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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
Visualizing and Working with Time Series Data

Regardless of whether the data source is from a file or database, we have now defined a repeatable analysis workflow. This is used to load the data into either an array or DataFrame and then answer business questions by running a few Python commands using their respective libraries.

This process has served us well so far and is a necessary step to up-skill our learning of how to work with data, which ultimately improves data literacy.Now, we are going to take yet another exciting step to help you communicate analysis by visualizing your data. In this chapter, we will learn how to create visual artifacts that can support structured data. We will break down the anatomy of a chart by uncovering the fundamentals of how data visualizations are created. Using the plotting features available in Python, you will create your first time series chart using the matplotlib library.

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