Pandas for Everyone: Python Data Analysis, 1st edition

Published by Addison-Wesley Professional (December 15, 2017) © 2018

  • Daniel Y. Chen
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This tutorial teaches students everything they need to get started with Python programming for the fast-growing field of data analysis. Daniel Chen tightly links each new concept with easy-to-apply, relevant examples from modern data analysis.

Unlike other beginner's books, this guide helps today's newcomers learn both Python and its popular Pandas data science toolset in the context of tasks they'll really want to perform. Following the proven Software Carpentry approach to teaching programming, Chen introduces each concept with a simple motivating example, slowly offering deeper insights and expanding your ability to handle concrete tasks.

  • 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

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