Practical Data Analysis Using Jupyter Notebook

By : Marc Wintjen

3.9 (9)

Buy this Book

Practical Data Analysis Using Jupyter Notebook

3.9 (9)

By: Marc Wintjen

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Section 1: Data Analysis Essentials

Free Chapter

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

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

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

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

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

Section 2: Solutions for Data Discovery

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

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

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">

Plotting, Visualization, and Storytelling

Technical requirements

Explaining distribution analysis

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

Tokenization explained

Counting words and exploring results

Normalizing text techniques

Excluding words from analysis

Summary

Further reading

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

Bringing It All Together

Technical requirements

Discovering real-world datasets

Reporting results

The Capstone project

Summary

Further reading

Works Cited

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Techniques for manipulating tabular data

Now that we have a better understanding of array data structures from using the NumPy library in Chapter 3, Getting Started with NumPy, we can now expand our data analysis expertise. We will do this by working with tabular data and focusing on a powerful library available in Python named pandas, which is available to use in our Jupyter notebooks.

The pandas library extends our ability to analyze structured data and was introduced as a Python library back in 2008 by Wes McKinney. McKinney recognized the power of extending the Python language by using libraries and the need to fill the gap that existed between data preparation and data insights by carrying out the entire data analysis workflow in Python without having to switch to a more domain-specific language such as R.

The pandas Python library name was taken from the term panel data (by McKinney) by shortening and combining the terms to get pan and da. Panel data is defined...