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Index
Title Page
Copyright and Credits
Practical Data Analysis using Jupyter Notebook
About Packt
Why subscribe?
Foreword
Contributors
About the author
About the reviewers
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: Data Analysis Essentials
Fundamentals of Data Analysis
The evolution of data analysis and why it is important
What makes a good data analyst?
Understanding data types and their significance
Common data types
Data classifications and data attributes explained
Understanding data literacy
Overview of Python and Installing Jupyter Notebook
Technical requirements
Installing Python and using Jupyter Notebook
Installing Anaconda
Running Jupyter and installing Python packages for data analysis
Hello World! – running your first Python code
Creating a project folder hierarchy
Uploading a file
Exploring Python packages
Checking for pandas
Checking for NumPy
Checking for sklearn
Checking for Matplotlib
Checking for SciPy
Summary
Future reading
Getting Started with NumPy
Technical requirements
Understanding a Python NumPy array and its importance
Differences between single and multiple dimensional arrays
Making your first NumPy array
Useful array functions
Practical use cases of NumPy and arrays
Assigning values to arrays manually
Assigning values to arrays directly
Assigning values to an array using a loop
Summary
Further reading
Creating Your First pandas DataFrame
Technical requirements
Techniques for manipulating tabular data
Understanding pandas and DataFrames
Handling essential data formats
CSV
XML
Data hierarchy
Defined schema
JSON
Data dictionaries and data types
Creating our first DataFrame
Summary
Further reading
Gathering and Loading Data in Python
Technical requirements
Introduction to SQL and relational databases
From SQL to pandas DataFrames
explained
Fundamental statistics
etadata explained
data lineage
Data flow
The input stage
The data ingestion stage
Business rules
Summary
Further reading
Section 2: Solutions for Data Discovery
Visualizing and Working with Time Series Data
Technical requirements
Data modeling for results
Introducing dimensions and measures
Anatomy of a chart and data viz best practices
Analyzing your data
Why pie charts have lost ground
Art versus science
Comparative analysis
Date and time trends explained
The shape of the curve
Creating your first time series chart
Summary
Further reading
Exploring, Cleaning, Refining, and Blending Datasets
Technical requirements
Retrieving, viewing, and storing tabular data
Retrieving
Viewing
Storing
Learning how to restrict, sort, and sift through data
Restricting
Sorting
Sifting
Cleaning, refining, and purifying data using Python
Combining and binning data
Binning
Summary
Further reading
Understanding Joins, Relationships, and Aggregates
Technical requirements
Foundations of join relationships
One-to-one relationships
Many-to-one relationships
Many-to-many relationship
Left join
Right join
Inner join
Outer join
Join types in action
Explaining data aggregation
Understanding the granularity of data
in action
Summary statistics and outliers
Summary
Further reading
Plotting, Visualization, and Storytelling
Technical requirements
Explaining distribution analysis
KYD
Shape of the curve
Understanding outliers and trends
Geoanalytical techniques and tips
Finding patterns in data
Summary
Further reading
Section 3: Working with Unstructured Big Data
Exploring Text Data and Unstructured Data
Technical requirements
Preparing to work with unstructured data
Corpus in action
Tokenization explained
Tokenize in action
Counting words and exploring results
Counting words
Normalizing text techniques
Stemming and lemmatization in action
Excluding words from analysis
Summary
Further reading
Practical Sentiment Analysis
Technical requirements
Why sentiment analysis is important
Elements of an NLP model
Creating a prediction output
Sentiment analysis packages
Sentiment analysis in action
Manual input
Social media file input
Summary
Further reading
Bringing It All Together
Technical requirements
Discovering real-world datasets
Data.gov
The Humanitarian Data Exchange
The World Bank
Our World in Data
Reporting results
Storytelling
The Capstone project
KYD sources
Exercise
Summary
Further reading
Works Cited
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