Pandas For Everyone: Python Data Analysis by Daniel Y. ChenPandas For Everyone: Python Data Analysis by Daniel Y. Chen

Pandas For Everyone: Python Data Analysis

byDaniel Y. Chen

Paperback | December 9, 2017

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This tutorial teaches everything you 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.

 

Each chapter is illuminated with a concept map: an intuitive visual index of what you'll learn -- and an easy way to refer back to what you've already learned. An extensive set of easy-to-read appendices help you fill knowledge gaps wherever they may exist. Coverage includes:

  • Setting up your Python and Pandas environment
  • Getting started with Pandas dataframes
  • Using dataframes to calculate and perform basic statistical tasks
  • Plotting in Matplotlib
  • Cleaning data, reshaping dataframes, handling missing values, working with dates, and more
  • Building basic data analytics models
  • Applying machine learning techniques: both supervised and unsupervised
  • Creating reproducible documents using literate programming techniques
Title:Pandas For Everyone: Python Data AnalysisFormat:PaperbackDimensions:350 pages, 8.75 × 6.35 × 0.68 inPublished:December 9, 2017Publisher:Pearson EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0134546938

ISBN - 13:9780134546933

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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