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Hands-On Exploratory Data Analysis with Python

You're reading from   Hands-On Exploratory Data Analysis with Python Perform EDA techniques to understand, summarize, and investigate your data

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789537253
Length 352 pages
Edition 1st Edition
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Authors (2):
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Suresh Kumar Mukhiya Suresh Kumar Mukhiya
Author Profile Icon Suresh Kumar Mukhiya
Suresh Kumar Mukhiya
Usman Ahmed Usman Ahmed
Author Profile Icon Usman Ahmed
Usman Ahmed
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Table of Contents (17) Chapters Close

Preface 1. Section 1: The Fundamentals of EDA
2. Exploratory Data Analysis Fundamentals FREE CHAPTER 3. Visual Aids for EDA 4. EDA with Personal Email 5. Data Transformation 6. Section 2: Descriptive Statistics
7. Descriptive Statistics 8. Grouping Datasets 9. Correlation 10. Time Series Analysis 11. Section 3: Model Development and Evaluation
12. Hypothesis Testing and Regression 13. Model Development and Evaluation 14. EDA on Wine Quality Data Analysis 15. Other Books You May Enjoy Appendix

Understanding the time series dataset

The most essential question would be, what do we mean by time series data? Of course, we have heard about it on several occasions. Perhaps we can define it? Sure we can. Essentially, a time series is a collection of observations made sequentially in time. Note that there are two important key phrases here—a collection of observations and sequentially in time. Since it is a series, it has to be a collection of observations, and since it deals with time, it has to deal with it in a sequential fashion.

Let's take an example of time series data:

The preceding screenshot illustrates solar energy production (measured in Gigawatt Hours (GWh)) for the first six months of 2016. It also shows the consumption of electricity on both a daily and weekly basis.

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