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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (11) Chapters Close

Preface 1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

Jupyter concepts

Jupyter is organized around a few basic concepts:

  • Notebook: A collection of statements (in a language). For example, this could be a complete R script that loads data, analyzes it, produces a graph, and records results elsewhere.
  • Cell: the lowest granular piece of a Jupyter Notebook that can be worked with:
    • Current Cell: The current cell being edited or the one(s) selected
  • Kernel: each notebook is associated with a specific language implementation. The part of Jupyter which processes the specific language involved is called a kernel.
You have been reading a chapter from
Jupyter for Data Science
Published in: Oct 2017
Publisher: Packt
ISBN-13: 9781785880070
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