更新时间：2021-06-24 16:45:36
封面
Title Page
Copyright and Credits
Hands-On Exploratory Data Analysis with Python
About Packt
Why subscribe?
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
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
EDA with Personal Email
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
Data Transformation
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
