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Hands-On Exploratory Data Analysis with Python

By : Kumar Mukhiya, Ahmed

2.5 (2)

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Section 1: The Fundamentals of EDA

Section 1: The Fundamentals of EDA

Free Chapter

Exploratory Data Analysis Fundamentals

Exploratory Data Analysis Fundamentals

Understanding data science

The significance of EDA

Making sense of data

Comparing EDA with classical and Bayesian analysis

Software tools available for EDA

Getting started with EDA

Summary

Further reading

Visual Aids for EDA

Visual Aids for EDA

Technical requirements

Line chart

Bar charts

Scatter plot

Area plot and stacked plot

Pie chart

Table chart

Polar chart

Histogram

Lollipop chart

Choosing the best chart

Other libraries to explore

Summary

Further reading

EDA with Personal Email

EDA with Personal Email

Technical requirements

Loading the dataset

Data transformation

Data analysis

Summary

Further reading

Data Transformation

Data Transformation

Technical requirements

Background

Merging database-style dataframes

Transformation techniques

Benefits of data transformation

Summary

Further reading

Section 2: Descriptive Statistics

Section 2: Descriptive Statistics

Descriptive Statistics

Descriptive Statistics

Technical requirements

Understanding statistics

Measures of central tendency

Measures of dispersion

Summary

Further reading

Grouping Datasets

Grouping Datasets

Technical requirements

Understanding groupby()

Groupby mechanics

Data aggregation

Pivot tables and cross-tabulations

Summary

Further reading

Correlation

Correlation

Technical requirements

Introducing correlation

Types of analysis

Discussing multivariate analysis using the Titanic dataset

Outlining Simpson's paradox

Correlation does not imply causation

Summary

Further reading

Time Series Analysis

Time Series Analysis

Technical requirements

Understanding the time series dataset

TSA with Open Power System Data

Summary

Further reading

Section 3: Model Development and Evaluation

Section 3: Model Development and Evaluation

Hypothesis Testing and Regression

Hypothesis Testing and Regression

Technical requirements

Hypothesis testing

p-hacking

Understanding regression

Model development and evaluation

Summary

Further reading

Model Development and Evaluation

Model Development and Evaluation

Technical requirements

Types of machine learning

Understanding supervised learning

Understanding unsupervised learning

Understanding reinforcement learning

Unified machine learning workflow

Summary

Further reading

EDA on Wine Quality Data Analysis

EDA on Wine Quality Data Analysis

Technical requirements

Disclosing the wine quality dataset

Analyzing red wine

Analyzing white wine

Model development and evaluation

Summary

Further reading

Other Books You May Enjoy

Other Books You May Enjoy

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Appendix

Appendix

String manipulation

Using pandas vectorized string functions

Using regular expressions

Further reading

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Data aggregation

Aggregation is the process of implementing any mathematical operation on a dataset or a subset of it. Aggregation is one of the many techniques in pandas that's used to manipulate the data in the dataframe for data analysis.

The Dataframe.aggregate() function is used to apply aggregation across one or more columns. Some of the most frequently used aggregations are as follows:

sum: Returns the sum of the values for the requested axis
min: Returns the minimum of the values for the requested axis
max: Returns the maximum of the values for the requested axis

We can apply aggregation in a DataFrame, df, as df.aggregate() or df.agg().

Since aggregation only works with numeric type columns, let's take some of the numeric columns from the dataset and apply some aggregation functions to them:

# new dataframe that consist length,width,height,curb-weight and...

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