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Pandas for Everyone


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Table of Contents

  • Part I. Introduction
  • 0. Setting Up
  • 1. Introduction to Panda's Dataframes
  • 2. Dataframe Components
  • 3. Performing Statistics and Calculations on Sliced and Grouped Dataframes
  • 4. Plotting in Matplotlib
  • Part II. Data Munging
  • 5. Basic Data Cleaning
  • 6. Reshaping Dataframes
  • 7. Missing Values
  • 8. Working with Dates
  • 9. Working with Multiple Dataframes
  • 10. Working with Databases
  • Part III. Modeling
  • 11. Basic Statistics
  • 12. Linear Models and Regression
  • 13. Survival Analysis
  • 14. Model Selection and Diagnostics
  • 15. Time Series
  • Part IV. Machine Learning
  • 16. Supervised Learning
  • 17. Unsupervised Learning
  • Part V. Reproducible Documents (Literate Programming)
  • 18. Jupyter Notebook
  • 19. Pweave
  • Appendices

About the Author

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.

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