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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

Comparative analysis

Now that we have a better understanding of the anatomy of a chart, we can start looking at time series charts in depth by explaining some of the differences between the date and time trends in charts.

Date and time trends explained

We'll begin with the example shown in the following graph, where we have a line chart with each data point represented by a single value. The first great thing about visualizing data is how easy it is to interpret the results without having all the context of how it was generated:

A best practice to emphasize in the preceding chart is the date values that are presented are in the standard and consistent format of YYYY-MM-DD. There are multiple reasons why this is important. For the producer of the chart, having a consistent data type ensures all the values are accurate for sorting and completion, which means the data visual matches the source data. Another factor to consider...

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