Hands-On Exploratory Data Analysis with Python

封面

Title Page

Copyright and Credits

Hands-On Exploratory Data Analysis with Python

About Packt

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Contributors

About the authors

About the reviewer

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

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Conventions used

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Reviews

Section 1: The Fundamentals of EDA

Exploratory Data Analysis Fundamentals

Understanding data science

The significance of EDA

Steps in EDA

Making sense of data

Numerical data

Discrete data

Continuous data

Categorical data

Measurement scales

Nominal

Ordinal

Interval

Ratio

Comparing EDA with classical and Bayesian analysis

Software tools available for EDA

Getting started with EDA

NumPy

Pandas

SciPy

Matplotlib

Summary

Further reading

Visual Aids for EDA

Technical requirements

Line chart

Steps involved

Bar charts

Scatter plot

Bubble chart

Scatter plot using seaborn

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

Technical requirements

Loading the dataset

Data transformation

Data cleansing

Loading the CSV file

Converting the date

Removing NaN values

Applying descriptive statistics

Data refactoring

Dropping columns

Refactoring timezones

Data analysis

Number of emails

Time of day

Average emails per day and hour

Number of emails per day

Most frequently used words

Summary

Further reading

Data Transformation

Technical requirements

Background

Merging database-style dataframes

Concatenating along with an axis

Using df.merge with an inner join

Using the pd.merge() method with a left join

Using the pd.merge() method with a right join

Using pd.merge() methods with outer join

Merging on index

Reshaping and pivoting

Transformation techniques

Performing data deduplication

Replacing values

Handling missing data

NaN values in pandas objects

Dropping missing values

Dropping by rows

Dropping by columns