Daniel Chen is a graduate student in the interdisciplinary PhD program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Tech. He is involved with Software Carpentry as an instructor and lesson maintainer. He completed his master's degree in public health at Columbia University Mailman School of Public Health in Epidemiology, and currently works at the Social and Decision Analytics Laboratory under the Biocomplexity Institute of Virginia Tech where he is working with data to inform policy decision-making. He is the author of Pandas for Everyone and Pandas Data Analysis with Python Fundamentals LiveLessons.


 

Part I: Introduction


Part II: Data Manipulation


Part III: Data Munging


Part IV: Data Modeling


Part V: Conclusion


Part VI: Appendixes


Appendix A: Installation


Appendix B: Command Line


Appendix C: Project Templates


Appendix D: Using Python


Appendix E: Working Directories


Appendix F: Environments


Appendix G: Install Packages


Appendix H: Importing Libraries


Appendix I: Lists


Appendix J: Tuples


Appendix K: Dictionaries


Appendix L: Slicing Values


Appendix M: Loops


Appendix N: Comprehensions


Appendix O: Functions


Appendix P: Ranges and Generators


Appendix Q: Multiple Assignment


Appendix R: numpy ndarray


Appendix S: Classes


Appendix T: Odo: The Shapeshifter

Establishes a solid foundation for all the Pandas basics needed to be effective


Covers dataframes, statistical calculations, data munging, modeling, machine learning, reproducible documents, and much more


Teaches step-by-step through easy, incremental examples, with plenty of opportunities to ""code along"""