean Mark Gawron is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Education isn’t how much you have committed to memory, or even how much you know. It’s being able to differentiate between what you do know and what you don’t.

—Anatole France

No matter where you go, there you are.

—Buckaroo Banzai

Contents

• 1. Preface
• 2. Introduction
  • 2.1. What will we learn?
  • 2.2. What is Python?
  • 2.3. Why Python?
  • 2.4. Python preliminaries
  • 2.5. Why open source?
  • 2.6. Python and Math
• 3. Introduction to Python
  • 3.1. Python example
  • 3.2. Your first Python program
  • 3.3. Python types
  • 3.4. More_Python types
  • 3.5. Python types assignment
  • 3.6. Variables
  • 3.7. Poetry break
  • 3.8. Summary
  • 3.9. Python types quiz
• 4. Introduction to basic Python constructions
  • 4.1. If-then construction (Conditionals)
  • 4.2. Operators that return Boolean results
  • 4.3. How Boolean tests work
  • 4.4. Loops
  • 4.5. List comprehension
  • 4.6. Functions
  • 4.7. Functions, loops, and types assignment
  • 4.8. Nims assignment
  • 4.9. Combination
  • 4.10. Recursion (advanced)
  • 4.11. Student scholarship problem
  • 4.12. Permutation (advanced)
  • 4.13. Summary
  • 4.14. Midterm
• 5. The Anatomy of a Python program
  • 5.1. Import statements
  • 5.2. Python names and namespaces
  • 5.3. Block structure
  • 5.4. Functions and function parameters
  • 5.5. Classes and class attributes
  • 5.6. Files and file IO streams
  • 5.7. Help and doc strings
  • 5.8. A Python program
  • 5.9. Putting it all together
  • 5.10. Summary
• 6. Numpy
  • 6.1. Numpy arrays
  • 6.2. Elementwise arithmetic operations and broadcasting
  • 6.3. More broadcasting
  • 6.4. Boolean operations
  • 6.5. Why use arrays?
• 7. Data and Data Frames
  • 7.1. Pandas Introduction
  • 7.2. Pivot Tables
  • 7.3. Merging
• 8. Regression and Classification
  • 8.1. Simple regression: Don’t think too hard!
  • 8.2. Regression Examples
  • 8.3. Classification Tutorial
  • 8.4. Text Classification: Insults with Naive Bayes
• 9. Visualization
  • 9.1. Plotting
  • 9.2. Box and Whisker Plots
  • 9.3. Color
  • 9.4. Visualization and Story-telling
  • 9.5. Visualizing Geographical Data
  • 9.6. Dimensionality reduction
  • 9.7. Visualizing Social Networks
  • 9.8. Word clouds
  • 9.9. Twitter Example
  • 9.10. Python tools for visualization
• 10. Introduction to Social Networks
  • 10.1. Social Networks
  • 10.2. Introduction to Networkx
  • 10.3. Using gephi
  • 10.4. Anna Karenina Network Assignment
• 11. References
• 12. Introduction to Python regular expressions
  • 12.1. Regular expression language
  • 12.2. Regular expression functions and methods
  • 12.3. Regular expression examples
  • 12.4. Regular Expression Assignment Practice Lab

Indices and tables

• Index

• Module Index

• Search Page