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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
Overview of Python and Installing Jupyter Notebook

Now that you have a better understanding of concepts behind data literacy and the evolution of data analysis, let's set up our own environment to allow you to work with data. In this chapter, we are going to provide an introduction to the Python programming language along with a popular tool called Jupyter Notebook that's used to run commands for data analysis. We will walk through the installation process step by step and discuss key concepts to understand why they are required for data analysis. By the end of this chapter, you will have a workstation available to run a hello world program that will help in building your confidence to move forward into deeper concepts.

In this chapter, we will cover the following:

  • Installing Python and using Jupyter Notebook
  • Storing and retrieving data files
  • Hello World! – running...
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