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Practical Data Analysis Using Jupyter Notebook

You're reading from   Practical Data Analysis Using Jupyter Notebook Learn how to speak the language of data by extracting useful and actionable insights using Python

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838826031
Length 322 pages
Edition 1st Edition
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Author (1):
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Marc Wintjen Marc Wintjen
Author Profile Icon Marc Wintjen
Marc Wintjen
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Data Analysis Essentials
2. Fundamentals of Data Analysis FREE CHAPTER 3. Overview of Python and Installing Jupyter Notebook 4. Getting Started with NumPy 5. Creating Your First pandas DataFrame 6. Gathering and Loading Data in Python 7. Section 2: Solutions for Data Discovery
8. Visualizing and Working with Time Series Data 9. Exploring, Cleaning, Refining, and Blending Datasets 10. Understanding Joins, Relationships, and Aggregates 11. Plotting, Visualization, and Storytelling 12. Section 3: Working with Unstructured Big Data
13. Exploring Text Data and Unstructured Data 14. Practical Sentiment Analysis 15. Bringing It All Together 16. Works Cited
17. Other Books You May Enjoy

Storing and retrieving data files

What I like about using Jupyter is that it is a self-contained solution for data analysis. What I mean by that statement is you can interact with the filesystem to add, update, and delete folders and files plus run Python commands all in one place. As you continue using this tool, I think you will find it much easier to navigate by staying in one ecosystem compared to hopping between multiple windows, apps, or systems on your workstation.

Let's begin with getting comfortable navigating the menu options to add, edit, or delete files. Jupyter defaults the dashboard by listing all files and folders that are accessible on your workstation from the directory paths it was installed. This is can be configured to change the starting folder but we will use the Windows default. In the following screenshot, I have highlighted the important sections of the Jupyter dashboard with letters for easy reference:

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