Pandas for Everyone
Pandas for Everyone
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
PRP: 250.58 Lei
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225.52Lei
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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
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