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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

Line chart

Do you remember what a continuous variable is and what a discrete variable is? If not, have a quick look at Chapter 1, Exploratory Data Analysis Fundamentals. Back to the main topic, a line chart is used to illustrate the relationship between two or more continuous variables.

We are going to use the matplotlib library and the stock price data to plot time series lines. First of all, let's understand the dataset. We have created a function using the faker Python library to generate the dataset. It is the simplest possible dataset you can imagine, with just two columns. The first column is Date and the second column is Price, indicating the stock price on that date.

Let's generate the dataset by calling the helper method. In addition to this, we have saved the CSV file. You can optionally load the CSV file using the pandas (read_csv) library and proceed with...

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