Book Image

Practical Data Analysis Using Jupyter Notebook

By : Marc Wintjen
Book Image

Practical Data Analysis Using Jupyter Notebook

By: Marc Wintjen

Overview of this book

Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.

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Practical Data Analysis Using Jupyter Notebook
Marc Wintjen
Jun, 2020 | 322 pages
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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Data Analysis Essentials
Section 1: Data Analysis Essentials
Free Chapter
2
Fundamentals of Data Analysis
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
Data classifications and data attributes explained
Understanding data literacy
Summary
Further reading
3
Overview of Python and Installing Jupyter Notebook
Overview of Python and Installing Jupyter Notebook
Technical requirements
Installing Python and using Jupyter Notebook
Storing and retrieving data files
Hello World! – running your first Python code
Exploring Python packages
Summary
Future reading
4
Getting Started with NumPy
Getting Started with NumPy
Technical requirements
Understanding a Python NumPy array and its importance
Making your first NumPy array
Practical use cases of NumPy and arrays
Summary
Further reading
5
Creating Your First pandas DataFrame
Creating Your First pandas DataFrame
Technical requirements
Techniques for manipulating tabular data
Understanding pandas and DataFrames
Handling essential data formats
Data dictionaries and data types
Creating our first DataFrame
Summary
Further reading
6
Gathering and Loading Data in Python
Gathering and Loading Data in Python
Technical requirements
Introduction to SQL and relational databases
From SQL to pandas DataFrames
Data about your data explained
The importance of data lineage
Summary
Further reading
7
Section 2: Solutions for Data Discovery
Section 2: Solutions for Data Discovery
8
Visualizing and Working with Time Series Data
Visualizing and Working with Time Series Data
Technical requirements
Data modeling for results
Anatomy of a chart and data viz best practices
Comparative analysis
The shape of the curve
Summary
Further reading
9
Exploring, Cleaning, Refining, and Blending Datasets
Exploring, Cleaning, Refining, and Blending Datasets
Technical requirements
Retrieving, viewing, and storing tabular data
Learning how to restrict, sort, and sift through data
Cleaning, refining, and purifying data using Python
Combining and binning data
Summary
Further reading
10
Understanding Joins, Relationships, and Aggregates
Understanding Joins, Relationships, and Aggregates
Technical requirements
Foundations of join relationships
Join types in action
Explaining data aggregation
Summary statistics and outliers
Summary
Further reading<a href="https://matplotlib.org/index.html">
11
Plotting, Visualization, and Storytelling
Plotting, Visualization, and Storytelling
Technical requirements
Explaining distribution analysis
Understanding outliers and trends
Geoanalytical techniques and tips
Finding patterns in data
Summary
Further reading
12
Section 3: Working with Unstructured Big Data
Section 3: Working with Unstructured Big Data
13
Exploring Text Data and Unstructured Data
Exploring Text Data and Unstructured Data
Technical requirements
Preparing to work with unstructured data
Tokenization explained
Counting words and exploring results
Normalizing text techniques
Excluding words from analysis
Summary
Further reading
14
Practical Sentiment Analysis
Practical Sentiment Analysis
Technical requirements
Why sentiment analysis is important
Elements of an NLP model
Sentiment analysis packages
Sentiment analysis in action
Summary
Further reading
15
Bringing It All Together
Bringing It All Together
Technical requirements
Discovering real-world datasets
Reporting results
The Capstone project
Summary
Further reading
16
Works Cited
Works Cited
17
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Understanding pandas and DataFrames

Now that we have a better understanding of tabular data and we have provided some background about panel data and the origins of why the pandas library was created, let's dive into some examples using pandas and explain how DataFrames are used.

The pandas library is a powerful Python library used for changing and analyzing data. A pandas DataFrame is a feature available in the library and is defined as a two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A DataFrame is a two-dimensional data structure—that is, data is aligned in a tabular fashion in rows and columns. It is commonly known that pandas DataFrame consists of three principal components: the data, the rows, and the columns. Being a visual learner myself, I created an example of this with the following diagram, which we can go through now:

DataFrames...

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