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  • Hands-On Exploratory Data Analysis with Python
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Hands-On Exploratory Data Analysis with Python

Hands-On Exploratory Data Analysis with Python

By : Kumar Mukhiya, Ahmed
2.5 (2)
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Hands-On Exploratory Data Analysis with Python

Hands-On Exploratory Data Analysis with Python

2.5 (2)
By: Kumar Mukhiya, Ahmed

Overview of this book

Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.
Table of Contents (17 chapters)
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1
Section 1: The Fundamentals of EDA
6
Section 2: Descriptive Statistics
11
Section 3: Model Development and Evaluation

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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